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.
