Patient Risk Prediction Model for Avoidable Healthcare Events
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Solution Overview
Problem
Healthcare facilities face challenges in managing capacity and resources due to a growing population, with a need to improve efficiency and maintain high-quality services while preventing avoidable hospital events.
Innovation Solution
A system and method for determining and presenting risk indicators of patients to avoidable healthcare events by correlating data with avoidable healthcare events, generating a model, and applying it to predict individual risks, thereby facilitating targeted resource allocation and interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If healthcare facilities increase capacity to serve growing population, then service coverage improves, but resource management complexity and costs increase
Solution Approach 1:
The patient population is segmented into high-risk and low-risk groups based on predictive modeling of avoidable healthcare events. This segmentation allows facilities to allocate resources differently to each segment, managing complexity by focusing intensive resources only where needed rather than uniformly across all patients.
Solution Approach 2:
The system performs preliminary identification and risk assessment of patients who are likely to experience avoidable healthcare events before those events occur. This preliminary action enables proactive resource allocation and preventive interventions, improving service coverage for high-risk patients without requiring increased overall facility capacity.
2Reliability
If healthcare facilities focus resources on high-risk patients, then preventable events are reduced, but resource allocation complexity increases
Solution Approach 1:
The predictive modeling system automatically identifies high-risk patients and generates risk assessments without requiring manual clinical evaluation for each patient. This self-service capability reduces the complexity of resource allocation by providing objective, data-driven risk stratification that clinicians can act upon directly.
Solution Approach 2:
The system provides feedback to healthcare providers about patient risk levels, enabling informed resource allocation decisions. This feedback mechanism simplifies the allocation process by giving providers clear guidance on which patients require intensive resources versus those who can be managed with standard care.
3Measurement precision
If comprehensive patient data is collected to improve prediction accuracy, then model precision improves, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the most relevant features and variables from comprehensive patient data that are necessary for predicting avoidable healthcare events. This extraction process maintains prediction accuracy by focusing on key predictors while reducing data processing complexity by eliminating redundant or less informative data elements.
Data Source
AI summary
Systems and methods for determining indicators of risk of patients to avoidable healthcare events and presentation of the same are disclosed. According to an aspect, a method includes receiving, from a database, data associated with a plurality of individuals. The method also includes correlating the data to an avoidable healthcare event for one or more of the individuals. Further, the method includes generating a model that relates the data to the avoidable healthcare event based on the correlation of the data to the avoidable healthcare event. The method also includes applying the model to data of another individual to generate an indicator of risk of the other individual to the avoidable healthcare event. Further, the method includes presenting the indicator of risk via a user interface.