Physician Attribution via Predictive Clinical Analysis
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Solution Overview
Problem
Current methods for attributing physician responsibility in inpatient care are inadequate, as they rely on expensive human-centered knowledge engineering and are not scalable or effective in identifying the primary physician responsible for a patient's care in hospital settings.
Innovation Solution
A system and method using machine learning algorithms to automatically learn attribution logic from a small expert-annotated dataset, leveraging clinical information from Electronic Medical Records (EMRs) to retrospectively identify the most responsible physician, with a computational model that computes weights for clinical features to assign scores and attribute patients to physicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-centered knowledge engineering is used to encode attribution logic, then attribution precision may be improved, but system complexity and cost increase significantly
Solution Approach 1:
The patent replaces manual knowledge engineering (mechanical/human process) with machine learning algorithms (automated computational process). The system uses supervised learning models to automatically learn attribution logic from annotated patient-physician data, eliminating the need for manual encoding of attribution rules while achieving comparable or superior precision.
Solution Approach 2:
The system enables self-service learning where the machine learning model automatically improves its attribution logic through training on annotated data without requiring continuous human intervention. The model self-adjusts its parameters and decision boundaries based on the training data, reducing ongoing manual effort.
2Reliability
If manual knowledge engineering is used for attribution, then attribution logic can be established, but scalability across hospitals and specialties is limited
Solution Approach 1:
The machine learning model is designed to be universal across different hospitals and specialties. By training on diverse annotated data from multiple sources, the model learns generalizable attribution patterns that can be applied across different healthcare settings without requiring re-engineering for each institution or specialty.
Solution Approach 2:
The system adapts to different hospitals and specialties by adjusting model parameters through training on local annotated data. The same base model can be fine-tuned with institution-specific data to achieve reliable attribution in diverse settings, enabling scalability through parameter adaptation rather than structural changes.
3Measurement precision
If traditional attribution methods are used, then physician responsibility can be identified, but the process is not cost-effective
Solution Approach 1:
The patent replaces expensive manual review processes with automated machine learning models that process electronic health record data at scale. This substitution dramatically reduces the labor costs and time required for attribution while maintaining or improving accuracy through consistent application of learned patterns.
Solution Approach 2:
The system creates computational copies of expert attribution logic through trained machine learning models. Once trained on annotated data, the model can replicate expert-level attribution decisions across unlimited cases without incurring additional expert time costs, achieving scale economies.
Data Source
AI summary
A system associated with determining physician attribution related to in-patient care based at least on prediction of attribution values associated with patient-physician attribution is disclosed. The system extracts clinical data associated with a patient from a first database, with the extracted clinical data comprising clinical information related to the patient and the clinical information including vector values indicative of clinical progress of the patient at various stages of treatment. The system stores the extracted clinical data in a second database and determines attribution values using the attribution manager with the attribution values being based on a predictive analysis using predetermined weights associated with the vector values. A training data set is generated based on learned weight values associated with the clinical progress of the patient. The system iteratively updates the vector values of the patient at least using a predictive analysis associated with the training data set. The updated vector values of the patient and the learned weight values are processed in order to cross-validate expert clinical information associated with a patient. At least one patient-physician attribution value is generated based on cross-validation of the expert clinical information and predictive analysis associated with the training data set. A corresponding method and computer readable medium are also disclosed.