Level of implementation of district health information system 2 at public health facilities in Eastern Ethiopia

Abstract

Objective The major aim of this study was to assess the level of District health information system 2 (DHIS 2) implementation in the public health facilities (HFs) in Dire Dawa City Administration. Methods This study was employed both quantitative (cross-sectional) and qualitative (phenomenological) study designs. All public HFs found in Dire Dawa City Administration and health workers were participated in the study. Quantitative data were collected using a pre-tested, structured, self-administered questionnaire. The collected data were entered into Epi-Data and analyzed using STATA version 14 software. A descriptive summary was computed using proportion and frequencies. Qualitative data were collected from in-depth interview with key informants (KIs), and the results were then analyzed thematically. Results The overall implementation level of DHIS 2 was 80%, which shows good implementation. The main difficulties encountered in implementing DHIS 2 were a lack of power backup (64.3%), unreliable internet connectivity (43%), and a lack of training (34.6%). According to an in-depth interview with a 32-year-old professional, “…there is offline and online DHIS 2 software for data collection and reporting that is an opportunity for the health center, but there is a challenge of interruption of electricity lost unsaved data and hinder data to enter and view for making a decision….” Conclusion The level of DHIS 2 implementation in this study was good compared to other studies in Ethiopia. However, more than half of the HFs require infrastructure maintenance and support.

Quality and sustainability of Ethiopia’s national surgical indicators

“…High rates of surgical data inconsistency were a major result of this research and are likely due to lack of consistent KPI reporting forms, and the considerable burden of aggregating substantive data elements into complex indicators as discussed previously by Adane et al 2020 [33]. Our observations were in alignment with other challenges with data collection in an Ethiopian setting previously discussed including turnover of hospital staff, poor understanding of data processes, modification or manipulation of data to compensate for the lack of data, and technological issues with DHIS2 [30][31].…”

“…al. and other studies [30][31][32]. Our observation of data flow processes demonstrated that SSC values are calculated by QI officers by selecting a sample of ten patient charts for review.…”

“…Our study calculates higher annual surgical volumes for Amhara and Sidama primary hospitals than the 37 per 100,000 found for primary health care units over a 90-day interval. The Amhara primary hospitals showed an overall increase in their surgical volume compared to the previous study during the data intervention in 2018, [30] which may be due to both growth in surgical capacity and population needs.…”