Assessing Health Information System Data Quality Management in LifeNet-supported Facilities in South Kivu, Democratic Republic of Congo

Résumé fourni par la source

Aims: To evaluate the quality of HIS data in LifeNet-supported facilities in South Kivu and correlate data completeness, accuracy, and timeliness with staff competency, training, and governance factors to validate current data management practices. This study is significant as it enhances understanding of data quality in LifeNet-supported health facilities, guiding improvements in information management and health service delivery in South Kivu. Study Design: A retrospective quantitative cross-sectional analytical research design. Place and Duration of Study: Study conducted in LifeNet International-supported health facilities across ten health districts in South Kivu Province, Democratic Republic of Congo, including Idjwi, Ibanda, Kabare, Kadutu, Miti-Murhesa, Nundu, Nyangezi, Nyatende, Uvira, and Walungu, between October 2023 and March 2024. Methodology: This study included 155 healthcare workers from 74 LifeNet International-supported health facilities across ten districts in South Kivu Province, DRC. Data were collected through a retrospective review of Maternal and Child Health (MCH) records and a structured HIS Assessment Questionnaire administered to healthcare workers. Data completeness, accuracy, and timeliness were evaluated using the Verification Factor (VF). Descriptive and inferential statistical analyses were performed using SPSS version 28 to assess relationships between HIS data quality and influencing factors. Results: MCH data quality was high: accuracy 89%, consistency 87%, completeness 93.3%, and timeliness 86.7%. Health worker competency showed high neutrality—data aggregation 62.6%, in-service training 65.2%, electronic skills 72.3%, HMIS usability 61.9%, pre-service training 75.5%—indicating limited confidence in HIS skills. Challenges in data collection were notable, with 46.5% neutral on cross-checking, 71.6% unsure about functional databases, and 62.6% unclear on monitoring roles. Regression analysis indicated systemic challenges strongly predicted health information use (β = 0.850, p < 0.001), while individual competency was not significant, highlighting the need for improved operational support. Conclusion: Systemic challenges, more than individual competency, significantly affect health information use, highlighting the need for stronger operational support, clear roles, and targeted HIS capacity-building in facilities.

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