Evaluation of Electronic Medical Record Implementation and Data Quality in HIV Programs in Kenya and Haiti

Abstract

The global HIV agenda is to end the AIDS epidemic. Robust health information systems (HIS) are essential for monitoring, evaluating, and strengthening universal efforts to achieve the Joint United Nations Programme on HIV/AIDS (UNAIDS) 95/95/95 targets by 2030 (to ensure that 95% of people living with HIV are diagnosed, 95% of those diagnosed are on ART and 95% of those on ART are virally suppressed) [1]. EMRs are considered essential building blocks for strong HIS by improving data capture, retrieval, reporting, and overall clinical decision making for enhanced patient care [2–4]. The World Health Organization's (WHO) global digital health strategy aims to “improve health for everyone, everywhere by accelerating the adoption of appropriate digital health” – this includes EMRs [5]. Funding agencies such as the United States (US) President’s Emergency Plan for AIDS Relief (PEPFAR) have funded scale up and implementation of EMRs in HIV clinics within low- and middle-income countries (LMICS) with a high HIV burden like Kenya and Haiti [6,7]. The HIV programs in these two contexts are expanding considerably along with the volume of data and reporting requirements. Phased introduction of EMRs over the past decade has led to migration from paper-based health records to electronic records in HIV clinics to increase data availability, data quality, and data use. However, there is a paucity of robust observational studies that evaluate the evidence of impact of EMRs on data quality in routine health management information systems (HMIS) and disparities in data quality across different age groups. This three-part evaluation was aimed at: a) developing and comparing novel composite data quality scoring systems; b) using interrupted time series analysis to investigate the impact of EMR implementation on the quality of data uploaded to the District Health Information System version 2 (DHIS2) and facility correlates of data quality in HIV clinics in Kenya; and c) exploring variations and disparities in data quality of EMR records across different age groups in Haiti. We used the tools of implementation science (e.g., conceptual framework and quasi-experimental design) to explore and measure data quality gaps, and make causal inference on the effect of EMRs. We generated data-driven composite data quality scores that can be used at scale to assess national DHIS2 data quality remotely. We found no evidence of impact of EMRs on the accuracy of the data uploaded to the DHIS2 in Kenya. In Haiti, we found disparities in data quality across children, adolescents, and adult age groups with poorest data quality being demonstrated among children. Our research demonstrates the need for further evaluation and investment in EMRs to harness their full potential for strengthening HIS and clinical patient management.