Patient Stay Prediction for Hospital Asset Availability
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Solution Overview
Problem
Care facilities face challenges in maintaining room availability due to unavailable assets, leading to treatment delays and elongated patient stays.
Innovation Solution
A computer system utilizing artificial intelligence to predict patient length of stay and associate it with asset management, optimizing asset availability through a length of stay model and asset model trained on patient and facility data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If assets are not available when needed, then room availability is maintained, but treatment delays occur and length of stay elongates
Solution Approach 1:
The system performs preliminary actions by predicting patient length of stay before discharge occurs, and proactively managing asset availability during the predicted stay period. The asset management system anticipates future asset needs based on predicted LOS, ensuring assets are available before they are actually needed, thereby preventing treatment delays while maintaining room availability.
Solution Approach 2:
The system implements feedback loops where actual patient outcomes and asset usage patterns are fed back into the prediction models. The length of stay model and asset model continuously learn from historical data and real-time observations, improving their accuracy over time. This feedback mechanism enables the system to adapt to changing patterns in patient care and asset utilization, optimizing both room availability and reducing unnecessary delays.
2Productivity
If assets are kept available for all patients, then treatment delays are reduced, but asset management efficiency decreases
Solution Approach 1:
The system changes key parameters by using data-driven predictions of length of stay to dynamically adjust asset allocation strategies. Instead of static asset management, the system varies asset availability parameters based on predicted patient needs, acuity levels, and anticipated discharge dates. This allows the system to optimize asset distribution according to actual predicted demand, improving treatment speed while managing complexity through intelligent parameter adjustment rather than blanket asset availability.
Solution Approach 2:
The asset management system transitions from static to dynamic management by continuously updating predictions and adjusting asset allocation in real-time. The system dynamically responds to changing patient conditions, predicted discharge dates, and asset availability patterns. This dynamic approach allows the system to provide assets when truly needed (improving treatment speed) while releasing or reallocating assets when no longer required (reducing management complexity and improving efficiency).
Data Source
AI summary
An example computer system for predicting a length of stay of a patient for asset management can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: train a length of stay model to estimate the length of stay of the patient; train an asset model for managing assets of a facility; and pair the length of stay model and the asset model to optimize management of the assets for the patient.