The relative benefits of claims and electronic health record data for predicting medication adherence trajectory
| Autor: | Alexis A. Krumme, Newell E. McElwee, Jessica M. Franklin, Joe Kimura, James R. Rogers, Niteesh K. Choudhry, Caroline McKay, Karandeep Singh, Chandrasekar Gopalakrishnan |
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| Rok vydání: | 2016 |
| Předmět: |
Male
medicine.medical_specialty Psychological intervention MEDLINE Insurance Claim Review Medication adherence 030204 cardiovascular system & hematology Medicare Advantage Medicare Medication Adherence 03 medical and health sciences 0302 clinical medicine Intervention (counseling) Outpatients Medicine Electronic Health Records Humans Hypoglycemic Agents 030212 general & internal medicine Medical prescription health care economics and organizations Antihypertensive Agents Aged business.industry United States Family medicine Evidence-Based Practice Needs assessment Chronic Disease Female Hydroxymethylglutaryl-CoA Reductase Inhibitors Cardiology and Cardiovascular Medicine business Needs Assessment |
| Zdroj: | American heart journal. 197 |
| ISSN: | 1097-6744 |
| Popis: | Background Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which to predict adherence than is currently available through claims. Methods In a linked database of complete Medicare Advantage claims and comprehensive EHR from a multi-specialty outpatient practice, we identified patients who filled a prescription for a statin, antihypertensive, or oral antidiabetic during 2011 to 2012. We followed patients to identify subsequent medication filling patterns and used group-based trajectory models to assign patients to adherence trajectories. We then identified potential predictors from both claims and EHR data and fit a series of models to evaluate the accuracy of each data source in predicting medication adherence. Results Claims were highly predictive of patients in the worst adherence trajectory (C = 0.78), but EHR data also provided good predictions (C = 0.72). Among claims predictors, presence of a prior gap in filling of at least 6 days was by far the most influential predictor. In contrast, good predictions from EHR data required complex models with many variables. Conclusion EHR data can provide good predictions of adherence trajectory and therefore may be useful for providers seeking to deploy resource-intensive interventions. However, prior adherence information derived from claims is most predictive, and can supplement EHR data when it is available. |
| Databáze: | OpenAIRE |
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