ArticleBMJ open2023
Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort study of the determinants of code frequency.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- A multidimensional framework for mapping social need to electronic health records in people with multimorbidity.Scientific reports · 2026Article
- Role of Chronic Conditions in Out-of-Pocket Costs for Preventive Care in the US.JAMA network open · 2026Article
- Chronic pain: prevalence, demographic inequalities, and healthcare utilisation. A primary care database analysis.BJGP open · 2025Article
- Remote Consultations in England During COVID-19: Challenges in Data Quality, Linkage, and Research Validity.Online journal of public health informatics · 2025Article
- Continuity of care in general practice and secondary care: retrospective cohort study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025Article
- Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study.NPJ digital medicine · 2025Article
- Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Identifying multi-resolution clusters of diseases in ten million patients with multimorbidity in primary care in England.Communications medicine · 2024Article
- Article
- Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions.Journal of multimorbidity and comorbidityArticle
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Authors and funding
7 authors.
Funding
Abstract
objectivesTo determine whether the frequency of diagnostic codes for long-term conditions (LTCs) in primary care electronic healthcare records (EHRs) is associated with (1) disease coding incentives, (2) General Practice (GP), (3) patient sociodemographic characteristics and (4) calendar year of diagnosis.
designRetrospective cohort study.
settingGPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset.
participantsAll patients registered to a GP with at least one incident LTC diagnosed between 1 January 2015 and 31 December 2019. PRIMARY AND SECONDARY OUTCOME MEASURES: The number of diagnostic codes for an LTC in (1) the first and (2) the second year following diagnosis, stratified by inclusion in the Quality and Outcomes Framework (QOF) financial incentive programme.
results3 113 724 patients were included, with 7 723 365 incident LTCs. Conditions included in QOF had higher rates of annual coding than conditions not included in QOF (1.03 vs 0.32 per year, p<0.0001). There was significant variation in code frequency by GP which was not explained by patient sociodemographics. We found significant associations with patient sociodemographics, with a trend towards higher coding rates in people living in areas of higher deprivation for both QOF and non-QOF conditions. Code frequency was lower for conditions with follow-up time in 2020, associated with the onset of the COVID-19 pandemic.
conclusionsThe frequency of diagnostic codes for newly diagnosed LTCs is influenced by factors including patient sociodemographics, disease inclusion in QOF, GP practice and the impact of the COVID-19 pandemic. Natural language processing or other methods using temporally ordered code sequences should account for these factors to minimise potential bias.
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