Intervention Benefit Ranking for Readmission Risk Prioritization
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Solution Overview
Problem
Modern healthcare systems face challenges in efficiently scheduling resources and making data-driven decisions for critically ill patients, particularly when resources are limited, leading to inefficient and ineffective healthcare delivery systems with high unplanned readmission rates.
Innovation Solution
A system that leverages causal inference techniques and machine learning models to predict intervention success probabilities and rank subjects based on net-benefit bounds, incorporating demographic, clinical, and historical health records to prioritize interventions that maximize clinical impact and financial sustainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clinicians use subjective judgement to prioritize patients, then individual clinical experience can be applied, but the reliability of determining likelihood of adverse outcomes deteriorates due to limited patient data
Solution Approach 1:
The patent introduces a computational model as an intermediary between clinical data and decision-making. The model processes electronic health record data and generates risk scores that assist clinicians in prioritizing patients, combining objective data analysis with clinical expertise to improve both reliability and adaptability in patient prioritization
Solution Approach 2:
The patent replaces the purely subjective mechanical process of clinical judgement with a hybrid system that incorporates computational algorithms. The system uses machine learning models to analyze patient data and generate objective risk assessments, which then inform clinical decisions, thereby enhancing the reliability of outcome predictions while preserving clinical flexibility
2Quantity of substance
If hospitals have limited supply of medications, medical equipment, and nursing staff, then resource constraints are reduced, but the challenge of scheduling and allocation deteriorates
Solution Approach 1:
The patent changes the parameters used for resource allocation from simple first-come-first-served or subjective clinical judgement to a multi-parameter risk scoring system. The system considers multiple factors including patient acuity, predicted length of stay, resource requirements, and potential outcomes to optimize the allocation of limited medications, equipment, and staff
Solution Approach 2:
The patent implements preliminary risk assessment and prioritization before resource allocation decisions are made. By pre-calculating risk scores and identifying high-priority patients in advance, the system enables more efficient real-time scheduling and allocation of limited resources, reducing the complexity of ad-hoc decision-making during resource constraints
3Speed
If reactive methods are used to assess patient severity, then immediate clinical observations can be utilized, but data-driven evidence in decision making is not considered leading to inefficient healthcare delivery
Solution Approach 1:
The patent performs preliminary analysis of electronic health record data to generate risk scores and prioritization lists before clinical teams need to make decisions. This pre-processing of data enables rapid assessment of patient severity using both historical data patterns and current observations, improving both speed and evidence-based decision-making in healthcare delivery
Solution Approach 2:
The patent implements feedback loops where outcomes of prioritization decisions are tracked and used to refine the risk prediction models. By continuously learning from actual patient outcomes, the system improves the accuracy of severity assessments over time, enhancing both the speed and efficiency of healthcare delivery through increasingly precise data-driven insights
Data Source
AI summary
The present disclosure relates to techniques for generating a ranked list of a set of subjects by predicting their potential health benefit from an intervention to prioritize subjects that may be at a risk of a negative outcome and likely to benefit from a proposed intervention. Additionally, the ranking may further account for potential cost-savings associated with early intervention to avoid acute-care utilization by applying a cost-modeling technique. The disclosed techniques may include analyzing subject-specific data, including demographic, clinical, and historical information, to compute a total net-benefit score by combining a predicted benefit probability with cost and revenue metrics. The benefit probability may be calculated using causal inference models to estimate a potential improvement in health outcomes from the proposed intervention or treatment. The disclosed techniques may further facilitate personalized subject care by dynamically updating rankings based on real-time data, enhancing clinical decision-making, and optimizing resource allocation.