(EHL Poster) SAS Health: Medication Adherence Risk Model
Wednesday, March 11, 2026 10:00 AM to 4:00 PM · 6 hr. (US/Pacific)
Level 5 | Palazzo G
Workforce ConneXtions
Information
This project adapts the SAS Medication Adherence Risk Model within the SAS Health platform to predict patients at risk of medication non-adherence. By integrating machine learning and standardized data structures through a Common Data Model, the solution streamlines deployment, enhances data sharing, and supports care coordination. The model enables healthcare providers to make data-driven decisions, target at-risk populations, and improve health outcomes through proactive, patient-centered interventions.
Level
Introductory
Format
Case Study
Learning Objective #1
Describe how machine learning models can be applied to predict patient medication non-adherence within a digital health platform
Learning Objective #2
Illustrate how standardized data integration through a Common data Model enhances interoperability and data-driven insights
Learning Objective #3
Analyze the workflow and technical components of adapting predictive analytics models into existing health platforms
Learning Objective #4
Evaluate the impact of data-driven decision-making on patient outcomes and care coordination
Learning Objective #5
Propose strategies for leveraging predictive models to improve medication adherence and patient engagement in healthcare settings
Session #
WFC-2.1
