Personalized Patient Engagement Engine Using Behavioral Phenotypes
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Solution Overview
Problem
Current care management systems rely on population-level evidence and struggle to incorporate individual-level patient engagement cues, making it difficult for care managers to adjust interventions for personalized care, as they lack indicators of patient engagement levels and efficient analysis tools for behavioral profiling.
Innovation Solution
A data processing system with a personalized patient engagement engine that develops models based on anonymized care management notes and records to match patients with behavioral phenotypes, estimate positive behavioral responses, and dynamically update intervention effectiveness rankings for personalized intervention recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If population-level evidence is used for care management, then general applicability is improved, but individual-level personalization deteriorates
Solution Approach 1:
The patent segments patients into distinct behavioral phenotypes (e.g., optimizers, delegators, maximizers, minimizers) based on their engagement patterns. This segmentation allows the system to apply population-level evidence while simultaneously providing individualized care recommendations tailored to each phenotype's characteristics, resolving the contradiction between general applicability and personalization.
Solution Approach 2:
The system applies different intervention strategies to different behavioral phenotypes based on their specific characteristics. For example, optimizers receive detailed information and education, while delegators prefer simplified guidance and provider decisions. This local quality approach enables personalized care within the framework of population-level evidence.
2Loss of information
If manual analysis of patient engagement cues is performed, then individual-level understanding is improved, but care manager workload deteriorates
Solution Approach 1:
The system automatically performs behavioral phenotype classification and intervention recommendation generation without requiring manual analysis by care managers. The automated natural language processing and machine learning algorithms analyze patient engagement cues, classify behaviors, and generate personalized recommendations, thereby maintaining individual-level understanding while eliminating the additional workload.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system using natural language processing and machine learning. This substitution maintains comprehensive individual-level analysis of engagement cues while freeing care managers from the time-consuming manual work of behavioral profiling and recommendation generation.
3Ease of manufacture
If behavioral phenotype classification is implemented, then intervention personalization is improved, but system complexity deteriorates
Solution Approach 1:
The system uses a unified behavioral phenotype classification framework that serves multiple functions: identifying patient engagement patterns, predicting intervention responses, and generating personalized recommendations. This multi-functional approach enables comprehensive intervention personalization while avoiding the need for separate complex systems for each function, thereby managing overall system complexity.
4Measurement precision
If comprehensive patient data analysis is performed, then model accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system performs preliminary behavioral phenotype classification using efficiently processed data, then applies pre-developed intervention effectiveness models specific to each phenotype. This preliminary action approach maintains high model accuracy by using comprehensive data for classification while reducing subsequent processing time by applying pre-trained phenotype-specific models rather than re-analyzing all data for each intervention decision.
Data Source
AI summary
A mechanism is provided in a data processing system to implement a personalized patient engagement engine. The personalized patient engagement engine develops a set of models for a plurality of behavioral phenotypes based on anonymized unstructured and structured patient-care management records for a plurality of patients over a period of time; matches a given patient to a behavioral phenotype; estimates a propensity of positive and/or negative behavioral responses of each of a plurality of targeted behaviors; dynamically updates personalized intervention effectiveness rankings in context for care manager and patient decision-making based on what has been shown to lead to positive responses for individuals with a similar behavioral profile; generates an intervention recommendation for the given patient based on the personalized intervention effectiveness rankings relative to the patient given an assigned goal and an individual intervention effect estimation; and provides the intervention recommendation to the care manager.