AI Healthcare Scheduling Optimizes Patient Outcomes
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Solution Overview
Problem
Existing healthcare scheduling systems fail to optimize employee schedules based on patient and employee attributes, leading to suboptimal patient outcomes and experiences.
Innovation Solution
A system and method that utilizes a computer-implemented machine learning model to simulate predicted outcome scores for employee combinations, selecting a preferred replacement employee to improve patient outcomes by considering employee and patient attributes, and automatically adjusting schedules to fill staffing gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling systems are used to assign healthcare employees, then scheduling simplicity is maintained, but patient outcomes are suboptimal
Solution Approach 1:
The patent replaces traditional manual scheduling mechanisms with an AI-based machine learning system that automatically analyzes employee and patient attributes to optimize schedule assignments, thereby improving patient outcomes without requiring complex human judgment processes
Solution Approach 2:
The scheduling system performs self-optimization by automatically generating and adjusting schedules based on input data from employee profiles and patient characteristics, eliminating the need for external intervention while maintaining high-quality decision-making
2Productivity
If manual schedule adjustment is performed when employees are absent, then scheduling flexibility is maintained, but time consumption increases
Solution Approach 1:
The system pre-calculates and stores optimal replacement assignments based on employee attributes and patient needs before absences occur, enabling immediate automated schedule adjustments when employees are absent without requiring time-consuming manual reevaluation
Solution Approach 2:
The AI scheduling system acts as an intermediary between employee availability and patient care needs, automatically matching replacements based on pre-analyzed compatibility data, thereby eliminating the time loss associated with manual matching processes
3Reliability
If employee schedules are optimized using AI-based prediction models, then patient outcomes are improved, but computational complexity increases
Solution Approach 1:
The system transforms complex qualitative attributes of employees and patients into quantifiable parameters that can be processed by machine learning models, enabling sophisticated outcome optimization through mathematical computation rather than complex procedural logic
Solution Approach 2:
The patent creates simplified digital representations (profiles) of employees and patients that capture essential attributes in a standardized format, allowing the computational model to efficiently process and analyze data without requiring complex real-world interpretations
Data Source
AI summary
A method of modifying a healthcare employee schedule includes receiving an indication that a pre-scheduled healthcare employee belonging to a first class of healthcare employees will be absent from a scheduled shift at a first healthcare facility, receiving a first plurality of patient health profiles for a first plurality of patients expected to visit the first healthcare facility during the scheduled shift, receiving a first plurality of employee profiles for a plurality of healthcare employees, simulating a first predicted outcome score for each employee of the plurality of healthcare employees using a simulator, selecting a preferred replacement employee from the plurality of healthcare employees based on the simulated first predicted outcome scores, and scheduling the preferred replacement employee during the scheduled shift. Each first predicted outcome score is simulated using a computer-implemented machine learning model configured to generate treatment outcome positivity values based on patient health profiles and employee profiles.