Patient Flow Simulation Using Clinical Parameter Extraction
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Solution Overview
Problem
Current patient flow simulation models in healthcare environments are limited in predicting future patient admissions and care pathways due to reliance on simplistic historical data and lack of clinical information for simulated patients, especially since electronic healthcare records are not available for future arrivals.
Innovation Solution
A system that receives and adapts healthcare records to generate patient parameters such as day of week, time of day, and patient type admission parameters, calculates length of stay and transition probabilities, and predicts patient admissions and care pathways using these parameters, providing accurate forecasts for healthcare resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simulation models use only straightforward historical admission/discharge/transfer data, then the model structure remains simple, but the prediction accuracy for simulated patients deteriorates
Solution Approach 1:
The system pre-processes historical electronic healthcare records to extract and store patient parameters (diagnosis, demographics, admission characteristics) before simulation runs. This preliminary action creates a reusable parameter set that can be applied to simulated patients, improving prediction accuracy without increasing real-time model complexity
Solution Approach 2:
The system creates parameter templates from actual patient records that can be copied and applied to simulated patients. These parameter copies capture clinical characteristics without requiring access to actual electronic records during simulation, maintaining model simplicity while improving predictive capability
2Ease of manufacture
If clinical information is used only for very small and specific populations, then the data processing becomes simpler, but the applicability across different patient types deteriorates
Solution Approach 1:
The system creates a universal parameter extraction framework that processes electronic healthcare records across multiple patient types and clinical populations. The same parameter extraction logic applies to different diagnoses and patient categories, making the system versatile while maintaining consistent data processing approaches
Solution Approach 2:
The system dynamically adjusts patient parameters based on the specific population and clinical context being simulated. By changing parameters such as admission characteristics, diagnosis types, and demographic distributions, the system adapts to different patient types while using the same underlying model structure
3Loss of information
If electronic healthcare records are not available for future arrivals at the time of running the simulation, then data availability for simulated patients is limited, but the simulation speed is improved
Solution Approach 1:
The system extracts and stores essential patient parameters from electronic healthcare records before the simulation run. This pre-extraction creates a parameter cache that can be quickly applied to simulated patients without requiring real-time access to electronic records during simulation, maintaining both data quality and simulation speed
Solution Approach 2:
The system extracts only the necessary patient parameters (diagnosis, demographics, admission characteristics) from electronic healthcare records, separating essential information from complete medical records. This extraction creates a streamlined parameter set that improves simulation speed while retaining clinically relevant information for accurate predictions
Data Source
AI summary
A method for predicting simulated patient admissions, comprising: receiving healthcare records for a plurality of patients; adapting the received healthcare records to a common data format; parameterizing the adapted healthcare records to generate a plurality of patient parameters comprising for each patient a day of the week admission parameter, a time of day admission parameter, and a patient type parameter; generating a length of stay parameter for each of the plurality of different patient types; generating a transition probability for each of the plurality of different patient types; predicting, for a time period in the healthcare environment, patient admissions; predicting a care pathway for some or all of the predicted patient admissions during the time period; and reporting, via a user interface, the predicted patient admissions and predicted care pathways.