Multi-Resource Scheduling System for Clinical Facilities
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Solution Overview
Problem
Current scheduling systems in healthcare delivery institutions, such as hospitals, lack the ability to proactively manage variations in patient loads and resource availability, leading to delays, overtime, and reduced care quality due to inadequate integration of resource scheduling across multiple resources like staff, equipment, and facilities.
Innovation Solution
A multi-resource scheduling system that uses a processor to generate schedules for clinical facilities by identifying optimal time slots for tasks based on resource availability, sub-task durations, and constraints, incorporating historical data and expert knowledge to ensure resource availability and reduce scheduling risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling systems are used in healthcare facilities, then simplicity of operation is maintained, but resource availability and care quality deteriorate due to inability to proactively manage variations in patient loads and resource availability
Solution Approach 1:
The scheduling system performs preliminary actions by proactively identifying potential resource conflicts and scheduling adjustments before they impact care delivery. The system analyzes historical data, current schedules, and resource availability to predict and prevent scheduling failures, allowing healthcare facilities to maintain reliable resource availability without requiring complex real-time intervention mechanisms.
2Productivity
If manual scheduling methods are used, then system complexity is low, but productivity and care quality deteriorate due to delays, overtime, and inability to dynamically reschedule resources
Solution Approach 1:
The scheduling system enables self-service functionality by automatically generating optimized schedules, identifying resource conflicts, and suggesting rescheduling options without requiring manual intervention. The system serves itself by using its own data and algorithms to improve scheduling efficiency, thereby increasing productivity while keeping the user interface simple and easy to operate.
3Reliability
If schedules are generated without considering multiple resource constraints, then ease of operation is maintained, but reliability and care quality worsen due to resource unavailability and scheduling conflicts
Solution Approach 1:
The system merges multiple resource constraints and scheduling criteria into a unified scheduling model that simultaneously considers staff availability, equipment status, facility capacity, and care requirements. By combining these diverse constraints into a single integrated system, the patent achieves high schedule reliability while maintaining ease of operation through automated conflict resolution and optimization algorithms.
Data Source
AI summary
Systems and methods for multi-resource scheduling are disclosed and described. An example apparatus includes a scheduler engine configured to enable clinical system(s) to operate with the scheduler engine in an analytical mode and an operating mode. When in the analytical mode, the scheduler engine is to dynamically calculate one or more binding constraints on the one or more clinical systems for scheduling. When in the operating mode, the scheduler engine is to manage and output a schedule for the one or more clinical systems based on the one or more binding constraints calculated in the analytical mode. The example scheduler engine is to dynamically switch between the analytical mode and the operating mode based at least in part on a probabilistic determination of delay associated with the schedule.