Personalized Nurse Shift Assignments for Staff Retention
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Solution Overview
Problem
Healthcare providers face significant challenges with high worker turnover, particularly among nursing staff, leading to substantial financial costs and declines in patient care quality due to the need for replacing and training new hires, as existing scheduling methods do not account for personalized nurse attributes and working conditions.
Innovation Solution
A system utilizing computer-implemented machine learning models to predict nurse resignation likelihood based on individual attributes and working conditions, optimizing schedules to minimize turnover by creating preferred work assignments that align with each nurse's preferences and reducing exposure to high-risk conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling methods are used, then scheduling simplicity is maintained, but nurse turnover increases and staff retention deteriorates
Solution Approach 1:
The patent replaces manual scheduling methods with an automated machine learning-based system that processes nurse attributes, shift constraints, and historical data to generate optimized schedules. This substitution of mechanical/manual processes with intelligent algorithms resolves the contradiction by automating complex analysis while maintaining system reliability for staff retention.
Solution Approach 2:
The system enables self-service scheduling by allowing nurses to input their preferences and attributes, which the machine learning model then uses to automatically generate personalized schedules. This self-service approach improves retention by giving nurses autonomy while the system handles the complexity of optimization independently.
2Reliability
If personalized work assignments are created using machine learning models, then nurse satisfaction and retention improve, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing nurse attributes, shift constraints, and historical data before the actual scheduling optimization. Machine learning models are trained in advance on historical turnover data, and nurse profiles are pre-analyzed. This preliminary preparation reduces the computational burden during runtime, resolving the contradiction between personalized scheduling benefits and energy consumption.
Solution Approach 2:
The system implements partial optimization by focusing computational resources on the most critical factors influencing nurse turnover, such as key shift constraints and nurse preferences, rather than optimizing every possible parameter. This selective approach maintains retention benefits while reducing overall computational energy requirements.
3Reliability
If shift constraints are optimized for individual nurses, then resignation probability decreases, but scheduling flexibility and adaptability reduce
Solution Approach 1:
The scheduling system is designed to be dynamic and adaptive, allowing shift constraints and nurse attributes to be updated in real-time. The machine learning model can re-optimize schedules as new information becomes available or conditions change, resolving the contradiction by making the system flexible rather than rigid while maintaining its ability to reduce resignation probability through personalized optimization.
Solution Approach 2:
The system changes parameters by adjusting shift constraints and work assignment variables based on individual nurse attributes and preferences. Rather than fixing schedules, the system dynamically modifies parameters like shift timing, duration, and type to optimize for each nurse while maintaining overall scheduling flexibility through controlled parameter variation.
Data Source
AI summary
An example of a method of creating preferred work assignments for nursing workers includes receiving a first set of attributes for a first nurse including one or more attributes that describe the first nurse, receiving a plurality of shift constraints for a plurality of shift variables, generating a preferred work assignment, and outputting an indication of the preferred work assignment. The preferred work assignment is generated by optimizing, using an optimization algorithm, shift variables for the first nurse using a computer-implemented machine learning model, the first set of nurse attributes, and the plurality of shift constraints. The first preferred work assignment is predicted by the optimization algorithm to reduce a first resignation probability of the first nurse according to outputs from the computer-implemented machine learning model and the computer-implemented machine learning model is configured to relate resignation likelihood to shift variables and nurse attributes.