Abstract:
Introduction: The health management information system is a system that supports the management and
use of routine health data in the health institutions and it has been implemented since 2008 in Ethiopia.
Data quality is a critical component of health management information system that enhances effective
decision making, but the level of routine data quality remains low or below the expectation in Ethiopia. In
the hospital settings, relatively much more data is produced and data management is complicated due to
the complex nature of hospitals (large departments and units).
Objective: To evaluate whether program resources availability, data quality assurance practices and the
level of data quality is congruent to the national standard or guideline in the public hospitals of Jimma
zone, Southwest Ethiopia.
Methods: A case study design using quantitative and qualitative evaluation methods was conducted from
15 th May, 2022 to July 30 th , 2022. An indicators-driven evaluation was conducted (38 indicators across
five dimensions of measurement were used) and the evaluation was guided by the Performance of Routine
Information System Management (PRISM) framework. Program resources inventory, document reviews,
observation, and key informant interviews were the main data collection methods. A total of 20
purposefully selected key informants from seven hospitals were interviewed to explore the barrier and
facilitators of data quality in hospitals. Evaluation of program performance in terms of quality was
evaluated using pre-set judgment parameters. Descriptive statistics was used to describe the
characteristics of hospitals. Quantitative data analysis was conducted with SPSS version 25. Qualitative
data were transcribed, translated and analyzed manually and presented narratively along with the
quantitative findings.
Results: The overall HMIS data quality according to the judgmental parameter was 71.1 %. Resource
Availability was 84 % and compliance was 73%. The overall data quality attribute was 60.3%.
Completeness, consistency and timeliness were 86.4%, 41.7% and 86.3% respectively. The perceived
barriers to data quality were trained and well experienced staffs turnover, inadequate supportive
supervision, problems with data extraction and unfamiliar with data quality assessment at department
level.
Conclusions: The HMIS data quality was fair as preset judgment parameters. It should achieve more by
availing internet access and give basic training for untrained providers. Moreover, regular supportive
supervision and follow up is better for program improvement
