Practitioner Value Assessment via Multiplicative Regression Model
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Solution Overview
Problem
Attributing the outcome of collaborative healthcare efforts to individual practitioners in multi-practitioner settings is challenging due to the lack of routine recording of actions and effort, making it difficult to assess each practitioner's value effectively for care management and improvement.
Innovation Solution
A method using a multiplicative regression model to derive practitioner outcome indices (POIs) and practitioner type indices (PTIs) from observational data, which assigns an outcome index to each practitioner, allowing for intuitive interpretation and consideration of different impact types, and providing indices for teams of practitioners.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If heuristic methods are used to assign patients to practitioners, then the assignment process is simple, but the measurement precision of practitioner value assessment deteriorates
Solution Approach 1:
The patent transforms the assessment from simple heuristic assignment to a mathematical model using multiple parameters including practitioner outcome indices (POIs) and practitioner type indices (PTIs). The model equation O_i = B_i × ∏(POI_j ^ PTI_j) allows precise quantification of each practitioner's contribution by changing from qualitative assignment to quantitative parameter-based evaluation.
2Measurement precision
If actions and effort of each practitioner are routinely recorded, then the measurement precision of individual contribution improves, but the device complexity and data management requirements increase
Solution Approach 1:
The patent extracts only the essential outcome data O_i from complex clinical processes, rather than recording all practitioner actions and efforts. By focusing on the final outcome and using the mathematical model to back-calculate individual contributions, the system avoids the complexity of comprehensive action recording while maintaining assessment precision.
3Measurement precision
If a multiplicative regression model is used to derive practitioner outcome indices, then the measurement precision and systematic assessment improve, but the difficulty of detecting and measuring individual practitioner value increases
Solution Approach 1:
The patent introduces practitioner outcome indices (POIs) and practitioner type indices (PTIs) as intermediary variables that mediate between observed outcomes O_i and individual practitioner contributions. These indices serve as measurable proxies that simplify the detection and measurement process while maintaining mathematical rigor and assessment precision.
Data Source
AI summary
A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations are solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.