Probabilistic Alerting System for Hospital Resource Coordination
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Solution Overview
Problem
Current hospital operations management systems lack the ability to efficiently reconcile the interdependencies of capacity and resource allocation across departments, leading to inefficiencies and delays in patient care due to the inability to forecast multiple feasible futures and optimize resource utilization.
Innovation Solution
A system-wide probabilistic alerting and activation system that uses cross-departmental data integration and simulation-based prediction to optimize resource allocation and sequencing, enabling real-time and forecasted control of hospital operations, reducing wait times and improving throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional departmental transactional systems are used, then each department can operate independently with its own systems, but the ability to forecast multiple feasible futures and optimize resource allocation across departments is lost
Solution Approach 1:
The patent merges multiple departmental transactional systems into a unified probabilistic alerting and activation system that operates across the entire healthcare organization. This integration enables the system to forecast multiple feasible futures and optimize resource allocation by considering interdependencies between departments, thereby resolving the contradiction between independent operation and cross-departmental coordination.
Solution Approach 2:
The system provides universal functionality by serving multiple departments simultaneously through a single integrated platform. The probabilistic alerting and activation system can manage resources, forecast scenarios, and coordinate operations across various healthcare functions, making the system multi-functional and adaptable to different departmental needs while maintaining overall optimization capability.
2Productivity
If manual rules of thumb or standard policies are used, then the complexity of calculating millions of combinations is avoided, but wait times increase and hospital throughput is restricted
Solution Approach 1:
The patent replaces manual mechanical decision-making processes with an automated computer-based probabilistic simulation system. Instead of relying on human operators to manually calculate and evaluate millions of possible scenarios, the system uses computational algorithms to automatically forecast multiple feasible futures, evaluate outcomes, and optimize resource allocation, thereby increasing productivity while managing computational complexity through efficient algorithms.
Solution Approach 2:
The system transforms the approach by changing from deterministic rules to probabilistic parameters. Rather than using fixed manual policies, the system employs probabilistic forecasts that consider multiple possible future scenarios and their likelihoods. This parameter change enables the system to handle complexity statistically, optimizing throughput by making data-driven decisions based on predicted outcomes rather than rigid rules.
3Reliability
If slack is scheduled into core operations, then the inability to forecast multiple futures is compensated for, but wait times accumulate and patient experience deteriorates
Solution Approach 1:
The system performs preliminary action by forecasting multiple feasible futures before operations occur. The probabilistic alerting and activation system proactively identifies potential delays and resource constraints by simulating different scenarios in advance, allowing the system to prepare and optimize resource allocation before actual operations begin, thereby reducing the need for reactive slack scheduling and minimizing patient wait times.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor actual operations against forecasted scenarios. By comparing predicted outcomes with actual performance, the system can dynamically adjust resource allocation and operational plans, providing real-time feedback that improves reliability without requiring excessive slack time. This feedback loop enables the system to learn from past performance and optimize future operations, reducing accumulated wait times.
Data Source
AI summary
Systems, methods, and computer program products that enable system-wide probabilistic forecasting, alerting, optimizing and activating resources in the delivery of care to address both immediate (near real-time) conditions as well as probabilistic forecasted operational states of the system over an interval that is selectable from the current time to minutes, hours and coming days or weeks ahead are provided. There are multiple probabilistic future states that are implemented in these different time intervals and these may be implemented concurrently for an instant in time control, near term, and long term. Those forecasts along with their optimized control of hospital capacity may be independently calculated and optimized, such as for a dynamic workflow direction over the next hour and also a patient's stay over a period of days. In the present application, a probabilistic and conditional workflow reasoning system enabling complex team-based decisions that improve capacity, satisfaction, and safety is provided. A means to consume user(s) judgment, implement control on specific resource assignments and tasks in a clinical workflow is enabled, as is the dynamical and optimal control of the other care delivery assets being managed by the system so as to more probably achieve operating criteria such as throughput, waiting and schedule risk.