Out-of-system Care and Recording of Patient Characteristics Critical for Comparative Effectiveness Research

Background: It is unclear how out-of-system care or electronic health record (EHR) discontinuity (i.e., receiving care outside of an EHR system) may affect validity of comparative effectiveness research using these data. We aimed to compare the misclassification of key variables in patients with high versus low EHR continuity. Methods: The study cohort comprised patients ages ≥65 identified in electronic health records from two US provider networks linked with Medicare insurance claims data from 2007 to 2014. By comparing electronic health records and claims data, we quantified EHR continuity by the proportion of encounters captured by the EHRs (i.e., “capture proportion”). Within levels of EHR continuity, for 40 key variables, we quantified misclassification by mean standardized differences between coding based on EHRs alone versus linked claims and EHR data. Results: Based on 183,739 patients, we found that mean capture proportion in a single electronic health record system was 16%–27% across two provider networks. Patients with highest level of EHR continuity (capture proportion ≥ 80%) had 11.4- to 17.4-fold less variable misclassification, when compared with those with lowest level of EHR continuity (capture proportion< 10%). Capturing at least 60% of the encounters in an EHR system was required to have reasonable variable classification (mean standardized difference <0.1). We found modest differences in comorbidity profiles between patients with high and low EHR continuity. Conclusions: EHR discontinuity may lead to substantial misclassification in key variables. Restricting comparative effectiveness research to patients with high EHR continuity may confer a favorable benefit (reducing information bias) to risk (losing generalizability) ratio.

[1]  B. Gage,et al.  Accuracy of ICD-9-CM Codes for Identifying Cardiovascular and Stroke Risk Factors , 2005, Medical care.

[2]  J. Avorn,et al.  A review of uses of health care utilization databases for epidemiologic research on therapeutics. , 2005, Journal of clinical epidemiology.

[3]  W. Ray,et al.  An automated database case definition for serious bleeding related to oral anticoagulant use , 2011, Pharmacoepidemiology and drug safety.

[4]  George Hripcsak,et al.  Defining and measuring completeness of electronic health records for secondary use , 2013, J. Biomed. Informatics.

[5]  A. Shaheen,et al.  Validation of ICD-9-CM/ICD-10 coding algorithms for the identification of patients with acetaminophen overdose and hepatotoxicity using administrative data , 2007, BMC Health Services Research.

[6]  Jeremy A Rassen,et al.  Metrics for covariate balance in cohort studies of causal effects , 2014, Statistics in medicine.

[7]  Sean Hennessy,et al.  Use of health care databases in pharmacoepidemiology. , 2006, Basic & clinical pharmacology & toxicology.

[8]  Betsy Jane Becker,et al.  Synthesizing standardized mean‐change measures , 1988 .

[9]  Sushrut S Waikar,et al.  Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure. , 2006, Journal of the American Society of Nephrology : JASN.

[10]  L. Tamariz,et al.  A systematic review of validated methods for identifying venous thromboembolism using administrative and claims data , 2012, Pharmacoepidemiology and Drug Safety.

[11]  P. Austin Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples , 2009, Statistics in medicine.

[12]  S. Schneeweiss Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics , 2006, Pharmacoepidemiology and drug safety.

[13]  Paul Enright,et al.  Deep vein thrombosis and pulmonary embolism in two cohorts: the longitudinal investigation of thromboembolism etiology. , 2004, The American journal of medicine.

[14]  Til Stürmer,et al.  Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. , 2005, American journal of epidemiology.

[15]  Sebastian Schneeweiss,et al.  A combined comorbidity score predicted mortality in elderly patients better than existing scores. , 2011, Journal of clinical epidemiology.

[16]  J. Gurwitz,et al.  A systematic review of validated methods for identifying cerebrovascular accident or transient ischemic attack using administrative data , 2012, Pharmacoepidemiology and drug safety.