Subjects -> MEDICAL SCIENCES (Total: 8690 journals)
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MEDICAL SCIENCES (2415 journals)            First | 3 4 5 6 7 8 9 10 | Last

Showing 1201 - 1400 of 3562 Journals sorted alphabetically
Journal of Evaluation In Clinical Practice     Hybrid Journal   (Followers: 6)
Journal of Evidence-Based Healthcare     Open Access   (Followers: 1)
Journal of Evidence-Based Integrative Medicine     Open Access   (Followers: 18)
Journal of Evidence-Based Medicine     Partially Free   (Followers: 4)
Journal of Exercise Science & Fitness     Open Access   (Followers: 29)
Journal of Experimental and Clinical Anatomy     Open Access   (Followers: 2)
Journal of Family and Community Medicine     Open Access   (Followers: 3)
Journal of Family Medicine and Primary Care     Open Access   (Followers: 11)
Journal of Foot and Ankle Research     Open Access   (Followers: 6)
Journal of Forensic Science and Research     Open Access   (Followers: 2)
Journal of Gandaki Medical College-Nepal     Open Access  
Journal of Generic Medicines     Hybrid Journal   (Followers: 2)
Journal of Geographical Sciences     Hybrid Journal   (Followers: 1)
Journal of Global Antimicrobial Resistance     Hybrid Journal   (Followers: 3)
Journal of Hand Therapy     Hybrid Journal   (Followers: 19)
Journal of Head & Neck Physicians and Surgeons     Open Access   (Followers: 2)
Journal of Health & Medical Informatics     Open Access   (Followers: 62)
Journal of Health and Biological Sciences     Open Access   (Followers: 1)
Journal of Health Design     Open Access   (Followers: 1)
Journal of Health Economics and Outcomes Research     Open Access   (Followers: 1)
Journal of Health Promotion and Behavior     Open Access  
Journal of Health Research and Reviews     Open Access  
Journal of Health Science and Medical Research     Open Access  
Journal of Health Science Research     Open Access  
Journal of Health Sciences     Open Access   (Followers: 1)
Journal of health sciences     Open Access  
Journal of Health Sciences / Sağlık Bilimleri Dergisi     Open Access  
Journal of Health Sciences and Medicine     Open Access  
Journal of Health Sciences and Medicine     Open Access   (Followers: 6)
Journal of Health Sciences and Surveillance System     Open Access  
Journal of Health Sciences Scholarship     Open Access  
Journal of Health Specialties     Open Access  
Journal of Health Studies     Open Access  
Journal of Healthcare Informatics Research     Hybrid Journal   (Followers: 1)
Journal of Heavy Metal Toxicity and Diseases     Open Access  
Journal of Helminthology     Hybrid Journal   (Followers: 2)
Journal of Herbs Spices & Medicinal Plants     Hybrid Journal  
Journal of HIV for Clinical and Scientific Research     Open Access   (Followers: 2)
Journal of Hospital Medicine     Hybrid Journal   (Followers: 11)
Journal of Huazhong University of Science and Technology [Medical Sciences]     Hybrid Journal  
Journal of Human Hypertension     Hybrid Journal   (Followers: 3)
Journal of Human Rhythm     Open Access  
Journal of Human Transcriptome     Open Access  
Journal of Ideas in Health     Open Access  
Journal of Inflammation     Open Access   (Followers: 2)
Journal of Inflammation Research     Open Access  
Journal of Injury and Violence Research     Open Access   (Followers: 6)
Journal of Innovation in Health Informatics     Open Access   (Followers: 17)
Journal of Institute of Medicine     Open Access  
Journal of Insulin Resistance     Open Access   (Followers: 1)
Journal of Interactional Research in Communication Disorders     Hybrid Journal   (Followers: 5)
Journal of Interferon & Cytokine Research     Hybrid Journal   (Followers: 3)
Journal of International Medical Research     Open Access   (Followers: 3)
Journal of Interventional Medicine     Open Access   (Followers: 1)
Journal of Investigative Medicine     Hybrid Journal   (Followers: 3)
Journal of Islamabad Medical & Dental College     Open Access   (Followers: 2)
Journal of Istanbul Faculty of Medicine     Open Access  
Journal of Karnali Academy of Health Sciences     Open Access   (Followers: 1)
Journal of Kathmandu Medical College     Open Access   (Followers: 1)
Journal of King Abdulaziz University : Medical Sciences     Open Access   (Followers: 2)
Journal of Laboratory Medicine     Hybrid Journal   (Followers: 27)
Journal of Laryngology and Voice     Open Access   (Followers: 11)
Journal of Lasers in Medical Sciences     Open Access  
Journal of Law, Medicine & Ethics     Hybrid Journal   (Followers: 28)
Journal of Legal Medicine     Hybrid Journal   (Followers: 7)
Journal of Limb Lengthening & Reconstruction     Open Access  
Journal of Lumbini Medical College     Open Access   (Followers: 1)
Journal of Mahatma Gandhi Institute of Medical Sciences     Open Access  
Journal of Manipulative and Physiological Therapeutics     Hybrid Journal   (Followers: 6)
Journal of Manmohan Memorial Institute of Health Sciences     Open Access   (Followers: 1)
Journal of Marine Medical Society     Open Access  
Journal of Materials Science : Materials in Medicine     Hybrid Journal   (Followers: 4)
Journal of Maternal and Child Health     Open Access  
Journal of Mechanics in Medicine and Biology     Hybrid Journal  
Journal of Medical and Biological Engineering     Hybrid Journal   (Followers: 4)
Journal of Medical and Biomedical Sciences     Open Access   (Followers: 2)
Journal of Medical Case Reports     Open Access   (Followers: 1)
Journal of Medical Cases     Open Access   (Followers: 6)
Journal of Medical Colleges of PLA     Full-text available via subscription  
Journal of Medical Disorders     Open Access  
Journal of Medical Economics     Hybrid Journal   (Followers: 8)
Journal of Medical Education and Curricular Development     Open Access   (Followers: 6)
Journal of Medical Ethics     Partially Free   (Followers: 27)
Journal of Medical Ethics and History of Medicine     Open Access   (Followers: 19)
Journal of Medical Humanities     Hybrid Journal   (Followers: 21)
Journal of Medical Hypotheses and Ideas     Open Access  
Journal of Medical Imaging and Health Informatics     Full-text available via subscription   (Followers: 1)
Journal of Medical Investigation and Practice     Open Access  
Journal of Medical Laboratory and Diagnosis     Open Access  
Journal of Medical Law and Ethics     Full-text available via subscription   (Followers: 17)
Journal of Medical Microbiology     Full-text available via subscription   (Followers: 6)
Journal of Medical Sciences     Open Access  
Journal of Medical Sciences     Open Access  
Journal of Medical Screening     Hybrid Journal   (Followers: 6)
Journal of Medical Signals and Sensors     Open Access   (Followers: 3)
Journal of Medical Society     Open Access  
Journal of Medical Systems     Hybrid Journal  
Journal of Medical Toxicology     Hybrid Journal   (Followers: 6)
Journal of Medical Ultrasound     Open Access   (Followers: 2)
Journal of Medicinal Botany     Open Access  
Journal of Medicinal Chemistry     Hybrid Journal   (Followers: 207)
Journal of Medicine     Open Access   (Followers: 1)
Journal of Medicine and Biomedical Research     Open Access   (Followers: 1)
Journal of Medicine and Philosophy     Hybrid Journal   (Followers: 9)
Journal of Medicine and the Person     Hybrid Journal  
Journal of Medicine in Scientific Research     Open Access  
Journal of Medicine in the Tropics     Open Access  
Journal of Medicine Research and Development     Open Access   (Followers: 3)
Journal of Medicine, Physiology and Biophysics     Open Access   (Followers: 5)
Journal of Medicines Development Sciences     Open Access   (Followers: 1)
Journal of Metabolomics & Systems Biology     Open Access   (Followers: 2)
Journal of Mind and Medical Sciences     Open Access   (Followers: 1)
Journal of Molecular Medicine     Hybrid Journal   (Followers: 11)
Journal of Movement Disorders     Open Access   (Followers: 2)
Journal of Multidisciplinary Research in Healthcare     Open Access   (Followers: 2)
Journal of Muscle Research and Cell Motility     Hybrid Journal   (Followers: 1)
Journal of Nanotechnology in Engineering and Medicine     Full-text available via subscription   (Followers: 6)
Journal of Nanotheranostics     Open Access   (Followers: 1)
Journal of Natural Medicines     Hybrid Journal  
Journal of Natural Science, Biology and Medicine     Open Access   (Followers: 3)
Journal of Nature and Science of Medicine     Open Access   (Followers: 4)
Journal of Negative and No Positive Results     Open Access  
Journal of Nepalgunj Medical College     Open Access  
Journal of Neurocritical Care     Open Access  
Journal of Neurodegenerative Diseases     Open Access   (Followers: 2)
Journal of Neurorestoratology     Open Access  
Journal of Neuroscience and Neurological Disorders     Open Access  
Journal of Nobel Medical College     Open Access  
Journal of Obesity and Bariatrics     Open Access   (Followers: 2)
Journal of Occupational Health     Open Access  
Journal of Occupational Therapy Education     Open Access   (Followers: 12)
Journal of Ocular Biology, Diseases, and Informatics     Hybrid Journal  
Journal of Oral Biology and Craniofacial Research     Full-text available via subscription  
Journal of Oral Health and Craniofacial Science     Open Access  
Journal of Orofacial Sciences     Open Access  
Journal of Otorhinolaryngology, Hearing and Balance Medicine     Open Access   (Followers: 1)
Journal of Ovarian Research     Open Access  
Journal of Ozone Therapy     Open Access  
Journal of Palliative Medicine     Hybrid Journal   (Followers: 47)
Journal of Paramedical Sciences & Rehabilitation     Open Access  
Journal of Parkinsonism and Restless Legs Syndrome     Open Access   (Followers: 2)
Journal of Parkinson’s Disease and Alzheimer’s Disease     Open Access   (Followers: 1)
Journal of Participatory Medicine     Open Access  
Journal of Patan Academy of Health Sciences     Open Access  
Journal of Pathogens     Open Access   (Followers: 1)
Journal of Patient Experience     Open Access  
Journal of Patient Safety and Risk Management     Hybrid Journal   (Followers: 2)
Journal of Patient-Centered Research and Reviews     Open Access  
Journal of Patient-Reported Outcomes     Open Access  
Journal of Periodontal Research     Hybrid Journal  
Journal of Personalized Medicine     Open Access   (Followers: 3)
Journal of Pest Science     Hybrid Journal   (Followers: 1)
Journal of Pharmaceutical Policy and Practice     Open Access   (Followers: 4)
Journal of Physiobiochemical Metabolism     Hybrid Journal   (Followers: 2)
Journal of Physiology-Paris     Hybrid Journal   (Followers: 2)
Journal of Pioneering Medical Sciences     Open Access  
Journal of Postgraduate Medicine     Open Access  
Journal of Pregnancy     Open Access   (Followers: 4)
Journal of Prevention & Intervention Community     Hybrid Journal   (Followers: 7)
Journal of Preventive Medicine and Public Health     Open Access  
Journal of Primary Prevention     Hybrid Journal   (Followers: 7)
Journal of Prosthodontic Research     Full-text available via subscription   (Followers: 1)
Journal of Prosthodontics     Hybrid Journal   (Followers: 2)
Journal of Receptor, Ligand and Channel Research     Open Access   (Followers: 1)
Journal of Regenerative Medicine     Partially Free   (Followers: 4)
Journal of Research in Medical Sciences     Open Access   (Followers: 2)
Journal of Science and Applications : Biomedicine     Open Access   (Followers: 1)
Journal of Science and Technology (Ghana)     Open Access   (Followers: 3)
Journal of Scientific Innovation in Medicine     Open Access  
Journal of Scientific Perspectives     Open Access   (Followers: 1)
Journal of Sensory Studies     Hybrid Journal   (Followers: 4)
Journal of Shaheed Suhrawardy Medical College     Open Access  
Journal of Shoulder and Elbow Arthroplasty     Open Access  
Journal of Sleep Disorders : Treatment & Care     Hybrid Journal   (Followers: 10)
Journal of South American Earth Sciences     Hybrid Journal   (Followers: 5)
Journal of Spinal Cord Medicine     Hybrid Journal   (Followers: 5)
Journal of Spinal Disorders & Techniques     Hybrid Journal   (Followers: 2)
Journal of Sports Medicine and Allied Health Sciences : Official Journal of the Ohio Athletic Trainers Association     Open Access   (Followers: 1)
Journal of Stem Cell Therapy and Transplantation     Open Access   (Followers: 1)
Journal of Stomal Therapy Australia     Full-text available via subscription   (Followers: 1)
Journal of Strength and Conditioning Research     Hybrid Journal   (Followers: 77)
Journal of Substance Use     Hybrid Journal   (Followers: 15)
Journal of Surgical Academia     Open Access   (Followers: 1)
Journal of Surgical and Clinical Research     Open Access  
Journal of Surgical Case Reports     Open Access  
Journal of Surgical Education     Full-text available via subscription   (Followers: 3)
Journal of Surgical Technique and Case Report     Open Access  
Journal of Systemic Therapies     Full-text available via subscription   (Followers: 3)
Journal of Taibah University Medical Sciences     Open Access  
Journal of Telemedicine and Telecare     Hybrid Journal   (Followers: 12)
Journal of The Academy of Clinical Microbiologists     Open Access  
Journal of the American Association for Laboratory Animal Science     Full-text available via subscription   (Followers: 9)
Journal of the American College of Certified Wound Specialists     Hybrid Journal   (Followers: 2)
Journal of the American College of Clinical Wound Specialists     Hybrid Journal   (Followers: 2)
Journal of the American Medical Directors Association     Hybrid Journal   (Followers: 5)
Journal of the American Medical Informatics Association : JAMIA     Hybrid Journal   (Followers: 36)
Journal of the American Podiatric Medical Association     Full-text available via subscription   (Followers: 7)
Journal of the Anatomical Society of India     Full-text available via subscription  
Journal of the Anus, Rectum and Colon     Open Access  
Journal of The Arab Society for Medical Research     Open Access  

  First | 3 4 5 6 7 8 9 10 | Last

Similar Journals
Journal Cover
Journal of the American Medical Informatics Association : JAMIA
Journal Prestige (SJR): 1.848
Citation Impact (citeScore): 4
Number of Followers: 36  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1067-5027 - ISSN (Online) 1527-974X
Published by Oxford University Press Homepage  [412 journals]
  • Celebrating G. Octo Barnett, MD
    • Pages: 1187 - 1189
      Abstract: In the eighth month of 2020, in which the COVID-19 (coronavirus disease 2019) pandemic remains a global health crisis and there is heightened awareness of structural racism in our society, I’ve chosen to step back from these critical issues and briefly reflect on the legacy of G. Octo Barnett, MD, medical informatics pioneer, who died at the end of June.
      PubDate: Wed, 15 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa170
      Issue No: Vol. 27, No. 8 (2020)
       
  • Characterizing outpatient problem list completeness and duplications in
           the electronic health record
    • Authors: Wang E; Wright A.
      Pages: 1190 - 1197
      Abstract: AbstractObjectiveThe study sought to characterize rates of problem list completeness and duplications in common chronic diseases and to identify any relationships that they may have with respect to disease type, demographics, and disease severity.Materials and MethodsWe performed a retrospective analysis of electronic health record data from Partners HealthCare. We selected 8 common chronic diseases and identified patients with each of those diseases. We then analyzed each patient’s problem list for completeness and duplications and also collected information regarding demographics and disease severity. Rates of completeness and duplications were calculated for each disease and compared according to disease type, demographics, and disease severity.ResultsA total of 327 695 unique patients and 383 404 problem list entries were identified. Problem list completeness varied from 72.9% in hypertension to 93.5% in asthma, whereas problem list duplications varied from 4.8% in hypertension to 28.2% in diabetes. There was a variable relationship between demographic factors and rates of completeness and duplication. Rates of completeness were positively correlated with disease severity for most diseases. Rates of duplication were consistently positively correlated with disease severity.ConclusionsIncompleteness and duplications are both important issues in problem lists. These issues vary widely across different diseases and can also be impacted by patient demographics and disease severity. Further studies are needed to investigate the effect of individual user behaviors and organizational policies on problem list utilization, which will aid the development of interventions that improve the utility of problem lists.
      PubDate: Sat, 04 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa125
      Issue No: Vol. 27, No. 8 (2020)
       
  • Reconsidering hospital EHR adoption at the dawn of HITECH: implications of
           the reported 9% adoption of a “basic” EHR
    • Authors: Everson J; Rubin J, Friedman C.
      Pages: 1198 - 1205
      Abstract: AbstractObjectiveIn 2009, a prominent national report stated that 9% of US hospitals had adopted a “basic” electronic health record (EHR) system. This statistic was widely cited and became a memetic anchor point for EHR adoption at the dawn of HITECH. However, its calculation relies on specific treatment of the data; alternative approaches may have led to a different sense of US hospitals’ EHR adoption and different subsequent public policy.Materials and MethodsWe reanalyzed the 2008 American Heart Association Information Technology supplement and complementary sources to produce a range of estimates of EHR adoption. Estimates included the mean and median number of EHR functionalities adopted, figures derived from an item response theory-based approach, and alternative estimates from the published literature. We then plotted an alternative definition of national progress toward hospital EHR adoption from 2008 to 2018.ResultsBy 2008, 73% of hospitals had begun the transition to an EHR, and the majority of hospitals had adopted at least 6 of the 10 functionalities of a basic system. In the aggregate, national progress toward basic EHR adoption was 58% complete, and, when accounting for measurement error, we estimate that 30% of hospitals may have adopted a basic EHR.DiscussionThe approach used to develop the 9% figure resulted in an estimate at the extreme lower bound of what could be derived from the available data and likely did not reflect hospitals’ overall progress in EHR adoption.ConclusionThe memetic 9% figure shaped nationwide thinking and policy making about EHR adoption; alternative representations of the data may have led to different policy.
      PubDate: Thu, 25 Jun 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa090
      Issue No: Vol. 27, No. 8 (2020)
       
  • Hospital adoption of electronic health record functions to support
           age-friendly care: results from a national survey
    • Authors: Adler-Milstein J; Raphael K, Bonner A, et al.
      Pages: 1206 - 1213
      Abstract: AbstractObjectiveTo measure US hospitals’ adoption of electronic health record (EHR) functions that support care for older adults, focusing on structured documentation of the 4Ms (What Matters, Medication, Mentation, and Mobility) and electronic health information exchange/communication with patients, caregivers, and long-term care providers.Materials and MethodsIn an online survey of a national, random sample of 797 US acute-care hospitals in 2018–2019, 479 (60.1%) responded. We calculated nationally representative measures of the percentages of hospitals with EHRs that include structured documentation of the 4Ms and exchange/communications functions.ResultsStructured EHR documentation of the 4Ms was fully implemented in at least 1 unit in 64.0% of hospitals and across all units in 41.5% of hospitals. Of the 4Ms, structured documentation was the highest for medications (91.3% in at least 1 unit) and the lowest for mentation (70.3% in at least 1 unit). All exchange/communication functions had been implemented in at least 1 unit in 16.2% of facilities and across all units in 7.6% of hospitals. Less than half of the hospitals had an EHR portal for long-term care facilities to access hospital information (45.4% in at least 1 unit), sent information electronically to long-term care facilities (44.6%), and had training for adults/caregivers on the patient portal (32.1%).DiscussionDespite significant national investment in EHRs, hospital EHRs do not yet include key documentation, exchange, and communication functions needed to support evidence-based care for the older adults who comprise the majority of the inpatient population. Additional policy efforts are likely needed to promote the expansion of EHR capabilities into these high-value domains.ConclusionsUS acute-care hospital EHRs are lacking key functions that support care for older adults.
      PubDate: Thu, 10 Sep 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa129
      Issue No: Vol. 27, No. 8 (2020)
       
  • Variation in electronic test results management and its implications for
           patient safety: A multisite investigation
    • Authors: Thomas J; Dahm M, Li J, et al.
      Pages: 1214 - 1224
      Abstract: AbstractObjectiveThe management and follow-up of diagnostic test results is a major patient safety concern. The aim of this qualitative study was to explore how clinicians manage test results on an everyday basis (work-as-done) in a health information technology–enabled emergency department setting. The objectives were to identify (1) variations in work-as-done in test results management and (2) the strategies clinicians use to ensure optimal management of diagnostic test results.Materials and MethodsQualitative interviews (n = 26) and field observations were conducted across 3 Australian emergency departments. Interview data coded for results management (ie, tracking, acknowledgment, and follow-up), and artifacts, were reviewed to identify variations in descriptions of work-as-done. Thematic analysis was performed to identify common themes.ResultsDespite using the same test result management application, there were variations in how the system was used. We identified 5 themes relating to electronic test results management: (1) tracking test results, (2) use and understanding of system functionality, (3) visibility of result actions and acknowledgment, (4) results inbox use, and (5) challenges associated with the absence of an inbox for results notifications for advanced practice nurses.DiscussionOur findings highlight that variations in work-as-done can function to overcome perceived impediments to managing test results in a HIT-enabled environment and thus identify potential risks in the process. By illuminating work-as-done, we identified strategies clinicians use to enhance test result management including paper-based manual processes, cognitive reminders, and adaptive use of electronic medical record functionality.ConclusionsTest results tracking and follow-up is a priority area in need of health information technology development and training to improve team-based collaboration/communication of results follow-up and diagnostic safety.
      PubDate: Mon, 27 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa093
      Issue No: Vol. 27, No. 8 (2020)
       
  • Integrated displays to improve chronic disease management in ambulatory
           care: A SMART on FHIR application informed by mixed-methods user testing
    • Authors: Curran R; Kukhareva P, Taft T, et al.
      Pages: 1225 - 1234
      Abstract: AbstractObjectiveThe study sought to evaluate a novel electronic health record (EHR) add-on application for chronic disease management that uses an integrated display to decrease user cognitive load, improve efficiency, and support clinical decision making.Materials and MethodsWe designed a chronic disease management application using the technology framework known as SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources). We used mixed methods to obtain user feedback on a prototype to support ambulatory providers managing chronic obstructive pulmonary disease. Each participant managed 2 patient scenarios using the regular EHR with and without access to our prototype in block-randomized order. The primary outcome was the percentage of expert-recommended ideal care tasks completed. Timing, keyboard and mouse use, and participant surveys were also collected. User experiences were captured using a retrospective think-aloud interview analyzed by concept coding.ResultsWith our prototype, the 13 participants completed more recommended care (81% vs 48%; P < .001) and recommended tasks per minute (0.8 vs 0.6; P = .03) over longer sessions (7.0 minutes vs 5.4 minutes; P = .006). Keystrokes per task were lower with the prototype (6 vs 18; P < .001). Qualitative themes elicited included the desire for reliable presentation of information which matches participants’ mental models of disease and for intuitive navigation in order to decrease cognitive load.DiscussionParticipants completed more recommended care by taking more time when using our prototype. Interviews identified a tension between using the inefficient but familiar EHR vs learning to use our novel prototype. Concept coding of user feedback generated actionable insights.ConclusionsMixed methods can support the design and evaluation of SMART on FHIR EHR add-on applications by enhancing understanding of the user experience.
      PubDate: Mon, 27 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa099
      Issue No: Vol. 27, No. 8 (2020)
       
  • sureLDA: A multidisease automated phenotyping method for the electronic
           health record
    • Authors: Ahuja Y; Zhou D, He Z, et al.
      Pages: 1235 - 1243
      Abstract: AbstractObjectiveA major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes.Materials and MethodsSurrogate-guided ensemble latent Dirichlet allocation (sureLDA) is a label-free multidimensional phenotyping method. It first uses the PheNorm algorithm to initialize probabilities based on 2 surrogate features for each target phenotype, and then leverages these probabilities to constrain the LDA topic model to generate phenotype-specific topics. Finally, it combines phenotype-feature counts with surrogates via clustering ensemble to yield final phenotype probabilities.ResultssureLDA achieves reliably high accuracy and precision across a range of simulated and real-world phenotypes. Its performance is robust to phenotype prevalence and relative informativeness of surogate vs nonsurrogate features. It also exhibits powerful feature selection properties.DiscussionsureLDA combines attractive properties of PheNorm and LDA to achieve high accuracy and precision robust to diverse phenotype characteristics. It offers particular improvement for phenotypes insufficiently captured by a few surrogate features. Moreover, sureLDA’s feature selection ability enables it to handle high feature dimensions and produce interpretable computational phenotypes.ConclusionssureLDA is well suited toward large-scale electronic health record phenotyping for highly multiphenotype applications such as phenome-wide association studies .
      PubDate: Wed, 17 Jun 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa079
      Issue No: Vol. 27, No. 8 (2020)
       
  • Fold-stratified cross-validation for unbiased and privacy-preserving
           federated learning
    • Authors: Bey R; Goussault R, Grolleau F, et al.
      Pages: 1244 - 1251
      Abstract: AbstractObjectiveWe introduce fold-stratified cross-validation, a validation methodology that is compatible with privacy-preserving federated learning and that prevents data leakage caused by duplicates of electronic health records (EHRs).Materials and MethodsFold-stratified cross-validation complements cross-validation with an initial stratification of EHRs in folds containing patients with similar characteristics, thus ensuring that duplicates of a record are jointly present either in training or in validation folds. Monte Carlo simulations are performed to investigate the properties of fold-stratified cross-validation in the case of a model data analysis using both synthetic data and MIMIC-III (Medical Information Mart for Intensive Care-III) medical records.ResultsIn situations in which duplicated EHRs could induce overoptimistic estimations of accuracy, applying fold-stratified cross-validation prevented this bias, while not requiring full deduplication. However, a pessimistic bias might appear if the covariate used for the stratification was strongly associated with the outcome.DiscussionAlthough fold-stratified cross-validation presents low computational overhead, to be efficient it requires the preliminary identification of a covariate that is both shared by duplicated records and weakly associated with the outcome. When available, the hash of a personal identifier or a patient’s date of birth provides such a covariate. On the contrary, pseudonymization interferes with fold-stratified cross-validation, as it may break the equality of the stratifying covariate among duplicates.ConclusionFold-stratified cross-validation is an easy-to-implement methodology that prevents data leakage when a model is trained on distributed EHRs that contain duplicates, while preserving privacy.
      PubDate: Sat, 04 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa096
      Issue No: Vol. 27, No. 8 (2020)
       
  • The tradeoffs between safety and alert fatigue: Data from a national
           evaluation of hospital medication-related clinical decision support
    • Authors: Co Z; Holmgren A, Classen D, et al.
      Pages: 1252 - 1258
      Abstract: AbstractObjectiveThe study sought to evaluate the overall performance of hospitals that used the Computerized Physician Order Entry Evaluation Tool in both 2017 and 2018, along with their performance against fatal orders and nuisance orders.Materials and MethodsWe evaluated 1599 hospitals that took the test in both 2017 and 2018 by using their overall percentage scores on the test, along with the percentage of fatal orders appropriately alerted on, and the percentage of nuisance orders incorrectly alerted on.ResultsHospitals showed overall improvement; the mean score in 2017 was 58.1%, and this increased to 66.2% in 2018. Fatal order performance improved slightly from 78.8% to 83.0% (P < .001), though there was almost no change in nuisance order performance (89.0% to 89.7%; P = .43). Hospitals alerting on one or more nuisance orders had a 3-percentage-point increase in their overall score.DiscussionDespite the improvement of overall scores in 2017 and 2018, there was little improvement in fatal order performance, suggesting that hospitals are not targeting the deadliest orders first. Nuisance order performance showed almost no improvement, and some hospitals may be achieving higher scores by overalerting, suggesting that the thresholds for which alerts are fired from are too low.ConclusionsAlthough hospitals improved overall from 2017 to 2018, there is still important room for improvement for both fatal and nuisance orders. Hospitals that incorrectly alerted on one or more nuisance orders had slightly higher overall performance, suggesting that some hospitals may be achieving higher scores at the cost of overalerting, which has the potential to cause clinician burnout and even worsen safety.
      PubDate: Sat, 04 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa098
      Issue No: Vol. 27, No. 8 (2020)
       
  • Constructing co-occurrence network embeddings to assist association
           extraction for COVID-19 and other coronavirus infectious diseases
    • Authors: Oniani D; Jiang G, Liu H, et al.
      Pages: 1259 - 1267
      Abstract: AbstractObjectiveAs coronavirus disease 2019 (COVID-19) started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine-readable dataset known as the COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection among COVID-19–related biomedical entities.Materials and MethodsLeveraging a Linked Data version of CORD-19 (ie, CORD-19-on-FHIR), we first utilized SPARQL to extract co-occurrences among chemicals, diseases, genes, and mutations and build a co-occurrence network. We then trained the representation of the derived co-occurrence network using node2vec with 4 edge embeddings operations (L1, L2, Average, and Hadamard). Six algorithms (decision tree, logistic regression, support vector machine, random forest, naïve Bayes, and multilayer perceptron) were applied to evaluate performance on link prediction. An unsupervised learning strategy was also developed incorporating the t-SNE (t-distributed stochastic neighbor embedding) and DBSCAN (density-based spatial clustering of applications with noise) algorithms for case studies.ResultsThe random forest classifier showed the best performance on link prediction across different network embeddings. For edge embeddings generated using the Average operation, random forest achieved the optimal average precision of 0.97 along with a F1 score of 0.90. For unsupervised learning, 63 clusters were formed with silhouette score of 0.128. Significant associations were detected for 5 coronavirus infectious diseases in their corresponding subgroups.ConclusionsIn this study, we constructed COVID-19–centered co-occurrence network embeddings. Results indicated that the generated embeddings were able to extract significant associations for COVID-19 and coronavirus infectious diseases.
      PubDate: Wed, 27 May 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa117
      Issue No: Vol. 27, No. 8 (2020)
       
  • Large-scale evidence generation and evaluation across a network of
           
    • Authors: Schuemie M; Ryan P, Pratt N, et al.
      Pages: 1268 - 1277
      Abstract: AbstractObjectivesTo demonstrate the application of the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) principles described in our companion article to hypertension treatments and assess internal and external validity of the generated evidence.Materials and MethodsLEGEND defines a process for high-quality observational research based on 10 guiding principles. We demonstrate how this process, here implemented through large-scale propensity score modeling, negative and positive control questions, empirical calibration, and full transparency, can be applied to compare antihypertensive drug therapies. We assess internal validity through covariate balance, confidence-interval coverage, between-database heterogeneity, and transitivity of results. We assess external validity through comparison to direct meta-analyses of randomized controlled trials (RCTs).ResultsFrom 21.6 million unique antihypertensive new users, we generate 6 076 775 effect size estimates for 699 872 research questions on 12 946 treatment comparisons. Through propensity score matching, we achieve balance on all baseline patient characteristics for 75% of estimates, observe 95.7% coverage in our effect-estimate 95% confidence intervals, find high between-database consistency, and achieve transitivity in 84.8% of triplet hypotheses. Compared with meta-analyses of RCTs, our results are consistent with 28 of 30 comparisons while providing narrower confidence intervals.ConclusionWe find that these LEGEND results show high internal validity and are congruent with meta-analyses of RCTs. For these reasons we believe that evidence generated by LEGEND is of high quality and can inform medical decision-making where evidence is currently lacking. Subsequent publications will explore the clinical interpretations of this evidence.
      PubDate: Thu, 10 Sep 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa124
      Issue No: Vol. 27, No. 8 (2020)
       
  • Incorporating home healthcare nurses’ admission information needs to
           inform data standards
    • Authors: Sockolow P; Bowles K, Wojciechowicz C, et al.
      Pages: 1278 - 1286
      Abstract: AbstractObjectivePatient transitions into home health care (HHC) often occur without the transfer of information needed for critical clinical decisions and the plan of care. Owing to a lack of universally implemented standards, there is wide variation in information transfer. We sought to characterize missing information at HHC admission.Materials and MethodsWe conducted a mixed methods study with 3 diverse HHC agencies. Focus groups with nurses at each agency identified what information supports patient care decisions at admission. Thirty-six in-home admissions with associated documentation review determined the available information. To inform information standards development for the HHC admission process, we compared the types of information desired and available to an international standard for transitions in care information, the Continuity of Care Document (CCD) enhanced with Office of the National Coordinator for Healthcare Information Technology summary terms (CCD/S).ResultsThree-quarters of the items from the focus groups mapped to the CCD/S. Regarding available information at admission, no observation included all CCD/S data items. While medication information was needed and often available for 4 important decisions, concepts related to patient medication self-management appeared in neither the CCD/S nor the admission documentation.DiscussionThe CCD/S mostly met HHC nurses’ information needs and is recommended to begin to fill the current information gap. Electronic health record recommendations include use of a data standard: the CCD or the proposed, more parsimonious U.S. Core Data for Interoperability.ConclusionsReferral source and HHC agency adoption of data standards is recommended to support structured, consistent data and information sharing.
      PubDate: Thu, 10 Sep 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa087
      Issue No: Vol. 27, No. 8 (2020)
       
  • Impact of integrated graphical display on expert and novice diagnostic
           performance in critical care
    • Authors: Reese T; Del Fiol G, Tonna J, et al.
      Pages: 1287 - 1292
      Abstract: AbstractObjectiveTo determine the impact of a graphical information display on diagnosing circulatory shock.Materials and MethodsThis was an experimental study comparing integrated and conventional information displays. Participants were intensivists or critical care fellows (experts) and first-year medical residents (novices).ResultsThe integrated display was associated with higher performance (87% vs 82%; P < .001), less time (2.9 vs 3.5 min; P = .008), and more accurate etiology (67% vs 54%; P = .048) compared to the conventional display. When stratified by experience, novice physicians using the integrated display had higher performance (86% vs 69%; P < .001), less time (2.9 vs 3.7 min; P = .03), and more accurate etiology (65% vs 42%; P = .02); expert physicians using the integrated display had nonsignificantly improved performance (87% vs 82%; P = .09), time (2.9 vs 3.3; P = .28), and etiology (69% vs 67%; P = .81).DiscussionThe integrated display appeared to support efficient information processing, which resulted in more rapid and accurate circulatory shock diagnosis. Evidence more strongly supported a difference for novices, suggesting that graphical displays may help reduce expert–novice performance gaps.
      PubDate: Wed, 17 Jun 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa086
      Issue No: Vol. 27, No. 8 (2020)
       
  • Laboratory information system requirements to manage the COVID-19
           pandemic: A report from the Belgian national reference testing center
    • Authors: Weemaes M; Martens S, Cuypers L, et al.
      Pages: 1293 - 1299
      Abstract: AbstractObjectiveThe study sought to describe the development, implementation, and requirements of laboratory information system (LIS) functionality to manage test ordering, registration, sample flow, and result reporting during the coronavirus disease 2019 (COVID-19) pandemic.Materials and MethodsOur large (>12 000 000 tests/y) academic hospital laboratory is the Belgian National Reference Center for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. We have performed a moving total of >25 000 SARS-CoV-2 polymerase chain reaction tests in parallel to standard routine testing since the start of the outbreak. A LIS implementation team dedicated to develop tools to remove the bottlenecks, primarily situated in the pre- and postanalytical phases, was established early in the crisis.ResultsWe outline the design, implementation, and requirements of LIS functionality related to managing increased test demand during the COVID-19 crisis, including tools for test ordering, standardized order sets integrated into a computerized provider order entry module, notifications on shipping requirements, automated triaging based on digital metadata forms, and the establishment of databases with contact details of other laboratories and primary care physicians to enable automated reporting. We also describe our approach to data mining and reporting of actionable daily summary statistics to governing bodies and other policymakers.ConclusionsRapidly developed, agile extendable LIS functionality and its meaningful use alleviates the administrative burden on laboratory personnel and improves turnaround time of SARS-CoV-2 testing. It will be important to maintain an environment that is conducive for the rapid adoption of meaningful LIS tools after the COVID-19 crisis.
      PubDate: Thu, 18 Jun 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa081
      Issue No: Vol. 27, No. 8 (2020)
       
  • Veterans’ response to an automated text messaging protocol during
           the COVID-19 pandemic
    • Authors: Saleem J; Read J, Loehr B, et al.
      Pages: 1300 - 1305
      Abstract: AbstractThe US Department of Veterans Affairs (VA) is using an automated short message service application named “Annie” as part of its coronavirus disease 2019 (COVID-19) response with a protocol for coronavirus precautions, which can help the veteran monitor symptoms and can advise the veteran when to contact his or her VA care team or a nurse triage line. We surveyed 1134 veterans on their use of the Annie application and coronavirus precautions protocol. Survey results support what is likely a substantial resource savings for the VA, as well as non-VA community healthcare. Moreover, the majority of veterans reported at least 1 positive sentiment (felt more connected to VA, confident, or educated and/or felt less anxious) by receiving the protocol messages. The findings from this study have implications for other healthcare systems to help manage a patient population during the coronavirus pandemic.
      PubDate: Sat, 04 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa122
      Issue No: Vol. 27, No. 8 (2020)
       
  • Barriers to hospital electronic public health reporting and implications
           for the COVID-19 pandemic
    • Authors: Holmgren A; Apathy N, Adler-Milstein J.
      Pages: 1306 - 1309
      Abstract: AbstractWe sought to identify barriers to hospital reporting of electronic surveillance data to local, state, and federal public health agencies and the impact on areas projected to be overwhelmed by the COVID-19 pandemic. Using 2018 American Hospital Association data, we identified barriers to surveillance data reporting and combined this with data on the projected impact of the COVID-19 pandemic on hospital capacity at the hospital referral region level.Our results find the most common barrier was public health agencies lacked the capacity to electronically receive data, with 41.2% of all hospitals reporting it. We also identified 31 hospital referral regions in the top quartile of projected bed capacity needed for COVID-19 patients in which over half of hospitals in the area reported that the relevant public health agency was unable to receive electronic data.Public health agencies’ inability to receive electronic data is the most prominent hospital-reported barrier to effective syndromic surveillance. This reflects the policy commitment of investing in information technology for hospitals without a concomitant investment in IT infrastructure for state and local public health agencies.
      PubDate: Mon, 01 Jun 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa112
      Issue No: Vol. 27, No. 8 (2020)
       
  • Self-reported COVID-19 symptoms on Twitter: an analysis and a research
           resource
    • Authors: Sarker A; Lakamana S, Hogg-Bremer W, et al.
      Pages: 1310 - 1315
      Abstract: AbstractObjectiveTo mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research.Materials and MethodsWe retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings.ResultsWe identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies.ConclusionThe spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.
      PubDate: Sat, 04 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa116
      Issue No: Vol. 27, No. 8 (2020)
       
  • Automatic detection of hand hygiene using computer vision technology
    • Authors: Singh A; Haque A, Alahi A, et al.
      Pages: 1316 - 1320
      Abstract: AbstractObjectiveHand hygiene is essential for preventing hospital-acquired infections but is difficult to accurately track. The gold-standard (human auditors) is insufficient for assessing true overall compliance. Computer vision technology has the ability to perform more accurate appraisals. Our primary objective was to evaluate if a computer vision algorithm could accurately observe hand hygiene dispenser use in images captured by depth sensors.Materials and MethodsSixteen depth sensors were installed on one hospital unit. Images were collected continuously from March to August 2017. Utilizing a convolutional neural network, a machine learning algorithm was trained to detect hand hygiene dispenser use in the images. The algorithm’s accuracy was then compared with simultaneous in-person observations of hand hygiene dispenser usage. Concordance rate between human observation and algorithm’s assessment was calculated. Ground truth was established by blinded annotation of the entire image set. Sensitivity and specificity were calculated for both human and machine-level observation.ResultsA concordance rate of 96.8% was observed between human and algorithm (kappa = 0.85). Concordance among the 3 independent auditors to establish ground truth was 95.4% (Fleiss’s kappa = 0.87). Sensitivity and specificity of the machine learning algorithm were 92.1% and 98.3%, respectively. Human observations showed sensitivity and specificity of 85.2% and 99.4%, respectively.ConclusionsA computer vision algorithm was equivalent to human observation in detecting hand hygiene dispenser use. Computer vision monitoring has the potential to provide a more complete appraisal of hand hygiene activity in hospitals than the current gold-standard given its ability for continuous coverage of a unit in space and time.
      PubDate: Sun, 26 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa115
      Issue No: Vol. 27, No. 8 (2020)
       
  • An artificial intelligence approach to COVID-19 infection risk assessment
           in virtual visits: A case report
    • Authors: Obeid J; Davis M, Turner M, et al.
      Pages: 1321 - 1325
      Abstract: AbstractObjectiveIn an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence–based methods with unstructured patient data collected through telehealth visits.Materials and MethodsAfter segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding–based convolutional neural network for predicting COVID-19 test results based on patients’ self-reported symptoms.ResultsText analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling.ConclusionsInformatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.
      PubDate: Sat, 04 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa105
      Issue No: Vol. 27, No. 8 (2020)
       
  • Rapid implementation of a COVID-19 remote patient monitoring program
    • Authors: Annis T; Pleasants S, Hultman G, et al.
      Pages: 1326 - 1330
      Abstract: AbstractObjectiveThe study sought to evaluate early lessons from a remote patient monitoring engagement and education technology solution for patients with coronavirus disease 2019 (COVID-19) symptoms.Materials and MethodsA COVID-19–specific remote patient monitoring solution (GetWell Loop) was offered to patients with COVID-19 symptoms. The program engaged patients and provided educational materials and the opportunity to share concerns. Alerts were resolved through a virtual care workforce of providers and medical students.ResultsBetween March 18 and April 20, 2020, 2255 of 3701 (60.93%) patients with COVID-19 symptoms enrolled, resulting in over 2303 alerts, 4613 messages, 13 hospital admissions, and 91 emergency room visits. A satisfaction survey was given to 300 patient respondents, 74% of whom would be extremely likely to recommend their doctor.DiscussionThis program provided a safe and satisfying experience for patients while minimizing COVID-19 exposure and in-person healthcare utilization.ConclusionsRemote patient monitoring appears to be an effective approach for managing COVID-19 symptoms at home.
      PubDate: Mon, 27 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa097
      Issue No: Vol. 27, No. 8 (2020)
       
  • Principles of Large-scale Evidence Generation and Evaluation across a
           Network of Databases (LEGEND)
    • Authors: Schuemie M; Ryan P, Pratt N, et al.
      Pages: 1331 - 1337
      Abstract: AbstractEvidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias.In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative. Its mission is to generate evidence on the effects of medical interventions using observational health-care databases while addressing the aforementioned concerns by following a recently proposed paradigm. We define 10 principles of LEGEND that enshrine this new paradigm, prescribing the generation and dissemination of evidence on many research questions at once; for example, comparing all treatments for a disease for many outcomes, thus preventing publication bias. These questions are answered using a prespecified and systematic approach, avoiding P hacking. Best-practice statistical methods address measured confounding, and control questions (research questions where the answer is known) quantify potential residual bias. Finally, the evidence is generated in a network of databases to assess consistency by sharing open-source analytics code to enhance transparency and reproducibility, but without sharing patient-level information.Here we detail the LEGEND principles and provide a generic overview of a LEGEND study. Our companion paper highlights an example study on the effects of hypertension treatments, and evaluates the internal and external validity of the evidence we generate.
      PubDate: Thu, 10 Sep 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa103
      Issue No: Vol. 27, No. 8 (2020)
       
  • It is time for computable evidence synthesis: The COVID-19 Knowledge
           Accelerator initiative
    • Authors: Alper B; Richardson J, Lehmann H, et al.
      Pages: 1338 - 1339
      Abstract: Dear JAMIA Editors,
      PubDate: Mon, 27 Jul 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa114
      Issue No: Vol. 27, No. 8 (2020)
       
  • Corrigendum to: Accounting for data variability in multi-institutional
           distributed deep learning for medical imaging
    • Authors: Balachandar N; Chang K, Kalpathy-Cramer J, et al.
      Pages: 1340 - 1340
      Abstract: Journal of the American Medical Informatics Association, 27(5), 2020, 700–708; doi: 10.1093/jamia/ocaa017
      PubDate: Thu, 10 Sep 2020 00:00:00 GMT
      DOI: 10.1093/jamia/ocaa118
      Issue No: Vol. 27, No. 8 (2020)
       
 
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