From Clinical Silos to Population Surveillance: Architecting Interoperability Between Electronic Health Records and Public Health Data Systems in Resource-Constrained Environments

Publication Details

Publication Date

11/11/2026

Volume / Issue

Vol 1, Issue 6 (2026)

Article No.

103

Journal

British Journal of Contemporary Research

DOI

https://doi.org/10.67693/BJCR-5XJVASMR

Received

01 Nov 2026

Views

62

Downloads

8

Abstract

Background: In low- and middle-income countries (LMICs), Electronic Health Record (EHR) platforms operate predominantly as disconnected, facility-level transaction ledgers. Simultaneously, national public health surveillance systems—principally the District Health Information Software 2 (DHIS2)—rely on retrospective, aggregated, manual reporting. This structural decoupling produces reporting latencies spanning weeks, introduces transcription error margins exceeding 20%, and discards critical clinical covariates necessary for precision epidemiology. Objective: To systematically synthesize empirical evidence on technical architectures, semantic standards, and socio-technical governance models required to establish automated, bidirectional interoperability between point-of-care EHRs and national public health surveillance systems in resource-constrained settings. Methods: Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE, Scopus, Web of Science, and Embase for peer-reviewed studies published between January 2016 and mid-2026. Eligible studies evaluated electronic health information exchanges, middleware data pipelines, semantic mediation, or automated case reporting linking clinical systems to population surveillance in resource-limited contexts. Quality appraisal was executed using the Mixed Methods Appraisal Tool (MMAT) and the Critical Appraisal Skills Programme (CASP) checklists. Results: Twenty-eight peer-reviewed publications met all inclusion criteria. The evidence identifies four systemic failure points: physical infrastructure fragility (electrical grid failures and network dropouts), semantic discordance between clinical narratives and structured reporting ontologies, clinical workforce documentation burnout, and legal/governance vacuums surrounding secondary health data custodianship. Successful implementations converged on a four-tier architecture: (1) clinical capture at the edge (e.g., OpenMRS, Bahmni); (2) decoupled edge middleware with store-and-forward caching (e.g., OpenHIM); (3) semantic mediation engines utilizing Fast Healthcare Interoperability Resources (HL7 FHIR) mapped to ICD, LOINC, and SNOMED-CT; and (4) automated electronic Case Reporting (eCR) pipelines directly populating DHIS2 aggregate and tracker databases. Deploying automated middleware reduced reporting latency from weeks to under 60 seconds while eliminating routine manual transcription errors. Conclusion: Transitioning from passive clinical silos to automated population surveillance requires abandoning monolithic national software procurements in favor of modular, standards-based middleware engineered for offline-first resilience. Achieving population-wide surveillance coverage further requires statutory policy frameworks that mandate the integration of private-sector healthcare facilities into national health data exchanges.

License

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.