Self-Updating Procedure Duration Estimation for Patient Scheduling
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Solution Overview
Problem
Current patient scheduling systems rely on static procedure duration estimates, which can lead to underutilization or overutilization of resources due to variations in medical procedures, causing delays, overtime, and impacting care quality.
Innovation Solution
A self-updating procedure duration estimation system that analyzes historical data using Exponentially Weighted Moving Average (EWMA) to provide users with default, expected, pessimistic, and optimistic duration options, allowing for more informed scheduling decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static procedure duration values are used for scheduling, then scheduling simplicity is maintained, but resource utilization efficiency deteriorates due to variations in actual procedure durations
Solution Approach 1:
The system transitions from static procedure duration values to dynamic duration estimates that are continuously updated based on historical data. The scheduling engine automatically adjusts procedure durations using exponentially weighted moving average calculations, allowing the system to adapt to variations in actual procedure times while maintaining ease of operation through automated updates.
Solution Approach 2:
The scheduling system performs self-updating of procedure duration estimates without requiring manual intervention. The scheduling engine automatically collects historical duration data, calculates updated estimates using EWMA, and applies these to future scheduling decisions, enabling the system to improve its own performance autonomously.
2Reliability
If protective capacity is added to schedules to manage variation, then care quality is maintained, but schedule efficiency deteriorates due to underutilized assets and staff
Solution Approach 1:
The system implements a feedback mechanism where actual procedure durations are continuously monitored and fed back to the scheduling engine. This feedback loop allows the system to learn from past performance and adjust future schedules more accurately, reducing the need for excessive protective capacity while maintaining care quality through data-driven scheduling decisions.
3Measurement precision
If more detailed procedure data is collected and analyzed, then scheduling accuracy is improved, but system complexity increases
Solution Approach 1:
The system replaces complex manual scheduling processes with an automated computational engine that uses exponentially weighted moving average algorithms. This substitution of mechanical/manual operations with automated computational methods enables sophisticated data analysis and continuous optimization without proportionally increasing operational complexity.
Data Source
AI summary
Certain embodiments provide systems and methods for self-updating procedure duration estimation for patient scheduling. A scheduling system for scheduling procedures in a clinical environment includes historical procedure data including stored duration values for a procedure. The scheduling system also includes a user interface providing scheduling and procedure duration information to a user for scheduling the procedure for a patient. The scheduling system further includes a scheduling engine estimating one or more duration values for the procedure for scheduling the procedure for a patient. The scheduling engine establishes a default duration value for a procedure and updating the default duration value based on the historical procedure duration data. The scheduling engine provides a plurality of procedure durations including the updated default duration value to the user for selection via the user interface. The scheduling engine schedules a procedure using a user selected value for procedure duration.