Machine Learning Patient Adherence Risk Scoring
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Solution Overview
Problem
Pharmacies face challenges in identifying patients at risk of complications due to gaps in prescription medication coverage, making it difficult to prioritize interventions effectively.
Innovation Solution
A predictive modeling system using machine learning to analyze historical prescription adherence data, predict the likelihood of days covered by medication, and assign patient risk scores to prioritize interventions based on individual patient profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pharmacies manually monitor and identify patients at risk of gaps in prescription coverage, then they can schedule interventions, but the process is time-consuming and lacks prioritization capability
Solution Approach 1:
The system enables self-service by having patients self-report their prescription refilling behavior through automated pharmacy system data collection. The machine learning model automatically analyzes this data and identifies at-risk patients without requiring manual assessment by pharmacy staff, thus improving monitoring accuracy while reducing time investment.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with an automated machine learning-based system. The machine learning model processes historical prescription data, predicts future adherence risks, and generates prioritized patient lists, eliminating the need for manual review of each patient's medication history and behavior patterns.
2Reliability
If pharmacies conduct interventions for all patients with gaps in coverage, then patient adherence improves, but resource allocation becomes inefficient
Solution Approach 1:
The system applies local quality by tailoring intervention strategies to specific patient segments based on their unique risk profiles and adherence patterns. The machine learning model identifies distinct patient subgroups with different adherence challenges, allowing pharmacies to customize intervention approaches for each segment rather than applying a uniform strategy to all patients.
Solution Approach 2:
The patent implements preliminary action by using the machine learning model to predict which patients are most likely to experience gaps in coverage before they actually occur. This allows pharmacies to proactively schedule interventions with high-risk patients before adherence problems manifest, improving both adherence outcomes and intervention efficiency by preventing rather than reacting to non-adherence.
3Ease of operation
If pharmacies prioritize patients for intervention based on current behavior only, then intervention scheduling is simplified, but prediction of future adherence risks is inaccurate
Solution Approach 1:
The system performs preliminary analysis by examining historical prescription refilling patterns, demographic data, and social determinants of health to predict future adherence risks before they manifest. The machine learning model processes this historical data to identify trends and patterns that indicate future non-adherence, enabling accurate prioritization based on predicted rather than just current behavior.
Solution Approach 2:
The patent applies dynamics by using a machine learning model that continuously learns from new data and adapts its predictions over time. The system dynamically adjusts risk assessments based on changing patient behaviors, life events, and external factors, maintaining high prediction accuracy while providing a manageable prioritization process that evolves with patient needs.
Data Source
AI summary
Systems and methods for using predictive modeling to improve patient adherence to prescription medication regimens are provided. Training data may be generated using historical prescription adherence data associated with a patient population. A prescription adherence machine learning model may be trained using the training data, and the trained model may be applied to current prescription adherence data associated with a patient to predict a likelihood that a proportion of days that the patient will be covered by a prescribed medication will be below a threshold value over a calendar year. A patient risk score may be generated for the patient based at least in part on the predicted likelihood that the proportion of days covered by the prescribed medication will be below the threshold value. Based on the patient's patient risk score, the patient may be automatically contacted for intervention.