Zach Landis-Lewis1, Ronald Manjomo2, Oliver J Gadabu2, Matthew Kam3, Bertha N Simwaka4, Susan L Zickmund5, Frank Chimbwandira6, Gerald P Douglas1, Rebecca S Jacobson7. 1. Center for Health Informatics for the Underserved, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. 2. Baobab Health Trust, Lilongwe, Malawi. 3. Google, New York, NY, USA. 4. The Global Fund to Fight AIDS, Tuberculosis and Malaria, Geneva, Switzerland. 5. Center for Health Equity Research and Promotion, VA Medical Center, Pittsburgh, PA, USA. 6. Department of HIV and AIDS, Ministry of Health, Lilongwe, Malawi. 7. Center for Health Informatics for the Underserved, Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: zjl1@pitt.edu.
Abstract
INTRODUCTION: Sub-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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: Using 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.
INTRODUCTION: Sub-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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: Using 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.
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