Predictive Adherence System for High-Risk Member Segmentation
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Solution Overview
Problem
Current healthcare systems face significant challenges in reducing healthcare costs due to low prescription drug adherence, which can lead to substantial waste and increased costs, affecting both individuals and the healthcare system as a whole, with non-adherence costing up to $100 billion annually.
Innovation Solution
The implementation of a system that utilizes predictive modeling and proactive outreach methods to identify high-risk individuals for non-adherence, tailoring interventions based on demographic and medical data, and promoting adherence through home delivery of pharmaceuticals, reminders, and cost reductions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prescription drug coverage is increased to improve adherence, then therapy adherence improves, but healthcare costs increase
Solution Approach 1:
The system performs preliminary actions by predicting adherence risks before they manifest as non-adherence events. It identifies members at risk of non-adherence in advance and proactively reaches out with tailored interventions, preventing costly non-adherence before it occurs rather than reacting after costs are already incurred.
Solution Approach 2:
The system continuously monitors member behavior patterns, refill history, and engagement data to provide feedback loops that refine adherence predictions. This feedback mechanism allows the system to adjust interventions dynamically based on real-time member responses and adherence outcomes, optimizing the balance between intervention costs and adherence improvements.
2Reliability
If proactive outreach interventions are implemented to improve adherence, then therapy adherence improves, but system complexity increases
Solution Approach 1:
The system segments the member population into distinct risk categories based on predicted adherence probability. By dividing members into segments (e.g., high risk, medium risk, low risk), the system can apply targeted interventions to specific segments rather than implementing complex universal programs, simplifying the overall system architecture while maintaining high adherence improvement effectiveness.
Solution Approach 2:
The system applies local quality by tailoring specific intervention types to specific member segments based on their predicted adherence risks and behavioral patterns. Different segments receive customized interventions (e.g., automated reminders for forgetful members, cost assistance for financially constrained members), allowing the system to maintain simplicity through standardized templates while achieving high personalization effectiveness.
3Productivity
If targeted interventions are applied to high-risk individuals, then adherence improvement efficiency increases, but member data processing complexity increases
Solution Approach 1:
The system extracts and isolates only the critical data elements necessary for adherence prediction, filtering out irrelevant information from the member database. By extracting only the essential features (e.g., refill patterns, engagement history, demographic risk factors), the system reduces data processing complexity while maintaining the accuracy needed for effective targeted interventions.
Solution Approach 2:
The system changes data parameters by transforming raw member data into standardized risk scores and adherence probabilities through predictive modeling. This parameter transformation simplifies data processing by converting complex behavioral patterns into single-value risk metrics that can be easily processed and used for segmentation, reducing computational complexity while maintaining productivity.
Data Source
AI summary
Methods and systems for improving therapy adherence are described. In an embodiment, a disease state associated with a member is identified. A member classification of the member is determined. The member classification may be based on past therapy the member received to treat a condition associated with the disease state. A diagnostic loop is selected based on the disease state associated with the member and the member classification. The diagnostic loop may include a plurality of operations. At least one of the plurality of operations of the diagnostic loop is performed. Other methods and systems are described.