Addressing Bias in Electronic Health Record-based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise

Abstract

PURPOSE OF REVIEW: Use of the electronic health record (EHR) for CVD surveillance is increasingly common. However, these data can introduce systematic error that influences the internal and external validity of study findings. We reviewed recent literature on EHR-based studies of CVD risk to summarize the most common types of bias that arise. Subsequently, we recommend strategies informed by work from others as well as our own to reduce the impact of these biases in future research. RECENT FINDINGS: Systematic error, or bias, is a concern in all observational research including EHR-based studies of CVD risk surveillance. Patients captured in an EHR system may not be representative of the general population, due to issues such as informed presence bias, perceptions about the healthcare system that influence entry, and access to health services. Further, the EHR may contain inaccurate information or be missing key data points of interest due to loss to follow-up or over-diagnosis bias. Several strategies, including implementation of unique patient identifiers, adoption of standardized rules for inclusion/exclusion criteria, statistical procedures for data harmonization and analysis, and incorporation of patient-reported data have been used to reduce the impact of these biases. SUMMARY: EHR data provide an opportunity to monitor and characterize CVD risk in populations. However, understanding the biases that arise from EHR datasets is instrumental in planning epidemiological studies and interpreting study findings. Strategies to reduce the impact of bias in the context of EHR data can increase the quality and utility of these data.

Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data

“… 5 , 6 They often are limited to patients presenting to a particular healthcare provider, which may further limit their representation of the general population. 7 In contrast, cohort studies often sample healthier participants who typically are more likely to respond to surveys. 8 …”

Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification

“…This problem has been discussed in the literature on HER data analysis (e.g. Bower et al, 2017; Goldstein et al, 2016; Phelan et al, 2017). Even so, statistical literature handling this covariate-related misclassification is sparse.…”

“…However, these methods do not address the systematic differences between patients in the population that are and are not included in the EHR itself. To bridge this gap, strategies in the survey sampling literature for dealing with unknown selection probabilities (termed non-probability sampling) such as calibration weighting, inverse probability of selection weighting, and propensity-score matching or covariate adjustment can be applied (Bower et al, 2017; Baker et al, 2013). Little work has been done to describe how such methods can be implemented in the specific EHR setting.…”

Should Electronic Health Record-Derived Social and Behavioral Data Be Used in Precision Medicine Research?

“…Within EHR research, frameworks for addressing bias for some types of clinical data have been developed; precision medicine researchers can utilize these methods when considering the biases of existing social and behavioral data in EHRs. 30,31 Without carefully accounting for all sources of bias, precision medicine researchers have the potential to exacerbate the existing injustices that underrepresented populations experience.…”