Machine Learning Referral Analytics for Patient-Service Matching
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Solution Overview
Problem
Conventional healthcare referral systems rely on subjective decision-making by providers, leading to sub-optimal care due to a lack of consideration for the specific suitability of referral targets for individual patients, resulting in inefficient resource allocation and potential adverse outcomes.
Innovation Solution
A machine learning-based system that accesses patient data and service-specific models to predict referral outcomes, facilitating informed decisions on referral acceptance or revision, and suggesting optimal healthcare services based on predicted patient suitability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional subjective referral selection is used, then provider autonomy and ease of operation are maintained, but referral outcome quality and patient suitability match deteriorate
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the referring provider and the referral decision. The model processes patient data, provider characteristics, and outcome data to generate predictions about referral success, serving as a mediator that objectively evaluates suitability while preserving provider autonomy in making final decisions.
Solution Approach 2:
The patent replaces the mechanical system of subjective human judgment with a computational machine learning system. The model uses trained algorithms to process structured data and generate objective predictions about referral outcomes, substituting human intuition with data-driven analysis while maintaining the referral workflow.
2Measurement precision
If comprehensive patient data analysis is performed, then referral suitability accuracy is improved, but computational resources and processing time are consumed
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring patient data, provider data, and outcome data before the actual referral prediction. The machine learning model is trained in advance on historical data, so that when a referral is needed, the system can quickly query pre-processed features rather than analyzing raw data from scratch, reducing real-time computational burden.
Solution Approach 2:
The patent applies local quality by focusing computational analysis on specific relevant features rather than processing all possible patient data uniformly. The system identifies and weights key predictors of referral success (such as patient demographics, condition severity, provider specialty match) while reducing or excluding less relevant data, optimizing the balance between accuracy and computational efficiency.
Data Source
AI summary
Techniques for machine learning-based data evaluation are provided. A patient referral of a patient to a healthcare service is accessed, and a referral condition of the patient is determined based on the patient referral. Using a machine learning model, a prediction indicating one or more referral outcomes is generated based on the first referral condition. Acceptance of the patient referral to the healthcare service is facilitated based on the prediction.