A Landscape Study Highlights the Urgent Need for Evidence Based Strategies to Enable Electronic Health Records Integration in the National Healthcare Systems of Low- and Middle-Income Countries
Bibliographic record
Abstract
A Review of: Kumar, M., & Mostafa, J. (2020). Electronic health records for better health in lower- and middle-income countries: A landscape study. Library Hi Tech, 38(4), 751–767. https://doi.org/10.1108/LHT-09-2019-0179 Abstract Objective – To identify how low- and middle-income countries (LMICs) approached the development of national and subnational electronic health records (EHRs) and to understand the challenges related to EHR research priorities and sustainability. Design – Landscape study consisting of a review of the scientific literature, country-focused grey literature, and consultation with international experts. Setting – Hospitals and healthcare systems within LMICs. Subjects – The 402 publications retrieved through a systematic search of four scientific electronic databases along with 49 publications found through a country-focused analysis of grey literature and 14 additional publications found through consultation with two international experts. Methods – On 15 May 2019, the authors comprehensively searched four major scientific databases: Global Health, PubMed, Scopus, and Web of Science. They also searched the grey literature and repositories in consultation with country-based international digital health experts. The authors subsequently used Mendeley reference management software to organize and remove duplicate publications. Peer-reviewed publications that focused on developing national EHRs within LMIC healthcare systems were included for the title and abstract screening. Data analysis was mainly qualitative, and the results were organized to highlight stakeholders, health information architecture (HIA), and sustainability. Main Results – The results were presented in three subsections. The first two described critical stakeholders for developing national and subnational EHRs and HIA, including country eHealth foundations, EHRs, and subsystems. The third section presented and discussed pressing challenges related to EHR sustainability. The findings of the three subsections were further explored through the presentation of three LMIC case studies that described stakeholders, HIA, and sustainability challenges. Conclusion – The results of this landscape study highlighted the scant evidence available to develop and sustain national and subnational EHRs within LMICs. The authors noted that there appears to be a gap in understanding how EHRs impact patient-level and population outcomes within the LMICs. The study revealed that EHRs were primarily designed to support monitoring and evaluating health programs focused on a particular disease or group of diseases rather than common health problems. While national governments and international donors focused on the role of EHRs to improve patient care, the authors highlighted the urgent need for further research on the development of EHRs, with a focus on efficiency, evaluation, monitoring, and quality within the national healthcare enterprise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.034 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".