Defection Probability Model for Patient Retention
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Solution Overview
Problem
Healthcare providers face challenges in predicting and addressing patient defection from at-home drug delivery systems to retail channels, particularly in identifying persistent defection, which can impact medication adherence and patient health outcomes.
Innovation Solution
A computerized method and system that use predictive modeling to determine a defection probability by analyzing user data from interactions with initial and alternative service channels, generating input encodings, and identifying remedial actions based on defection thresholds to prevent service defection opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive modeling is used to identify patients at risk of defection, then patient retention can be improved, but system complexity increases
Solution Approach 1:
The system segments patients into different risk categories based on their defection probability scores, allowing targeted retention strategies for high-risk patients while avoiding unnecessary complexity for low-risk patients. The model divides the patient population into distinct groups based on behavioral patterns and risk factors.
Solution Approach 2:
The predictive modeling system acts as an intermediary between patient behavior data and retention actions. It processes raw interaction data, generates risk scores, and triggers appropriate retention interventions, thereby managing system complexity through a structured intermediate layer.
2Measurement precision
If comprehensive user data is analyzed to determine defection probability, then prediction accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features and data points from comprehensive user interactions to build the predictive model. It identifies and extracts key behavioral indicators, interaction patterns, and risk factors while discarding redundant information, thereby maintaining prediction accuracy with reduced data processing requirements.
Solution Approach 2:
The model applies different levels of data analysis to different patient segments based on their specific risk profiles. High-risk patients receive more comprehensive data analysis while low-risk patients undergo lighter processing, optimizing the balance between prediction accuracy and data processing requirements.
3Reliability
If remedial actions are implemented based on defection probability, then patient retention improves, but operational costs increase
Solution Approach 1:
The system dynamically adjusts remedial actions based on real-time defection probability assessments. Retention interventions are activated only when the model predicts a high risk of defection, and the intensity of interventions is adjusted according to the specific risk level, optimizing the balance between retention effectiveness and operational costs.
Solution Approach 2:
The system changes operational parameters (such as intervention type, timing, and intensity) based on the calculated defection probability. High-probability patients receive more intensive and costly interventions, while low-probability patients receive minimal or no intervention, thereby optimizing resource allocation and reducing overall operational costs.
Data Source
AI summary
A method for determining a defection probability includes receiving user data. The method also includes determining, from the user data, a set of features characterizing a likelihood of the user to change from the initial service channel to an alternative service channel. The method further includes generating an input encoding for the set of features. The method additionally includes determining, using a predictive model, a probability that indicates how likely the user is to change from the initial service channel, wherein the predictive model is trained to receive, as input, the input encoding and to generate, as output, a respective probability that indicates how likely a respective user is to change from the initial service channel to the alternative service channel. The method also includes determining whether the probability from the predictive model satisfies a defection threshold and selectively generating a remedial action based on the probability.