The Role of Data Processing on Decision-Making within Logistics Management Information Systems for Effective Quality Care Management

Background Effective healthcare treatment relies on efficient Logistics Management Information Systems (LMIS). LMIS is crucial for overseeing supply chains in healthcare, as it supplies decision-makers with precise and timely information necessary for delivering quality services. Nonetheless, problems in data collection, aggregation, and transfer can compromise the quality of the information used for patient care management, especially in settings with limited resources. Purpose of the study To emphasize to stakeholders about the significance of effective data processing in LMIS for enhancing the quality of care and treatment. Methods A cross-sectional study took place from April to July 2024 at Kisii Teaching and Referral Hospital (KTRH) and Naivasha Sub-County Hospital, involving a sample of 201 participants from different healthcare departments. Data collection was carried out using structured questionnaires and interview guides. The research utilized both quantitative and qualitative methods. Data was analyzed with SPSS version 29.0.0. Results are presented in both descriptive and inferential statistics. The agenda was to investigate the correlation between efficiency in LMIS data processing and the quality of decision-making in care management and treatment. Results The study found that data processing was strongly correlated as well as significant with the efficiency of LMIS in supporting decision making on patient care management and treatment. Particularly concerning timeliness, completeness, and accuracy of information (r = -0.801, p < 0.01). Key findings indicated that at KTRH and Naivasha Sub-County Hospital there was the use of hybrid LMIS systems for information dissemination and the limited use of computerized data analysis at service delivery points. Poor decision-making on patient care management and treatment issues were primarily attributed to inadequate tools and equipment, lack of training, and disjointed reporting frameworks. Conclusions Evidence from this study findings shows that data processing is a crucial determinant of the quality of decisions made in healthcare settings. Improving data aggregation, analysis, information use, and transfer processes, with a focus on complete digitization, is essential for enhancing patient care management and treatment. Digital systems in key departments were in place and this improved decision-making. The implications of not digitizing these processes may result in elevated costs, operational inefficiencies, impeded innovation, and fragmented data systems, thereby complicating informed decision-making and hindering effective collaboration.