Abstract 13604: Patient Centered Prescription Using Optimization Algorithms for Statin Users

Abstract

Introduction: Clinical decision support (CDS) that does not optimize individual variability with best practice and treatment outcomes, cannot support patient-centered care. For example, a statin treatment CDS focused on cholesterol reduction without accounting for patient comorbidities, at risk lifestyle, or genetics, may well suggest a treatment that increases adverse effects, has a high cost, and has a low potential for improved outcome. We present a novel patient-centered decision support (PCDS) system for statin initiation that accounts for best practice and patient variation and ranks treatment options by their predicted ability to improve cardiac outcomes and reduce adverse effects for individual patients. Method: A total of 30 M de-identified claim records from 1.4 M adults receiving statin treatment over 1990-2020 are available in the OptumLabs® Data Warehouse and were used to train our counterfactual prediction algorithm. For a new patient, the PCDS system predicts LDL reduction, cardiac events, and other outcomes at 1 and 5 years, for every potential statin treatment. The PCDS then integrates doctor and patient preferences into a multi-objective optimization algorithm to generate a ranked list of treatment options. Conclusion: Our novel patient-centered decision support (PCDS) adds sophisticated AI modeling and optimization to account for evidenced-based best practice, patient variability, and adverse effect risk to produce a clear ranking of treatment options that can be re-ranked upon input of patient preferences and provider’s lifestyle, socioeconomics, culture, experience and environment factors. This PCDS approach leverages traditional CDS adjusted to individual preferences to aligned with the patient-centered care model.