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

Abstract

This study assessed the quality and challenges of health data before and after the implementation of the Hospital Information System (HIS) for the fistula program at the Comprehensive Community-based Rehabilitation Hospital in Tanzania. This was a mixed methods study. Data were collected at baseline and end-line (before and after the implementation of the HIS) study using quantitative Data Quality Assessment (DQA) checklists and individual interviews. At the baseline, the DQA checklists were assessed using files registered from 2017 to May 2019. At the end-line, the DQA checklists used data for a patient registered at the established HIS from June 2019 to December 2020. Interviews with clinical and non-clinical staff were conducted at both baseline and end-line studies. The study found that data quality improved after the implementation of the HIS. At baseline, data completeness declined from 85% in 2017 to 44.9% in 2019 while accuracy declined from 44% in 2017 to 31% in 2019. At the end-line, data completeness and accuracy were >80% and 100% annually respectively. Data preciseness increased from 0% at baseline to 100% at the end-line. Both data completeness and accuracy had inconsistent tend over months, although preciseness could not be evaluated at baseline as no breakdown of indicators was reported. The major challenges related to data quality included language barrier, incompleteness of data, incorrect data entry, use of both manual and electronic systems, as well as unreliable electricity and network. In general, the HIS had improved rates of data quality in terms of completeness, accuracy, and preciseness of data as compared to the manual system. Policy-makers and hospital administrators should ensure that the HIS are integrated into national hospital information systems, build staff capacity, ensure reliable sources of electrical power, and regular training on data collection.