Patient Risk Prediction Model Using Integrated Healthcare Data
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Solution Overview
Problem
Conventional machine learning models for healthcare data analysis are limited by their access to patient data, implicit bias, computational intensity, lack of transparency, and inability to effectively reduce health disparities, leading to inadequate risk assessment and intervention guidance for adverse medical events.
Innovation Solution
A system utilizing a predictive model that integrates accident, inpatient, and outpatient data, normalized and validated with clinical expertise, to identify patients at high risk of adverse medical events, enabling targeted healthcare interventions to mitigate these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models utilize Accident and Emergency (A&E) healthcare data or inpatient healthcare data, then the model can be built with limited data access, but the model performance is limited due to inability to access patient's past clinical history
Solution Approach 1:
The patent combines multiple data sources (A&E data, inpatient data, outpatient data, and primary care data) into a unified predictive model. This merging of previously separate data silos enables comprehensive access to patient clinical history while maintaining a single integrated model structure, thereby improving risk assessment accuracy without proportionally increasing system complexity.
Solution Approach 2:
The predictive model is designed to process and analyze multiple types of healthcare data (A&E, inpatient, outpatient, primary care) through a single unified framework. This multi-functional capability allows the model to leverage diverse data sources for comprehensive risk assessment, improving measurement precision while avoiding the need for separate specialized models for each data type.
2Measurement precision
If conventional machine learning models utilize computationally intensive algorithms such as random forests and support vector machines (SVMs), then the model can handle complex healthcare data, but the algorithms are convoluted and less transparent, therefore may not provide clear and in-depth understanding of the machine learning models to users
Solution Approach 1:
The patent transitions from using complex algorithms like random forests and SVMs to simpler algorithms such as logistic regression or gradient boosting. This parameter change in algorithm selection maintains adequate discriminatory performance while significantly improving model transparency and interpretability, allowing healthcare professionals to understand the decision-making process clearly.
Solution Approach 2:
The patent adopts simpler, more interpretable algorithms that can be easily understood and explained to users, sacrificing some of the computational complexity of advanced algorithms. This approach prioritizes model transparency and clinical acceptability over maximizing discriminatory performance through complex black-box algorithms.
3Productivity
If conventional machine learning models use routinely collected healthcare data, then the data is readily available, but the data inherits implicit bias, which leads the model to exacerbate existing health disparities
Solution Approach 1:
The patent modifies the data processing parameters by implementing normalization techniques and bias adjustment mechanisms during data preprocessing. These parameter changes ensure that routinely collected healthcare data are processed in a way that reduces implicit bias while maintaining processing efficiency, thereby improving health equity outcomes without sacrificing productivity.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor model predictions for signs of bias or health disparities. This feedback loop allows the system to identify and correct biased predictions, ensuring that the use of routinely collected data does not exacerbate existing health disparities while maintaining efficient data processing.
4Device complexity
If conventional machine learning models do not provide clear guidance for effective treatment measures, then the model development is simpler, but the model is ineffective to provide proper guidance for effective treatment measures to patients
Solution Approach 1:
The predictive model is designed to identify high-risk patients before adverse events occur, enabling preliminary intervention. By predicting risk in advance, the system provides timely guidance for preventive measures and early intervention, improving ease of operation and clinical effectiveness without significantly increasing model complexity.
Solution Approach 2:
The system acts as an intermediary between raw predictive outputs and clinical decision-making by providing structured risk scores and actionable recommendations. This intermediary layer translates complex model predictions into clear, interpretable guidance that healthcare professionals can easily understand and act upon, improving intervention guidance quality while maintaining reasonable model simplicity.
Data Source
AI summary
Disclosed is a system for assessing risk of adverse medical events leading to unplanned healthcare and/or death, for facilitating mitigation of adverse medical events, system comprising processor(s) configured to: obtain existing healthcare data from data source(s), existing healthcare data comprises accident and emergency data, inpatient data, outpatient data; build predictive model for estimating individual patients'risks for adverse medical events using existing healthcare data and pre-defined set of medical conditions and pre-defined patient characteristics; deploy predictive model for use; obtain first healthcare data, first healthcare data being generated later in time than existing healthcare data; process first healthcare data using predictive model, to predict risk level of adverse medical event(s); identify target set of patients, send communication indicative of target set of patients to data source(s) and/or first device(s) associated with healthcare professional(s), for enabling determination of healthcare intervention(s), healthcare intervention(s) mitigates adverse medical event(s) which reduces mortality of patient.