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Applied Medical Informatics
Journal Prestige (SJR): 0.106

Number of Followers: 12

This is an Open Access Journal

Open Access journal
ISSN (Print) 1224-5593 - ISSN (Online) 2067-7855
Published by SRIMA Publishing House

[1 journal]

The Role of Electronic Medical Records on Aggregate Data Reporting and Use
in the Tanzanian Health System
- Authors: Bigten KIKOBA, Masoud MAHUNDI, Wilfred SENYONI
  Abstract: Background: Globally, integrating routine data use into its health system is critical for informed decisions and better healthcare outcomes. Electronic Medical Records (EMRs) are crucial for this goal, particularly for individual data generation. While EMR adoption is growing, the effectiveness of EMRs in supporting the Health Management Information System (HMIS) for collecting, synthesizing, and reporting routine health data remains unknown. This study investigates how EMRs contribute to these HMIS functions. Methods: This study employed a descriptive qualitative approach to examine the implementation and usage of Electronic Medical Records (EMRs) in the Tanzania health system, using Dodoma and Bahi districts as case studies. The Task-Technology Fit (TTF) theory guided the development of key themes, focusing on how EMR functionalities support core Health Management Information System (HMIS) functions: data collection, synthesis, utilization, and dissemination. Data were gathered through key informant interviews with relevant stakeholders and a comparative review of EMR and HMIS monthly reports. Results: The study revealed that EMRs effectively manage individual patient data and primarily support direct patient care. However, their contribution to aggregate data reporting and use for broader health management is limited. This limitation is due to incompatible report formats, insufficient user capabilities, lack of interoperability, and the absence of aggregate data visualization dashboards and analytical tools, all hinder secondary data utilization and support for HMIS functions. Conclusion: This study emphasizes the need to address the identified challenges to realize the transformative potential of Electronic Medical Records (EMRs). By overcoming these obstacles, Tanzania's health system can effectively leverage EMRs for routine data reporting and utilization, leading to robust evidence-based decision-making and institutionalized data use.
  PubDate: 2025-05-26
Discovery of a Sinusoidal Pattern in the Cancer Antigen 15-3 Time Series
of a Patient with Metastatic Breast Cancer
- Authors: Alexandros CLOUVAS
  Abstract: Cancer Antigen 15-3 (CA 15-3) is a glycoprotein commonly linked to breast cancer. Monitoring CA 15-3 levels is crucial for tracking disease progression and evaluating treatment response in patients with metastatic breast cancer. Although fluctuations in CA 15-3 levels are common in patients with metastatic breast cancer, no periodic pattern has been identified to date. In this study, a sinusoidal pattern was observed for the first time in CA 15-3 time-series data of a patient with metastatic breast cancer. This pattern recurred every 175 days, from February 2024 to January 2025. Statistical analysis strongly indicated that the observed sinusoidal variation was highly unlikely to have occurred by chance, thus providing an excellent fit to the data. This finding is both novel and captivating, as changes in CA 15-3 levels are usually linked to disease progression or treatment responses rather than following a predictable sinusoidal rhythm.
  PubDate: 2025-05-24
Boosting Cognitive Focus via Attention Types Detection using
Brain-Computer Interfaces: A Pilot Study
- Authors: Mihai-Robert BEU, Tudor DURDUMAN-BURTESCU, David GHEORGHICĂ ISTRATE
  Abstract: This study leverages Brain-Computer Interfaces (BCIs) and electroencephalography (EEG) to enhance cognitive focus in adolescents (12–17 years) by classifying effective (task-oriented) and ineffective (distracted) attention states. Addressing declining attention spans in Generation Alpha/Z, we integrate augmented reality (AR) environments with personality-adaptive machine learning models. Sixteen participants performed cognitive tasks while EEG data was captured via a 16-channel BrainAccess MIDI headset. Signal preprocessing (filtering, ICA- independent component analysis, CSP -common spatial patterns) tied with data augmentation improved dataset robustness by 40%. Results demonstrated a 57% concentration increase in AR versus VR (where participants performed identical tasks in a non-adaptive virtual environment) with personality-tailored models boosting classification accuracy by 10%. High-performing classifiers (e.g., Deep Neural Networks, XGBoost) achieved 87% accuracy, underscoring BCIs’ potential for personalized cognitive interventions in education and therapy.
  PubDate: 2025-05-24
Bioinformatics in Age-Related Macular Degeneration: A Narrative Review
- Authors: Mehrdad MOTAMED SHARIATI
  Abstract: Purpose: Age-related macular degeneration (AMD) is the leading cause of irreversible blindness worldwide. Understanding AMD's complex pathophysiology of AMD and its associated genetic and molecular mechanisms is critical for the development of effective diagnostics and therapeutics. This review explores the evolving role of bioinformatics in AMD with a focus on current applications and future opportunities in clinical research and patient care. Methods: A comprehensive literature search was conducted using major databases to identify articles related to bioinformatic applications in AMD. Selected studies were evaluated to extract data on bioinformatics tools and techniques, including genomic analysis, artificial intelligence (AI), and systems biology, utilized in AMD research. Results: Bioinformatics has advanced AMD research through genome-wide association studies (GWAS) that identify susceptibility genes, and transcriptomics and proteomics analyses that reveal biomarkers and pathways. AI and machine learning models enhance disease prediction and classification, whereas integrative omics approaches facilitate personalized treatment strategies. Conclusion: Bioinformatics is revolutionizing AMD research by providing insights into its molecular basis, enabling early diagnosis, and aiding in the development of personalized therapies. Continued advancements in computational tools and data integration will further our understanding of AMD and improve preventive and therapeutic outcomes.
  PubDate: 2025-05-24
Time Series measurements of White Blood Cells (WBC) and plasma Cancer
Antigen 15-3 (CA 15-3) in a patient with metastatic breast cancer, serving
as a reference for assessing the clinical relevance of different
clustering algorithms.
- Authors: Alexandros CLOUVAS
  Abstract: Clustering is highly valuable for analyzing cancer marker time series data. In a recent study, the significance of the K-means clustering algorithm was examined for the first time in evaluating time-series measurements of plasma Cancer Antigen 15-3 (CA 15-3) in a male patient with metastatic breast cancer. CA 15-3 is a glycoprotein commonly associated with breast cancer. The present study marks a significant advancement, as the time-series measurements of White Blood Cells (WBC) and plasma CA 15-3 from the same patient served as a reference for assessing the clinical relevance of various clustering algorithms. The time-series measurements of WBC are particularly noteworthy because the specific therapy administered to the patient has clearly defined all clustering parameters, including the number of clusters and their boundaries. This provides a unique opportunity for rigorous testing of nine different clustering algorithms. Remarkably, seven to nine of these algorithms demonstrate perfect alignment between the observed number of clusters and their boundaries, and the expected results based on the therapy administered to the patient. Furthermore, applying the nine clustering methods to the CA 15-3 time-series data revealed that K-means with Euclidean distance, K-means with Manhattan distance, and K-medoids are the most suitable algorithms for analyzing both the specific CA 15-3 and WBC time-series data.
  PubDate: 2024-12-26

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