The Role of Electronic Medical Records on Aggregate Data Reporting and Use in the Tanzanian Health System

Abstract

Background: Globally, integrating routine data use into its health system is critical for informed decisions and better healthcare outcomes. Electronic Medical Records (EMRs) are crucial for this goal, particularly for individual data generation. While EMR adoption is growing, the effectiveness of EMRs in supporting the Health Management Information System (HMIS) for collecting, synthesizing, and reporting routine health data remains unknown. This study investigates how EMRs contribute to these HMIS functions. Methods: This study employed a descriptive qualitative approach to examine the implementation and usage of Electronic Medical Records (EMRs) in the Tanzania health system, using Dodoma and Bahi districts as case studies. The Task-Technology Fit (TTF) theory guided the development of key themes, focusing on how EMR functionalities support core Health Management Information System (HMIS) functions: data collection, synthesis, utilization, and dissemination. Data were gathered through key informant interviews with relevant stakeholders and a comparative review of EMR and HMIS monthly reports. Results: The study revealed that EMRs effectively manage individual patient data and primarily support direct patient care. However, their contribution to aggregate data reporting and use for broader health management is limited. This limitation is due to incompatible report formats, insufficient user capabilities, lack of interoperability, and the absence of aggregate data visualization dashboards and analytical tools, all hinder secondary data utilization and support for HMIS functions. Conclusion: This study emphasizes the need to address the identified challenges to realize the transformative potential of Electronic Medical Records (EMRs). By overcoming these obstacles, Tanzania's health system can effectively leverage EMRs for routine data reporting and utilization, leading to robust evidence-based decision-making and institutionalized data use.