Identifying barriers to using eHealth data for individualized clinical performance feedback in Malawi : A case study

Seminar Date: 
Seminar Time: 
11am - 12pm
Seminar Location: 
5607 Baum Boulevard, Room 407A
Zach Landis-Lewis, PhD


Introduction: Sub-optimal performance of healthcare providers in low-income countries is a critical and persistent global problem. The use of eHealth in these settings is creating unprecedented opportunities to automate healthcare performance measurement and the creation of performance feedback for healthcare providers to support clinical learning and behavior change. However, barriers to generating individualized, eHealth-based performance feedback in low-resource settings are not well understood.

Objective: The aim of this study was 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. We developed a code book through the analysis of the qualitative data that was informed by conceptual models of cognitive processing of performance feedback and an implementation science research framework. We used an adjudication process to resolve differences between coders and interpreted the thematic findings using the lens of our conceptual models.

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: Technology designed to use eHealth data to generate performance feedback in low-resource settings must adaptively accommodate barriers that affect the quality of eHealth data. Tools that enable supervisors to tailor feedback messages for individual and situational differences may improve the effectiveness of performance feedback in this setting.