Barriers to using eHealth data for clinical performance feedback in Malawi: A case study

• We qualitatively analyzed barriers to EMR data use for audit and feedback in Malawi.
• Primary barriers included provider rotations and disruptions to care processes.
• User acceptance of eHealth and performance indicator lifespan were also key barriers.
• The barriers are dynamic and could inform routine data quality assessment.
• Understanding the clinical setting as a complex adaptive system yielded key insights.

IntroductionSub-optimal performance of healthcare providers in low-income countries is a critical and persistent global problem. The use of electronic health information technology (eHealth) in these settings is creating large-scale opportunities to automate performance measurement and provision of feedback to individual healthcare providers, to support clinical learning and behavior change. An electronic medical record system (EMR) deployed in 66 antiretroviral therapy clinics in Malawi collects data that supervisors use to provide quarterly, clinic-level performance feedback. Understanding barriers to provision of eHealth-based performance feedback for individual healthcare providers in this setting could present a relatively low-cost opportunity to significantly improve the quality of care.ObjectiveThe aims of this study were to identify and describe barriers to using EMR data for individualized audit and feedback for healthcare providers in Malawi and to consider how to design technology to overcome these barriers.MethodsWe conducted a qualitative study using interviews, observations, and informant feedback in eight public hospitals in Malawi where an EMR system is used. We interviewed 32 healthcare providers and conducted seven hours of observation of system use.ResultsWe identified four key barriers to the use of EMR data for clinical performance feedback: provider rotations, disruptions to care processes, user acceptance of eHealth, and performance indicator lifespan. Each of these factors varied across sites and affected the quality of EMR data that could be used for the purpose of generating performance feedback for individual healthcare providers.ConclusionUsing routinely collected eHealth data to generate individualized performance feedback shows potential at large-scale for improving clinical performance in low-resource settings. However, technology used for this purpose must accommodate ongoing changes in barriers to eHealth data use. Understanding the clinical setting as a complex adaptive system (CAS) may enable designers of technology to effectively model change processes to mitigate these barriers.