Development of guidelines for the utilization of health management information system for quality immunization service in Ethiopia

Abstract

Health Management Information System (HMIS) is a major source of information for estimating immunisation service coverage; however, most studies argue that the Routine Health Management Information System (RHIS) inherently produces poor-quality data, which deters its wider use. Some literature asserts that no data from any source can prove the best quality; rather, using available information to provide quality service can sustain information use, thereby achieving higher data quality. This, in turn, promotes usability; nevertheless, the nexus between quality improvement and information use is not yet established. This study was intended to develop guidelines to improve HMIS use for quality immunisation service. The Performance of Routine Information System Management-Model For Improvement (PRISM-MFI) framework, guided by an explanatory sequential design, informed the mixed-methods data collection, analysis, and interpretation. The study was conducted in 42 health facilities in Addis Ababa, Ethiopia, enrolling 412 participants among health and HMIS workers. A multistage cluster, stratified with Probability Proportion to the Size (PPS) sampling, was used. Data collected through a self-administered questionnaire were entered and cleaned with EpiInfo version 7.2.5 and thoroughly analysed using SPSS for Windows 28. Qualitative strand applied Nested Criterion sampling. Data collected from nine focus group interviews composed of Information Technicians (HITs), health professionals, and health managers. Each of the categories had three groups interviewed using a thematic guide, audio recordings, and notes were manually transcribed, and codes and themes were subsequently generated using the software ATLAS.ti version 24, and were detail-analysed applying a descriptive qualitative design. Integration of findings from two studies; used a narrative joint display table to generate meta-inferences. The result revealed weak data management and data quality, hence the need to use information to ensure quality immunisation services. RHIS processes are affected by the use of hybrid, disaggregated data tools, redundant entry into the District Health Information System (DHIS2), and parallel reporting; they fail to systematically assess data quality, perform timely analysis, and interpret gaps and mitigation plans. The feedback mechanism is not constructive. The system update neglects frontline staff participation, worsening the complexity of forms, DHIS2 software, and indicators. Subpar HMIS staffing, training resources, and data use for budgeting and staff appraisal negate accountability. Data demand prioritises monitoring program output over improving service quality. Health staff lack selfreliance in interpreting and using data to evidence the design and implementation of quality initiatives. In sum, information is underused for immunisation planning, resource optimisation, supportive supervision, and community engagement in services. The interlinked guidelines were developed to address current challenges in data quality assurance and in the use of information to improve immunisation service quality.