Deep Learning Model for Palliative Care Risk Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current healthcare systems face challenges in accurately identifying patients near the end of life who would benefit from palliative care, leading to aggressive and costly treatments, and the COVID-19 pandemic has exacerbated the difficulty in prognostication, especially for hospitalized patients with and without COVID-19 infection.
Innovation Solution
A deep learning model trained on electronic health records (EHRs) and administrative claims data to predict short-term mortality and hospice outcomes, providing a patient risk score that helps healthcare providers determine appropriate care levels, including palliative and hospice care, by integrating historical and ongoing patient data features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning model integrates multiple EHR features and administrative claims data to improve prediction accuracy, then measurement precision of mortality risk assessment is improved, but device complexity of the system increases
Solution Approach 1:
The system segments the complex prediction task into distinct functional modules: data retrieval module that collects EHR and claims data, feature extraction module that processes clinical and administrative features, model execution module that runs the deep learning prediction, and output module that delivers risk scores. This segmentation manages complexity by organizing functions into separate, manageable components while maintaining high prediction accuracy through integrated processing.
Solution Approach 2:
The system introduces an intermediary processing layer between raw data and final predictions. This layer includes feature extraction and preprocessing components that transform heterogeneous EHR and claims data into standardized features suitable for the deep learning model. This intermediary layer simplifies the overall system by handling data integration and transformation complexities separately from the core prediction function.
2Loss of information
If the system retrieves and processes extensive EHR and claims data for each patient, then information completeness for prognostication is improved, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary data retrieval and processing operations in advance. It proactively queries and retrieves relevant EHR and claims data before the prediction is needed, pre-processes the data into standardized formats, and prepares feature sets ahead of time. This preliminary action ensures complete information is available while reducing actual processing time when predictions are required for clinical decisions.
Solution Approach 2:
The system implements continuous data processing pipelines that operate in the background, continuously retrieving and preprocessing patient data as it becomes available. This continuous action ensures data completeness is maintained without interrupting clinical workflows, as the processing occurs continuously rather than in discrete batches when needed.
3Adaptability or versatility
If the model is trained on diverse patient populations including COVID-19 patients to improve adaptability, then adaptability of the prognostic tool is improved, but reliability of predictions may be affected by changing mortality rates
Solution Approach 1:
The system implements dynamic model training and updating mechanisms that adapt to changing patient populations and disease patterns. The deep learning model is continuously retrained on new data including COVID-19 patients, allowing it to adapt to evolving mortality rates and clinical presentations. This dynamic approach maintains reliability by ensuring the model reflects current population characteristics while preserving adaptability across diverse patient groups.
Solution Approach 2:
The system adjusts model parameters and training configurations based on the specific patient population being analyzed. When training on diverse populations including COVID-19 patients, the model dynamically modifies its parameter weights and processing thresholds to account for different disease severities and mortality patterns. This parameter adaptation maintains predictive reliability across varying population characteristics while preserving broad adaptability.
Data Source
AI summary
A system is provided. The system includes a patient analysis (“PA”) computer device including at least one processor in communication with at least one memory device. The at least one processor is programmed to a) store a patient analysis model, b) receive a patent identifier associated with the patient; c) retrieve a plurality of claim information associated with the patient identifier; d) retrieve a plurality of electronic healthcare records (EHR) associated with the patient identifier; e) execute the patient analysis model to determine a patient risk score based on the plurality of claim information and the plurality of EHR; and f) present information about the patient to a healthcare provider.