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Identifying potential biases in code sequences in primary care electronic healthcare records: a retrospective cohort
Thomas Beaney1,2, Jonathan Clarke2, David Salman3,4
1Department of Primary Care and Public Health, Imperial College London, London, UK thomas.beaney@imperial.ac.uk.
Insights
Diagnostic coding for long-term conditions (LTCs) in primary care is influenced by financial incentives, patient demographics, and the COVID-19 pandemic. These factors must be considered in health record analysis.
Area of Science:
- Health Informatics
- Primary Care Research
- Electronic Health Records (EHRs)
Background:
- Accurate coding of long-term conditions (LTCs) in primary care electronic health records (EHRs) is crucial for clinical care and research.
- Understanding factors influencing diagnostic code frequency is essential for reliable data analysis and quality improvement initiatives.
Purpose of the Study:
- To investigate associations between the frequency of LTC diagnostic codes in primary care EHRs and disease coding incentives.
- To examine the influence of General Practice (GP) characteristics, patient sociodemographic factors, and diagnosis year on coding frequency.
- To assess the impact of the Quality and Outcomes Framework (QOF) on LTC diagnostic coding rates.
Main Methods:
- Retrospective cohort study utilizing the Clinical Practice Research Datalink Aurum dataset in England (2015-2022).
- Inclusion of patients with at least one incident LTC diagnosed between January 2015 and December 2019.
- Analysis of diagnostic code frequency in the first two years post-diagnosis, stratified by QOF inclusion.
Main Results:
- Conditions included in the QOF exhibited significantly higher annual coding rates compared to non-QOF conditions (1.03 vs. 0.32 codes/year).
- Significant variation in coding frequency was observed across GPs, independent of patient sociodemographics.
- Higher coding rates were associated with increased patient deprivation for both QOF and non-QOF conditions; coding frequency decreased in 2020 due to the COVID-19 pandemic.
Conclusions:
- LTC diagnostic code frequency is multifactorial, influenced by QOF incentives, patient sociodemographics, GP practice variations, and external events like the COVID-19 pandemic.
- Future analyses using natural language processing or code sequence analysis should account for these identified influencing factors to mitigate potential bias in EHR data.
Objectives:
To 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.
Design:
Retrospective cohort study.
Setting:
GPs in England from 2015 to 2022 contributing to the Clinical Practice Research Datalink Aurum dataset.
Participants:
All 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.
Results:
3 113 724 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.
Conclusions:
The 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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