An algorithm to predict data completeness in oncology electronic medical records for comparative effectiveness research

Introduction: Electronic health record (EHR) discontinuity (missing out-of-network encounters) can lead to information bias. We sought to construct an algorithm that identifies high EHR-continuity among on-cology patients. Methods: Using a linked Medicare-EHR database and regression, we sought to 1) measure how often Medicare claims for outpatient encounters were substantiated by visits recorded in the EHR, and 2) pre-dict continuity ratio, defined as the yearly proportion of outpatient encounters reported to Medicare that were captured by EHR data. The prediction model's performance was evaluated with the coefficient of de-termination and Spearman's correlation. We quantified variable misclassification by decile of continuity ratio using standardized difference and sensitivity. Results: A total of 79,678 subjects met all eligibility criteria. Predicted and observed continuity was highly correlated (sigma Spearman = 0 . 86 ). On average across all variables measured, MSD was reduced by a factor of 1/7th and sensitivity was improved 35-fold comparing subjects in the highest vs. lowest decile of CR. Conclusion: In the oncology population, restricting EHR-based study cohorts to subjects with high conti-nuity may reduce misclassification without greatly impacting representativeness. Further work is needed to elucidate the best manner of implementing continuity prediction rules in cohort studies. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This study aims to develop an algorithm that can identify oncology patients with high electronic health record (EHR) continuity. The results show that using this algorithm for prediction can significantly reduce variable misclassification and improve sensitivity without greatly impacting representativeness.