Perceptions and experiences with district health information system software to collect and utilize health data in Bangladesh: A qualitative exploratory study

Abstract

Background: Accurate and high-quality data are important for improving program effectiveness and informing policy.In 2009 Bangladesh’s health management information system (HMIS) adopted the District Health Information Software, Version 2 (DHIS2) to capture real-time health service utilization data. However, routinely collected data are being underused because of poor data quality and reporting. We aimed to understand the facilitators and barriers to implementing DHIS2 as a way to retrieve meaningful and accurate data for reproductive, maternal, newborn, child, and adolescent health (RMNCAH) services. Methods: This qualitative study was conducted in two districts of Bangladesh from September 2017 to 2018. Data collection included key informant interviews (n=11), in-depth interviews (n=23), and focus group discussions (n=2). The study participants were involved with DHIS2 implementation from the community level to the national level. The data were analyzed thematically.Results: DHIS2 could improve the timeliness and completeness of data reporting over time. The reported facilitating factors were strong government commitment, extensive donor support, and positive attitudes toward technology among staff. Quality checks and feedback loops at multiple levels of data gathering points are helpful for minimizing data errors. Introducing a dashboard makes DHIS2 compatible to use as a monitoring tool. Barriers to effective DHIS2 implementation were lack of human resources, slow Internet connectivity, frequent changes to DHIS2 versions, and maintaining both manual and electronic system side-by-side. The data in DHIS2 remains incomplete because it does not capture data from private health facilities. Having two parallel HMIS reporting the same RMNCAH indicators threatens data quality and increases the reporting workload. Conclusion: The overall insights from this study are expected to contribute to the development of effective strategies for successful DHIS2 implementation and building a responsive HMIS. Focused strategic direction is needed to sustain the achievements of digital data culture. Periodic refresher trainings, incentives for increased performance, and an automated single reporting system for multiple stakeholders could make the system more user-friendly. A national electronic health strategy and implementation framework can facilitate creating a culture of DHIS2 use for planning, setting priorities, and decision making among stakeholder groups.

The quality of health data before and after the implementation of the electronic health management information system for the fistula program in Tanzania

“…Generally, the study findings indicate that the quality of data collected manually in paper based and reported in HIS was poor as compared to the data collected directly using the current electronic system. These findings support evidence from previous observations which showed that the quality of HIS data gathering and reporting was poor in low and middle income countries (Assefa Ayalew et al, 2020; Begum et al, 2020; Manya & Nielsen, 2016; Ouedraogo et al, 2019) when using the paper system as compared to using the electronic system. Indications are that inadequate information may be available for decision‐making purposes at the health facility if data are collected using paper based systems.…”

“…Quality data leads to better health systems performance as well as accurate decision‐making. Scholars defined HIS in different aspects, generally referring it as a tool facilitating data collection, management and reporting in a hospital setting (Begum et al, 2020; Liang et al, 2017; Shiferaw et al, 2017). For instance, Liang et al (2017) defined HIS as a “massive, integrated computer systems designed to support the comprehensive information requirements of hospitals, including patient, clinical, ancillary and financial management” (Liang et al, 2017).…”

“…For instance, Liang et al (2017) defined HIS as a “massive, integrated computer systems designed to support the comprehensive information requirements of hospitals, including patient, clinical, ancillary and financial management” (Liang et al, 2017). Furthermore, HIS was defined as a tool whereby health data is captured, stored, processed, and used for developing policies, evaluating health programs, and improve decision making (Begum et al, 2020; Shiferaw et al, 2017) in a hospital setting. While quality data has been evidenced to originate from electronic collected data as compared to the manual system of data collection, not all health facilities have HIS.…”

“…With the paper‐based system of data collection, it is difficult to attain the required standards of data quality (Kikoba et al, 2019a). However, the research shows that the quality of HIS data gathering and reporting in a health facility is often poor in low and middle‐income countries (Assefa Ayalew et al, 2020; Begum et al, 2020; Manya & Nielsen, 2016; Ouedraogo et al, 2019). According to Mashoufi et al (2019) it was found that that the data quality in emergency medical services was not at a high level, with the following ratings: sound data (6.23), dependable data (6.28), useful data (6.30), and usable data (6.35) in Iran, with 0 being the lowest possible score and 10 being the highest.…”

Are socio-demographic factors associated with electronic Health Information System utilization?A cross sectional study of the District Health Information System -2 (DHIS-2) in Burundi

“…Regardless of the financial and technical efforts of the Ministry of Health and funding agencies to operationalize the DHIS-2 platform at local levels, the HIS is still facing many challenges such as: (i) utilization of paper-based system when collecting data in the health district offices and health facilities -this leads to several number of mistakes in data reporting system; and (ii) the lack of connection or network for data entry and reporting in such offices and facilities. This is compromising health data quality for robust UHCrelated decision making process [12,13].…”