Conference Agenda

Purpose: Access to essential medicines remains a major challenge in developing countries due to inefficient supply chains and weak Logistics Management Information Systems (LMIS) (Stevens & Huys, 2017; WHO, 2010). In Ethiopia, persistent problems related to data quality, infrastructure, workforce capacity, and system integration continue to hinder health commodities supply chain performance despite ongoing health sector reforms. This study employed a grounded theory approach to develop a conceptual model for evaluating LMIS performance in public healthcare facilities in Ethiopia. The model aims to strengthen digital logistics data management, improve supply chain efficiency, and support evidence-based healthcare delivery aligned with Ethiopia’s Health Information Revolution agenda.

Methodology: An institution-based concurrent exploratory mixed-methods design with a qualitative emphasis was employed. The study was conducted across 102 public healthcare facilities in the Amhara Region. Eighty-three key informants, including health facility heads, medical directors, pharmacy heads, supply chain coordinators, district coordinators, and chief executive officers, were purposively selected based on their experience in health commodities supply chain management. Data were collected through in-depth interviews using open-ended questions. Qualitative data were analyzed using grounded theory methodology, including open, axial, and selective coding based on the coding paradigm model of Corbin and Strauss (Corbin J & Strauss A, 2015). MAXQDA version 24.4.1 was used for coding and analysis. Ethical approval was obtained from Addis Ababa University, and participant confidentiality was maintained throughout the study.

Findings and Analysis: The analysis generated 1,427 coding segments, 187 first-level concepts, 64 second-level categories, and 21 high-level categories. The resulting conceptual model identified key factors influencing LMIS performance and health commodities supply chain efficiency. Major causal factors included budget limitations, poor data quality, inadequate workforce training, and LMIS implementation challenges. Contextual conditions such as government commitment, infrastructure availability, public health emergencies, and security concerns also significantly affected system performance. The model further highlighted strategic interventions including digital innovation, LMIS integration, workforce development, and enhanced implementation support. Overall, the model demonstrated that an effective LMIS improves healthcare delivery through timely, reliable, and high-quality data for informed decision-making.

Originality: This study contributes original evidence by developing a grounded theory-based conceptual model for evaluating health commodities LMIS performance in a low-resource healthcare setting. Unlike previous studies that primarily focused on isolated operational challenges, this study integrates causal, contextual, and strategic dimensions into a comprehensive logistics performance framework. The model provides a practical foundation for strengthening digital logistics platforms, improving supply chain data systems, and supporting evidence-based decision-making in healthcare logistics management.

Conclusion and Recommendations: The developed conceptual model provides a practical framework for evaluating and improving LMIS performance in Ethiopian healthcare facilities. The study recommends piloting the model prior to large-scale implementation and conducting further quantitative validation studies to establish measurable key performance indicators for routine LMIS monitoring and evaluation.