Patient Status Prediction Using EMR-Based ML Triage
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Solution Overview
Problem
Current methods for determining patient status in hospitals are inefficient, often relying on incomplete data and subjective UM nurse experience, leading to incorrect patient care levels and resource waste.
Innovation Solution
A system utilizing machine learning models to predict patient status transitions between Inpatient and Observation statuses by analyzing electronic medical records, leveraging binary classification models to quantify and streamline the review process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If UM nurses manually review patient charts to determine status, then they can make clinical judgments based on experience, but the process is time-consuming and prone to subjectivity
Solution Approach 1:
The system performs preliminary automated analysis of patient data before UM nurse review. Machine learning models pre-process electronic health records, clinical notes, and billing data to generate predicted status recommendations, allowing nurses to focus only on borderline cases rather than reviewing every patient from scratch
Solution Approach 2:
The system enables self-service status determination through automated machine learning models that independently analyze patient data and generate status predictions. The models continuously learn from historical data and automatically update their predictions, reducing reliance on manual nurse review while maintaining accuracy
2Measurement precision
If UM nurses review all patient cases thoroughly, then status determination accuracy improves, but productivity decreases due to limited time
Solution Approach 1:
The system segments patients into different risk categories based on predicted status change probability. High-risk patients requiring thorough review are separated from low-risk patients that can be processed automatically, allowing nurses to concentrate their expertise where it is most needed while maintaining high overall throughput
Solution Approach 2:
The machine learning model acts as an intermediary between raw patient data and final status determination. It processes and structures unstructured clinical data, generates evidence-based recommendations, and presents prioritized cases to nurses, thereby increasing both accuracy and productivity simultaneously
3Productivity
If automated systems are used for status prediction, then efficiency and consistency improve, but the system complexity increases
Solution Approach 1:
The machine learning model is designed as a universal system that handles multiple patient types, conditions, and data formats through a single unified architecture. It processes diverse inputs including clinical notes, lab results, and billing data using the same underlying model, reducing overall system complexity despite the variety of functions performed
Data Source
AI summary
A prediction cycle controller queries a database for patient visits that are eligible for admit status prediction (ASP) and extracts, from the patient visits eligible for the ASP, ASP features and major diagnosis category (MDC) prediction features for each of the patient visits. The ASP features include observations of prediction-eligible patients of a healthcare provider. The MDC prediction features include data points for determining a MDC. The ASP features are provided to an admit status predictor which examines, utilizing a machine learning model, the observations of the prediction-eligible patients and generates an ASP for each prediction-eligible patient. The MDC prediction features are provided to an MDC predictor which examines the MDC prediction features and the ASP thus generated by the admit status predictor for each prediction-eligible patient and generates a MDC prediction (MDCP). The ASP and the MDCP are then presented, via a user interface, on a user device.