ArticleBMC medical informatics and decision making2020
Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Common data quality elements for health information systems: a systematic review.BMC medical informatics and decision making · 2024Pooled it
- Effect of the data-informed platform for health intervention on the culture of data use for decision-making among district health office staff in North Shewa Zone, Ethiopia: a cluster-randomised controlled trial.BMC medical informatics and decision making · 2024Trial
- Augmented Capacity Development Interventions (ACDI) Improved Data Quality Performance in the Routine Health Information System (RHIS): A Cluster Randomized Trial.Inquiry : a journal of medical care organization, provision and financingTrial
- Attitude and practices of district-level HMIS managers towards malaria routine reporting in Uganda: a 2024 cross-sectional study.Malaria journal · 2026Article
- Evaluating data quality at a primary healthcare rehabilitation centre in a low-resource context.BMC primary care · 2026Article
- The relationship between self-awareness skills of healthcare workers and healthcare data quality: a cross-sectional study in Tanzanian regional referral hospitals.BMC psychology · 2026Article
- Strategic analysis of the health information system for communicable disease control in Afghanistan.BMC health services research · 2026Article
- Evaluation of the first malaria vaccine introduction: ongoing learning opportunities during the introduction into routine immunisation in Cameroon.BMJ global health · 2026Article
- Variability in Perceived Healthcare Data Quality Across Tanzanian Regional Referral Hospitals: A Hospital-Based Cross-Sectional Study.Health science reports · 2026Article
- Health data reporting challenges in public health facilities in the age of DHIS2: a mixed-methods study in the Teso sub-region of Uganda.BMC health services research · 2026Article
- Evaluation of performance information management in primary health care, Malawi.BMC health services research · 2026Article
- Assessing the impact of a facility-based digital intervention on district-level data quality in rural Burkina Faso.BMJ global health · 2026Article
- Article
- Implementation strategies to promote e-Tracker use in the childhood immunization program in Rwanda - a mixed methods study.Frontiers in health services · 2026Article
- Formal and informal digital health in Tanzania: healthcare workforce, regulatory and trust implications of e-patients and virtual care and the development of the TRUST framework.Frontiers in health services · 2026Review
- Surviving institutional contradictions: routine data use practices at the subnational levels.Oxford open digital health · 2026Article
- Optimizing adolescent HIV care: a review of EMR system quality for clinical monitoring in Zambia.Frontiers in public health · 2026Article
- Article
- Utilisation of data and factors affecting the performance of the Health Management Information System (HMIS). The case of six Synods of Livingstonia health facilities in Malawi.Archives of public health = Archives belges de sante publique · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEffective planning for disease prevention and control requires accurate, adequately-analysed, interpreted and communicated data. In recent years, efforts have been put in strengthening health management information systems (HMIS) in Sub-Saharan Africa to improve data accessibility to decision-makers. This study assessed the quality of routine HMIS data at primary healthcare facility (HF) and district levels in Tanzania.
methodsThis cross-sectional study involved reviews of documents, information systems and databases, and collection of primary data from facility-level registers, tally sheets and monthly summary reports. Thirty-four indicators from Outpatient, Inpatient, Antenatal care, Family Planning, Post-natal care, Labour and Delivery, and Provider-Initiated Testing and Counselling service areas were assessed. Indicator records were tracked and compared across the process of data collection, compilation and submission to the district office. Copies of monthly report forms submitted by facilities to the district were also reviewed. The availability and utilization of HMIS tools were assessed, while completeness and data accuracy levels were quantified for each phase of the reporting system.
resultsA total of 115 HFs (including hospitals, health centres, dispensaries) in 11 districts were involved. Registers (availability rate = 91.1%; interquartile range (IQR) 66.7-100%) and report forms (86.9%; IQR 62.2-100%) were the most utilized tools. There was a limited use of tally-sheets (77.8%; IQR 35.6-100%). Tools availability at the dispensary was 91.1%, health centre 82.2% and hospital 77.8%, and was low in urban districts. The availability rate at the district level was 65% (IQR 48-75%). Wrongly filled or empty cells in registers and poor adherence to the coding procedures were observed. Reports were highly over-represented in comparison to registers' records, with large differences observed at the HF phase of the reporting system. The OPD and IPD areas indicated the highest levels of mismatch between data source and district office. Indicators with large number of clients, multiple variables, disease categorization, or those linked with dispensing medicine performed poorly.
conclusionThere are high variations in the tool utilisation and data accuracy at facility and district levels. The routine HMIS is weak and data at district level inaccurately reflects what is available at the source. These results highlight the need to design tailored and inter-service strategies for improving data quality.
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