Electronic healthcare records (EHRs) are increasingly being used to collect and store data on patient care. This data can be used for a variety of purposes, such as improving clinical care, conducting research, and monitoring population health. However, it is important to be aware of potential biases in EHR data, as these can lead to inaccurate or misleading results..
The reliability of diagnostic codes in primary care EHRs is a subject of ongoing debate and a topic we investigated in paper published in BMJ Open.
These codes not only guide clinical decisions but also shape healthcare policies, research, and even financial incentives in the healthcare system. A recent retrospective cohort study explored whether the frequency of these codes for long-term conditions (LTCs) is influenced by various factors such as financial incentives, general practices, patient sociodemographic data, and the calendar year of diagnosis. The study comes at a crucial time, shedding light on significant biases that need to be addressed.
The study, which involved data from 3,113,724 patients diagnosed with 7,723,365 incident LTCs from 2015 to 2022, revealed some significant findings:
Influence of Financial Incentives: Conditions included in the Quality and Outcomes Framework (QOF), a financial incentive program, had higher rates of annual coding than those not included (1.03 vs 0.32 per year, p<0.0001).
Variability Across GPs: There was a significant variation in the frequency of coding across different General Practices, which was not explained solely by patient sociodemographic factors.
Impact of Sociodemographic factors: Higher coding rates were observed in people living in areas of greater deprivation, irrespective of whether the conditions were part of QOF or not.
Covid-19: The study noted a decrease in code frequency for conditions that had follow-up time in the year 2020, likely due to the COVID-19 pandemic affecting healthcare services.
Implications for Healthcare Providers and Researchers
The findings of the study raise some pertinent questions:
Addressing Financial Incentives: If the QOF influences coding rates, how can we ensure a level playing field for conditions not included in such programs? This could impact resource allocation and healthcare planning.
Standardizing Practices: The variability in coding across GPs implies that there might be inconsistencies in how conditions are diagnosed and recorded. These inconsistencies need to be addressed to improve the quality of healthcare.
Considering Sociodemographic factors: The influence of patient sociodemographic factors suggests a need for tailored interventions, especially in areas with higher deprivation levels.
Navigating Pandemic-related Challenges: The reduction in coding during the COVID-19 pandemic indicates that external factors can significantly affect healthcare data. This demands adaptive strategies to ensure the ongoing reliability of EHRs.
Conclusions and Future Steps
As we move towards a more data-driven healthcare system, understanding the biases in primary care EHRs becomes crucial. The study suggests that natural language processing or other analytical methods using temporally ordered code sequences should account for these biases to provide a more accurate and comprehensive picture. By doing so, healthcare providers and policymakers can better tailor their strategies, ensuring more effective and equitable healthcare delivery.