Predictive Model for Adverse Patient Outcomes Using Biometric and EHR Data
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Solution Overview
Problem
Critically ill patients in ICUs experience rapid changes in severity of illness, making frequent clinical and laboratory data evaluations necessary for effective triage and resource mobilization, and lack of recognition of clinical deterioration contributes to cardiac arrest events, highlighting the need for predictive models to anticipate adverse outcomes.
Innovation Solution
A predictive model that monitors biometric parameters and retrieves electronic health records (EHR) data to generate categorical parameters, using a feature extractor and machine learning algorithms to calculate a risk score for adverse patient outcomes, providing continuous and real-time risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent evaluation of clinical and laboratory data is performed to assess severity of illness, then prediction accuracy of adverse outcomes is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary processing of clinical and laboratory data by automatically extracting relevant features and generating risk scores in advance, so that when adverse outcomes need to be predicted, the analysis is already prepared and can be delivered quickly to clinicians
Solution Approach 2:
The patent replaces manual clinical evaluation with an automated machine learning model that processes patient data, eliminating the need for clinicians to manually analyze numerous clinical and laboratory parameters while maintaining high prediction accuracy
2Adaptability or versatility
If manual assessment of severity of illness is performed by clinicians, then flexibility in evaluation is maintained, but productivity and response speed decrease
Solution Approach 1:
The system enables self-service by automatically performing the severity of illness assessment without requiring clinician intervention. The machine learning model independently processes patient data, generates risk scores, and provides predictions, freeing clinicians from manual evaluation tasks while maintaining adaptability through configurable model parameters
3Reliability
If comprehensive clinical and laboratory data are analyzed to prevent cardiac arrest events, then prediction reliability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from comprehensive clinical and laboratory data that are necessary for predicting adverse outcomes. The machine learning model identifies and processes key predictors while filtering out redundant information, maintaining prediction reliability without requiring analysis of all available data
Solution Approach 2:
The patent segments the complex prediction task into distinct components: data extraction, feature engineering, model training, and score generation. This modular approach breaks down the complexity of analyzing comprehensive patient data into manageable stages, making the system more implementable and maintainable
Data Source
AI summary
Systems and methods are provided for predicting an adverse patient outcome. A set of biometric parameters associated with a patient are monitored and at least one electronic health records (EHR) parameter is retrieved from an EHR database. A set of categorical parameters are generated from the set of biometric parameters and one or more EHR parameters according to a predefined rule set. A score, representing a risk that a patient will experience an adverse patient outcome, is generated from the set of categorical parameters.