Clinical Risk Prediction System for Heart Failure Readmission Reduction
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Solution Overview
Problem
Hospital readmissions, particularly for patients with heart failure, are common due to fragmented care environments and the lack of effective identification of high-risk patients, leading to inefficient allocation of resources and increased costs.
Innovation Solution
A clinical predictive and monitoring system that utilizes a computer system to integrate and analyze real-time and historical clinical and non-clinical data from various sources, including electronic medical records, health information exchanges, and social services, to calculate disease risk scores and identify high-risk patients, enabling targeted interventions and improved care coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods are used to identify high-risk patients, then implementation simplicity is maintained, but identification accuracy and reliability deteriorate
Solution Approach 1:
The patent replaces manual identification methods with an automated computer-based system that processes clinical and non-clinical data to generate risk scores. This substitution of mechanical/manual processes with automated computational systems directly improves identification accuracy while managing system complexity through software-based solutions.
Solution Approach 2:
The patent introduces a computer system as an intermediary between raw patient data and clinical decision-making. This intermediary automatically processes and analyzes multiple data sources, generating standardized risk scores that improve identification accuracy without requiring direct manual analysis of all underlying data.
2Reliability
If comprehensive data integration from multiple sources is implemented, then patient risk assessment accuracy improves, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent creates a multi-functional computer system that handles diverse data types (clinical records, lab results, social determinants) through unified processing algorithms. This universal approach allows comprehensive data integration while managing complexity through standardized processing methods applicable across different data sources.
Solution Approach 2:
The patent transforms diverse, unstructured clinical and non-clinical data into standardized risk score parameters through automated processing. By changing the state of raw data into standardized quantitative parameters, the system achieves comprehensive data integration while managing complexity through consistent parameter transformation.
3Productivity
If targeted interventions are implemented for identified high-risk patients, then readmission rates decrease, but resource allocation complexity increases
Solution Approach 1:
The patent applies targeted interventions specifically to identified high-risk patient populations rather than uniformly across all patients. This local quality approach concentrates resources on those most likely to benefit, improving readmission reduction efficiency while managing resource allocation complexity through precise patient targeting.
Solution Approach 2:
The patent identifies and flags high-risk patients during the hospital stay before discharge, enabling preliminary intervention planning. This preliminary action allows care teams to prepare appropriate interventions in advance, improving coordination efficiency while reducing the complexity of last-minute decision-making.
Data Source
AI summary
A clinical predictive and monitoring system comprising a data store operable to receive and store data associated with a plurality of patients selected from medical and health data; and a number of social, behavioral, lifestyle, and economic data; at least one predictive model to identify at least one high-risk patient associated with at least one medical condition; a risk logic module operable to apply the at least one predictive model to the patient data to determine at least one risk score associated the at least one medical condition and identify at least one high-risk patient; a data presentation module operable to present notification and information to an intervention coordination team about the identified at least one high-risk patient; and an artificial intelligence tuning module adapted to automatically adjust parameters in the predictive model in response to trends in the patient data.