Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation.

Importance: Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD). Objective: To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. Design, Setting, and Participants: This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020. Main Outcomes and Measures: The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system. Results: Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes. Conclusions and Relevance: These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Hidden Burden of Electronic Health Record-Identified Familial Hypercholesterolemia: Clinical Outcomes and Cost of Medical Care.

Background Familial hypercholesterolemia ( FH ), is a historically underdiagnosed, undertreated, high-risk condition that is associated with a high burden of cardiovascular morbidity and mortality. In this study, we use a population-based approach using electronic health record ( EHR )-based algorithms to identify FH . We report the major adverse cardiovascular events, mortality, and cost of medical care associated with this diagnosis. Methods and Results In our 1.18 million EHR- eligible cohort, International Classification of Diseases, Ninth Revision ( ICD -9) code-defined hyperlipidemia was categorized into FH and non- FH groups using an EHR algorithm designed using the modified Dutch Lipid Clinic Network criteria. Major adverse cardiovascular events, mortality, and cost of medical care were analyzed. A priori associated variables/confounders were used for multivariate analyses using binary logistic regression and linear regression with propensity score-based weighted methods as appropriate. EHR FH was identified in 32 613 individuals, which was 2.7% of the 1.18 million EHR cohort and 13.7% of 237 903 patients with hyperlipidemia. FH had higher rates of myocardial infarction (14.77% versus 8.33%; P<0.0001), heart failure (11.82% versus 10.50%; P<0.0001), and, after adjusting for traditional risk factors, significantly correlated to a composite major adverse cardiovascular events variable (odds ratio, 4.02; 95% CI, 3.88-4.16; P<0.0001), mortality (odds ratio, 1.20; CI, 1.15-1.26; P<0.0001), and higher total revenue per-year (incidence rate ratio, 1.30; 95% CI, 1.28-1.33; P<0.0001). Conclusions EHR -based algorithms discovered a disproportionately high prevalence of FH in our medical cohort, which was associated with worse outcomes and higher costs of medical care. This data-driven approach allows for a more precise method to identify traditionally high-risk groups within large populations allowing for targeted prevention and therapeutic strategies.