Publisher: Sage Publications   (Total: 1086 journals)

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 3 4 5 6 | Last   [Sort by number of followers]   [Restore default list]

Showing 1 - 200 of 1086 Journals sorted alphabetically
AADE in Practice     Hybrid Journal   (Followers: 6)
Abstracts in Anthropology     Full-text available via subscription   (Followers: 24)
Academic Pathology     Open Access   (Followers: 5)
Accounting History     Hybrid Journal   (Followers: 17, SJR: 0.527, CiteScore: 1)
Acta Radiologica     Hybrid Journal   (Followers: 2, SJR: 0.754, CiteScore: 2)
Acta Radiologica Open     Open Access   (Followers: 3)
Acta Sociologica     Hybrid Journal   (Followers: 37, SJR: 0.939, CiteScore: 2)
Action Research     Hybrid Journal   (Followers: 51, SJR: 0.308, CiteScore: 1)
Active Learning in Higher Education     Hybrid Journal   (Followers: 349, SJR: 1.397, CiteScore: 2)
Adaptive Behavior     Hybrid Journal   (Followers: 9, SJR: 0.288, CiteScore: 1)
Administration & Society     Hybrid Journal   (Followers: 14, SJR: 0.675, CiteScore: 1)
Adoption & Fostering     Hybrid Journal   (Followers: 23, SJR: 0.313, CiteScore: 0)
Adsorption Science & Technology     Open Access   (Followers: 8, SJR: 0.258, CiteScore: 1)
Adult Education Quarterly     Hybrid Journal   (Followers: 227, SJR: 0.566, CiteScore: 2)
Adult Learning     Hybrid Journal   (Followers: 43)
Advances in Dental Research     Hybrid Journal   (Followers: 8, SJR: 1.791, CiteScore: 4)
Advances in Developing Human Resources     Hybrid Journal   (Followers: 30, SJR: 0.614, CiteScore: 2)
Advances in Mechanical Engineering     Open Access   (Followers: 138, SJR: 0.272, CiteScore: 1)
Advances in Methods and Practices in Psychological Science     Full-text available via subscription   (Followers: 10)
Advances in Structural Engineering     Full-text available via subscription   (Followers: 46, SJR: 0.599, CiteScore: 1)
Advances in Tumor Virology     Open Access   (Followers: 3, SJR: 0.108, CiteScore: 0)
AERA Open     Open Access   (Followers: 10)
Affilia     Hybrid Journal   (Followers: 5, SJR: 0.496, CiteScore: 1)
Agrarian South : J. of Political Economy     Hybrid Journal   (Followers: 2)
Air, Soil & Water Research     Open Access   (Followers: 13, SJR: 0.214, CiteScore: 1)
Alexandria : The J. of National and Intl. Library and Information Issues     Full-text available via subscription   (Followers: 65)
Allergy & Rhinology     Open Access   (Followers: 4)
AlterNative : An Intl. J. of Indigenous Peoples     Full-text available via subscription   (Followers: 12, SJR: 0.194, CiteScore: 0)
Alternative Law J.     Hybrid Journal   (Followers: 10, SJR: 0.176, CiteScore: 0)
Alternatives : Global, Local, Political     Hybrid Journal   (Followers: 12, SJR: 0.351, CiteScore: 1)
American Behavioral Scientist     Hybrid Journal   (Followers: 23, SJR: 0.982, CiteScore: 2)
American Economist     Hybrid Journal   (Followers: 8)
American Educational Research J.     Hybrid Journal   (Followers: 220, SJR: 2.913, CiteScore: 3)
American J. of Alzheimer's Disease and Other Dementias     Hybrid Journal   (Followers: 18, SJR: 0.67, CiteScore: 2)
American J. of Cosmetic Surgery     Hybrid Journal   (Followers: 6)
American J. of Evaluation     Hybrid Journal   (Followers: 17, SJR: 0.646, CiteScore: 2)
American J. of Health Promotion     Hybrid Journal   (Followers: 34, SJR: 0.807, CiteScore: 1)
American J. of Hospice and Palliative Medicine     Hybrid Journal   (Followers: 43, SJR: 0.65, CiteScore: 1)
American J. of Law & Medicine     Full-text available via subscription   (Followers: 11, SJR: 0.204, CiteScore: 1)
American J. of Lifestyle Medicine     Hybrid Journal   (Followers: 6, SJR: 0.431, CiteScore: 1)
American J. of Medical Quality     Hybrid Journal   (Followers: 11, SJR: 0.777, CiteScore: 1)
American J. of Men's Health     Open Access   (Followers: 8, SJR: 0.595, CiteScore: 2)
American J. of Rhinology and Allergy     Hybrid Journal   (Followers: 9, SJR: 0.972, CiteScore: 2)
American J. of Sports Medicine     Hybrid Journal   (Followers: 207, SJR: 3.949, CiteScore: 6)
American Politics Research     Hybrid Journal   (Followers: 33, SJR: 1.313, CiteScore: 1)
American Review of Public Administration     Hybrid Journal   (Followers: 20, SJR: 2.062, CiteScore: 2)
American Sociological Review     Hybrid Journal   (Followers: 316, SJR: 6.333, CiteScore: 6)
American String Teacher     Full-text available via subscription   (Followers: 2)
Analytical Chemistry Insights     Open Access   (Followers: 25, SJR: 0.224, CiteScore: 1)
Angiology     Hybrid Journal   (Followers: 3, SJR: 0.849, CiteScore: 2)
Animation     Hybrid Journal   (Followers: 14, SJR: 0.197, CiteScore: 0)
Annals of Clinical Biochemistry     Hybrid Journal   (Followers: 10, SJR: 0.634, CiteScore: 1)
Annals of Otology, Rhinology & Laryngology     Hybrid Journal   (Followers: 17, SJR: 0.807, CiteScore: 1)
Annals of Pharmacotherapy     Hybrid Journal   (Followers: 53, SJR: 1.096, CiteScore: 2)
Annals of the American Academy of Political and Social Science     Hybrid Journal   (Followers: 46, SJR: 1.225, CiteScore: 3)
Annals of the ICRP     Hybrid Journal   (Followers: 4, SJR: 0.548, CiteScore: 1)
Anthropocene Review     Hybrid Journal   (Followers: 9, SJR: 3.341, CiteScore: 7)
Anthropological Theory     Hybrid Journal   (Followers: 42, SJR: 0.739, CiteScore: 1)
Antitrust Bulletin     Hybrid Journal   (Followers: 11)
Antiviral Chemistry and Chemotherapy     Open Access   (Followers: 2, SJR: 0.635, CiteScore: 2)
Antyajaa : Indian J. of Women and Social Change     Hybrid Journal  
Applied Biosafety     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Applied Psychological Measurement     Hybrid Journal   (Followers: 23, SJR: 1.17, CiteScore: 1)
Applied Spectroscopy     Full-text available via subscription   (Followers: 26, SJR: 0.489, CiteScore: 2)
Armed Forces & Society     Hybrid Journal   (Followers: 21, SJR: 0.29, CiteScore: 1)
Arts and Humanities in Higher Education     Hybrid Journal   (Followers: 42, SJR: 0.305, CiteScore: 1)
Asia Pacific Media Educator     Hybrid Journal   (Followers: 1, SJR: 0.23, CiteScore: 0)
Asia-Pacific J. of Management Research and Innovation     Full-text available via subscription   (Followers: 3)
Asia-Pacific J. of Public Health     Hybrid Journal   (Followers: 11, SJR: 0.558, CiteScore: 1)
Asian and Pacific Migration J.     Full-text available via subscription   (Followers: 106, SJR: 0.324, CiteScore: 1)
Asian Cardiovascular and Thoracic Annals     Hybrid Journal   (Followers: 2, SJR: 0.305, CiteScore: 0)
Asian J. of Comparative Politics     Hybrid Journal   (Followers: 4)
Asian J. of Legal Education     Full-text available via subscription   (Followers: 4)
Asian J. of Management Cases     Hybrid Journal   (Followers: 6, SJR: 0.101, CiteScore: 0)
ASN Neuro     Open Access   (Followers: 2, SJR: 1.534, CiteScore: 3)
Assessment     Hybrid Journal   (Followers: 17, SJR: 1.519, CiteScore: 3)
Assessment for Effective Intervention     Hybrid Journal   (Followers: 16, SJR: 0.578, CiteScore: 1)
Australasian Psychiatry     Hybrid Journal   (Followers: 18, SJR: 0.433, CiteScore: 1)
Australian & New Zealand J. of Psychiatry     Hybrid Journal   (Followers: 29, SJR: 1.801, CiteScore: 2)
Australian and New Zealand J. of Criminology     Hybrid Journal   (Followers: 528, SJR: 0.612, CiteScore: 1)
Australian J. of Career Development     Hybrid Journal   (Followers: 4)
Australian J. of Education     Hybrid Journal   (Followers: 42, SJR: 0.403, CiteScore: 1)
Australian J. of Management     Hybrid Journal   (Followers: 13, SJR: 0.497, CiteScore: 1)
Autism     Hybrid Journal   (Followers: 327, SJR: 1.739, CiteScore: 4)
Autism & Developmental Language Impairments     Open Access   (Followers: 11)
Behavior Modification     Hybrid Journal   (Followers: 12, SJR: 0.877, CiteScore: 2)
Behavioral and Cognitive Neuroscience Reviews     Hybrid Journal   (Followers: 26)
Bible Translator     Hybrid Journal   (Followers: 13)
Biblical Theology Bulletin     Hybrid Journal   (Followers: 18, SJR: 0.184, CiteScore: 0)
Big Data & Society     Open Access   (Followers: 50)
Biochemistry Insights     Open Access   (Followers: 7)
Bioinformatics and Biology Insights     Open Access   (Followers: 12, SJR: 1.141, CiteScore: 2)
Biological Research for Nursing     Hybrid Journal   (Followers: 7, SJR: 0.685, CiteScore: 2)
Biomarker Insights     Open Access   (Followers: 1, SJR: 0.81, CiteScore: 2)
Biomarkers in Cancer     Open Access   (Followers: 10)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Informatics Insights     Open Access   (Followers: 8)
Bioscope: South Asian Screen Studies     Hybrid Journal   (Followers: 3, SJR: 0.235, CiteScore: 0)
BMS: Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique     Hybrid Journal   (Followers: 4, SJR: 0.226, CiteScore: 0)
Body & Society     Hybrid Journal   (Followers: 27, SJR: 1.531, CiteScore: 3)
Bone and Tissue Regeneration Insights     Open Access   (Followers: 2)
Brain and Neuroscience Advances     Open Access  
Breast Cancer : Basic and Clinical Research     Open Access   (Followers: 10, SJR: 0.823, CiteScore: 2)
British J. of Music Therapy     Hybrid Journal   (Followers: 8)
British J. of Occupational Therapy     Hybrid Journal   (Followers: 202, SJR: 0.323, CiteScore: 1)
British J. of Pain     Hybrid Journal   (Followers: 26, SJR: 0.579, CiteScore: 2)
British J. of Politics and Intl. Relations     Hybrid Journal   (Followers: 32, SJR: 0.91, CiteScore: 2)
British J. of Visual Impairment     Hybrid Journal   (Followers: 13, SJR: 0.337, CiteScore: 1)
British J.ism Review     Hybrid Journal   (Followers: 18)
BRQ Business Review Quarterly     Open Access   (Followers: 1)
Building Acoustics     Hybrid Journal   (Followers: 4, SJR: 0.215, CiteScore: 1)
Building Services Engineering Research & Technology     Hybrid Journal   (Followers: 3, SJR: 0.583, CiteScore: 1)
Bulletin of Science, Technology & Society     Hybrid Journal   (Followers: 8)
Business & Society     Hybrid Journal   (Followers: 12)
Business and Professional Communication Quarterly     Hybrid Journal   (Followers: 8, SJR: 0.348, CiteScore: 1)
Business Information Review     Hybrid Journal   (Followers: 16, SJR: 0.279, CiteScore: 0)
Business Perspectives and Research     Hybrid Journal   (Followers: 3)
Cahiers Élisabéthains     Hybrid Journal   (Followers: 1, SJR: 0.111, CiteScore: 0)
Calcutta Statistical Association Bulletin     Full-text available via subscription   (Followers: 1)
California Management Review     Hybrid Journal   (Followers: 31, SJR: 2.209, CiteScore: 4)
Canadian J. of Kidney Health and Disease     Open Access   (Followers: 6, SJR: 1.007, CiteScore: 2)
Canadian J. of Nursing Research (CJNR)     Hybrid Journal   (Followers: 13)
Canadian J. of Occupational Therapy     Hybrid Journal   (Followers: 140, SJR: 0.626, CiteScore: 1)
Canadian J. of Psychiatry     Hybrid Journal   (Followers: 28, SJR: 1.769, CiteScore: 3)
Canadian J. of School Psychology     Hybrid Journal   (Followers: 11, SJR: 0.266, CiteScore: 1)
Canadian Pharmacists J. / Revue des Pharmaciens du Canada     Hybrid Journal   (Followers: 3, SJR: 0.536, CiteScore: 1)
Cancer Control     Open Access   (Followers: 1)
Cancer Growth and Metastasis     Open Access   (Followers: 1)
Cancer Informatics     Open Access   (Followers: 4, SJR: 0.64, CiteScore: 1)
Capital and Class     Hybrid Journal   (Followers: 7, SJR: 0.282, CiteScore: 1)
Cardiac Cath Lab Director     Full-text available via subscription  
Cardiovascular and Thoracic Open     Open Access  
Career Development and Transition for Exceptional Individuals     Hybrid Journal   (Followers: 9, SJR: 0.44, CiteScore: 1)
Cartilage     Hybrid Journal   (Followers: 5, SJR: 0.889, CiteScore: 3)
Cell and Tissue Transplantation and Therapy     Open Access   (Followers: 2)
Cell Transplantation     Open Access   (Followers: 4, SJR: 1.023, CiteScore: 3)
Cephalalgia     Hybrid Journal   (Followers: 7, SJR: 1.581, CiteScore: 3)
Child Language Teaching and Therapy     Hybrid Journal   (Followers: 35, SJR: 0.501, CiteScore: 1)
Child Maltreatment     Hybrid Journal   (Followers: 9, SJR: 1.22, CiteScore: 3)
Child Neurology Open     Open Access   (Followers: 6)
Childhood     Hybrid Journal   (Followers: 19, SJR: 0.894, CiteScore: 2)
Childhood Obesity and Nutrition     Open Access   (Followers: 11)
China Information     Hybrid Journal   (Followers: 7, SJR: 0.767, CiteScore: 2)
China Report     Hybrid Journal   (Followers: 10, SJR: 0.221, CiteScore: 0)
Chinese J. of Sociology     Full-text available via subscription   (Followers: 4)
Chronic Illness     Hybrid Journal   (Followers: 6, SJR: 0.672, CiteScore: 2)
Chronic Respiratory Disease     Hybrid Journal   (Followers: 9, SJR: 0.808, CiteScore: 2)
Chronic Stress     Open Access  
Citizenship, Social and Economics Education     Full-text available via subscription   (Followers: 6, SJR: 0.145, CiteScore: 0)
Cleft Palate-Craniofacial J.     Hybrid Journal   (Followers: 8, SJR: 0.757, CiteScore: 1)
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical and Applied Thrombosis/Hemostasis     Open Access   (Followers: 16, SJR: 0.49, CiteScore: 1)
Clinical and Translational Neuroscience     Open Access  
Clinical Case Studies     Hybrid Journal   (Followers: 3, SJR: 0.364, CiteScore: 1)
Clinical Child Psychology and Psychiatry     Hybrid Journal   (Followers: 45, SJR: 0.73, CiteScore: 2)
Clinical EEG and Neuroscience     Hybrid Journal   (Followers: 6, SJR: 0.552, CiteScore: 2)
Clinical Ethics     Hybrid Journal   (Followers: 10, SJR: 0.296, CiteScore: 1)
Clinical Medicine Insights : Arthritis and Musculoskeletal Disorders     Open Access   (Followers: 3, SJR: 0.537, CiteScore: 2)
Clinical Medicine Insights : Blood Disorders     Open Access   (SJR: 0.314, CiteScore: 2)
Clinical Medicine Insights : Cardiology     Open Access   (Followers: 6, SJR: 0.686, CiteScore: 2)
Clinical Medicine Insights : Case Reports     Open Access   (Followers: 1, SJR: 0.283, CiteScore: 1)
Clinical Medicine Insights : Circulatory, Respiratory and Pulmonary Medicine     Open Access   (Followers: 3, SJR: 0.425, CiteScore: 2)
Clinical Medicine Insights : Ear, Nose and Throat     Open Access   (Followers: 1)
Clinical Medicine Insights : Endocrinology and Diabetes     Open Access   (Followers: 33, SJR: 0.63, CiteScore: 2)
Clinical Medicine Insights : Oncology     Open Access   (Followers: 3, SJR: 1.129, CiteScore: 3)
Clinical Medicine Insights : Pediatrics     Open Access   (Followers: 3)
Clinical Medicine Insights : Psychiatry     Open Access   (Followers: 10)
Clinical Medicine Insights : Reproductive Health     Open Access   (Followers: 2, SJR: 0.776, CiteScore: 0)
Clinical Medicine Insights : Therapeutics     Open Access   (Followers: 1, SJR: 0.172, CiteScore: 0)
Clinical Medicine Insights : Trauma and Intensive Medicine     Open Access   (Followers: 4)
Clinical Medicine Insights : Urology     Open Access   (Followers: 2)
Clinical Medicine Insights : Women's Health     Open Access   (Followers: 4)
Clinical Nursing Research     Hybrid Journal   (Followers: 30, SJR: 0.471, CiteScore: 1)
Clinical Pathology     Open Access   (Followers: 3)
Clinical Pediatrics     Hybrid Journal   (Followers: 22, SJR: 0.487, CiteScore: 1)
Clinical Psychological Science     Hybrid Journal   (Followers: 11, SJR: 3.281, CiteScore: 5)
Clinical Rehabilitation     Hybrid Journal   (Followers: 75, SJR: 1.322, CiteScore: 3)
Clinical Risk     Hybrid Journal   (Followers: 5, SJR: 0.133, CiteScore: 0)
Clinical Trials     Hybrid Journal   (Followers: 21, SJR: 2.399, CiteScore: 2)
Clothing and Textiles Research J.     Hybrid Journal   (Followers: 25, SJR: 0.36, CiteScore: 1)
Common Law World Review     Full-text available via subscription   (Followers: 18)
Communication & Sport     Hybrid Journal   (Followers: 8, SJR: 0.385, CiteScore: 1)
Communication and the Public     Hybrid Journal   (Followers: 1)
Communication Disorders Quarterly     Hybrid Journal   (Followers: 17, SJR: 0.458, CiteScore: 1)
Communication Research     Hybrid Journal   (Followers: 20, SJR: 2.171, CiteScore: 3)
Community College Review     Hybrid Journal   (Followers: 9, SJR: 1.451, CiteScore: 1)
Comparative Political Studies     Hybrid Journal   (Followers: 246, SJR: 3.772, CiteScore: 3)
Compensation & Benefits Review     Hybrid Journal   (Followers: 8)
Competition & Change     Hybrid Journal   (Followers: 11, SJR: 0.843, CiteScore: 2)
Competition and Regulation in Network Industries     Full-text available via subscription   (Followers: 8, SJR: 0.143, CiteScore: 0)
Concurrent Engineering     Hybrid Journal   (Followers: 3, SJR: 0.642, CiteScore: 2)
Conflict Management and Peace Science     Hybrid Journal   (Followers: 38, SJR: 2.441, CiteScore: 1)
Contemporary Drug Problems     Full-text available via subscription   (Followers: 3, SJR: 0.609, CiteScore: 2)
Contemporary Education Dialogue     Hybrid Journal   (Followers: 5, SJR: 0.102, CiteScore: 0)
Contemporary Issues in Early Childhood     Full-text available via subscription   (Followers: 6, SJR: 0.766, CiteScore: 1)
Contemporary Review of the Middle East     Full-text available via subscription   (Followers: 12)
Contemporary Sociology : A J. of Reviews     Full-text available via subscription   (Followers: 34, SJR: 0.195, CiteScore: 0)
Contemporary Voice of Dalit     Full-text available via subscription   (Followers: 1)
Contexts     Hybrid Journal   (Followers: 6)
Contributions to Indian Sociology     Hybrid Journal   (Followers: 4, SJR: 0.376, CiteScore: 0)

        1 2 3 4 5 6 | Last   [Sort by number of followers]   [Restore default list]

Similar Journals
Journal Cover
Health Informatics Journal
Journal Prestige (SJR): 0.612
Citation Impact (citeScore): 2
Number of Followers: 27  
 
Hybrid Journal Hybrid journal   * Containing 96 Open Access Open Access article(s) in this issue *
ISSN (Print) 1460-4582 - ISSN (Online) 1741-2811
Published by Sage Publications Homepage  [1086 journals]
  • An intelligent real-time scheduler for out-patient clinics: A multi-agent
           system model

         This is an Open Access Article Open Access Article

    • Authors: Jyoti R Munavalli, Shyam Vasudeva Rao, Aravind Srinivasan, GG van Merode
      Abstract: Health Informatics Journal, Ahead of Print.
      Scheduling of resources and patients are crucial in outpatient clinics, particularly when the patient demand is high and patient arrivals are random. Generally, outpatient clinic systems are push systems where scheduling is based on average demand prediction and is considered for long term (monthly or bimonthly). Often, planning and actual scenario vary due to uncertainty and variability in demand and this mismatch results in prolonged waiting times and under-utilization of resources. In this article, we model an outpatient clinics as a multi-agent system and propose an intelligent real-time scheduler that schedules patients and resources based on the actual status of departments. Two algorithms are implemented: one for resource scheduling that is based on predictive demand and the other is patient scheduling which performs path optimization depending on the actual status of departments. In order to match resources with stochastic demand, a coordination mechanism is developed that reschedules the resources in the outpatient clinics in real time through auction-bidding procedures. First, a simulation study of intelligent real-time scheduler is carried out followed by implementation of the same in an outpatient clinic of Aravind Eye Hospital, Madurai, India. This hospital has huge patient demand and the patient arrivals are random. The results show that the intelligent real-time scheduler improved the performance measures like waiting time, cycle time, and utilization significantly compared to scheduling of resources and patients in isolation. By scheduling resources and patients, based on system status and demand, the outpatient clinic system becomes a pull system. This scheduler transforms outpatient clinics from open loop system to closed-loop system.
      Citation: Health Informatics Journal
      PubDate: 2020-02-21T12:58:11Z
      DOI: 10.1177/1460458220905380
       
  • Quality of antimicrobial prescribing improved by the introduction of
           ePrescribing at Auckland City Hospital

         This is an Open Access Article Open Access Article

    • Authors: Tayla R Bowers, Eamon J Duffy
      Abstract: Health Informatics Journal, Ahead of Print.
      Improving antimicrobial prescribing is a difficult process often requiring labour-intensive, multi-modal interventions. Many hospitals have introduced ePrescribing systems but the effect on antimicrobial prescribing, without treatment choice decision support systems, has not been well described. We sought to determine whether the introduction of ePrescribing improved prescribing quality. Patient records for inpatients on four rehabilitation wards, two using ePrescribing and two using the National Medication Chart, during February 2017, were retrospectively reviewed to identify all antimicrobial prescriptions, which were then reviewed for quality. Documentation of indication was significantly better on ePrescribing wards (45/46, 98%) compared to National Medication Chart wards (47/59, 80%). Adherence to guidelines (32/46, 70% vs 33/59, 56%), appropriateness of therapy (42/46, 91% vs 50/59, 85%) and documentation of duration, stop or review dates (35/46, 76% vs 38/59, 64%) did not significantly differ. ePrescribing can improve the quality of antimicrobial prescribing when Antimicrobial Stewardship principles are used in system customisation but cannot address all factors impacting on prescribing quality.
      Citation: Health Informatics Journal
      PubDate: 2020-02-20T10:35:06Z
      DOI: 10.1177/1460458220905163
       
  • Telemedicine adoption issues in the United States and Brazil: Perception
           of healthcare professionals

         This is an Open Access Article Open Access Article

    • Authors: Edimara Luciano, M Adam Mahmood, Parand Mansouri Rad
      Abstract: Health Informatics Journal, Ahead of Print.
      Telemedicine has recently garnered more attention from healthcare professionals because it provides access to health services to patients in rural areas while making patient healthcare information more vulnerable to security breaches. The objective of this research is to identify factors that play a critical role in possible adoption of telemedicine in the United States and Brazil. A model with eight hypotheses was used to establish a research framework. A survey was conducted involving healthcare professionals in the aforementioned countries. The results show that telemedicine adoption is influenced by policies and culture in both countries and influenced by security and privacy in the United States. It can be inferred from the research that perceptions of the American and Brazilian healthcare professionals are similar in telemedicine issues covered in this research. These healthcare professionals, however, disagree on how patients’ privacy should be preserved in the two countries.
      Citation: Health Informatics Journal
      PubDate: 2020-02-19T10:26:06Z
      DOI: 10.1177/1460458220902957
       
  • Emergency patient flow forecasting in the radiology department
         This is an Open Access Article Open Access Article

    • Authors: Yumeng Zhang, Li Luo, Fengyi Zhang, Ruixiao Kong, Jianchao Yang, Yabing Feng, Huili Guo
      Abstract: Health Informatics Journal, Ahead of Print.
      The accurate forecast of radiology emergency patient flow is of great importance to optimize appointment scheduling decisions. This study used a multi-model approach to forecast daily radiology emergency patient flow with consideration of different patient sources. We constructed six linear and nonlinear models by considering the lag effects and corresponding time factors. The autoregressive integrated moving average and least absolute shrinkage and selection operator (Lasso) were selected from the category of linear models, whereas linear-and-radial support vector regression models, random forests and adaptive boosting were chosen from the category of nonlinear models. The models were applied to 4-year daily emergency visits data in the radiology department of West China Hospital in Chengdu, China. The mean absolute percentage error of six models ranged from 8.56 to 9.36 percent for emergency department patients, whereas it varied from 10.90 to 14.39 percent for ward patients. The best-performing model for total radiology visits was Lasso, which yielded a mean absolute percentage error of 7.06 percent. The arrival patterns of emergency department and total radiology emergency patient flows could be modeled by linear processes. By contrast, the nonlinear model performed best for ward patient flow. These findings will benefit hospital managers in managing efficient patient flow, thus improving service quality and increasing patient satisfaction.
      Citation: Health Informatics Journal
      PubDate: 2020-02-19T10:24:27Z
      DOI: 10.1177/1460458220901889
       
  • Telemonitoring readiness among Austrian diabetic patients: A
           cross-sectional validation study

         This is an Open Access Article Open Access Article

    • Authors: Domenik Muigg, Georg Duftschmid, Peter Kastner, Robert Modre-Osprian, Daniela Haluza
      Abstract: Health Informatics Journal, Ahead of Print.
      Digitalized healthcare services offer remote and cost-effective treatment of diabetes patients. Thus, the present online study analyzed the readiness to use telemonitoring among Austrian diabetes patients. We developed and validated a German version of the patient telehealth readiness assessment tool and performed quantitative context analysis of free-text comments on perceived barriers and benefits of telemonitoring. Participants (n = 41, 42.6% females) achieved a medium average readiness level for telemonitoring. The three top benefits were intensified care, shorter travel and waiting times, and better therapy adjustment. The top three barriers were data privacy issues, loss of personal communication and focus on blood sugar, and teledoctor competence. Diabetes patients represent a suitable target group for remote treatment opportunities. However, a shift from traditional face-to-face medical care to exclusive telemonitoring treatment from diagnosis to consultation and treatment requires fundamental new legal framework conditions.
      Citation: Health Informatics Journal
      PubDate: 2020-02-12T10:00:17Z
      DOI: 10.1177/1460458219894094
       
  • Combining big data search analytics and the FDA Adverse Event Reporting
           System database to detect a potential safety signal of mirtazapine abuse

         This is an Open Access Article Open Access Article

    • Authors: Dimitrios Spachos, Spyridon Siafis, Panagiotis Bamidis, Dimitrios Kouvelas, Georgios Papazisis
      Abstract: Health Informatics Journal, Ahead of Print.
      This study sought to detect a potential safety signal of mirtazapine abuse by combining two different sources of surveillance, specifically Google Analytics (Google, Inc., Mountain View, CA, USA) and the FDA Adverse Event Reporting System database. Data from the first quarter of 2004 to the second quarter of 2017 were collected and analysed. The search interest over time, the frequencies of abuse-related terms in the search analytics domain, and the odds ratio of abuse events in FDA Adverse Event Reporting System were determined. Correlations between the two aforementioned domains using quarterly data from the timeline series were also assessed. Our results suggest a positive correlation between abuse-related searches in the Google domain and abuse-related events in FDA Adverse Event Reporting System database. These results indicate that these methods can be used in combination with each other as a pharmacovigilance supplementary tool to detect drug safety signals.
      Citation: Health Informatics Journal
      PubDate: 2020-02-06T12:04:36Z
      DOI: 10.1177/1460458219901232
       
  • Refining an algorithm-powered just-in-time adaptive weight control
           intervention: A randomized controlled trial evaluating model performance
           and behavioral outcomes
    • Authors: Stephanie P Goldstein, J Graham Thomas, Gary D Foster, Gabrielle Turner-McGrievy, Meghan L Butryn, James D Herbert, Gerald J Martin, Evan M Forman
      Abstract: Health Informatics Journal, Ahead of Print.
      Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (n = 121) used a 10-week mobile weight loss program and were randomized to OnTrack-short (i.e. 8 questions/survey) or OnTrack-long (i.e. 17 questions/survey). Additional questions reduced ecological momentary assessment adherence; however, increased data completeness improved algorithm performance. There were no differences in perceived effectiveness, app utilization, or behavioral outcomes. Minimal differences in utilization and perceived effectiveness likely contributed to similar behavioral outcomes across various conditions.
      Citation: Health Informatics Journal
      PubDate: 2020-02-06T12:03:58Z
      DOI: 10.1177/1460458220902330
       
  • The popularity of eating broadcast: Content analysis of “mukbang”
           YouTube videos, media coverage, and the health impact of “mukbang” on
           public

         This is an Open Access Article Open Access Article

    • Authors: EunKyo Kang, Jihye Lee, Kyae Hyung Kim, Young Ho Yun
      Abstract: Health Informatics Journal, Ahead of Print.
      As “mukbang” (eating broadcast) becomes increasingly widespread, there is growing interest about the impact of mukbang on public health. This study aimed to analyze the content of mukbang YouTube videos, as well as news articles related to mukbang and the association between watching mukbang videos and health habits. We analyzed 5952 YouTube mukbang videos, 5265 news articles, and a survey of 1200 people in Korea. In this study, we confirmed that the provocative content of mukbang YouTube videos, such as overeating, was related to video popularity (p 
      Citation: Health Informatics Journal
      PubDate: 2020-01-29T09:23:32Z
      DOI: 10.1177/1460458220901360
       
  • Outcomes of health information technology utilization in nursing homes: Do
           implementation processes matter'

         This is an Open Access Article Open Access Article

    • Authors: Darla J Hamann, Karabi C Bezboruah
      Abstract: Health Informatics Journal, Ahead of Print.
      We examined several outcomes of health information technology utilization in nursing homes and how the processes used to implement health information technology affected these outcomes. We hypothesized that one type of health information technology, electronic medical records, will improve efficiency and quality-related outcomes, and that the use of effective implementation processes and change leadership strategies will improve these outcomes. We tested these hypotheses by creating an original survey based on the case study literature, which we sent to the top executives of nursing homes in seven US states. The administrators reported that electronic medical record adoption led to moderately positive efficiency and quality outcomes, but its adoption was unrelated to objective quality indicators obtained from regulatory agencies. Improved electronic medical record implementation processes, however, were positively related to administrator-reported efficiency and quality outcomes and to decreased deficiency citations at the next regulatory visit to the nursing home. Change leadership processes did not matter as much as technological implementation processes.
      Citation: Health Informatics Journal
      PubDate: 2020-01-29T01:31:48Z
      DOI: 10.1177/1460458219899556
       
  • Computational time series analysis of patient referrals to a primary
           percutaneous coronary intervention service

         This is an Open Access Article Open Access Article

    • Authors: Aleeha Iftikhar, Raymond R Bond, Victoria McGilligan, Stephen J Leslie, Anne McShane, Charles Knoery, Khaled Rjoob, Aaron Peace
      Abstract: Health Informatics Journal, Ahead of Print.
      This article retrospectively analyses a primary percutaneous coronary intervention dataset comprising patient referrals that were accepted for percutaneous coronary intervention and those who were turned down between January 2015 and December 2018 at Altnagelvin Hospital (United Kingdom). Time series analysis of these referrals was undertaken for analysing the referral rates per year, month, day and per hour. The overall referrals have 70 per cent (n = 1466, p 
      Citation: Health Informatics Journal
      PubDate: 2020-01-24T08:04:05Z
      DOI: 10.1177/1460458219899762
       
  • Using photos for public health communication: A computational analysis of
           the Centers for Disease Control and Prevention Instagram photos and public
           responses

         This is an Open Access Article Open Access Article

    • Authors: Yunhwan Kim, Jang Hyun Kim
      Abstract: Health Informatics Journal, Ahead of Print.
      This study aims to explore the use of Instagram by the Centers for Disease Control and Prevention, one of the representative public health authorities in the United States. For this aim, all of the photos uploaded on the Centers for Disease Control and Prevention Instagram account were crawled and the content of them were analyzed using Microsoft Azure Cognitive Services. Also, engagement was measured by the sum of numbers of likes and comments to each photo, and sentiment analysis of comments was conducted. Results suggest that the photos that can be categorized into “text” and “people” took the largest share in the Centers for Disease Control and Prevention Instagram photos. And it was found that the Centers for Disease Control and Prevention’s major way of delivering messages on Instagram was to imprint key messages that call for actions for better health on photos and to provide the source of complementary information on text component of each post. It was also found that photos with more and bigger human faces had lower level of engagement than the others, and happiness and neutral emotions expressed on the faces in photos were negatively associated with engagement. The features whose high value would make the photos look splendid and gaudy were negatively correlated with engagement, but sharpness was positively correlated.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:31:23Z
      DOI: 10.1177/1460458219896673
       
  • Assessing the utility of a smart thermometer and mobile application as a
           surveillance tool for influenza and influenza-like illness

         This is an Open Access Article Open Access Article

    • Authors: Sarah F Ackley, Sarah Pilewski, Vladimir S Petrovic, Lee Worden, Erin Murray, Travis C Porco
      Abstract: Health Informatics Journal, Ahead of Print.
      Kinsa Inc. sells Food and Drug Administration–cleared smart thermometers, which synchronize with a mobile application, and may aid influenza forecasting efforts. We compare smart thermometer and mobile application data to regional influenza and influenza-like illness surveillance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Smart thermometer readings are highly correlated with regional surveillance data, are more predictive of surveillance data for their own region and season than for other times and places, and improve predictions of influenza, but not predictions of influenza-like illness. These results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:29:23Z
      DOI: 10.1177/1460458219897152
       
  • Error by omission: A lack of integration across implementation and use in
           structuring health information technology contracts

         This is an Open Access Article Open Access Article

    • Authors: Thomas R Martin, Hamlet Gasoyan, David J Wierz
      Abstract: Health Informatics Journal, Ahead of Print.
      Limited work identifies best practices to assess functional electronic health record system performance when contracting for health information technology and information technology–related services. Without a set of best practices or specific contracting provisions to assess the performance of electronic health record systems, healthcare providers will not be able to fully leverage the performance of these systems to reduce the cost of care and improve patient outcomes. This work seeks to provide operational considerations and best practices when forming teams to negotiate health information technology system specifications in contracts. To better understand the contracting and performance assessment process, we conducted a cross-sectional survey of eligible healthcare personnel. Our study highlights a potential disconnect between respondents setting contract structure, knowledge of ongoing functional performance assessments in practice, and the relationship to those with direct system involvement to avoid potential legal risk.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:27:03Z
      DOI: 10.1177/1460458219898095
       
  • Speech enhancement method using deep learning approach for
           hearing-impaired listeners

         This is an Open Access Article Open Access Article

    • Authors: PF Khaleelur Rahiman, VS Jayanthi, AN Jayanthi
      Abstract: Health Informatics Journal, Ahead of Print.
      A deep learning-based speech enhancement method is proposed to aid hearing-impaired listeners by improving speech intelligibility. The algorithm decomposes the noisy speech signal into frames (as features). Subsequently, a deep convolutional neural network is fed with decomposed noisy speech signal frames to produce frequency channel estimation. However, a higher signal-to-noise ratio information is contained in produced frequency channel estimation. Using this estimate, speech-dominated cochlear implant channels are taken to produce electrical stimulation. This process is the same as that of the conventional n-of-m cochlear implant coding strategies. To determine the speech-in-noise performance of 12 cochlear implant users, the fan and music sound applied are considered as background noises. Performance of the proposed algorithm is evaluated by considering these background noises. Low processing delay and reliable architecture are the best characteristics of the deep learning-based speech enhancement algorithm; hence, this can be suitably applied for all applications of hearing devices. Experimental results demonstrate that deep convolutional neural network approach appeared more promising than conventional approaches.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:25:23Z
      DOI: 10.1177/1460458219893850
       
  • Shaping technologies for older adults with and without dementia:
           Reflections on ethics and preferences

         This is an Open Access Article Open Access Article

    • Authors: Unai Diaz-Orueta, Louise Hopper, Evdokimos Konstantinidis
      Abstract: Health Informatics Journal, Ahead of Print.
      As a result of several years of European funding, progressive introduction of assistive technologies in our society has provided many researchers and companies with opportunities to develop new information and communication technologies aimed at overcoming the digital divide of those at a greater risk of being left behind, as can be the case with healthy older people and those developing cognitive decline and dementia. Moreover, in recent years, when considering how information and communication technologies have been integrated into older people’s lives, and how technology has influenced these individuals, doubts remain regarding whether technologies really fulfil older users’ needs and wishes and whether technologies developed specifically for older users necessarily protect and consider main ethical values. In this article, we address the relevance of privacy, vulnerability and preservation of autonomy as key factors when involving older individuals as target users for information and communication technology research and development. We provide explanatory examples on ethical issues involved in the particular case of developing different types of information and communication technology for older people (from robotics to serious games), what previously performed research tells us about older adults’ preferences and wishes for information and communication technology and what steps should be taken into consideration in the near future.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:20:42Z
      DOI: 10.1177/1460458219899590
       
  • The association between hospital ownership and postoperative
           complications: Does it matter who owns the hospital'

         This is an Open Access Article Open Access Article

    • Authors: Robby Atala, Philip J Kroth
      Abstract: Health Informatics Journal, Ahead of Print.
      Postoperative complications place a major burden on the healthcare systems. The type of hospital’s ownership could be one factor associated with this adverse outcome. Using CMS’s publicly available “Complications and Deaths—Hospitals” and “Hospital General Information” datasets, we analyzed the association between four postoperative complications (venous thromboembolism, joint replacement complications, wound dehiscence, postoperative sepsis) and hospital ownership. These data were collected by Medicare between April 2013 and March 2016. We found a significant association (p = 0.029) between ownership types and the postoperative complication score. A 6-percent drop in the share of not-for-profit ownership, accompanied by a 3-percent increase in each of the government and for-profit ownership, resulted in a 20-percent drop in postoperative complication scores (from 5.75 to 4.6). There is an association between hospital ownership type and postoperative complications. Creating this awareness in leadership should prompt for redesigning of hospitals’ operations and workflows to become more compatible with safe and effective care delivery.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:17:42Z
      DOI: 10.1177/1460458219899827
       
  • A clinician’s perspective on co-developing and co-implementing a
           digital tumor board solution

         This is an Open Access Article Open Access Article

    • Authors: Richard D Hammer, Matthew S Prime
      Abstract: Health Informatics Journal, Ahead of Print.
      Healthcare has entered the information age. This will deliver huge opportunities for healthcare providers to deliver more individualized treatments for patients, and as such improve outcomes. Nowhere is the prospect greater than in cancer care. Healthcare providers now need to manage the challenge of how to best capture, interpret and exploit insights from real-world clinical data. A significant aspect of cancer care is the challenge of preparing and conducting tumor boards. Currently, data are distributed across multiple systems and cannot be easily aggregated or integrated. In recognition that no suitable solution existed, the University of Missouri School of Medicine, in partnership with Roche, have co-developed and co-implemented a digital tumor board solution. This article describes the development process and the enablers and barriers for adoption from a clinician’s perspective. In addition, it reflects on some of the key factors for success and some of the future opportunities.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:14:22Z
      DOI: 10.1177/1460458219899841
       
  • Linear discriminant analysis and principal component analysis to predict
           coronary artery disease

         This is an Open Access Article Open Access Article

    • Authors: Carlo Ricciardi, Antonio Saverio Valente, Kyle Edmund, Valeria Cantoni, Roberta Green, Antonella Fiorillo, Ilaria Picone, Stefania Santini, Mario Cesarelli
      Abstract: Health Informatics Journal, Ahead of Print.
      Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.
      Citation: Health Informatics Journal
      PubDate: 2020-01-23T09:12:22Z
      DOI: 10.1177/1460458219899210
       
  • Investigating the use of data-driven artificial intelligence in
           computerised decision support systems for health and social care: A
           systematic review

         This is an Open Access Article Open Access Article

    • Authors: Kathrin Cresswell, Margaret Callaghan, Sheraz Khan, Zakariya Sheikh, Hajar Mozaffar, Aziz Sheikh
      Abstract: Health Informatics Journal, Ahead of Print.
      There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a systematic literature review to identify relevant randomised controlled trials conducted between 2013 and 2018. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, Cochrane Library, ASSIA, Emerald, Health Business Fulltext Elite, ProQuest Public Health, Social Care Online, and grey literature sources. Search terms were conceptualised into three groups: artificial intelligence-related terms, computerised decision support -related terms, and terms relating to health and social care. Terms within groups were combined using the Boolean operator OR, and groups were combined using the Boolean operator AND. Two reviewers independently screened studies against the eligibility criteria and two independent reviewers extracted data on eligible studies onto a customised sheet. We assessed the quality of studies through the Critical Appraisal Skills Programme checklist for randomised controlled trials. We then conducted a narrative synthesis. We identified 68 hits of which five studies satisfied the inclusion criteria. These studies varied substantially in relation to quality, settings, outcomes, and technologies. None of the studies was conducted in social care settings, and three randomised controlled trials showed no difference in patient outcomes. Of these, one investigated the use of Bayesian triage algorithms on forced expiratory volume in 1 second (FEV1) and health-related quality of life in lung transplant patients. Another investigated the effect of image pattern recognition on neonatal development outcomes in pregnant women, and another investigated the effect of the Kalman filter technique for warfarin dosing suggestions on time in therapeutic range. The remaining two randomised controlled trials, investigating computer vision and neural networks on medication adherence and the impact of learning algorithms on assessment time of patients with gestational diabetes, showed statistically significant and clinically important differences to the control groups receiving standard care. However, these studies tended to be of low quality lacking detailed descriptions of methods and only one study used a double-blind design. Although the evidence of effectiveness of data-driven artificial intelligence to support decision-making in health and social care settings is limited, this work provides important insights on how a meaningful evidence base in this emerging field needs to be developed going forward. It is unlikely that any single overall message surrounding effectiveness will emerge - rather effectiveness of interventions is likely to be context-specific and calls for inclusion of a range of study designs to investigate mechanisms of action.
      Citation: Health Informatics Journal
      PubDate: 2020-01-22T08:47:57Z
      DOI: 10.1177/1460458219900452
       
  • HoneyDetails: A prototype for ensuring patient’s information privacy and
           thwarting electronic health record threats based on decoys

         This is an Open Access Article Open Access Article

    • Authors: Abiodun Esther Omolara, Aman Jantan, Oludare Isaac Abiodun, Humaira Arshad, Kemi Victoria Dada, Etuh Emmanuel
      Abstract: Health Informatics Journal, Ahead of Print.
      Advancements in electronic health record system allow patients to store and selectively share their medical records as needed with doctors. However, privacy concerns represent one of the major threats facing the electronic health record system. For instance, a cybercriminal may use a brute-force attack to authenticate into a patient’s account to steal the patient’s personal, medical or genetic details. This threat is amplified given that an individual’s genetic content is connected to their family, thus leading to security risks for their family members as well. Several cases of patient’s data theft have been reported where cybercriminals authenticated into the patient’s account, stole the patient’s medical data and assumed the identity of the patients. In some cases, the stolen data were used to access the patient’s accounts on other platforms and in other cases, to make fraudulent health insurance claims. Several measures have been suggested to address the security issues in electronic health record systems. Nevertheless, we emphasize that current measures proffer security in the short-term. This work studies the feasibility of using a decoy-based system named HoneyDetails in the security of the electronic health record system. HoneyDetails will serve fictitious medical data to the adversary during his hacking attempt to steal the patient’s data. However, the adversary will remain oblivious to the deceit due to the realistic structure of the data. Our findings indicate that the proposed system may serve as a potential measure for safeguarding against patient’s information theft.
      Citation: Health Informatics Journal
      PubDate: 2020-01-20T12:51:28Z
      DOI: 10.1177/1460458219894479
       
  • Using radar plots for performance benchmarking at patient and hospital
           levels using an Australian orthopaedics dataset

         This is an Open Access Article Open Access Article

    • Authors: Daniel M Morales-Silva, Cameron S McPherson, Guillermo Pineda-Villavicencio, Rory Atchison
      Abstract: Health Informatics Journal, Ahead of Print.
      This study will highlight the diagnostic potential that radar plots display for reporting on performance benchmarking from patient admissions to hospital for surgical procedures. Two drawbacks of radar plots – the presence of missing information and ordering of indicators – are addressed. Ten different orthopaedic surgery procedures were considered in this study. Moreover, twelve outcome indicators were provided for each of the 10 surgeries of interest. These indicators were displayed using a radar plot, which we call a scorecard. At the hospital level, we propose a facile process by which to consolidate our 10 scorecards into one. We addressed the ordering of indicators in our scorecards by considering the national median of the indicators as a benchmark. Furthermore, our the consolidated scorecard facilitates concise visualisation and dissemination of complex data. It also enables the classification of providers into potential low and high performers that warrant further investigation. In conclusion, radar plots provide a clear and effective comparative tool for discerning multiple outcome indicators against the benchmarks of patient admission. A case study between two top and bottom performers on a consolidated scorecard (at hospital level) showed that medical provider charges varied more than other outcome indicators.
      Citation: Health Informatics Journal
      PubDate: 2020-01-20T12:50:44Z
      DOI: 10.1177/1460458219895190
       
  • PredictMed: A logistic regression–based model to predict health
           conditions in cerebral palsy

         This is an Open Access Article Open Access Article

    • Authors: Carlo M Bertoncelli, Paola Altamura, Edgar Ramos Vieira, Sundaraja Sitharama Iyengar, Federico Solla, Domenico Bertoncelli
      Abstract: Health Informatics Journal, Ahead of Print.
      Logistic regression–based predictive models are widely used in the healthcare field but just recently are used to predict comorbidities in children with cerebral palsy. This article presents a logistic regression approach to predict health conditions in children with cerebral palsy and a few examples from recent research. The model named PredictMed was trained, tested, and validated for predicting the development of scoliosis, intellectual disabilities, autistic features, and in the present study, feeding disorders needing gastrostomy. This was a multinational, cross-sectional descriptive study. Data of 130 children (aged 12–18 years) with cerebral palsy were collected between June 2005 and June 2015. The logistic regression–based model uses an algorithm implemented in R programming language. After splitting the patients in training and testing sets, logistic regressions are performed on every possible subset (tuple) of independent variables. The tuple that shows the best predictive performance in terms of accuracy, sensitivity, and specificity is chosen as a set of independent variables in another logistic regression to calculate the probability to develop the specific health condition (e.g. the need for gastrostomy). The average of accuracy, sensitivity, and specificity score was 90%. Our model represents a novelty in the field of some cerebral palsy–related health outcomes treatment, and it should significantly help doctors’ decision-making process regarding patient prognosis.
      Citation: Health Informatics Journal
      PubDate: 2020-01-20T12:46:49Z
      DOI: 10.1177/1460458219898568
       
  • The rapid growth of intelligent systems in health and health care
         This is an Open Access Article Open Access Article

    • Authors: Jiang Bian, François Modave
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2020-01-11T11:35:45Z
      DOI: 10.1177/1460458219896899
       
  • Exploring the semantics in mental health research
         This is an Open Access Article Open Access Article

    • Authors: Jiang Bian, Yaoyun Zhang, Cui Tao
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2020-01-11T11:31:04Z
      DOI: 10.1177/1460458219896886
       
  • Mental health professional perspectives on health data sharing: Mixed
           methods study

         This is an Open Access Article Open Access Article

    • Authors: Adela Grando, Julia Ivanova, Megan Hiestand, Hiral Soni, Anita Murcko, Michael Saks, David Kaufman, Mary Jo Whitfield, Christy Dye, Darwyn Chern, Jonathan Maupin
      Abstract: Health Informatics Journal, Ahead of Print.
      This study explores behavioral health professionals’ perceptions of granular data. Semi-structured in-person interviews of 20 health professionals were conducted at two different sites. Qualitative and quantitative analysis was performed. While most health professionals agreed that patients should control who accesses their personal medical record (70%), there are certain types of health information that should never be restricted (65%). Emergent themes, including perceived reasons that patients might share or withhold certain types of health information (65%), care coordination (12%), patient comprehension (11%), stigma (5%), trust (3%), sociocultural understanding (3%), and dissatisfaction with consent processes (1%), are explored. The impact of care role (prescriber or non-prescriber) on data-sharing perception is explored as well. This study informs the discussion on developing technology that helps balance provider and patient data-sharing and access needs.
      Citation: Health Informatics Journal
      PubDate: 2020-01-11T11:28:44Z
      DOI: 10.1177/1460458219893848
       
  • Insights from user reviews to improve mental health apps
         This is an Open Access Article Open Access Article

    • Authors: Felwah Alqahtani, Rita Orji
      Abstract: Health Informatics Journal, Ahead of Print.
      Mental health applications hold great promise as interventions for addressing common mental issues. Although many people with mental health issues use mobile app interventions, their adherence level remains low. Low engagement affects the effectiveness of mobile interventions. However, there is still a dearth of research to explain the reasons for low engagement. User experience and usability are two factors that determine the adoption and usage of apps. Analyzing user reviews of mobile apps for mental health issues reveals user experience and what features users liked and disliked in the apps and hence informs future app design and refinements. This research aims to analyze user reviews of publicly available mental health applications to uncover their strengths, weaknesses, and gaps, hence revealing why users are likely to cease using these applications. We mined reviews of 106 mental health apps retrieved from Apple’s App Store and Google Play and employed thematic analysis on 13,549 reviews. The review analysis shows that users placed more emphasis on the user interface and the user-friendliness of the app. Users also appreciated apps that present them with a variety of options, functionalities, and content that they can choose. Again, apps that offer adaptive functionalities that allow users to adapt some app features also received high ratings. In contrast, poor usability emerged as the most common reason for abandoning mental health apps. Other pitfalls include lack of a content variety, lack of personalization, lack of customer service and trust, and security and privacy issues.
      Citation: Health Informatics Journal
      PubDate: 2020-01-10T10:06:23Z
      DOI: 10.1177/1460458219896492
       
  • Mental health professionals’ perceptions on patients control of data
           sharing

         This is an Open Access Article Open Access Article

    • Authors: Julia Ivanova, Adela Grando, Anita Murcko, Michael Saks, Mary Jo Whitfield, Christy Dye, Darwyn Chern
      Abstract: Health Informatics Journal, Ahead of Print.
      Integrated mental and physical care environments require data sharing, but little is known about health professionals’ perceptions of patient-controlled health data sharing. We describe mental health professionals’ views on patient-controlled data sharing using semi-structured interviews and a mixed-method analysis with thematic coding. Health information rights, specifically those of patients and health care professionals, emerged as a key theme. Behavioral health professionals identified patient motivations for non-sharing sensitive mental health records relating to substance use, emergency treatment, and serious mental illness (94%). We explore conflicts between professional need for timely access to health information and patient desire to withhold some data categories. Health professionals’ views on data sharing are integral to the redesign of health data sharing and informed consent. As well, they seek clarity about the impact of patient-controlled sharing on health professionals’ roles and scope of practice.
      Citation: Health Informatics Journal
      PubDate: 2020-01-08T10:41:49Z
      DOI: 10.1177/1460458219893845
       
  • School-based telemedicine: Perceptions about a telemedicine model of care
         This is an Open Access Article Open Access Article

    • Authors: May Lin Tye, Michelle Honey, Karen Day
      Abstract: Health Informatics Journal, Ahead of Print.
      In New Zealand, a store-and-forward telemedicine programme is implemented in schools to address common health conditions. This study aimed to investigate perceptions of the non-clinical school staff involved on this telemedicine model of care. Interviews and analysis were framed by sociotechnical theory under constructs of identities, affiliations, interactions and environments. Findings show that telemedicine aligned with identities of staff as carers. Affiliations via close relationships with children and community support enabled the programme. Delivering telemedicine enhanced interactions with children. Environments related to practices and physical characteristics of the school were viewed as constrainers and enablers for delivery. School-based telemedicine delivered by school staff is perceived as an acceptable model of care. Benefits include empowerment, school cohesion and potential improvement in health literacy, with no major issues perceived. Telemedicine may be effective for treating common health conditions in school children, with potential for community members to be involved in health care.
      Citation: Health Informatics Journal
      PubDate: 2020-01-08T10:40:01Z
      DOI: 10.1177/1460458219895380
       
  • Risks and medication errors analysis to evaluate the impact of a
           chemotherapy compounding workflow management system on cancer patients’
           safety

         This is an Open Access Article Open Access Article

    • Authors: MBelén Marzal-Alfaro, Carmen Guadalupe Rodriguez-Gonzalez, Vicente Escudero-Vilaplana, José Luis Revuelta-Herrero, Eva González-Haba, Sara Ibáñez-Garcia, Irene Iglesias-Peinado, Ana Herranz-Alonso, Maria Sanjurjo Saez
      Abstract: Health Informatics Journal, Ahead of Print.
      A failure modes, effects and criticality analysis was supported by an observational medication error rate study to analyze the impact of Phocus Rx®, a new image-based workflow software system, on chemotherapy compounding error rates. Residual risks that should be a target for additional action were identified and prioritized and pharmacy staff satisfaction with the new system was evaluated. In total, 16 potential failure modes were recognized in the pre-implementation phase and 21 after Phocus Rx® implementation. The total reduction of the criticality index was 67 percent, with a reduction of 46 percent in material preparation, 76 percent in drug production and 48 percent in quality control subprocesses. The relative risk reduction of compounding error rate was 63 percent after the implementation of Phocus Rx®, from 0.045 to 0.017 percent. The high-priority recommendations defined were identification of the product with batch and expiration date from scanned bidimensional barcodes on drug vials and process improvements in image-based quality control. Overall satisfaction index was 8.30 (SD 1.06) for technicians and 8.56 (SD 1.42) for pharmacists (p = 0.655). The introduction of a new workflow management software system was an effective approach to increasing safety in the compounding procedures in the pharmacy department, according to the failure modes, effects and criticality analysis method.
      Citation: Health Informatics Journal
      PubDate: 2020-01-08T10:39:02Z
      DOI: 10.1177/1460458219895434
       
  • Usability of electronic patient record systems: Instrument validation
           study conducted for hospitals in Germany

         This is an Open Access Article Open Access Article

    • Authors: Anke Simon
      Abstract: Health Informatics Journal, Ahead of Print.
      In Germany and elsewhere, few hospital electronic patient record usability surveys are available. Moreover, there seems to be a complete lack of validated instruments in the respective literature. Hence, this study’s purpose is to validate a scale for measuring the usability of hospital electronic patient record systems. The data used for the study’s analyses (n = 949) were originally obtained during the German national initiative ‘Hospital IT User Questionnaire’. In the course of the study, reliability and exploratory factor analyses were conducted and psychometric tests showed a reliable, valid and suitable instrument. Descriptive data analysis suggests a generally low user perception and variances hint at a high potential for future improvements. To our knowledge, this study shows that a general inventory (subscales from the IsoMetrics inventory) can be applied to measuring hospital electronic patient record usability as well. The validated instrument can be used to inform healthcare providers, decision makers and politicians of a given state of usability, discrepancies between different hospitals or systems providers, and serve as a basis for improvements.
      Citation: Health Informatics Journal
      PubDate: 2020-01-08T10:37:42Z
      DOI: 10.1177/1460458219895910
       
  • Evaluation of mobile apps for treatment of patients at risk of developing
           gestational diabetes

         This is an Open Access Article Open Access Article

    • Authors: Cristina Tassone, Karim Keshavjee, Alessia Paglialonga, Nimia Moreira, Jennifer Pinto, Yuri Quintana
      Abstract: Health Informatics Journal, Ahead of Print.
      This study evaluates mobile apps using a theory-based evaluation framework to discover their applicability for patients at risk of gestational diabetes. This study assessed how well the existing mobile apps on the market meet the information and tracking needs of patients with gestational diabetes and evaluated the feasibility of how to integrate these apps into patient care. A search was conducted in the Apple iTunes and Google Play store for mobile apps that contained keywords related to the following concepts of nutrition: diet, tracking, diabetes, and pregnancy. Evaluation criteria were developed to assess the mobile apps on five dimensions. Overall, the apps scored well on education and information functions and scored poorly on engagement functions. There are few apps that provide comprehensive evidence-based educational content, tracking tools, and integration with electronic health records. This study demonstrates the need to develop apps that have comprehensive content, tracking tools, and ability to bidirectionally share data.
      Citation: Health Informatics Journal
      PubDate: 2020-01-08T10:35:58Z
      DOI: 10.1177/1460458219896639
       
  • Validity and usability of a smart ball–driven serious game to monitor
           grip strength in independent elderlies

         This is an Open Access Article Open Access Article

    • Authors: Francesca Lunardini, N. Alberto Borghese, Luca Piccini, Giuseppina Bernardelli, Matteo Cesari, Simona Ferrante
      Abstract: Health Informatics Journal, Ahead of Print.
      Telemonitoring is one of the most expedient answers to the strong need for preventive care imposed by the rapidly aging society. We propose an innovative solution to the detection of early signs of frailty by presenting a serious game controlled by a smart sensorized soft plastic ball, designed to achieve continuous home-based monitoring of muscle weakness in older adults. Design, development, and testing of the smart ball and of the game interface devised to guide the monitoring procedure are presented. Reliability and concurrent validity of the system in measuring maximal grip strength against the clinical standard Jamar® were evaluated. Serious game usability and acceptance were investigated on 26 elderlies. Smart ball and Jamar measurements were well correlated (0.76 and 0.80 for dominant and non-dominant hands) and test–retest reliability of pressure measurements was excellent (intraclass correlation coefficient>0.94). The serious game was well accepted by the 96.1 percent of participants, who provided a strongly positive usability score (87.7/100). The smart ball–driven serious game demonstrated excellent reliability and good validity in measuring grip strength. The proposed smart ball–driven serious game can be used for home self-monitoring of grip strength in elderlies.
      Citation: Health Informatics Journal
      PubDate: 2020-01-06T12:07:26Z
      DOI: 10.1177/1460458219895381
       
  • A literature review of current technologies on health data integration for
           patient-centered health management

         This is an Open Access Article Open Access Article

    • Authors: Cong Peng, Prashant Goswami, Guohua Bai
      Abstract: Health Informatics Journal, Ahead of Print.
      Health data integration enables a collaborative utilization of data across different systems. It not only provides a comprehensive view of a patient’s health but can also potentially cope with challenges faced by the current healthcare system. In this literature review, we investigated the existing work on heterogeneous health data integration as well as the methods of utilizing the integrated health data. Our search was narrowed down to 32 articles for analysis. The integration approaches in the reviewed articles were classified into three classifications, and the utilization approaches were classified into five classifications. The topic of health data integration is still under debate and problems are far from being resolved. This review suggests the need for a more efficient way to invoke the various services for aggregating health data, as well as a more effective way to integrate the aggregated health data for supporting collaborative utilization. We have found that the combination of Web Application Programming Interface and Semantic Web technologies has the potential to cope with the challenges based on our analysis of the review result.
      Citation: Health Informatics Journal
      PubDate: 2019-12-30T09:21:23Z
      DOI: 10.1177/1460458219892387
       
  • Multicenter validation of a machine-learning algorithm for 48-h all-cause
           mortality prediction

         This is an Open Access Article Open Access Article

    • Authors: Hamid Mohamadlou, Saarang Panchavati, Jacob Calvert, Anna Lynn-Palevsky, Sidney Le, Angier Allen, Emily Pellegrini, Abigail Green-Saxena, Christopher Barton, Grant Fletcher, Lisa Shieh, Philip B Stark, Uli Chettipally, David Shimabukuro, Mitchell Feldman, Ritankar Das
      Abstract: Health Informatics Journal, Ahead of Print.
      In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.
      Citation: Health Informatics Journal
      PubDate: 2019-12-30T09:20:03Z
      DOI: 10.1177/1460458219894494
       
  • Methodologies for designing healthcare analytics solutions: A literature
           analysis

         This is an Open Access Article Open Access Article

    • Authors: Shah J Miah, John Gammack, Najmul Hasan
      Abstract: Health Informatics Journal, Ahead of Print.
      Healthcare analytics has been a rapidly emerging research domain in recent years. In general, healthcare solution design studies focus on developing analytic solutions that enhance product, process and practice values for clinical and non-clinical decision support. The objective of this study is to explore the scope of healthcare analytics research and in particular its utilisation of design and development methodologies. Using six prominent electronic databases, qualifying articles between 2010 and mid-2018 were sourced and categorised. A total of 52 articles on healthcare analytics solutions were selected for relevant content on public healthcare. The research team scrutinised the articles, using established content analysis protocols. Analysis identified that various methodologies have been used for developing analytics solutions, such as prototyping, traditional software engineering, agile approaches and others, but despite its clear advantages, few show the use of design science. Key topic areas are also identified throughout the content analysis suggesting topical research priorities in the field.
      Citation: Health Informatics Journal
      PubDate: 2019-12-26T11:17:56Z
      DOI: 10.1177/1460458219895386
       
  • Electronic Health Record and Problem Lists in Leeds, United Kingdom:
           Variability of general practitioners’ views

         This is an Open Access Article Open Access Article

    • Authors: Pablo Millares Martin, Laura Sbaffi
      Abstract: Health Informatics Journal, Ahead of Print.
      Data sharing of Electronic Health Records from general practices to secondary care in Leeds occurs through the so-called Leeds Care Records, which collects a specific set of codes from primary care, known as ‘Active Problems’, and presents it to the user. Variability on its content is a known issue. To explore general practitioners’ views on their use of ‘Active Problems’ and on sharing data, so lessons could be learnt on how to homogenise and improve shared data. Assessing Leeds general practitioners’ views through two parallel processes (60 online surveys and 17 interviews). General practitioners feel they do not have the time nor the training required for keeping a shared approach to concise and current Problem Lists in electronic patient records. Action is needed to reduce current variability, and to improve the quality of shared information. Some types of codes currently present in Problem Lists have very little support among general practitioners who consider the focus should be on long-term conditions and probably adding current acute diagnoses and life expectancy items and not omitting sensitive information. There is a perceived need of training and time to update Problem Lists if their quality is to improve.
      Citation: Health Informatics Journal
      PubDate: 2019-12-25T12:12:01Z
      DOI: 10.1177/1460458219895184
       
  • ‘WP2Cochrane’, a tool linking Wikipedia to the Cochrane Library:
           Results of a bibliometric analysis evaluating article quality and
           importance

         This is an Open Access Article Open Access Article

    • Authors: Arash Joorabchi, Cailbhe Doherty, Jennifer Dawson
      Abstract: Health Informatics Journal, Ahead of Print.
      Medical information on English Wikipedia was accessed over 2 billion times in 2018. Our goal was to develop an automated system to assist Wikipedia volunteers to improve articles with high-quality sources from journals such as The Cochrane Library. We created an automated indexing system by linking available reviews from the Cochrane library with disease-related Wikipedia articles and evaluating the relationship between the quality and importance of these articles with the number of relevant and cited Cochrane reviews. We first conducted a bibliometric analysis, identifying disease-related Wikipedia articles and relevant/cited Cochrane reviews. Citations were thematically coded, and descriptive statistics were calculated. Finally, separate multinomial logistic regression analyses were conducted for article quality and importance. The indexing system identified 4381 disease-related Wikipedia articles, 1193 (27%) of which cited a Cochrane review. Higher quality Wikipedia articles were more likely to cite a Cochrane review (p = 0.002), while lower quality articles were less likely to cite a Cochrane review (p < 0.0005). A greater number of Cochrane reviews are available for more ‘important’ Wikipedia articles (p < 0.005), and these articles were more likely to cite a Cochrane review (p < 0.005). This approach to an indexing system can be leveraged by Wikipedia contributors and editors seeking to update disease-related Wikipedia articles with relevant Cochrane reviews (thus improving their quality), and online information seekers in need of additional information to supplement their Wikipedia search.
      Citation: Health Informatics Journal
      PubDate: 2019-12-23T12:57:46Z
      DOI: 10.1177/1460458219892711
       
  • Predictive factors of physicians’ satisfaction with telemedicine
           services acceptance

         This is an Open Access Article Open Access Article

    • Authors: Jonathan Kissi, Baozhen Dai, Courage SK Dogbe, Jonathan Banahene, Oyeh Ernest
      Abstract: Health Informatics Journal, Ahead of Print.
      Despite the significant increase in telemedicine services technology, its adoption and use have been quite slow in some healthcare settings. It is generally accepted in today’s globalizing world that the success of telemedicine services relies on users’ satisfaction. Satisfying physicians and patients is one of the crucial objectives of telemedicine success. This study seeks to evaluate physicians’ satisfaction with telemedicine services adoption and utilization using the technology acceptance model. A structured questionnaire based on the construct of technology acceptance model was used to solicit for data from participants in four different government health institutions. Purposive and convenience sampling techniques were employed to select healthcare professionals from various medical fields. Structural equation modeling was utilized in the data analysis. Perceived ease of use and perceived usefulness of telemedicine services were found to influence physicians’ behavioral intentions. This resulted in increased efficiency, quality of services, quality patient care delivery, and satisfaction among physicians in using telemedicine services. We noted that the adoption of telemedicine services in clinical settings depends on physicians’ and patients’ satisfaction with the use of the service. The study contributes to empirical knowledge by identifying the vital predictive factors affecting telemedicine services satisfaction among physicians.
      Citation: Health Informatics Journal
      PubDate: 2019-12-19T11:13:57Z
      DOI: 10.1177/1460458219892162
       
  • Enablers and obstacles to implementing remote monitoring technology in
           cardiac care: A report from an interactive workshop

         This is an Open Access Article Open Access Article

    • Authors: Yohanca Diaz-Skeete, Oonagh M Giggins, David McQuaid, Paul Beaney
      Abstract: Health Informatics Journal, Ahead of Print.
      An ageing population and chronic disease are putting pressure on the Irish health system. The field of eHealth is rapidly evolving and has the potential to become an important component of healthcare, but there appears to be a gap currently between research in this field and the integration of eHealth technology into clinical practice. During the eHealth Ireland Ecosystem Conference held in April 2018, a workshop was conducted to explore the barriers and facilitators to the adoption of eHealth technology, particularly remote monitoring systems in community and home cardiac care. Participants included clinicians, academic researchers, technologists, patient advocates, policy makers, and representatives from the health service. The conversations in the workshop pivoted around why technology systems in cardiac care rarely moved beyond the research project stage and what can be done to address this issue. The discussions in the workshop focused around the lack of funding available, the need for reimbursement models, the lack of awareness about remote monitoring, the angst about who is responsible for the data generated, the design of systems, regulatory standards, and the increasing demand on services, education, and patient empowerment.
      Citation: Health Informatics Journal
      PubDate: 2019-12-19T11:09:37Z
      DOI: 10.1177/1460458219892175
       
  • Using the Onitor® Track for weight loss: A mixed methods study among
           overweight and obese women

         This is an Open Access Article Open Access Article

    • Authors: Kelly Buchan, Heather M Morgan
      Abstract: Health Informatics Journal, Ahead of Print.
      Non-communicable disease rates associated with being overweight or obese are rising. Technologies warrant consideration as weight loss interventions. Cloudtag’s® Onitor® Track, a dual-position wearable plus smartphone application, monitors energy expenditure and provides tailored exercise programmes. This research aimed to undertake an experimental study of 20 overweight and obese women using the technology; explore its feasibility and acceptability through interviews; and investigate its behaviour change techniques. The primary outcome was decreased body weight (kg) at 4 weeks. Secondary outcomes were body mass index (kg/m2) and waist circumference (cm) changes. 15 overweight and obese women took part. Results indicated weight loss (median 2.7 kg, p 
      Citation: Health Informatics Journal
      PubDate: 2019-12-17T02:17:16Z
      DOI: 10.1177/1460458219890790
       
  • Atrial fibrillation classification using deep learning algorithm in
           Internet of Things–based smart healthcare system

         This is an Open Access Article Open Access Article

    • Authors: Pandia Rajan Jeyaraj, Edward Rajan Samuel Nadar
      Abstract: Health Informatics Journal, Ahead of Print.
      Detecting the electrocardiogram pattern in Internet of Things–based healthcare system and notifying this to the user is a challenging task. Using advance computing methods for classification of electrocardiogram signal is a notable research topic. In this research work, an intelligent electrocardiogram signal classification, employing deep learning algorithm, developed and tested in Internet of Things–based smart healthcare system was proposed. For classification of acquired electrocardiogram signal, a partitioned deep convolutional neural network was proposed. The electrocardiogram feature continuously in the Internet of Things–based monitoring system was learnt. To make use of learned features in the continuous time series data, it forms a higher order space in the server. We have made quantifiable comparative analysis with other classification algorithm with the same time series data collected from different atrial fibrillation samples in the Internet of Things–based e-health system. Our proposed algorithm learned features were tested in atrial fibrillation classified signal with other conventional classifiers with various performance indices. We obtained an accuracy of 96.3 percent with 93.5-percent sensitivity and 97.5-percent precision. From the obtained result, processing with proposed deep convolutional neural network provides reliable timely assist and accurate classification of electrocardiogram signal in Internet of Things–based smart healthcare system.
      Citation: Health Informatics Journal
      PubDate: 2019-12-16T12:45:09Z
      DOI: 10.1177/1460458219891384
       
  • Computational prediction of implantation outcome after embryo transfer
         This is an Open Access Article Open Access Article

    • Authors: Behnaz Raef, Masoud Maleki, Reza Ferdousi
      Abstract: Health Informatics Journal, Ahead of Print.
      The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.
      Citation: Health Informatics Journal
      PubDate: 2019-12-12T09:11:55Z
      DOI: 10.1177/1460458219892138
       
  • Optimizing smartphone intervention features to improve chronic disease
           management: A rapid review

         This is an Open Access Article Open Access Article

    • Authors: Arieh Gomolin, Bertrand Lebouché, Kim Engler, Isabelle Vedel
      Abstract: Health Informatics Journal, Ahead of Print.
      While there are an increasing number of mobile health applications to facilitate self-management in patients with chronic disease, little is known about which application features are responsible for impact. The objective was to uncover application features associated with increased usability or improved patient outcomes. A rapid review was conducted in MEDLINE for recent studies on smartphone applications. Eligible studies examined applications for adult chronic disease populations, with self-management content, and assessed specific features. The features studied and their impacts on usability and patient outcomes were extracted. From 3661 records, 19 studies were eligible. Numerous application features related to interface (e.g. reduced number of screens, limited manual data entry) and content (e.g. simplicity, self-tracking features) were linked to improved usability. Only three studies examined patient outcomes. Specific features were shown to have a higher impact. Implementing them can improve chronic disease management and reduce app development efforts.
      Citation: Health Informatics Journal
      PubDate: 2019-12-12T09:10:15Z
      DOI: 10.1177/1460458219891377
       
  • Medication-rights detection using incident reports: A natural language
           processing and deep neural network approach

         This is an Open Access Article Open Access Article

    • Authors: Zoie Shui-Yee Wong, HY So, Belinda SC Kwok, Mavis WS Lai, David TF Sun
      Abstract: Health Informatics Journal, Ahead of Print.
      Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using natural language processing and deep neural networks to automate medication-incident identification using free-text incident reports. We assessed the performance of deep neural network models in classifying the Advanced Incident Reporting System reports and compared the models’ performance with that of other common classification methods (including logistic regression, support vector machines and the decision-tree method). We also evaluated the effects on prediction outcomes of several deep neural network model settings, including number of layers, number of neurons and activation regularisation functions. The accuracy of the models was measured at 0.9 or above across model settings and algorithms. The average values obtained for accuracy and area under the curve were 0.940 (standard deviation: 0.011) and 0.911 (standard deviation: 0.019), respectively. It is shown that deep neural network models were more accurate than the other classifiers across all of the tested class labels (including wrong patient, wrong drug, wrong time, wrong dose and wrong route). The deep neural network method outperformed other binary classifiers and our default base case model, and parameter arguments setting generally performed well for the five medication-rights datasets. The medication-rights detection system developed in this study successfully uses a natural language processing and deep-learning approach to classify patient-safety incidents using the Advanced Incident Reporting System reports, which may be transferable to other mandatory and voluntary incident reporting systems worldwide.
      Citation: Health Informatics Journal
      PubDate: 2019-12-10T11:02:26Z
      DOI: 10.1177/1460458219889798
       
  • A smartphone-based support group for alcoholism: Effects of giving and
           receiving emotional support on coping self-efficacy and risky drinking

         This is an Open Access Article Open Access Article

    • Authors: Woohyun Yoo, Dhavan V Shah, Ming-Yuan Chih, David H Gustafson
      Abstract: Health Informatics Journal, Ahead of Print.
      The purpose of this study was to investigate the nature and effects of exchanging emotional support via a smartphone-based support group for patients with alcohol dependence. Of the 349 patients who met the Diagnostic and Statistical Manual of Mental Disorders (4th ed.) criteria for alcohol dependence, 153 patients participated in the discussion group within the Addiction-Comprehensive Health Enhancement Support System, a smartphone application aimed at reducing relapse. This was developed to prevent problem drinking by offering individuals in recovery for alcohol dependence automated 24/7 recovery support services and frequent assessment of their symptom status as part of their addiction care. The results showed that receiving emotional support from health care providers improved coping self-efficacy. Giving emotional support and receiving emotional support from health care providers acted as a buffer, protecting patients from the harmful effects of emotional distress on risky drinking. Clinicians and researchers should use the features of smartphone-based support groups to reach out to alcoholic patients in need and encourage them to participate in the exchange of emotional support with others.
      Citation: Health Informatics Journal
      PubDate: 2019-12-09T12:05:40Z
      DOI: 10.1177/1460458219888403
       
  • Usability evaluation of the eHealth Long Lasting Memories program in
           Spanish elderly people

         This is an Open Access Article Open Access Article

    • Authors: Susel Góngora Alonso, José Miguel Toribio Guzmán, Beatriz Sainz de Abajo, Juan Luis Muñoz Sánchez, Manuel Franco Martín, Isabel de la Torre Díez
      Abstract: Health Informatics Journal, Ahead of Print.
      In recent years, there has been a great development of software technology in the field of psychogeriatric research, helping to improve the quality of life of the elderly and preventing cognitive deterioration associated with aging, and thus decrease the possible dependence. The main objective of the present study is to evaluate the usability of the Long Lasting Memories program in elderly people with or without cognitive impairment in a region of Spain. For the study, users were classified into three groups: subjects with no cognitive impairment, with mild cognitive impairment and mild dementia, and they were given a usability questionnaire covering different variables. Of the 157 Spanish participants in the study, 84.1 percent answered the usability questionnaire, obtaining wide acceptance in all study groups regarding the usability of the Long Lasting Memories program. Current research begins to mark a new perspective that recognizes the need to establish a preventive strategy for degenerative diseases.
      Citation: Health Informatics Journal
      PubDate: 2019-12-06T02:24:02Z
      DOI: 10.1177/1460458219889501
       
  • Tailored texts: An application of regulatory fit to text messages designed
           to reduce high-risk drinking

         This is an Open Access Article Open Access Article

    • Authors: Elizabeth M Glowacki, Jay M Bernhardt, Matthew S McGlone
      Abstract: Health Informatics Journal, Ahead of Print.
      This study used the regulatory focus/fit framework to compare the impact of text message wording on college students’ drinking behaviors. In this 2 × 3 × 2 pre-test/post-test experiment, participants (N = 279) were randomly assigned to one of the three groups: messages matching regulatory focus (congruent group), messages mismatching regulatory focus (incongruent group), and general health messages (control group). Messages were tailored by regulatory fit (prevention-oriented or promotion-oriented). Mixed factorial analyses of covariance revealed that prevention-oriented individuals who received text messages incongruent with their regulatory focus reported drinking alcohol for more hours and were more likely to consume a higher quantity of drinks than participants in the congruent or control group. These findings suggest that health messages mismatched to a receiver’s regulatory focus might exacerbate unhealthy behavior.
      Citation: Health Informatics Journal
      PubDate: 2019-12-06T02:23:43Z
      DOI: 10.1177/1460458219889279
       
  • Perceptions and needs regarding technologies in nursing homes: An
           exploratory study

         This is an Open Access Article Open Access Article

    • Authors: Anne Bourbonnais, Jacqueline Rousseau, Marie-Hélène Lalonde, Jean Meunier, Nolwenn Lapierre, Marie-Pierre Gagnon
      Abstract: Health Informatics Journal, Ahead of Print.
      Two of the most salient problems in nursing homes are the responsive behaviours and falls of older people living with Alzheimer’s disease and related disorders. Intelligent videomonitoring and mobile applications are potential technologies that may help prevent and manage these problems. However, evidence for the needs for technologies in nursing homes is scarce. This study aimed to explore the perceptions and needs of care managers, and of formal and family caregivers in nursing homes regarding these potential technologies. With an exploratory qualitative design based on Rogers’ diffusion of innovation theory, individual interviews and a content analysis were conducted. Results show that the potential users of these technologies consider them relevant in nursing homes. The characteristics that would make these technologies useful in nursing homes are described. These results could be used to develop useful technologies to improve the quality of clinical practice in nursing homes.
      Citation: Health Informatics Journal
      PubDate: 2019-12-04T12:45:32Z
      DOI: 10.1177/1460458219889499
       
  • Exploring perceptions on medical app use in clinical communication among
           Austrian physicians: Results of a validation study

         This is an Open Access Article Open Access Article

    • Authors: Daniela Haluza, Fanni Hofer
      Abstract: Health Informatics Journal, Ahead of Print.
      Physicians increasingly use medical applications to facilitate clinical information management. The respective effect on clinical communication and quality of healthcare provision has not been studied in the Austrian context so far. Thus, the current cross-sectional online study analyzed prevalent medical applications use and views on clinical communication competence in everyday medical practice among Austrian physicians (n = 151) and validated the survey tool. More than half of the participants used medical applications in daily clinical practice. The top three benefits of medical applications use were higher quality of healthcare, location-independent health service access, and higher efficiency in healthcare resource allocation. Moreover, study participants felt that communication competence acquired during medical studies inadequately prepared them for daily clinical practices. Medical applications use certainly affects the therapeutic alliance between patients and physician. This study supports the importance of initiating an open, constructive discussion among healthcare stakeholders and developing according to evidence-based guidelines.
      Citation: Health Informatics Journal
      PubDate: 2019-12-03T05:07:14Z
      DOI: 10.1177/1460458219888420
       
  • An investigation into the use of infant feeding tracker apps by
           breastfeeding mothers

         This is an Open Access Article Open Access Article

    • Authors: Kaitlyn Dienelt, Carly J Moores, Jacqueline Miller, Kaye Mehta
      Abstract: Health Informatics Journal, Ahead of Print.
      Sufficient information and support for breastfeeding mothers is vital to encourage optimal infant feeding practices. Infant feeding apps give breastfeeding instructions and access to information however, little is known about mothers’ perceptions about these resources. This study investigated mothers’ use and experiences of infant feeding apps with a feeding tracker component, including how information within these apps is used, initial reasons for downloading, the role of the app in infant feeding, and perceived benefits and disadvantages of infant feeding apps. In-depth interviews were conducted with nine Australian breastfeeding mothers who had used an infant feeding app in the last year. Interviews were recorded, transcribed verbatim and coded prior to thematic analysis. The findings revealed that infant feeding apps provide mothers with objective information to guide their breastfeeding decisions and other aspects of baby care. This objective approach to infant feeding gives mothers a perception of greater control, confidence and efficacy at a time of transition and stress in the early stages of parenting an infant. While, overall, the mothers were positive about infant feeding apps, they also expressed concerns regarding overreliance on the app, feeling overwhelmed with the data and questioning the credibility of the information.
      Citation: Health Informatics Journal
      PubDate: 2019-12-03T01:53:05Z
      DOI: 10.1177/1460458219888402
       
  • FoodKnight: A mobile educational game and analyses of obesity awareness in
           children

         This is an Open Access Article Open Access Article

    • Authors: Thomas Bailey, Fadi Thabtah, Marcus Wright, Duy Anh Tran
      Abstract: Health Informatics Journal, Ahead of Print.
      One of the main contributing factors to child obesity is the absence of education and knowledge children have towards certain foods when they are making food choices. In most cases, children will pick energy-dense food over foods with more nutritional value and do not understand the consequences of their decisions. Our proposed solution to help overcome this problem is an educational gaming application called FoodKnight. Games have the ability to engage children more than traditional teaching methods used in schools, and capitalising on this exciting approach would be beneficial for children. FoodKnight incorporates stealth learning to disguise the teaching of healthy food choices while playing a game; a step-counter is also included to encourage the user to be active. The overall feedback FoodKnight received from 38 participants regarding the initial prototype was positive. Minor issues found with the game were addressed and implemented in an update. FoodKnight has been implemented in the Android mobile platform to increase accessibility.
      Citation: Health Informatics Journal
      PubDate: 2019-12-03T01:51:57Z
      DOI: 10.1177/1460458219888405
       
  • A comparative study of the utilisation of an electronic test–result
           management system in emergency and intensive care settings

         This is an Open Access Article Open Access Article

    • Authors: Judith Thomas, Maria R Dahm, Julie Li, Johanna I Westbrook, Andrew Georgiou
      Abstract: Health Informatics Journal, Ahead of Print.
      The purpose of this qualitative study was to identify differences in the utilisation of an electronic medical record test–result management system between two acute care departments. Field observations (130 min) and semi-structured interviews (n = 24) were conducted in the Intensive Care Unit and Emergency Department of an Australian hospital. Work processes identified from audio transcripts were modelled using business process modelling. Comparison of the Emergency Department and Intensive Care Unit identified the following: (1) test ordering variations according to clinical roles, (2) differences in the use of electronic medical record functionality according to specific demands of the clinical environment and (3) the non-linear components of the test–result management process. Variations were identified in the number of process decisions, external collaborations and temporal process workflows. Modelling the business processes, collaboration and communication needs of individual clinical environments can aid in enhancing the quality and appositeness of health information technology interventions and thus contribute to improving patient safety. Future health information technology interventions/evaluations aimed at improving the safety of test–result management processes need to address both the nuances of the clinical environment and accommodate the individual work practices of clinicians within that environment.
      Citation: Health Informatics Journal
      PubDate: 2019-12-03T01:51:25Z
      DOI: 10.1177/1460458219889223
       
  • The efficiency analysis and spatial implications of health information
           technology: A regional exploratory study in China

         This is an Open Access Article Open Access Article

    • Authors: Dan Li, Jianqian Chao, Jing Kong, Gui Cao, Mengru Lv, Man Zhang
      Abstract: Health Informatics Journal, Ahead of Print.
      The new adoption of healthcare information technology is costly, and effects on healthcare performance can be questionable. This nationwide study in China investigated the efficient performance of healthcare information technology and examined its spatial correlation. Panel data were extracted from the Annual Investigation Report on Hospital Information in China and the China Health Statistics Yearbook for 2007 through 2015 (279 observations). Stochastic frontier analysis was employed to estimate the technical efficiency of healthcare information technology performance and related factors at the regional level. Healthcare information technology performance was positively associated with electronic medical records, total input, and cost of inpatient stay, while picture archiving and communication systems and net assets were negatively related. Local Indicators of Spatial Association showed that there existed significant spatial autocorrelation. Governmental policies would best make distinctions among different forms of healthcare information technology, especially between electronic medical records and picture archiving and communication systems. Policies should be formulated to improve healthcare information technology adoption and reduce regional differences.
      Citation: Health Informatics Journal
      PubDate: 2019-12-03T01:49:45Z
      DOI: 10.1177/1460458219889794
       
  • Network analysis of medical care services
         This is an Open Access Article Open Access Article

    • Authors: Jerome Niyirora, Ossayne Aragones
      Abstract: Health Informatics Journal, Ahead of Print.
      Medical care services can be organized into a network. Understanding the structure of this network cannot only help analyze common clinical protocols but can also help reveal previously unknown patterns of care. The objective of this research is to introduce the concept and methods for constructing and analyzing the network of medical care services. We start by demonstrating how to build the network itself and then develop algorithms, based on principal component analysis and social network analysis, to detect communities of services. Finally, we propose novel graphical techniques for representing and assessing patterns of care. We demonstrate the application of our algorithms using data from an Emergency Department in New York State. One of the implications of our research is that clinical experts could use our algorithms to detect deviations from either existing protocols of care or administrative norms.
      Citation: Health Informatics Journal
      PubDate: 2019-11-18T08:54:41Z
      DOI: 10.1177/1460458219887047
       
  • How data provided by the Brazilian information system of primary care have
           been used by researchers

         This is an Open Access Article Open Access Article

    • Authors: Fernando Rocha Lucena Lopes, Karolinne Souza Monteiro, Silvana Santos
      Abstract: Health Informatics Journal, Ahead of Print.
      In this article, we have investigated how researchers use the data provided by the Brazilian Information System of Primary Care . We also searched, for the first time, studies that evaluated the quality and reliability of the information provided by the Primary Care Information System. An integrative review of the literature was performed using the keywords ‘information systems, primary care and SIAB’ on search databases, and 53 of 174 articles were selected. These publications were classified into two large subgroups: those using the Primary Care Information System as ‘data source’ and those that took it as the ‘object of study’. The first group included 35 studies, 18 of which used demographic and social health data records, and nine described data about diseases, specifically hypertension and diabetes. These data were used by researchers for association with health indicators (20%) or comparison with other information systems (17%), sample or population calculus (9%), estimation of prevalence and characterization of the epidemiological profile of a population (26%) or, more generally, to carry out the assessment of health status (29%). The Primary Care Information System as the ‘object of study’ group included 18 works, describing the knowledge and practices of professionals in relation to the information system. These researchers pointed out issues in the process of production and information consolidation, mainly due to the lack of training and supervision of community health workers and bureaucratization of their work process. Although some issues in the quality of data provided by the Primary Care Information System were reported by researchers, these findings were not corroborated by two studies that assessed the reliability of information disclosed by this system. Despite changes in the Brazilian health policies, the issue of data quality in health information systems continues to be a challenge preventing data from being used for decision-making and knowledge production.
      Citation: Health Informatics Journal
      PubDate: 2019-11-15T12:07:02Z
      DOI: 10.1177/1460458219882273
       
  • YouTube as a resource for evaluating the unmet needs of caregivers of
           stroke survivors

         This is an Open Access Article Open Access Article

    • Authors: Alexandra MJ Denham, Amanda L Baker, Neil J Spratt, Olivia Wynne, Sally A Hunt, Billie Bonevski, Ratika Kumar
      Abstract: Health Informatics Journal, Ahead of Print.
      Content produced by caregivers of stroke survivors on the online video-sharing platform YouTube may be a good source of knowledge regarding caregivers’ unmet needs. We aimed to examine the content, quantity and quality of YouTube videos that target and discuss the needs and concerns of caregivers of stroke survivors. YouTube was systematically searched using six search strings, and the first 20 videos retrieved from each search were screened against the inclusion criteria. A pre-determined coding schedule was used to report the rate of unmet needs in each video. Twenty-six videos were included in the analysis. In total, 291 unmet needs were reported by caregivers of stroke survivors, an average of 11.2 unmet needs per video. The most common unmet needs domain was ‘Impact of Caregiving on Daily Activities’ (44%). Most videos were developed in the United States (61.5%) and featured spouses of stroke survivors (65.47%). Content produced by caregivers of stroke survivors on YouTube may be used as a tool for caregivers to provide and receive support through online communication. YouTube videos offer insight into the unmet needs of caregivers of stroke survivors and may be used as an additional resource for stroke services to support caregivers.
      Citation: Health Informatics Journal
      PubDate: 2019-11-14T07:29:01Z
      DOI: 10.1177/1460458219873538
       
  • Using machine learning approaches to predict high-cost chronic obstructive
           pulmonary disease patients in China

         This is an Open Access Article Open Access Article

    • Authors: Li Luo, Jialing Li, Shuhao Lian, Xiaoxi Zeng, Lin Sun, Chunyang Li, Debin Huang, Wei Zhang
      Abstract: Health Informatics Journal, Ahead of Print.
      The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic burden of COPD. The objectives of this study were to use machine learning approaches to identify and predict potential high-cost patients and explore the key variables of the forecasting model, by comparing differences in the predictive performance of different variable sets. Machine learning approaches were used to estimate the medical costs of COPD patients using the Medical Insurance Data of a large city in western China. The prediction models used were logistic regression, random forest (RF), and extreme gradient boosting (XGBoost). All three models had good predictive performance. The XGBoost model outperformed the others. The areas under the ROC curve for Logistic Regression, RF and XGBoost were 0.787, 0.792 and 0.801. The precision and accuracy metrics indicated that the methods achieved correct and reliable results. The results of this study can be used by healthcare data analysts, policy makers, insurers, and healthcare planners to improve the delivery of health services.
      Citation: Health Informatics Journal
      PubDate: 2019-11-11T12:35:15Z
      DOI: 10.1177/1460458219881335
       
  • Design of a Mindfulness Virtual Community: A focus-group analysis
         This is an Open Access Article Open Access Article

    • Authors: Christo El Morr, Catherine Maule, Iqra Ashfaq, Paul Ritvo, Farah Ahmad
      Abstract: Health Informatics Journal, Ahead of Print.
      Mental illnesses are on the rise on campuses worldwide. There is a need for a scalable and economically sound innovation to address these mental health challenges. The aim of this study was to explore university students’ needs and concerns in relation to an online mental health virtual community. Eight focus groups (N = 72, 55.6% female) were conducted with university students aged 18–47 (mean = 23.38, SD = 5.82) years. Participants were asked about their views in relation to online mental health platform. Three major themes and subthemes emerged: (1) perceived concerns: potential loss of personal encounter and relationships, fear of cyber bullying, engagement challenge, and privacy and distraction; (2) perceived advantages: anonymity and privacy, convenience and flexibility, filling a gap, and togetherness; and (3) desired features: user-centered design, practical trustworthy support, and online moderation. The analysis informed design features for a mindfulness virtual community.
      Citation: Health Informatics Journal
      PubDate: 2019-11-11T12:34:56Z
      DOI: 10.1177/1460458219884840
       
  • Is there an app for that' A cluster randomised controlled trial of a
           mobile app–based mental health intervention

         This is an Open Access Article Open Access Article

    • Authors: Rachel Kenny, Amanda Fitzgerald, Ricardo Segurado, Barbara Dooley
      Abstract: Health Informatics Journal, Ahead of Print.
      Demand for the use of mobile apps in mental health interventions has grown in recent years, particularly among adolescents who experience elevated levels of distress. However, there is a scarcity of evidence for the effectiveness of these tools within this population. The aim of this study was to test the effectiveness of CopeSmart, a mental health mobile app, using a multicentre cluster randomised controlled trial design. Participants were 15–18-years-olds (N = 560) recruited from 10 schools randomly assigned to an intervention or control condition. Intervention participants used the app over a 4-week period. Multi-level modelling analyses revealed no significant changes in the intervention group from pre-test to post-test, when compared to the control group, in terms of emotional distress, well-being, emotional self-awareness or coping strategies. Findings suggest that a 4-week app-based intervention may not be enough to elicit intra-personal changes in mental health outcomes in a general adolescent population.
      Citation: Health Informatics Journal
      PubDate: 2019-11-08T04:44:15Z
      DOI: 10.1177/1460458219884195
       
  • Kind mobile notifications for healthcare professionals
         This is an Open Access Article Open Access Article

    • Authors: Estefanía Serral, Pedro Valderas, Jan Derboven
      Abstract: Health Informatics Journal, Ahead of Print.
      The inclusion of the Internet of Things in healthcare is producing numerous automatic notifications for health professionals. These notifications must be delivered in the right moment and in the right way to be appropriately attended, and at the same time, ensuring no important task is interrupted. In this work, we have applied a human-centred design method to deal with this issue. By collaborating with health professionals in Belgium, we have designed and validated DELICATE, a conceptual framework that categorizes the different attention needs for each notification, and links them with the delivery mechanisms that are more appropriate for each particular context. As an aid for designers, we also define methodological guidelines to clearly determine how DELICATE can be used to develop a notification system. Finally, as a proof-of-concept validation of the framework, we have implemented it in an Android application and tested it using real scenarios. This validation has shown that DELICATE can be used to design a notification system that delivers kind healthcare notifications.
      Citation: Health Informatics Journal
      PubDate: 2019-11-08T04:37:42Z
      DOI: 10.1177/1460458219884184
       
  • Validity of Veterans Health Administration structured data to determine
           accurate smoking status

         This is an Open Access Article Open Access Article

    • Authors: Sara E Golden, Elizabeth R Hooker, Sarah Shull, Matthew Howard, Kristina Crothers, Reid F Thompson, Christopher G Slatore
      Abstract: Health Informatics Journal, Ahead of Print.
      We compared smoking status from Veterans Health Administration (VHA) structured data with text in electronic health record (EHR) to assess validity. We manually abstracted the smoking status of 5,610 VHA patients. Only those with a smoking status found in both EHR text data and VHA structured data were included (n=5,289). We calculated agreement and kappa statistics to compare structured data vs. manually abstracted EHR text smoking status. We found a kappa statistic of 0.70 and total agreement of 81.1% between EHR text data and structured data for Current, Former, and Never smoking categories. Comparing EHR text data and structured data between Never and Ever smokers revealed a kappa statistic of 0.62 and total agreement of 89.1%. For comparison between Current and Never/Former smokers, the kappa statistic was 0.80 and total agreement was 90.2%. We found substantial and significant agreement between smoking status in EHR text data and structured data that may aid in future research.
      Citation: Health Informatics Journal
      PubDate: 2019-11-07T01:49:54Z
      DOI: 10.1177/1460458219882259
       
  • Multimodal mental models: Understanding users’ design expectations
           for mHealth apps

         This is an Open Access Article Open Access Article

    • Authors: J Scott Brennen, Allison J Lazard, Elizabeth Troutman Adams
      Abstract: Health Informatics Journal, Ahead of Print.
      Employing qualitative structured interviews with mobile health app users, this research describes shared mental models for mHealth and reveals their complexity. The findings uncover prototypical design components common to mental models beyond health apps and suggest that users’ mental models are multimodal, containing distinct and often contradictory dimensions for evaluations of aesthetics and for craftsmanship. The findings also indicate that users’ mental models are informed by experiences with apps from across the mobile landscape. This research suggests that designers of consumer mobile health apps and mobile health interventions should incorporate prototypical or salient features. In doing so, they should index designs to trends across the larger app landscape and innovate the means to balance between multidimensional and conflicting mental models.
      Citation: Health Informatics Journal
      PubDate: 2019-11-01T01:43:26Z
      DOI: 10.1177/1460458219882271
       
  • An evidence-based strategy to achieve equivalency and interoperability for
           social-behavioral determinants of health assessment, storage, exchange,
           and use

         This is an Open Access Article Open Access Article

    • Authors: Ruth E Wetta, Roberta D Severin, Heidi Gruhler
      Abstract: Health Informatics Journal, Ahead of Print.
      The interoperable exchange of social-behavioral determinants of health data is challenging due to complex factors including multiple recommendations, multiple tools with varying domains, scoring, and cutpoints, and lack of terminology code sets for storing assessments and findings. This article describes a strategy that permits scoring by social-behavioral determinants of health domain to create interoperability and equivalency across tools, settings, and populations. The three-tier scoring strategy converts social-behavioral determinants of health data to (1) be used immediately at point of care by identifying social needs or social risk factors, (2) be consumed within analytics and algorithms and for secondary analysis, and (3) produce total scores that reflect social determinant burden and behavioral determinant burden across populations and settings within a healthcare system. The strategy supports the six uses recommended by the National Academy of Medicine, provides flexibility in choice of social-behavioral determinants of health tool, and leverages the power of social-behavioral determinants of health data in healthcare delivery.
      Citation: Health Informatics Journal
      PubDate: 2019-10-29T10:38:04Z
      DOI: 10.1177/1460458219882265
       
  • Symptom monitoring of childhood illnesses and referrals: A pilot study on
           the feasibility of a mobile phone-based system as a disease surveillance
           tool in a rural health district of Ghana

         This is an Open Access Article Open Access Article

    • Authors: Aliyu Mohammed, Princess Ruhama Acheampong, Easmon Otupiri, Ellis Owusu-Dabo
      Abstract: Health Informatics Journal, Ahead of Print.
      Despite the potential of mobile technology in improving health systems, its use as a surveillance tool is still unclear. This study aimed to examine the feasibility of a mobile phone-based system as a surveillance tool for identifying common symptoms of childhood illnesses. We conducted a community-based cross-sectional study involving caregivers (n = 161) of children under 5 years. The system was designed to assess disease symptoms of the sick children and provide health advice to caregivers regarding what to do with the sick child. The capacity of the system to correctly assess the disease symptoms of sick children, and provide referral was examined using Kappa statistics. Of the 126 calls recorded by the system, 52 (41.3%) were valid with complete data. The level of agreement between the system and clinicians’ report with respect to common symptoms of childhood illnesses varied: fever (kappa = 0.70, p 
      Citation: Health Informatics Journal
      PubDate: 2019-10-23T11:33:42Z
      DOI: 10.1177/1460458219879329
       
  • A case study of applying text analysis to identify possible adverse drug
           interactions: The case of Adalat (Nifedipine)

         This is an Open Access Article Open Access Article

    • Authors: David Gefen, Ofir Ben-Assuli, Nir Shlomo, Noreen Robertson, Robert Klempfner
      Abstract: Health Informatics Journal, Ahead of Print.
      Adalat (Nifedipine) is a calcium-channel blocker that is also used as an antihypertensive drug. The drug was approved by the US Food and Drug Administration in 1985 but was discontinued in 1996 on account, among other things, of interactions with other medications. Nonetheless, Adalat is still used in other countries to treat congestive heart failure. We examine all the congestive heart failure electronic health records of the largest medical center in Israel to discover whether, possibly, taking Adalat with other medications is associated with patient death. This study examines a semantic space built by running latent semantic analysis on the entire corpus of congestive heart failure electronic health records of that medical center, encompassing 8 years of data on almost 12,000 patients. Through this semantic space, the most highly correlated medications and medical conditions that co-occurred with Adalat were identified. This was done separately for men and women. The results show that Adalat is correlated with different medications and conditions across genders. The data also suggest that taking Adalat with Captopril (angiotensin-converting enzyme inhibitor) or Rulid (antibiotic) might be dangerous in both genders. The study thus demonstrates the potential of applying latent semantic analysis to identify potentially dangerous drug interactions that may have otherwise gone under the radar.
      Citation: Health Informatics Journal
      PubDate: 2019-10-22T07:12:56Z
      DOI: 10.1177/1460458219882269
       
  • Similarity of medical concepts in question and answering of health
           communities

         This is an Open Access Article Open Access Article

    • Authors: Hamid Naderi, Sina Madani, Behzad Kiani, Kobra Etminani
      Abstract: Health Informatics Journal, Ahead of Print.
      The ability to automatically categorize submitted questions based on topics and suggest similar question and answer to the users reduces the number of redundant questions. Our objective was to compare intra-topic and inter-topic similarity between question and answers by using concept-based similarity computing analysis. We gathered existing question and answers from several popular online health communities. Then, Unified Medical Language System concepts related to selected questions and experts in different topics were extracted and weighted by term frequency -inverse document frequency values. Finally, the similarity between weighted vectors of Unified Medical Language System concepts was computed. Our result showed a considerable gap between intra-topic and inter-topic similarities in such a way that the average of intra-topic similarity (0.095, 0.192, and 0.110, respectively) was higher than the average of inter-topic similarity (0.012, 0.025, and 0.018, respectively) for questions of the top 3 popular online communities including NetWellness, WebMD, and Yahoo Answers. Similarity scores between the content of questions answered by experts in the same and different topics were calculated as 0.51 and 0.11, respectively. Concept-based similarity computing methods can be used in developing intelligent question and answering retrieval systems that contain auto recommendation functionality for similar questions and experts.
      Citation: Health Informatics Journal
      PubDate: 2019-10-22T07:11:56Z
      DOI: 10.1177/1460458219881333
       
  • A usability study to test the effectiveness, efficiency and simplicity of
           a newly developed Internet-based Exercise-focused Health App for Lung
           cancer survivors (iEXHALE): Protocol paper

         This is an Open Access Article Open Access Article

    • Authors: Catherine Henshall, Zoe Davey, Cynthia Jacelon, Clare Martin
      Abstract: Health Informatics Journal, Ahead of Print.
      The Internet-based Exercise-focused Health App for Lung cancer survivors (iEXHALE) is a mobile web app being developed to provide lung cancer survivors with an algorithm-based, tailor-made, self-management programme to inform their exercise choices and improve symptom severity. The aim of this protocol paper is to detail the plan for conducting the usability study to test the effectiveness, efficiency and simplicity of an exercise-focused self-management mobile web app for lung cancer survivors. The mixed methods study will consist of three consecutive phases, each interspersed with elements of data analysis and app prototype redevelopment. The study will take place in Oxford, United Kingdom. Ethical approvals have been obtained. The study will contribute to lung cancer survivorship research and is important in the app developmental process. This study contributes to the international forum for the exchange of practice, innovation and research, increases transparency in mobile health developmental processes and contributes to the methodological evidence base.
      Citation: Health Informatics Journal
      PubDate: 2019-10-21T11:31:46Z
      DOI: 10.1177/1460458219882268
       
  • Impact of an educational intervention on eye gaze behaviour in retinal
           image interpretation by consultant and trainee ophthalmologists

         This is an Open Access Article Open Access Article

    • Authors: Kate Shirley, Michael Williams, Laura McLaughlin, Nicola Parker, Raymond Bond
      Abstract: Health Informatics Journal, Ahead of Print.
      This study uses eye-tracking technology to assess the differences in gaze behaviours between ophthalmologists of different experience levels while interpreting retinal images of diabetic retinopathy. The differences in gaze behaviours before and after a teaching intervention which introduced a suggested search strategy is also investigated. A total of 9 trainees and 10 consultant ophthalmologists interpreted six retinal images. They were then shown a 5-min tutorial that demonstrated a search strategy. This was followed by six further retinal image interpretations. Participants completed questionnaires indicating clinical signs seen, appropriate retinopathy grade, and confidence. Eye movements were tracked during each interpretation.Overall, trainees compared to consultants demonstrated more uncertain and unstructured gaze behaviours. Trainee eye gaze metrics included: longer interpretation time, 36.5 s (SD = 6.2 vs. 31.4 s) (SD = 4.2) (p = 0.024), higher visit count, 17.38 visits (SD = 5.13) versus 12.18 visits(SD = 2.64) (p = 0.01), higher proportion of fixation, 57.0 per cent (SD = 5) versus 50.5 per cent (SD = 5) (p = 0.05) and shorter time to first fixation, 0.232 s (SD = 0.10) versus 0.821 s (SD = 0.77) (p = 0.001), respectively. The teaching intervention resulted in more focused gaze patterns in both groups. Pre-intervention and post-intervention mean proportion fixation on areas of interest were 38.6 per cent (SD = 6.8) and 51.8 per cent (SD = 13.9) for the trainee group, respectively, and 39.9 per cent (SD = 4.1) and 50.9 per cent (SD = 9.3) for the consultant group (p = 0.01).Consultants used more systematic and efficient approaches than trainees during interpretation. After the introduction of a suggested search strategy, trainees showed trends towards consultant eye gaze behaviours. Eye tracking gives an interesting insight into the thought processes of physicians carrying out complex tasks. The implication is that eye tracking may have future use in teaching and assessment. Its use in objectively assessing different teaching strategies could be a valuable tool for medical education.
      Citation: Health Informatics Journal
      PubDate: 2019-10-19T11:46:46Z
      DOI: 10.1177/1460458219881337
       
  • Trajectory analysis for postoperative pain using electronic health
           records: A nonparametric method with robust linear regression and
           K-medians cluster analysis

         This is an Open Access Article Open Access Article

    • Authors: Yingjie Weng, Lu Tian, Dario Tedesco, Karishma Desai, Steven M Asch, Ian Carroll, Catherine Curtin, Kathryn M McDonald, Tina Hernandez-Boussard
      Abstract: Health Informatics Journal, Ahead of Print.
      Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement. The estimated trajectories were used for a subsequent K-medians cluster analysis to categorize the longitudinal pain score patterns into distinct clusters. For each cluster, a mixture regression model estimated the association between pain score and time to discharge adjusting for confounding. The fitted regression model generated the pain trajectory pattern for given cluster. Finally, regression analyses examined the association between pain trajectories and patient outcomes. A total of 3442 surgeries were identified with a median of 22 pain scores at an academic hospital during 2009–2016. Four pain trajectory patterns were identified and one was associated with higher rates of outcomes. In conclusion, we described a novel approach with fast implementation to model patients’ pain experience using electronic health records. In the era of big data science, clinical research should be learning from all available data regarding a patient’s episode of care instead of focusing on the “average” patient outcomes.
      Citation: Health Informatics Journal
      PubDate: 2019-10-17T09:24:52Z
      DOI: 10.1177/1460458219881339
       
  • Oncology health-care professionals’ perceived effects of patient
           accessible electronic health records 6 years after launch: A survey study
           at a major university hospital in Sweden

         This is an Open Access Article Open Access Article

    • Authors: Jonas Moll, Åsa Cajander
      Abstract: Health Informatics Journal, Ahead of Print.
      Patient accessible electronic health records have been launched in many countries, and generally, health-care professionals have had strong initial concerns related to the areas patient contact, documentation practices and quality of care. Especially, oncology care was discussed in media when launching patient accessible electronic health records in Sweden. However, few studies have investigated clinician-perceived effects several years after the launch. A survey covering these areas, as well as supposed effects for patients, was distributed to oncology health-care professionals 6 years after the launch of patient accessible electronic health records and answered by N = 176. Results show that patient accessible electronic health records have had small effects within the covered areas, and that the area most affected was documentation practices. Very few significant differences could be found between physicians and nurses. A comparison with results from interviews and surveys conducted shortly after the launch of patient accessible electronic health records clearly indicates that the experienced negative effects are not as big as originally feared.
      Citation: Health Informatics Journal
      PubDate: 2019-10-17T09:24:51Z
      DOI: 10.1177/1460458219881007
       
  • Evaluating the factors that influence cloud technology
           adoption—comparative case analysis of health and non-health sectors: A
           systematic review

         This is an Open Access Article Open Access Article

    • Authors: Farahnaz Sadoughi, Omar Ali, Leila Erfannia
      Abstract: Health Informatics Journal, Ahead of Print.
      Cloud technology has brought great benefits to the health industry, including enabling improvement in the quality of services. The objective of this review study is to investigate the reported factors affecting the adoption of cloud in the health sector by comparing studies in the health and non-health sectors. This article is a systematized review of studies conducted in 2018. From 541 articles, 47 final articles were selected and classified into two categories: health and non-health studies; conclusions were drawn from the two sectors by comparing their effective factors. Based on the results of this review, the factors were categorized as technological, organizational, environmental, and individual. The results of this review study could be a beneficial guide to the health empirical research on cloud adoption. Individual domains have not been examined in health sector studies. Since the process of adoption of new technologies in organizations is time-consuming, due to the lack of managerial knowledge about the efficient factors, recognition of these factors by decision-makers while planning for cloud adoption becomes of great importance. The findings of this review study aim to help health decision-makers by increasing their awareness of the cloud and of the factors that impact decisions at both the organizational and individual levels.
      Citation: Health Informatics Journal
      PubDate: 2019-10-14T10:24:46Z
      DOI: 10.1177/1460458219879340
       
  • Integrating usability and social cognitive theories with the technology
           acceptance model to understand young users’ acceptance of a health
           information portal

         This is an Open Access Article Open Access Article

    • Authors: Da Tao, Fenglian Shao, Hailiang Wang, Mian Yan, Xingda Qu
      Abstract: Health Informatics Journal, Ahead of Print.
      The past decade has seen the proliferation of health information portals; however, consumer acceptance of the portals has proven difficult and rate of use has been limited. This study developed a consumer acceptance model by integrating usability and social cognitive theories with the technology acceptance model to explain young Internet users’ acceptance of health information portals. Participants (n = 201) completed a self-report questionnaire measuring model constructs after attending a usability testing with a typical health information portal. Results showed that the hypothesized model accounted for 56 percent of the variance in behavioral intention to use the portal and explained consumer acceptance well. Both subjective usability and application-specific self-efficacy served as significant antecedents in the model, while application-specific self-efficacy also moderated the effect of subjective usability on perceived ease of use. The findings can help practitioners with the design and implementation of health information portals and other health informatics applications in support of consumer acceptance.
      Citation: Health Informatics Journal
      PubDate: 2019-10-11T01:31:54Z
      DOI: 10.1177/1460458219879337
       
  • Business Process Management for optimizing clinical processes: A
           systematic literature review

         This is an Open Access Article Open Access Article

    • Authors: Alberto De Ramón Fernández, Daniel Ruiz Fernández, Yolanda Sabuco García
      Abstract: Health Informatics Journal, Ahead of Print.
      Business Process Management is a new strategy for process management that is having a major impact today. Mainly, its use is focused on the industrial, services, and business sector. However, in recent years, it has begun to apply for optimizing clinical processes. So far, no studies that evaluate its true impact on the healthcare sector have been found. This systematic review aims to assess the results of the application of Business Process Management methodology on clinical processes, analyzing whether it can become a useful tool to improve the effectiveness and quality of processes. We conducted a systematic literature review using ScienceDirect, Web of Science, Scopus, PubMed, and Springer databases. After the electronic search process in different databases, 18 articles met the pre-established requirements. The findings support the use of Business Process Management as an effective methodology to optimize clinical processes. Business Process Management has proven to be a feasible and useful methodology to design and optimize clinical processes, as well as to automate tasks. However, a more comprehensive follow-up of this methodology, better technological support, and greater involvement of all the clinical staff are factors that play a key role for the development of its true potential.
      Citation: Health Informatics Journal
      PubDate: 2019-10-04T07:02:49Z
      DOI: 10.1177/1460458219877092
       
  • Healthcare data warehouse system supporting cross-border interoperability
         This is an Open Access Article Open Access Article

    • Authors: Goce Gavrilov, Elena Vlahu-Gjorgievska, Vladimir Trajkovik
      Abstract: Health Informatics Journal, Ahead of Print.
      The free movement of European citizens across member states of the European Union adds an important level of complexity to strategic efforts of health interoperability. The use of electronic health data has been marked as an important strategic activity and policy to improve healthcare in European countries. Cross-border healthcare depends on the ability to set up shared practices with respect to patient data exchange across the countries. Data flow must comply with demanding security, legal and interoperability requirements, as defined by the European Patients Smart Open Services project specifications. The aim of this article is to propose a novel design of healthcare data warehouse based on the restructured Extract–Transform–Load process. We describe a portal framework that offers a comprehensive set of interoperability services to enable national e-Health platforms to set up cross-border health information networks compliant with European Patients Smart Open Services. The presented approach incorporates the technical and organizational interoperability by interconnecting Health Level Seven standard and Open National Contact Points framework in order to provide a modular, scalable and inter-operating architecture.
      Citation: Health Informatics Journal
      PubDate: 2019-10-04T07:01:46Z
      DOI: 10.1177/1460458219876793
       
  • Digital health and patient safety: Technology is not a magic wand
         This is an Open Access Article Open Access Article

    • Authors: Mark Sujan, Philip Scott, Kathrin Cresswell
      Abstract: Health Informatics Journal, Ahead of Print.
      The use of novel health information technology provides avenues for potentially significant patient benefit. However, it is also timely to take a step back and to consider whether the use of these technologies is safe – or more precisely what the current evidence for their safety is, and what kinds of evidence we should be looking for in order to create a convincing argument for patient safety. This special issue on patient safety includes eight papers that demonstrate an increasing focus on qualitative approaches and a growing recognition that the sociotechnical lens of examining health information technology–associated change is important. We encourage a balanced approach to technology adoption that embraces innovation, but nonetheless insists upon suitable concerns for safety and evaluation of outcomes.
      Citation: Health Informatics Journal
      PubDate: 2019-10-04T07:00:05Z
      DOI: 10.1177/1460458219876183
       
  • An observational study on the rate of reporting of adverse event on
           healthcare staff in a mental health setting: An application of Poisson
           expectation maximisation analysis on nurse staffing data

         This is an Open Access Article Open Access Article

    • Authors: Robert M Cook, Sarahjane Jones, Gemma C Williams, Daniel Worsley, Ray Walker, Mark Radford, Alison Leary
      Abstract: Health Informatics Journal, Ahead of Print.
      Evidence highlights the intrinsic link between nurse staffing and expertise, and outcomes for service users of healthcare, and that workforce retention is linked to the clinical and organisational experiences of employees. However, this understanding is less well established in mental health. This study comprises a retrospective observational study carried out on routinely collected data from a large mental healthcare provider. Two databases comprising nurse staffing levels and adverse events were modelled using latent variable methods to account for the presence of multiple underlying behaviours. The analysis reveals a strong dependence of the rate of adverse events on the location and perceived clinical demand of the wards, and a reduction in adverse events where registered nurses exceed ‘clinically required levels’. In the first study of its kind, these findings present significant implications for nursing workforce policy and present an opportunity to not only improve safety but potentially impact nurse retention.
      Citation: Health Informatics Journal
      PubDate: 2019-10-04T06:58:44Z
      DOI: 10.1177/1460458219874637
       
  • Design for mobile mental health: Exploring the informed participation
           approach

         This is an Open Access Article Open Access Article

    • Authors: Bijan Aryana, Liz Brewster
      Abstract: Health Informatics Journal, Ahead of Print.
      Mobile applications (apps) have the potential to improve mental health services. However, there is limited evidence of efficacy or responsiveness to user needs for existing apps. A lack of design methods has contributed to this issue. Developers view mental health apps as stand-alone products and dismiss the complex context of use. Participatory design, particularly an informed participation approach, has potential to improve the design of mental health apps. In this study, we worked with young mobile users and mental health practitioners to examine the informed participation approach for designing apps. Using auto-ethnography and a set of design workshops, the project focused on eliciting design requirements as a factor for successful implementation. We compared resultant ideas and designs with existing apps. Many user requirements revealed were absent in existing apps, suggesting potential advantages to informed participation. The observation of the process, however, showed challenges in engagement that need to be overcome.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T12:54:45Z
      DOI: 10.1177/1460458219873540
       
  • User involvement in the development of a telehealth solution to improve
           the kidney transplantation process: A participatory design study

         This is an Open Access Article Open Access Article

    • Authors: Charlotte Nielsen, Hanne Agerskov, Claus Bistrup, Jane Clemensen
      Abstract: Health Informatics Journal, Ahead of Print.
      Kidney transplantation is the treatment of choice for patients with end-stage renal disease, and leads to everyday self-management of this chronic condition. This article aims to provide documentation for a participatory design study of a telehealth solution to improve the kidney transplantation process, and to identify the impact from the different participants in the participatory design study. Through a participatory design approach, a smartphone application (app) was developed for the entire kidney transplantation process together with a workflow for post-transplantation follow-up. A core element in participatory design is user involvement. By way of workshops and laboratory tests, the telehealth solution was developed in close cooperation with patients, their families, healthcare professionals, kidney association representatives, and Information Technology designers. The participatory design approach means that the telehealth solution was designed to be functional in a clinical setting, address patients’ needs, and support their self-management.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T12:53:25Z
      DOI: 10.1177/1460458219876188
       
  • The effect of telehealth interventions on quality of life of cancer
           survivors: A systematic review and meta-analysis

         This is an Open Access Article Open Access Article

    • Authors: Jamie L Larson, Adam B Rosen, Fernando A Wilson
      Abstract: Health Informatics Journal, Ahead of Print.
      The objective of this study was to perform a systematic review and meta-analysis comparing the effect of telehealth interventions to usual care for cancer survivors’ quality of life. A comprehensive search of four different databases was conducted. Manuscripts were included if they assessed telehealth interventions and usual care for adult cancer survivors and reported a measure of quality of life. Pooled random effects models were used to calculate overall mean effects for quality of life pre- and post-intervention. Eleven articles fit all systematic review and meta-analysis criteria. Initial analyses indicated that telehealth interventions demonstrated large improvements compared with usual care in quality of life measures (Δ = 0.750, p = 0.007), albeit with substantial heterogeneity. Upon further analysis and outlier removal, telehealth interventions demonstrated significant improvements in quality of life compared with usual care (Δ = 0.141–0.144, p < 0.05). The results of the systematic review with meta-analysis indicate that supplementary interventions through telehealth may have a positive impact on quality of life compared with in-person usual care.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:16:55Z
      DOI: 10.1177/1460458219863604
       
  • Pharmacology and social media: Potentials and biases of web forums for
           drug mention analysis—case study of France

         This is an Open Access Article Open Access Article

    • Authors: Bissan Audeh, François-Elie Calvier, Florelle Bellet, Marie-Noëlle Beyens, Antoine Pariente, Agnès Lillo-Le Louet, Cedric Bousquet
      Abstract: Health Informatics Journal, Ahead of Print.
      The aim of this study is to analyze drug mentions in web forums to evaluate the utility of this data source for drug post-marketing studies. We automatically annotated over 60 million posts extracted from 21 French web forums. Drug mentions detected in this corpus were matched to drug names in a French drug database (Theriaque®). Our analysis showed that a high proportion of the most frequent drug mentions in the selected web forums correspond to drugs that are usually prescribed to young women, such as combined oral contraceptives. The most mentioned drugs in our corpus correlated weakly to the most prescribed drugs in France but seemed to be influenced by events widely reported in traditional media. In this article, we conclude that web forums have high potential for post-marketing drug-related studies, such as pharmacovigilance, and observation of drug utilization. However, the bias related to forum selection and the corresponding population representativeness should always be taken into account.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:15:32Z
      DOI: 10.1177/1460458219865128
       
  • Electronic health records in a Blockchain: A systematic review
         This is an Open Access Article Open Access Article

    • Authors: André Henrique Mayer, Cristiano André da Costa, Rodrigo da Rosa Righi
      Abstract: Health Informatics Journal, Ahead of Print.
      Blockchain could reinvent the way patient’s electronic health records are shared and stored by providing safer mechanisms for health information exchange of medical data in the healthcare industry, by securing it over a decentralized peer-to-peer network. Intending to support and ease the understanding of this distributed ledger technology, a solid Systematic Literature Review was conducted, aiming to explore the recent literature on Blockchain and healthcare domain and identify existing challenges and open questions, guided by the raise of research questions regarding EHR in a Blockchain. More than 300 scientific studies published in the last ten years were surveyed, resulting in an up-to-date taxonomy creation, challenges and open questions identified, and the most significant approaches, data types, standards and architectures regarding the use of Blockchain for EHR were assessed and discussed.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:14:32Z
      DOI: 10.1177/1460458219866350
       
  • A preliminary study on the acceptability of a brief SMS program for
           perinatal women

         This is an Open Access Article Open Access Article

    • Authors: Alinne Z Barrera, Adrian Aguilera, Nicole Inlow, Joanna Servin
      Abstract: Health Informatics Journal, Ahead of Print.
      This study examined the acceptability of the BabyText program, a Spanish and English textmessaging program adapted from a prevention of postpartum depression group intervention. Ten ethnically and racially diverse pregnant and postpartum women (mean age = 31.3, standard deviation = 5.25) recruited from a metropolitan, urban area of the United States received the BabyText program over a 69-day period (between October 2015 and April 2016). Each tip was assessed for the helpfulness of the content, and all women were invited to provide qualitative feedback about the program. Eighteen of the tips received a positive endorsement of helpfulness from 75 to 100 percent of the women, 12 tips received a positive endorsement of helpfulness from 50 percent of the women, and one tip was rated negatively by those who responded. Qualitative feedback described the need to personalize the tips to reflect the characteristics of women such as planned/unplanned pregnancy status, available economic resources, and current psychological distress. Women in this study favored tips that described stress management skills and emphasized caring for the self (vs only the baby). Data from this study are preliminary but add to the growing sentiment that digital tools should continue to be developed and tested, and personalization of intervention content is important to users.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:14:12Z
      DOI: 10.1177/1460458219866560
       
  • Detecting associations between dietary supplement intake and sentiments
           within mental disorder tweets

         This is an Open Access Article Open Access Article

    • Authors: Yefeng Wang, Yunpeng Zhao, Jianqiu Zhang, Jiang Bian, Rui Zhang
      Abstract: Health Informatics Journal, Ahead of Print.
      Many patients with mental disorders take dietary supplement, but their use patterns remain unclear. In this study, we developed a method to detect signals of associations between dietary supplement intake and mental disorder in Twitter data. We developed an annotated dataset and trained a convolutional neural network classifier that can identify language use pattern of dietary supplement intake with an F1-score of 0.899, a precision of 0.900, and a recall of 0.900. Using the classifier, we discovered that melatonin and vitamin D were the most commonly used supplements among Twitter users who self-diagnosed mental disorders. Sentiment analysis using Linguistic Inquiry and Word Count has shown that among Twitter users who posted mental disorder self-diagnosis, users who indicated supplement intake are more active and express more negative emotions and fewer positive emotions than those who have not mentioned supplement intake.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:13:32Z
      DOI: 10.1177/1460458219867231
       
  • Adoption mechanism of telemedicine in underdeveloped country
         This is an Open Access Article Open Access Article

    • Authors: Xiang Zhang, Badee uz Zaman
      Abstract: Health Informatics Journal, Ahead of Print.
      The purpose of this study is to reveal the influential mechanism on patients’ adoption intention of telemedicine in the underdeveloped areas. Based on 896 patients’ data collected in Pakistan, we found that patients’ adoption intention is a function of traveling cost, attitudes, and perceived usefulness. High traveling cost is found to have the most significant negative influence on adoption intention. Patients with shorter distance prefer more to use telemedicine. Traveling cost, traveling time, and traveling distance have indirect influences on adoption intention through their significant impacts on perceived usefulness and perceived ease of use. Our findings indicate that geographic locations does matter during promotion of telemedicine. This study also helps identify the true barriers and facilitators to large-scale adoption of telemedicine in developing countries and reduce the gap of healthcare equity as concerned by both UN Millennium Development Goals and UN Sustainable Development Goals.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:12:33Z
      DOI: 10.1177/1460458219868353
       
  • Developing an mHealth app for post-stroke upper limb rehabilitation:
           Feedback from US and Ethiopian rehabilitation clinicians

         This is an Open Access Article Open Access Article

    • Authors: Charmayne Mary Lee Hughes, Alejandra Padilla, Amy Hintze, Tatiana Mariscal Raymundo, Michael Sera, Sven Weidner, Jonathan Ontiveros, Tony Peng, Aaron Encarcion, Zeus A Cruz, Sam Warner, Kedir Sany, Moges Baye
      Abstract: Health Informatics Journal, Ahead of Print.
      Stroke is the leading cause of adult disability worldwide, with 70 percent of survivors exhibiting residual impairments of the upper limb that require frequent in-person visits to rehabilitation clinic over several months. This study explored rehabilitation clinician’s preferences for design features to be included in an mHealth-enabled app for post-stroke upper limb rehabilitation. Data were collected via online survey, sampling participants from Ethiopia (n = 69) and the United States (n = 75). Survey results indicated that Ethiopian and US rehabilitation clinicians have different opinions about the importance of design features that should be included in a stroke tele-rehabilitation system which are likely due to differences in culture, the availability of human and physical resources, and how the field of rehabilitation is organized and managed. Our results, thus, indicate that mHealth technologies must be tailored to the geographical and cultural context of the end users.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:11:13Z
      DOI: 10.1177/1460458219868356
       
  • Implementation of a decision aid for localized prostate cancer in routine
           care: A successful implementation strategy

         This is an Open Access Article Open Access Article

    • Authors: Julia J van Tol-Geerdink, Inge M van Oort, Diederik M Somford, Carl J Wijburg, Arno Geboers, Cornelia F van Uden-Kraan, Marieke de Vries, Peep FM Stalmeier
      Abstract: Health Informatics Journal, Ahead of Print.
      For the treatment choice of localized prostate cancer, effective patient decision aids have been developed. The implementation of decision aids in routine care, however, lags behind. Main known barriers are lack of confidence in the tool, lack of training on its use, lack of resources and lack of time. A new implementation strategy addresses these barriers. Using this implementation strategy, the implementation rate of a decision aid was measured in eight hospitals and questionnaires were filled out by 24 care providers and 255 patients. The average implementation rate was 60 per cent (range 31%–100%). Hardly any barriers remained for care providers. Patients who did not use the decision aid appeared to be more unwilling than unable to use the decision aid. By addressing known barriers, that is, informing care providers on the effectiveness of the decision aid, providing instructions on its use, embedding it in the existing workflow and making it available free of charge, a successful implementation of a prostate cancer decision aid was reached.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:10:11Z
      DOI: 10.1177/1460458219873528
       
  • A systematic analysis of the optimization of computerized physician order
           entry and clinical decision support systems: A qualitative study in
           English hospitals

         This is an Open Access Article Open Access Article

    • Authors: Valeri Wiegel, Abby King, Hajar Mozaffar, Kathrin Cresswell, Robin Williams, Aziz Sheik
      Abstract: Health Informatics Journal, Ahead of Print.
      This article analyzes the range of system optimization activities taking place over an extended period following the implementation of computerized physician order entry and clinical decision support systems. We undertook 207 qualitative semi-structured interviews, 24 rounds of non-participant observations of meetings and system use, and collected 17 organizational documents in five hospitals over three time periods between 2011 and 2016. We developed a systematic analysis of system optimization activities with eight sub-categories grouped into three main categories. This delineates the range of system optimization activities including resolving misalignments between technology and clinical practices, enhancing the adopted system, and improving user capabilities to utilize/further optimize systems. This study highlights the optimization efforts by user organizations adopting multi-user, organization-spanning information technologies. Hospitals must continue to attend to change management for an extended period (up to 5 years post-implementation) and develop a strategy for long-term system optimization including sustained user engagement, training, and broader capability development to ensure smoother and quicker realization of benefits.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:09:11Z
      DOI: 10.1177/1460458219868650
       
  • Smart applications for diabetes management: A comprehensive survey and
           ranking

         This is an Open Access Article Open Access Article

    • Authors: Muhammad Ubaid Ur Rehman, Muhammad Aleem, Muhammad Arshad Islam, Salman Ahmed
      Abstract: Health Informatics Journal, Ahead of Print.
      Diabetes is a chronic disease, and its treatment requires intensive management of medication, diet, and exercise. Nowadays, information and communication technology provides diverse facilities to patients and medical specialists to manage different diseases in an efficient manner with the help of smartphone technology. Earlier studies have not ranked diabetes management apps by correlating each app feature, and their review is not comprehensive. Therefore, this study presents a comprehensive analysis of the existing diabetes-related smartphone applications. Moreover, we examine the factors based on which most of the users provide a higher rank to a particular application. We classify the diabetes mobile applications with respect to the application features and perform rigorous analysis of the top 15 applications. The results indicate that there exists a weak correlation between the number of downloads and user ratings. For evaluation, we calculate the normalized discounted cumulative gain score to rank applications based on its features. The results demonstrate that a higher normalized discounted cumulative gain score is attained by those mobile applications that contain the data-sharing feature.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:08:31Z
      DOI: 10.1177/1460458219869159
       
  • Recognizing software names in biomedical literature using machine learning
         This is an Open Access Article Open Access Article

    • Authors: Qiang Wei, Yaoyun Zhang, Muhammad Amith, Rebecca Lin, Jenay Lapeyrolerie, Cui Tao, Hua Xu
      Abstract: Health Informatics Journal, Ahead of Print.
      Software tools now are essential to research and applications in the biomedical domain. However, existing software repositories are mainly built using manual curation, which is time-consuming and unscalable. This study took the initiative to manually annotate software names in 1,120 MEDLINE abstracts and titles and used this corpus to develop and evaluate machine learning–based named entity recognition systems for biomedical software. Specifically, two strategies were proposed for feature engineering: (1) domain knowledge features and (2) unsupervised word representation features of clustered and binarized word embeddings. Our best system achieved an F-measure of 91.79% for recognizing software from titles and an F-measure of 86.35% for recognizing software from both titles and abstracts using inexact matching criteria. We then created a biomedical software catalog with 19,557 entries using the developed system. This study demonstrates the feasibility of using natural language processing methods to automatically build a high-quality software index from biomedical literature.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:06:51Z
      DOI: 10.1177/1460458219869490
       
  • The role of non-medical factors in physicians’ decision-making process
           in a pediatric telemedicine service

         This is an Open Access Article Open Access Article

    • Authors: Motti Haimi, Shuli Brammli-Greenberg, Yehezkel Waisman, Nili Stein, Orna Baron-Epel
      Abstract: Health Informatics Journal, Ahead of Print.
      The complex process of medical decision-making is prone also to medically extraneous influences or “non-medical” factors. We aimed to investigate the possible role of non-medical factors in doctors’ decision-making process in a telemedicine setting. Interviews with 15 physicians who work in a pediatric telemedicine service were conducted. Those included a qualitative section, in which the physicians were asked about the role of non-medical factors in their decisions. Their responses to three clinical scenarios were also analyzed. In an additional quantitative section, a random sample of 339 parent -physician consultations, held during 2014–2017, was analyzed retrospectively. Various non-medical factors were identified with respect to their possible effect on primary and secondary decisions, the accuracy of diagnosis, and “reasonability” of the decisions. Various non-medical factors were found to influence physicians’ decisions. Those factors were related to the child, the applying parent, the physician, the interaction between the doctor and parents, the shift, and to demographic considerations, and were also found to influence the ability to make an accurate diagnosis and “reasonable” decisions. Our conclusion was that non-medical factors have an impact on doctor’s decisions, even in the setting of telemedicine, and should be considered for improving medical decisions in this milieu.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:06:10Z
      DOI: 10.1177/1460458219870660
       
  • Highlighting the rules between diagnosis types and laboratory diagnostic
           tests for patients of an emergency department: Use of association rule
           mining

         This is an Open Access Article Open Access Article

    • Authors: Görkem Sarıyer, Ceren Öcal Taşar
      Abstract: Health Informatics Journal, Ahead of Print.
      Diagnostic tests are widely used in emergency departments to make detailed investigations on diagnosis and treat patients correctly. However, since these tests are expensive and time-consuming, ordering correct tests for patients is crucial for efficient use of hospital resources. Thus, understanding the relation between diagnosis and diagnostic test requirement becomes an important issue in emergency departments. Association rule mining was used to extract hidden patterns and relation between diagnosis and diagnostic test requirement in real-life medical data received from an emergency department. Apriori was used as an association rule mining algorithm. Diagnosis was grouped into 21 categories based on International Classification of Disease, and laboratory tests were grouped into four main categories (hemogram, biochemistry, cardiac enzyme, urine and human excrement related). Both positive and negative rules were discovered. Since the nature of the data had the dominance of negative values, higher number of negative rules with higher confidences were discovered compared to positive ones. The extracted rules were validated by emergency department experts and practitioners. It was concluded that understanding the association between patient’s diagnosis and diagnostic test requirement can improve decision-making and efficient use of resources in emergency departments. Association rules can also be used for supporting physicians to treat patients.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:05:31Z
      DOI: 10.1177/1460458219871135
       
  • A machine learning–based 1-year mortality prediction model after
           hospital discharge for clinical patients with acute coronary syndrome

         This is an Open Access Article Open Access Article

    • Authors: Syed Waseem Abbas Sherazi, Yu Jun Jeong, Moon Hyun Jae, Jang-Whan Bae, Jong Yun Lee
      Abstract: Health Informatics Journal, Ahead of Print.
      Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning–based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005–30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning–based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning–based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:04:40Z
      DOI: 10.1177/1460458219871780
       
  • Exploring healthcare professionals’ understanding and experiences of
           artificial intelligence technology use in the delivery of healthcare: An
           integrative review

         This is an Open Access Article Open Access Article

    • Authors: Lucy Shinners, Christina Aggar, Sandra Grace, Stuart Smith
      Abstract: Health Informatics Journal, Ahead of Print.
      Background:The integration of artificial intelligence (AI) into our digital healthcare system is seen as a significant strategy to contain Australia’s rising healthcare costs, support clinical decision making, manage chronic disease burden and support our ageing population. With the increasing roll-out of ‘digital hospitals’, electronic medical records, new data capture and analysis technologies, as well as a digitally enabled health consumer, the Australian healthcare workforce is required to become digitally literate to manage the significant changes in the healthcare landscape. To ensure that new innovations such as AI are inclusive of clinicians, an understanding of how the technology will impact the healthcare professions is imperative.Method:In order to explore the complex phenomenon of healthcare professionals’ understanding and experiences of AI use in the delivery of healthcare, an integrative review inclusive of quantitative and qualitative studies was undertaken in June 2018.Results:One study met all inclusion criteria. This study was an observational study which used a questionnaire to measure healthcare professional’s intrinsic motivation in adoption behaviour when using an artificially intelligent medical diagnosis support system (AIMDSS).Discussion:The study found that healthcare professionals were less likely to use AI in the delivery of healthcare if they did not trust the technology or understand how it was used to improve patient outcomes or the delivery of care which is specific to the healthcare setting. The perception that AI would replace them in the healthcare setting was not evident. This may be due to the fact that AI is not yet at the forefront of technology use in healthcare setting. More research is needed to examine the experiences and perceptions of healthcare professionals using AI in the delivery of healthcare.
      Citation: Health Informatics Journal
      PubDate: 2019-09-30T01:00:49Z
      DOI: 10.1177/1460458219874641
       
  • Predicting hospital mortality for intensive care unit patients:
           Time-series analysis
    • Authors: Aya Awad, Mohamed Bader-El-Den, James McNicholas, Jim Briggs, Yasser El-Sonbaty
      Abstract: Health Informatics Journal, Ahead of Print.
      Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.
      Citation: Health Informatics Journal
      PubDate: 2019-07-26T09:15:36Z
      DOI: 10.1177/1460458219850323
       
  • Analysis of medication dosing error related to Computerised Provider Order
           Entry system: A comparison of ECF, HFACS, STAMP and AcciMap approaches

         This is an Open Access Article Open Access Article

    • Authors: Oseghale Osezua Igene, Christopher Johnson
      Abstract: Health Informatics Journal, Ahead of Print.
      Different accident analytical approaches have been utilised in safety-critical industries for analysing accidents and formulating safety recommendations. This study presents a ‘health informatics’ case incident of a patient adversely affected due to a medication dosing error resulting from a combination of contributing factors including those relating to the Computerised Order Provider Entry System. A comparative study was carried out using selected accident analytical approaches: Human Factors and Classification System, System-Theoretic Accident Modelling and Processes and Accident Modelling. Each resulting output was compared using the model characteristic criteria developed by Underwood and Waterson. Safety recommendations developed based on the outputs from the models/methods were also compared for any similar findings. It was acknowledged that while accident models incorporating ‘systems thinking’ can prove to be beneficial for healthcare in providing insight on systemic factors, there is a need for improving the reliability and validity of these models. This particularly applies to Rasmussen’s Accident Modelling approach to be considered useful in the healthcare domain.
      Citation: Health Informatics Journal
      PubDate: 2019-07-17T04:39:45Z
      DOI: 10.1177/1460458219859992
       
  • SuperOrder: Provider order recommendation system for outpatient clinics
    • Authors: Yi-Shan Sung, Ronald W Dravenstott, Jonathan D Darer, Priyantha D Devapriya, Soundar Kumara
      Abstract: Health Informatics Journal, Ahead of Print.
      This study aims at developing SuperOrder, an order recommendation system for outpatient clinics. Using the electronic health record data available at midnight, SuperOrder predicts the order contents for each upcoming appointment on a daily basis. A two-level prediction framework is proposed. At the base-level, the predictions are produced by aggregating three machine learning methods. The meta-level predictions are generated by integrating the base-level predictions with the order co-occurrence network. We used the retrospective data between 1 April 2014 and 31 March 2015 in pulmonary clinics from five hospital sites within a large rural health care facility in Pennsylvania to test the feasibility. With a decrease of 6 per cent in the precision, the improvement of the recall at the meta-level is approximately 20 per cent from the base-level. This demonstrates that the proposed order co-occurrence network helps in increasing the performance of order predictions. The implementation will bring a more effective and efficient way to place outpatient orders.
      Citation: Health Informatics Journal
      PubDate: 2019-07-03T08:02:46Z
      DOI: 10.1177/1460458219857383
       
  • A study exploring the usability of an exergaming platform for senior
           fitness testing
    • Authors: Tsai-Hsuan Tsai, Kevin C Tseng, Alice MK Wong, Hsien-Jui Chang
      Abstract: Health Informatics Journal, Ahead of Print.
      This study proposes a structural usability model to identify the relationship between the user interface design and the usability of an exergame system that includes a software system and a separate hardware device. The model consisted of two dimensions: the interface design, which was evaluated using Nielsen’s heuristic evaluation method, and the usability, as defined by ISO 9241-11. An empirical study used the iFit exergame system to test the physical fitness of 101 seniors in order to evaluate the model’s validity. The results showed a strong correlation between the interface design and the usability of the exergame system. An improved interface enabled users to interact with the system better, and the usability of the whole system was enhanced, including the device and the system itself. The results show that the proposed usability model can be used to evaluate other exergame systems.
      Citation: Health Informatics Journal
      PubDate: 2019-07-02T09:49:16Z
      DOI: 10.1177/1460458219853369
       
  • Hidden big data analytics issues in the healthcare industry
    • Authors: Kenneth David Strang, Zhaohao Sun
      Abstract: Health Informatics Journal, Ahead of Print.
      We extended the big data body of knowledge by analyzing the longitudinal literature to highlight important research topics and identify critical gaps. We initially collected 79,012 articles from 1900 to 2016 related to big data. We refined our sample to 13,029 articles allowing us to determine that the big data paradigm commenced in late 2011 and the research production exponentially rose starting in 2012, which approximated a Weibull distribution that captured 82 percent of the variance (p 
      Citation: Health Informatics Journal
      PubDate: 2019-07-02T09:46:56Z
      DOI: 10.1177/1460458219854603
       
  • Combination possibility and deep learning model as clinical decision-aided
           approach for prostate cancer
    • Authors: Okyaz Eminaga, Omran Al-Hamad, Martin Boegemann, Bernhard Breil, Axel Semjonow
      Abstract: Health Informatics Journal, Ahead of Print.
      This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient’s subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.
      Citation: Health Informatics Journal
      PubDate: 2019-06-26T07:11:42Z
      DOI: 10.1177/1460458219855884
       
  • Digital brief interventions for risky drinkers are not the panacea: A
           pilot study exploring barriers for its implementation according to
           professionals’ perceptions
    • Authors: Hugo López-Pelayo, Elsa Caballeria, Estela Díaz, Ariadna Sánchez, Lidia Segura, Joan Colom, Paul Wallace, Antoni Gual
      Abstract: Health Informatics Journal, Ahead of Print.
      Digital brief interventions have emerged as an instrument to improve the implementation of Screening, Brief Intervention and Referral to Treatment programs for risky drinkers. However, trials in Catalonia have been unsuccessful. This study was aimed at researching professionals’ perceptions regarding the usefulness of digital brief interventions in overcoming traditional barriers of face-to-face Screening, Brief Intervention and Referral to Treatment and new barriers posed by the use of digital brief interventions. Professionals who participated in the Effectiveness of primary care based Facilitated Access to alcohol Reduction website (EFAR)digital brief intervention clinical trial were surveyed on April 2017 on the following areas: (1) satisfaction, (2) usefulness, (3) perceived ability of digital interventions on overcoming traditional barriers and (4) perceived new barriers of digital interventions. Sixty-eight professionals completed the survey. Univariate and multivariate analyses were performed using the level of professional engagement with the project as the dependent variable, barriers as independent variables and socio-demographic characteristics as covariables. Of all professionals, 79.4 percent were satisfied with their participation in the project, but only 26.5 percent perceived the website as useful. Low engagement was associated with the perceived lack of feedback (0.22; 95% confidence interval: 0.05 -0.88), perception that it was difficult to use among the elderly(0.22; 95 confidence interval: 0.05 -0.091) and among low socioeconomic population (0.14; 95% confidence interval: 0.03 -0.64). The majority of the participants indicated that digital brief intervention for risky drinkers succeeded in overcoming most of the traditional barriers. However, new barriers emerged as difficulties for implementing digital brief interventions in the Catalan Primary Health Care System. Usefulness perception is a key factor, which must be addressed in any proposed intervention in primary care.
      Citation: Health Informatics Journal
      PubDate: 2019-06-19T06:59:37Z
      DOI: 10.1177/1460458219855177
       
  • The unmet needs of carers of stroke survivors: An evaluation of Google
           search results
    • Authors: Alexandra MJ Denham, Olivia Wynne, Amanda L Baker, Neil J Spratt, Billie Bonevski
      Abstract: Health Informatics Journal, Ahead of Print.
      Google is the most used search engine in the world, and likely to be used by caregivers of stroke survivors to find online forums and online communities to connect with other caregivers. This study aims to identify the types of websites accessed by caregivers of stroke survivors to connect with other caregivers, and analyse the online content produced by caregivers to identify their unmet needs. The first 20 websites from eight search strings entered into Google were systematically reviewed. Unmet needs on included websites were identified using a pre-determined coding schedule. Six websites were analysed. Most were discussion boards (n = 5, 83%) developed by organisations in the United States (n = 4, 66.6%). Overall, 2124 unmet needs appeared in 896 posts from caregivers. ‘Emotional and psychological’ were the most reported needs across posts (n = 765, 36%). Content produced on websites may address social isolation and provide insight into delivering and developing services to meet the needs of caregivers of stroke survivors.
      Citation: Health Informatics Journal
      PubDate: 2019-06-19T06:59:17Z
      DOI: 10.1177/1460458219852530
       
  • A virtual second opinion: Acceptability of a computer-based decision tool
           to assess older drivers with dementia
    • Authors: Mark J Rapoport, Carla Zucchero Sarracini, Benoit M Mulsant, Dallas P Seitz, Frank Molnar, Gary Naglie, Nathan Herrmann, Linda Rozmovits
      Abstract: Health Informatics Journal, Ahead of Print.
      Clinicians face challenges in deciding which older patients with dementia to report to transportation administrators. This study used a qualitative thematic analysis to understand the utility and limitations of implementing a computer-based Driving in Dementia Decision Tool in clinical practice. Thirteen physicians and eight nurse practitioners participated in an interview to discuss their experience using the tool. While many participants felt the tool provided a useful ‘virtual second opinion’, specialist physicians felt that the tool did not add value to their clinical practice. Barriers to using the Driving in Dementia Decision Tool included lack of integration with electronic medical records and inability to capture certain contextual nuances. Opinions varied about the impact of the tool on the relationship of clinicians with patients and their families. The Driving in Dementia Decision Tool was judged most useful by nurse practitioners and least useful by specialist physicians. This work highlights the importance of tailoring knowledge translation interventions to particular practices.
      Citation: Health Informatics Journal
      PubDate: 2019-06-18T12:42:17Z
      DOI: 10.1177/1460458219852870
       
  • A regulatory perspective on the influence of health information technology
           on organisational quality and safety in England
    • Authors: Guy Martin, Sonal Arora, Nisha Shah, Dominic King, Ara Darzi
      Abstract: Health Informatics Journal, Ahead of Print.
      Health information technology can transform and enhance the quality and safety of care, but it may also introduce new risks. This study analysed 130 healthcare regulator inspection reports and organisational digital maturity scores in order to characterise the impact of health information technology on quality and safety from a regulatory perspective. Although digital maturity and the positive use of health information technology are significantly associated with overall organisational quality, the negative effects of health information technology are frequently and more commonly identified by regulators. The poor usability of technology, lack of easy access to systems and data and the incorrect use of health information technology are the most commonly identified areas adversely affecting quality and safety. There is a need to understand the full risks and benefits of health information technology from the perspective of all stakeholders, including patients, end-users, providers and regulators in order to best inform future practice and regulation.
      Citation: Health Informatics Journal
      PubDate: 2019-06-15T07:20:55Z
      DOI: 10.1177/1460458219854602
       
  • Feasibility and acceptability of a mobile messaging program within a
           church-based healthy living intervention for African Americans and Latinos
           
    • Authors: Margaret D Whitley, Denise D Payán, Karen R Flórez, Malcolm V Williams, Eunice C Wong, Cheryl A Branch, Kathryn P Derose
      Abstract: Health Informatics Journal, Ahead of Print.
      Church-based programs can act on multiple levels to improve dietary and physical activity behaviors among African Americans and Latinos. However, the effectiveness of these interventions may be limited due to challenges in reaching all congregants or influencing behavior outside of the church setting. To increase intervention impact, we sent mobile messages (text and email) in English or Spanish to congregants (n = 131) from predominantly African American or Latino churches participating in a multi-level, church-based program. To assess feasibility and acceptability, we collected feedback throughout the 4-month messaging intervention and conducted a process evaluation using the messaging platform. We found that the intervention was feasible to implement and acceptable to a racially ethnically diverse study sample with high obesity and overweight rates. While the process evaluation had some limitations (e.g. low response rate), we conclude that mobile messaging is a promising, feasible addition to church-based programs aiming to improve dietary and physical activity behaviors.
      Citation: Health Informatics Journal
      PubDate: 2019-06-15T07:20:35Z
      DOI: 10.1177/1460458219853408
       
  • Prediction and prevention of hypoglycaemic events in type-1 diabetic
           patients using machine learning
    • Authors: Josep Vehí, Iván Contreras, Silvia Oviedo, Lyvia Biagi, Arthur Bertachi
      Abstract: Health Informatics Journal, Ahead of Print.
      Tight blood glucose control reduces the risk of microvascular and macrovascular complications in patients with type 1 diabetes. However, this is very difficult due to the large intra-individual variability and other factors that affect glycaemic control. The main limiting factor to achieve strict control of glucose levels in patients on intensive insulin therapy is the risk of severe hypoglycaemia. Therefore, hypoglycaemia is the main safety problem in the treatment of type 1 diabetes, negatively affecting the quality of life of patients suffering from this disease. Decision support tools based on machine learning methods have become a viable way to enhance patient safety by anticipating adverse glycaemic events. This study proposes the application of four machine learning algorithms to tackle the problem of safety in diabetes management: (1) grammatical evolution for the mid-term continuous prediction of blood glucose levels, (2) support vector machines to predict hypoglycaemic events during postprandial periods, (3) artificial neural networks to predict hypoglycaemic episodes overnight, and (4) data mining to profile diabetes management scenarios. The proposal consists of the combination of prediction and classification capabilities of the implemented approaches. The resulting system significantly reduces the number of episodes of hypoglycaemia, improving safety and providing patients with greater confidence in decision-making.
      Citation: Health Informatics Journal
      PubDate: 2019-06-14T06:23:48Z
      DOI: 10.1177/1460458219850682
       
  • Calorie counting smart phone apps: Effectiveness in nutritional awareness,
           lifestyle modification and weight management among young Indian adults
    • Authors: Paromita Banerjee, Vishnu Vardhana Rao Mendu, Damayanthi Korrapati, SubbaRao M Gavaravarapu
      Abstract: Health Informatics Journal, Ahead of Print.
      Calorie counting mobile apps claim to assist in weight management by helping users monitor their diets and track activity. This study assessed quality and effectiveness of popular calorie counting apps in weight management and behaviour change. Top 20 apps were selected from Google Play store and their quality was assessed using a 55-point scoring scale on attributes like standards used, content accuracy, user interface and sources of database. The mean (±SD (standard deviation)) quality score was 36.95 (±5.65). The calorie and activity recommendations were compared with standards and over 65 per cent apps over/underestimated calorie intake. To assess effectiveness, 60 young volunteers were recruited and divided into two groups. The intervention group (n = 30) was asked to use one of the top 3 apps for 8 weeks. Pre- and post-comparisons were made with the control group (n = 28). No significant difference was noted in anthropometry or food consumption. There was increasing trend (13.33%) in physical activity in the intervention group.
      Citation: Health Informatics Journal
      PubDate: 2019-06-14T06:23:08Z
      DOI: 10.1177/1460458219852531
       
  • Willingness to pay for pharmacist-provided home telemonitoring among
           patients with chronic diseases in Enugu metropolis
    • Authors: Chibueze Anosike, Maxwell Ogochukwu Adibe, Abdulmuminu Isah, Onyinye Blessing Ukoha-Kalu
      Abstract: Health Informatics Journal, Ahead of Print.
      Home telemonitoring is a promising approach in the management of patients with chronic diseases. However, no study has assessed its acceptability and possible service charge in Nigeria. Therefore, this study aimed to evaluate willingness to pay for pharmacist-provided telemonitoring among patients with chronic diseases and to explore its determinants. Hence, using the contingent valuation method, a cross-sectional study was conducted among eligible patients visiting 15 selected community pharmacies in Enugu metropolis, over a period of 3 months. Of the 335 patients who participated in the study, about 40 percent (i.e. 39.4%) were willing to pay an average monthly fee of ₦915.91 ± 485.49 (US$2.99 ± 1.59) for home telemonitoring services. Significant predictors of willingness to pay for home telemonitoring were perceived insufficient income (odds ratio = 0.20, 95% confidence interval = 0.07–0.60, p = 0.040) and health insurance status (odds ratio = 0.39, 95% confidence interval = 0.18–0.86, p = 0.019). Our findings suggest a promising potential for adopting home telemonitoring services among patients with chronic diseases in Enugu metropolis.
      Citation: Health Informatics Journal
      PubDate: 2019-06-14T06:22:28Z
      DOI: 10.1177/1460458219852534
       
  • Clinical decision support system to assess the risk of sepsis using Tree
           Augmented Bayesian networks and electronic medical record data
    • Authors: Akash Gupta, Tieming Liu, Scott Shepherd
      Abstract: Health Informatics Journal, Ahead of Print.
      Early and accurate diagnoses of sepsis enable practitioners to take timely preventive actions. The existing diagnostic criteria suffer from deficiencies, such as triggering false alarms or leaving conditions undiagnosed. This study aims to develop a clinical decision support system to predict the risk of sepsis using tree augmented naive Bayesian network by identifying the optimal set of biomarkers. The key feature of our approach is that we captured the dynamics among biomarkers. With an area under receiver operating characteristic of 0.84, the proposed model outperformed the competing diagnostic criteria (systemic inflammatory response syndrome = 0.59, quick sepsis-related organ failure assessment = 0.65, modified early warning system = 0.75, sepsis-related organ failure assessment = 0.80). The richness of our proposed model is measured not only by achieving high accuracy, but also by utilizing fewer biomarkers. We also propose a left-center-right imputation method suitable for electronic medical record data. This method uses the individual patient’s visit, instead of aggregated (mean or median) value, to impute the missing data.
      Citation: Health Informatics Journal
      PubDate: 2019-06-14T06:21:48Z
      DOI: 10.1177/1460458219852872
       
  • Time to change the paradigm' A mixed method study of the preferred and
           potential features of an asthma self-management app

         This is an Open Access Article Open Access Article

    • Authors: Chi Yan Hui, Robert Walton, Brian McKinstry, Hilary Pinnock
      Abstract: Health Informatics Journal, Ahead of Print.
      We explored the potential of asthma apps to support self-management and identified preferred features that enable users to live with asthma. We recruited patients from five UK practices and social media; observed their usage of our app, administered a questionnaire and interviewed a purposive sample of patients and professionals to explore preferred features. Thematic analysis of interview was synthesised with quantitative data. A total of 111 patients used our app for 3 months. We interviewed 15 patients and 16 professionals. Participants were interested in a broad range of self-management support strategies, including action plans, monitoring with feedback, allergy/weather warnings and tailor-made running coaching. Professionals wanted to integrate patients’ logs with practice records, though were concerned about data overload and risk of patient dependency. We propose a paradigm shift - from apps developed to provide features that are easy to implement technologically, to an approach in which apps are designed to deliver theoretically grounded preferred components.
      Citation: Health Informatics Journal
      PubDate: 2019-06-14T06:21:08Z
      DOI: 10.1177/1460458219853381
       
  • Development and piloting of a software tool to facilitate proactive hazard
           and risk analysis of Health Information Technology
    • Authors: Ibrahim Habli, Yan Jia, Sean White, George Gabriel, Tom Lawton, Mark Sujan, Clive Tomsett
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-06-05T10:35:54Z
      DOI: 10.1177/1460458219852789
       
  • Identification of the essential components of quality in the data
           collection process for public health information systems
    • Authors: Hong Chen, Ping Yu, David Hailey, Tingru Cui
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-05-29T12:28:15Z
      DOI: 10.1177/1460458219848622
       
  • Predicting nationwide obesity from food sales using machine learning
    • Authors: Jocelyn Dunstan, Marcela Aguirre, Magdalena Bastías, Claudia Nau, Thomas A Glass, Felipe Tobar
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-05-20T06:59:00Z
      DOI: 10.1177/1460458219845959
       
  • Predicting substance use disorder using long-term attention deficit
           hyperactivity disorder medication records in Truven
    • Authors: Sajjad Fouladvand, Emily R Hankosky, Heather Bush, Jin Chen, Linda P Dwoskin, Patricia R Freeman, Darren W Henderson, Kathleen Kantak, Jeffery Talbert, Shiqiang Tao, Guo-Qiang Zhang
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-05-20T06:58:21Z
      DOI: 10.1177/1460458219844075
       
  • Informatics and interaction: Applying human factors principles to optimize
           the design of clinical decision support for sepsis
    • Authors: Laura Schubel, Danielle L Mosby, Joseph Blumenthal, Muge Capan, Ryan Arnold, Rebecca Kowalski, F Jacob Seagull, Ken Catchpole, J Sanford Schwartz, Ella Franklin, Robin Littlejohn, Kristen E Miller
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-05-13T11:58:22Z
      DOI: 10.1177/1460458219839623
       
  • Sustainable improvement of HbA1c and satisfaction with diabetes care after
           adding telemedicine in patients on adaptable insulin regimens: Results of
           the TeleDiabetes randomized controlled trial
    • Authors: Heidi Buysse, Peter Coremans, Frans Pouwer, Johannes Ruige
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-05-03T11:09:54Z
      DOI: 10.1177/1460458219844369
       
  • The usability aspects of medication-related decision support systems in
           the inpatient setting: A systematic review
    • Authors: Bram Knols, Mathijs Louws, Alec Hardenbol, Jamshid Dehmeshki, Marjan Askari
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-24T07:09:21Z
      DOI: 10.1177/1460458219841167
       
  • Healthcare staff digital literacy levels and their attitudes towards
           information systems
    • Authors: Angeline Kuek, Sharon Hakkennes
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-15T09:22:23Z
      DOI: 10.1177/1460458219839613
       
  • The devil is in the detail: How a closed-loop documentation system for IV
           infusion administration contributes to and compromises patient safety

         This is an Open Access Article Open Access Article

    • Authors: Dominic Furniss, Bryony Dean Franklin, Ann Blandford
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-15T09:21:23Z
      DOI: 10.1177/1460458219839574
       
  • Feelings, opinions and experiences of Turkish women with infertility: A
           qualitative study
    • Authors: Samiye Mete, Sevcan Fata, Merlinda Aluş Tokat
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-11T12:44:55Z
      DOI: 10.1177/1460458219839628
       
  • A novel optimized initial cluster center and enhanced objective function:
           Medical diagnosis through classification
    • Authors: Binay Subedi, Abeer Alsadoon, PWC Prasad, Omar Hisham Alsadoon, Sami Haddad, Ahmad Alrubaie
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-11T12:44:35Z
      DOI: 10.1177/1460458219839629
       
  • Do user preferences align with human factors assessment scores of
           drug–drug interaction alerts'
    • Authors: David Lowenstein, Wu Yi Zheng, Rosemary Burke, Eliza Kenny, Anmol Sandhu, Meredith Makeham, Johanna Westbrook, Richard O Day, Melissa T Baysari
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-11T12:44:15Z
      DOI: 10.1177/1460458219840210
       
  • Assessing mental health signals among sexual and gender minorities using
           Twitter data
    • Authors: Yunpeng Zhao, Yi Guo, Xing He, Yonghui Wu, Xi Yang, Mattia Prosperi, Yanghua Jin, Jiang Bian
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-10T02:25:52Z
      DOI: 10.1177/1460458219839621
       
  • The training game SALIENCE for the therapy of alcohol use disorder
    • Authors: Sabine Vollstädt-Klein, Philip Mildner, Jan Malte Bumb, Damian Karl, Christoph Ueberle, Yury Shevchenko, Falk Kiefer, Wolfgang Effelsberg
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-08T12:45:10Z
      DOI: 10.1177/1460458219839612
       
  • Drivers of participation in Facebook long-term care groups: Applying the
           use and gratification theory, social identification theory, and the
           modulating role of group diversity
    • Authors: Tung-Cheng Lin, Davis Fang, Siang-Ying Hsueh, Ming-Cheng Lai
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-04-08T12:44:30Z
      DOI: 10.1177/1460458219839618
       
  • Development and usability testing of a multi-criteria value clarification
           methods for patients with localized prostate cancer
    • Authors: Isabel B de Angst, Marieke GM Weernink, Paul JM Kil, Janine A van Til, Erik B Cornel, Johanna JM Takkenberg
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-27T10:24:46Z
      DOI: 10.1177/1460458219832055
       
  • On the evidence consistency of pharmacovigilance outcomes between Food and
           Drug Administration Adverse Event Reporting System and electronic medical
           record data for acute mania patients
    • Authors: Rui Duan, Xinyuan Zhang, Jingcheng Du, Jing Huang, Cui Tao, Yong Chen
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-19T09:47:27Z
      DOI: 10.1177/1460458219833093
       
  • Patient-specific factors associated with pressure injuries revealed by
           electronic health record analyses
    • Authors: Megan W Miller, Rebecca T Emeny, Jennifer A Snide, Gary L Freed
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-18T11:31:03Z
      DOI: 10.1177/1460458219832053
       
  • What is the impact of introducing inpatient electronic prescribing on
           prescribing errors' A naturalistic stepped wedge study in an English
           teaching hospital
    • Authors: Bryony Dean Franklin, Seetal Puaar
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-18T11:30:15Z
      DOI: 10.1177/1460458219833112
       
  • Information security climate and the assessment of information security
           risk among healthcare employees
    • Authors: Stacey R Kessler, Shani Pindek, Gary Kleinman, Stephanie A Andel, Paul E Spector
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-14T07:03:48Z
      DOI: 10.1177/1460458219832048
       
  • Exploring temporal suicidal behavior patterns on social media: Insight
           from Twitter analytics
    • Authors: Jianhong Luo, Jingcheng Du, Cui Tao, Hua Xu, Yaoyun Zhang
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-14T07:03:08Z
      DOI: 10.1177/1460458219832043
       
  • Using predictive analytics to identify drug-resistant epilepsy patients
    • Authors: Dursun Delen, Behrooz Davazdahemami, Enes Eryarsoy, Leman Tomak, Abhinav Valluru
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-12T09:36:05Z
      DOI: 10.1177/1460458219833120
       
  • Essential activities for electronic health record safety: A qualitative
           study
    • Authors: Joan S Ash, Hardeep Singh, Adam Wright, Dian Chase, Dean F Sittig
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-08T02:25:44Z
      DOI: 10.1177/1460458219833109
       
  • Integral patient scheduling in outpatient clinics under demand uncertainty
           to minimize patient waiting times

         This is an Open Access Article Open Access Article

    • Authors: Jyoti R Munavalli, Shyam Vasudeva Rao, Aravind Srinivasan, GG van Merode
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-08T02:25:21Z
      DOI: 10.1177/1460458219832044
       
  • Automated classification of primary care patient safety incident report
           content and severity using supervised machine learning (ML) approaches
    • Authors: Huw Prosser Evans, Athanasios Anastasiou, Adrian Edwards, Peter Hibbert, Meredith Makeham, Saturnino Luz, Aziz Sheikh, Liam Donaldson, Andrew Carson-Stevens
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-07T12:48:31Z
      DOI: 10.1177/1460458219833102
       
  • Healthcare practitioner behaviours that influence unsafe use of hospital
           information systems
    • Authors: Lizawati Salahuddin, Zuraini Ismail, Ummi Rabaah Hashim, Nor Haslinda Ismail, Raja Rina Raja Ikram, Fiza Abdul Rahim, Noor Hafizah Hassan
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-07T12:27:14Z
      DOI: 10.1177/1460458219833090
       
  • Ontological representation–oriented term normalization and
           standardization of the Research Domain Criteria
    • Authors: Fang Li, Guozheng Rao, Jingcheng Du, Yang Xiang, Yaoyun Zhang, Salih Selek, Jane Elizabeth Hamilton, Hua Xu, Cui Tao
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-07T12:26:30Z
      DOI: 10.1177/1460458219832059
       
  • Enablers and barriers for hospital pharmacy information systems
    • Authors: Noel Carroll, Ita Richardson
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-03-07T10:00:41Z
      DOI: 10.1177/1460458219832056
       
  • Natural language processing of lifestyle modification documentation
    • Authors: Kimberly Shoenbill, Yiqiang Song, Lisa Gress, Heather Johnson, Maureen Smith, Eneida A Mendonca
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-02-22T09:03:03Z
      DOI: 10.1177/1460458218824742
       
  • Sleep stage detection using only heart rate
    • Authors: Yasue Mitsukura, Koichi Fukunaga, Masato Yasui, Masaru Mimura
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-02-20T05:46:24Z
      DOI: 10.1177/1460458219827349
       
  • Using visualisation methods to analyse referral networks within community
           health care among patients aged 65 years and over
    • Authors: Ryan Palmer, Martin Utley, Naomi J Fulop, Stephen O’Connor
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-02-09T06:46:11Z
      DOI: 10.1177/1460458218824717
       
  • I have most of my asthma under control and I know how my asthma acts:
           Users’ perceptions of asthma self-management mobile app tailored for
           adolescents
    • Authors: Tali Schneider, Laura Baum, Alman Amy, Couluris Marisa
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-02-08T09:23:33Z
      DOI: 10.1177/1460458218824734
       
  • Real-time tracking and documentation in trauma management
    • Authors: Sara Montagna, Angelo Croatti, Alessandro Ricci, Vanni Agnoletti, Vittorio Albarello, Emiliano Gamberini
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-02-06T05:30:54Z
      DOI: 10.1177/1460458219825507
       
  • Continuity of care and multiple chronic conditions impact frequent use of
           outpatient services
    • Authors: Chi Wang, Hsiao-Ching Kuo, Su-Fen Cheng, Jui-Lan Hung, Jia-Hui Xiong, Pei-Ling Tang
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-31T12:10:46Z
      DOI: 10.1177/1460458218824720
       
  • Application of machine learning to predict obstructive sleep apnea
           syndrome severity
    • Authors: Corrado Mencar, Crescenzio Gallo, Marco Mantero, Paolo Tarsia, Giovanna E Carpagnano, Maria P Foschino Barbaro, Donato Lacedonia
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-30T08:54:50Z
      DOI: 10.1177/1460458218824725
       
  • Assessing quality of glycemic control: Hypo- and hyperglycemia, and
           
    • Authors: Sophie Huey-Ming Guo
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-29T10:49:33Z
      DOI: 10.1177/1460458218824756
       
  • A new machine learning model based on induction of rules for autism
           detection
    • Authors: Fadi Thabtah, David Peebles
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-29T10:47:52Z
      DOI: 10.1177/1460458218824711
       
  • Speaking the same language: Development of a Nutrition Minimum Data Set
           for healthcare professionals in primary healthcare
    • Authors: Sasja Jul Håkonsen, Preben Ulrich Pedersen, Ann Bygholm, Micah DJ Peters, Merete Bjerrum
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-25T12:03:43Z
      DOI: 10.1177/1460458218824707
       
  • Development process and patient usability preferences for a touch screen
           tablet–based questionnaire
    • Authors: Victor Lam Shin Cheung, Monika Kastner, Joanna EM Sale, Sharon Straus, Alan Kaplan, Louis-Philippe Boulet, Samir Gupta
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-23T12:21:50Z
      DOI: 10.1177/1460458218824749
       
  • The use of analytic hierarchy process for measuring the complexity of
           medical diagnosis
    • Authors: Ofir Ben-Assuli, Nanda Kumar, Ofer Arazy, Itamar Shabtai
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-23T12:19:30Z
      DOI: 10.1177/1460458218824708
       
  • Assessing the effect of data integration on predictive ability of cancer
           survival models
    • Authors: Yi Guo, Jiang Bian, Francois Modave, Qian Li, Thomas J George, Mattia Prosperi, Elizabeth Shenkman
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-23T12:16:50Z
      DOI: 10.1177/1460458218824692
       
  • Exploring trajectories of emergency department visits using a
           laboratory-based indicator of serious illness
    • Authors: Ofir Ben-Assuli, Rema Padman, Itamar Shabtai
      Abstract: Health Informatics Journal, Ahead of Print.

      Citation: Health Informatics Journal
      PubDate: 2019-01-22T01:02:20Z
      DOI: 10.1177/1460458218824751
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 35.175.113.29
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-