AI Care Plan System Prioritizing Gaps
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Solution Overview
Problem
Current healthcare systems face challenges in generating effective care plans due to information overload, lack of prioritization, and inefficiencies in analyzing patient health histories, leading to uncoordinated and non-personalized healthcare recommendations.
Innovation Solution
An AI-based system that processes electronic patient data to identify care gaps, assigns treatment effect scores, prioritizes gaps, and generates personalized, actionable recommendations, which are then communicated to patients or care managers through optimized channels, incorporating social determinants of health to address barriers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If healthcare providers manually review healthcare data and generate care plans, then they can personalize recommendations for patients, but the process becomes time-consuming and inefficient
Solution Approach 1:
The machine learning model performs self-service by automatically analyzing healthcare data, identifying care gaps, and generating personalized care plan recommendations without requiring manual intervention from healthcare providers for each step of the analysis process
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated machine learning system that processes healthcare data, identifies patterns, and generates care plan recommendations, thereby reducing the time investment required while maintaining personalization
2Reliability
If healthcare systems analyze comprehensive patient health histories, then they can identify more care gaps and provide thorough recommendations, but the complexity of analysis increases
Solution Approach 1:
The patent segments the comprehensive health history analysis into distinct components: extracting patient data from multiple sources, identifying care gaps based on predefined criteria, determining treatment effects, and generating recommendations. This segmentation reduces overall complexity by breaking down the complex analysis into manageable steps handled by specialized model components
Solution Approach 2:
The machine learning model acts as an intermediary between the raw healthcare data and the final care plan recommendations, processing and translating complex data patterns into actionable insights through automated analysis
3Reliability
If care plans include multiple recommended actions, then they can address more care gaps, but the information overload increases for providers and patients
Solution Approach 1:
The patent applies partial action by prioritizing care gaps and recommending actions based on treatment effect scores and patient-specific factors, providing only the most relevant and impactful recommendations rather than listing all possible actions, thereby reducing information overload while maintaining comprehensive coverage of critical care gaps
Data Source
AI summary
A method is provided that includes: providing data inputs to a machine learning model, where the data inputs include electronic patient data obtained from electronic records describing a health history of the patient. The method includes receiving an output from the machine learning model, where the output is generated based on the machine learning model processing the data inputs and includes identified care gaps for the patient. The method includes determining a treatment effect for each of the identified care gaps and assigning a treatment effect score to each of the identified care gaps. The method includes prioritizing the identified care gaps based on the treatment effect score assigned thereto and, based on the prioritization, determining one or more recommended patient actions for the patient. The method includes generating and transmitting an electronic communication that describes the one or more recommended patient actions for the patient.