Clinical Risk Model Bias Calibration
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Solution Overview
Problem
Current healthcare systems face inefficiencies in identifying patients at high risk for hospital or acute care services, making it challenging to preemptively provide interventions and reduce the need for costly hospital visits, especially for patients with compromised immune systems like cancer patients.
Innovation Solution
A model-assisted system using a processor to analyze medical records with a trained machine learning model based on logistic regression, determining patient risk levels and generating reports for recommended interventions, while also detecting biases in the model's predictions across different patient groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of medical charts is performed to identify patients at high risk for hospital services, then identification accuracy may be improved, but time consumption and operational efficiency deteriorate significantly
Solution Approach 1:
The patent replaces manual mechanical review of medical charts with an automated machine learning system that processes electronic health records. The system uses trained models to automatically identify patients at high risk for hospital services, substituting human analysts with computational algorithms that can process large volumes of data quickly and consistently without time constraints.
2Loss of energy
If preemptive care interventions are provided to reduce hospital visits, then healthcare costs and patient exposure to infectious diseases are reduced, but resource allocation complexity increases
Solution Approach 1:
The patent implements a feedback-driven resource allocation system where the machine learning model continuously identifies high-risk patients, triggers preemptive care interventions, and monitors outcomes. This closed-loop feedback mechanism automatically adjusts resource allocation based on real-time risk assessments, reducing the need for complex manual planning while ensuring resources are directed to patients who need them most.
3Productivity
If machine learning models are used to predict patient risk levels, then identification efficiency is improved, but potential biases in predictions across different patient groups may arise
Solution Approach 1:
The patent addresses prediction biases by implementing calibration factors that adjust model predictions for different patient groups. The system dynamically modifies prediction parameters based on group-specific characteristics, ensuring that risk assessments remain accurate and fair across diverse populations. This parameter adjustment mechanism maintains high identification efficiency while correcting for potential biases in the underlying data or model assumptions.
Data Source
AI summary
A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.