Illustrating Informed Presence Bias in Electronic Health Records Data: How Patient Interactions with a Health System Can Impact Inference

Abstract

Electronic health record (EHR) data are becoming a primary resource for clinical research. Compared to traditional research data, such as those from clinical trials and epidemiologic cohorts, EHR data have a number of appealing characteristics. However, because they do not have mechanisms set in place to ensure that the appropriate data are collected, they also pose a number of analytic challenges. In this paper, we illustrate that how a patient interacts with a health system influences which data are recorded in the EHR. These interactions are typically informative, potentially resulting in bias. We term the overall set of induced biases informed presence. To illustrate this, we use examples from EHR based analyses. Specifically, we show that: 1) Where a patient receives services within a health facility can induce selection bias; 2) Which health system a patient chooses for an encounter can result in information bias; and 3) Referral encounters can create an admixture bias. While often times addressing these biases can be straightforward, it is important to understand how they are induced in any EHR based analysis.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…One challenge for research using EHR-linked biobank data is that the mechanism by which a patient from the population enters the biobank and when a visit appears in the EHR is often unknown and inherently patient-driven. 30,31 This phenomenon, called nonprobability sampling, has been studied extensively in the statistical literature, and certain mechanisms governing self-selected patient recruitment can introduce bias. 32 The extent to which the selection mechanism impacts study results depends on the estimand of interest and remains an open question.…”

Evaluation of associations between asthma exacerbations and distance to roadways using geocoded electronic health records data

“…Finally, while we believe that the use of EHR data produces more objective data measurements, EHR data are prone to their own biases. 52 In particular, type of health service utilization is often a re ection of both severity of disease as well as other social factors such as SES and health seeking behavior. Of note, though, our analyses did not indicate confounding due to nSES.…”

Evaluation of associations between asthma exacerbations and distance to roadways using geocoded electronic health records data

“…Finally, while we believe that the use of EHR data produces more objective data measurements, EHR data are prone to their own biases. 54 In particular, type of health service utilization is often a re ection of both severity of disease as well as other social factors such as SES and health seeking behavior. Of note, our analyses did not indicate confounding due to nSES.…”