Electronic medical record systems, data quality and loss to follow-up: survey of antiretroviral therapy programmes in resource-limited settings
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OBJECTIVE: To describe the electronic medical databases used in antiretroviral therapy (ART) programmes in lower-income countries and assess the measures such programmes employ to maintain and improve data quality and reduce the loss of patients to follow-up. METHODS: In 15 countries of Africa, South America and Asia, a survey was conducted from December 2006 to February 2007 on the use of electronic medical record systems in ART programmes. Patients enrolled in the sites at the time of the survey but not seen during the previous 12 months were considered lost to follow-up. The quality of the data was assessed by computing the percentage of missing key variables (age, sex, clinical stage of HIV infection, CD4+ lymphocyte count and year of ART initiation). Associations between site characteristics (such as number of staff members dedicated to data management), measures to reduce loss to follow-up (such as the presence of staff dedicated to tracing patients) and data quality and loss to follow-up were analysed using multivariate logit models. FINDINGS: Twenty-one sites that together provided ART to 50 060 patients were included (median number of patients per site: 1000; interquartile range, IQR: 72-19 320). Eighteen sites (86%) used an electronic database for medical record-keeping; 15 (83%) such sites relied on software intended for personal or small business use. The median percentage of missing data for key variables per site was 10.9% (IQR: 2.0-18.9%) and declined with training in data management (odds ratio, OR: 0.58; 95% confidence interval, CI: 0.37-0.90) and weekly hours spent by a clerk on the database per 100 patients on ART (OR: 0.95; 95% CI: 0.90-0.99). About 10 weekly hours per 100 patients on ART were required to reduce missing data for key variables to below 10%. The median percentage of patients lost to follow-up 1 year after starting ART was 8.5% (IQR: 4.2-19.7%). Strategies to reduce loss to follow-up included outreach teams, community-based organizations and checking death registry data. Implementation of all three strategies substantially reduced losses to follow-up (OR: 0.17; 95% CI: 0.15-0.20). CONCLUSION: The quality of the data collected and the retention of patients in ART treatment programmes are unsatisfactory for many sites involved in the scale-up of ART in resource-limited settings, mainly because of insufficient staff trained to manage data and trace patients lost to follow-up.
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A study of HIV clinical data collection systems at 21 HFs (18 in Africa) showed a reduction in missing data associated with training in data management (OR 0.58, 95% CI 0.37-0.90) and weekly hours spent by a data clerk working on the data collection system (OR 0.95, 95% CI 0.90-0.99).
“A study of HIV clinical data collection systems at 21 HFs (18 in Africa) showed a reduction in missing data associated with training in data management (OR 0.58, 95% CI 0.37-0.90) and weekly hours spent by a data clerk working on the data collection system (OR 0.95, 95% CI 0.90-0.99) [16].”
From Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study (2024) · cites this paper as [16]
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Experimental — accuracy not yet measuredPoor completion or accuracy of key data items impacts quality of care metrics (eg, in cancer care in the United States) and efficient reporting of data for clinical teams, MOH, and funders.
“Furthermore, it impacts quality of care metrics (eg, in cancer care in the United States [43]) and efficient reporting of data for clinical teams, MOH, and funders [16,42].”
From Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study (2024) · cites this paper as [16]
The Results section (‘Database characteristics and data quality measures’) says reporting was an important purpose of the databases. The Discussion (‘Antiretroviral database anarchy?’ and ‘The price of quality’) describes inefficient reporting-related database practices and time-consuming correction of data errors, but does not establish that incomplete or inaccurate data impedes efficient reporting to clinical teams, ministries of health, and funders. The US cancer-care example is attached to a different citation, [43]. (LLM, conf=0.83)
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- Authors
- Egger, M.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Egger, Matthias
- ORCID
- Egger, Matthias
- Venue
- Bulletin of the World Health Organization
- Publisher
- WHO Press
- Published
- 2008-12-01
- Volume
- 86
- Issue
- 12
- Pages
- 939-947
- ISSN
- 0042-9686 (ISSN-L)
- PMC
- PMC2649575
- OpenAlex
- W2142665794
- Cited by
- 177
- References
- 24
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 1 · 2023: 5 · 2022: 1 · 2021: 5 · 2020: 10 · 2019: 3 · 2018: 6 · 2017: 10 · 2016: 8 · 2015: 13 · 2014: 19 · 2013: 9 · 2012: 23
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Bulletin of the World Health Organization (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Centers for Disease Control and Prevention
- National Institute of Allergy and Infectious Diseases (U01AI069924)
- Topics
- Health Sciences › Medicine › Infectious Diseases › HIV/AIDS Research and Interventions
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- electronic health records, HIV, data quality, loss to follow-up, missing data, resource-limited settings, data management training, patient tracing, community organizations, outreach teams, CD4 count, HIV/AIDS surveillance, health information systems, Africa, South America, Asia, low-income countries
- MeSH terms
- Developing Countries, Humans, Poverty, Research Design, Socioeconomic Factors, Models, Statistical, Program Evaluation, HIV Infections, Multivariate Analysis, Confidence Intervals, Odds Ratio, Medical Records Systems, Computerized, Anti-HIV Agents, Health Care Surveys, Databases as Topic, Anti-Retroviral Agents
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