Hybrid Predictive Model for Postoperative Bed Type Allocation
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Solution Overview
Problem
Current predictive models for postoperative bed type allocation often result in erroneous predictions, leading to suboptimal resource allocation and potential health risks due to misclassification of care levels, which can cause elective surgeries to be postponed despite available resources.
Innovation Solution
A hybrid predictive model that combines machine learning with expert selection by establishing upper and lower thresholds on a probability scale, where predictions below the lower threshold default to a lower care bed, above the upper threshold default to a higher care bed, and within the threshold range recommend expert selection, optimized to minimize health risks and resource misallocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a predictive model is used to predict postoperative bed type, then resource allocation efficiency is improved, but prediction accuracy deteriorates leading to erroneous classifications
Solution Approach 1:
The patent segments the prediction output into distinct categories (ICU bed, PACU bed, general ward) based on probability thresholds. This segmentation allows the system to handle different care levels appropriately and improves overall prediction accuracy by avoiding forced binary choices when multiple bed types are possible.
Solution Approach 2:
The system uses feedback from actual patient outcomes and bed allocation results to continuously refine the predictive model. By analyzing whether predictions match actual needs, the model iteratively improves its accuracy, resolving the contradiction between efficiency and precision.
2Quantity of substance
If all non-occupied ICU beds are allocated to patients undergoing surgery, then bed availability is improved, but elective surgeries must be postponed
Solution Approach 1:
The system performs preliminary prediction of postoperative bed requirements before surgery scheduling. By accurately predicting which patients will need ICU beds versus PACU or general ward beds in advance, the system optimizes bed allocation plans, ensuring ICU beds are reserved for patients who actually need them while maintaining surgery schedules.
Solution Approach 2:
The bed allocation system dynamically adjusts based on real-time factors including surgical urgency, patient condition, and actual bed availability. This dynamic approach allows the system to maximize ICU bed utilization for critical cases while maintaining flexibility to accommodate elective surgeries when resources permit.
3Ease of operation
If a simple prediction system is used, then ease of operation is improved, but prediction reliability deteriorates
Solution Approach 1:
The patent introduces an intermediary layer of probability thresholds between the raw model output and final bed type assignment. This intermediary mechanism translates complex model predictions into actionable, reliable decisions, maintaining system simplicity while improving reliability through clear decision rules.
Solution Approach 2:
The system adjusts prediction parameters and thresholds based on hospital-specific characteristics, surgical specialty, and patient population. By optimizing parameters for each context, the system maintains simplicity in operation while achieving high reliability through customized parameter settings.
Data Source
AI summary
A system and method are provided for generating a predictive model for predicting a postoperative bed type to be used by a patient after surgery. The predictive model is trained on features extracted from medical data and using a postoperative bed type as prediction target in the training. The predictive model is configured to output a probability on a scale 400 which corresponds to, at its lower end, a prediction of a first postoperative bed type and, at its upper end, a prediction of a second postoperative bed. A hybrid model is generated which applies a lower 410 and an upper threshold 420 to the probability scale. If the output probability of the predictive model is in between both thresholds, an expert selection of the bed type is recommended, while otherwise, the prediction of the predictive model is output. The values of the thresholds are optimized using a performance metric.