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Jerris Heaton

How EHR Data and Survey Responses Mitigate Clinical Research Bias

June 15, 2022

Uncategorized 4 Minute Read

The disparity between EHR data and patient survey responses, as part of the unique All of Us Research Program, sponsored by the National Institutes of Health, is helping to identify and mitigate clinical bias. The study published in JAMIA, A Scholarly Journal of Informatics in Health and Biomedicine, was the 4th iteration and included datasets curated from the participation of 314,994 individuals.

Participants are asked to complete a total of seven surveys that collect medical history and information and cover 150-plus medical conditions in twelve disease categories, including a survey dedicated to assessing an individual’s well-being during the pandemic. This information is then compared to EHR data to create datasets that can help researchers address treatment biases. Overall, 28.3% of the participants completed surveys and 65.5% had EHR data.

Collected Data Identifies Discrepancies Between Surveys and EHRs

Patient surveys collect data that may not be sought after during medical visits. Coupled with documented EHR data, the research is able to collect valuable information regarding bias in medical care for underserved communities.

Both of these sources are likely to have missing or inaccurate information. EHR data can be missing due to lack of accessibility or failure to accurately document patient responses. Surveys have the potential to be incorrect for simple reasons such as lack of recollection or failed memories.

Overall, the outcome identified which categories had the most and least responses. It also identified how closely information provided in the collected surveys matched the collected EHR data.

Research Findings Demonstrate Value of Patient Surveys

The basis of the study is to identify the most and least self-reported conditions and the accuracy of these reports. Interestingly enough, vision and hearing related surveys had the highest response rates but had the second least amount of agreement with documented EHR data. Whereas infectious diseases had the lowest agreement rates and cancer conditions had the highest agreement rates.

EHR Data and Survey Responses Mitigate Clinical Research Bias , EHR Data, Medical Surveys, Patient Surveys

All of this data allows researchers to identify the reasons behind the disparate information as well as possible solutions to improve patient data. This has the potential to increase visibility into clinical bias based on minimal data which is a problem experienced primarily by minority groups in underserved communities.

“Disagreement might also occur from high levels of EHR code aggregation and generalized disease names in surveys” (EHR Intelligence). One suggested solution is to add more specific EHR codes to increase the accuracy of documentation. In the meantime, “disagreement between EHR and survey can help identify possible missing records and guide researchers to adjust for biases” (JAMIA).

The All About Us Research Program is Redefining Patient Care

So, do surveys really work when it comes to identifying ways to increase patient care? The All About Us research initiative is certainly bringing the benefits of approaching research studies with greater participant involvement to the forefront of modern medicine.

“All of Us is part of a new era in which researchers, health care providers, technology experts, community partners, and the public work together to develop individualized health care,” according to the organizations website.

The reality is that medicine is an art and there is no one-size-fits all approach to treating patients. Coordinating patient care amongst difference practices and facilities can be difficult leading to missed diagnosis. The organization is not focusing on one specific disease or demographic group like most medical research programs. Rather they are working to create a unique database that is truly revolutionary.

The database is collecting information on over 150 conditions alongside demographic information including race, age, and gender-identity to provide information for thousands of studies. Patients benefit from treatment plans that are specific to their race and age, can connect patients to studies specific to their conditions, understand disease risk factors, and learn how advancing technology can support treatment plans while giving physicians valuable data that can improve care and patient outcomes.

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