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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,010 | 0,012 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,013 | 0,016 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».