Readmission Risk Score System Using Real-Time Clinical Data
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Solution Overview
Problem
Current medical systems lack effective methods to predict and mitigate hospital readmission risks for patients, relying on administrative data that is not available in real-time and failing to incorporate clinical data for accurate risk assessment.
Innovation Solution
A system that integrates patient-specific data, including laboratory results and medical history, to determine a readmission risk score, which is used to configure medical device parameters for testing and treatment protocols, providing real-time decision support to minimize readmission risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If administrative data is used for risk assessment, then data availability is improved, but real-time accuracy deteriorates
Solution Approach 1:
The patent combines administrative data with real-time clinical data (laboratory results, vital signs, medication information) to create a comprehensive risk assessment system. This merging allows the system to maintain data availability while achieving real-time accuracy by integrating multiple data sources that provide both historical context and current patient status.
Solution Approach 2:
The system performs preliminary risk stratification during the hospital admission period using available clinical data, enabling proactive identification of high-risk patients before discharge. This preliminary action allows the system to prepare and implement personalized discharge plans that prevent readmissions, rather than reacting after readmission occurs.
2Measurement precision
If clinical data is incorporated for risk assessment, then real-time accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the risk assessment system into distinct modules: data collection components (EHR integration, laboratory systems, medication databases), risk calculation algorithms (predictive models weighted by patient characteristics), and intervention strategies (personalized discharge plans). This segmentation allows complex clinical data processing to be organized into manageable, functional components that can be implemented systematically.
Solution Approach 2:
The system introduces an intermediary processing layer that automatically extracts, validates, and weight clinical data from multiple sources before feeding it into predictive models. This intermediary layer simplifies the overall system architecture by providing a standardized interface between diverse data sources and the risk assessment algorithms, reducing the complexity of direct integration.
3Reliability
If personalized treatment protocols are implemented, then patient care quality is improved, but implementation difficulty deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor patient outcomes and readmission rates, using this information to refine and update the predictive models and treatment protocols. This feedback loop ensures that personalized care plans remain effective and adaptable to changing patient conditions, improving care quality while providing guidance for implementation through data-driven optimization.
Solution Approach 2:
The system changes key parameters in treatment protocols based on individual patient risk scores and characteristics. By adjusting parameters such as discharge timing, follow-up frequency, and intervention intensity according to calculated risk levels, the system achieves personalized care without requiring completely new treatment paradigms, thereby reducing implementation difficulty.
Data Source
AI summary
Systems for use with a medical device for reducing medical facility readmission risks are provided. In one aspect, a system includes a medical device that is configurable with operating limit parameters for providing testing or treatment to a patient, and a limiting system. The limiting system includes a memory that includes patient-specific information for the patient and a database that includes readmission risk information, and a processor. The processor is configured to compare readmission risk parameters with the patient-specific information, and provide a readmission risk score for integration with medical devices and processes corresponding to the patient. Methods and machine-readable media are also provided.