Healthcare Staffing Allocation Using AI for Cross-Entity Scheduling
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Solution Overview
Problem
Current work force scheduling in healthcare entities is inefficient and ineffective, often relying on historical practices and location-specific methods, leading to staff shortages and limited access to services, without considering individual employee skills, preferences, or dependencies, resulting in increased costs and diminished patient care.
Innovation Solution
A method and system for allocating staffing resources across multiple healthcare entities using data analytics and artificial intelligence to identify staffing surpluses and demands by analyzing structured and unstructured data, including staff qualifications, client demographics, and operational status, and reallocating resources to entities with the greatest need.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual scheduling methods are used, then ease of operation is maintained, but productivity and staffing efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical scheduling processes with an automated AI-based system that uses machine learning models to predict staffing needs and optimize schedules. The system processes structured and unstructured data from multiple sources to automatically generate optimized schedules, eliminating the need for manual calling and coordination while significantly improving staffing efficiency and productivity.
Solution Approach 2:
The scheduling system performs self-service by automatically analyzing data from multiple operating entities, predicting staffing demands, and generating optimized schedules without requiring manual intervention. The AI model continuously learns from historical data and adjusts scheduling decisions autonomously, reducing the need for human involvement in the scheduling process while maintaining high productivity.
2Adaptability or versatility
If location-specific scheduling is used, then ease of operation is maintained, but adaptability across multiple entities deteriorates
Solution Approach 1:
The patent implements a universal scheduling system that operates across multiple operating entities (hospitals, clinics, pharmacies) rather than being location-specific. The AI model processes data from diverse sources including staff qualifications, client demographics, operational status, and historical patterns to generate optimized schedules that adapt to different entities' unique characteristics while maintaining a unified approach, thereby improving cross-entity adaptability.
Solution Approach 2:
The system segments the scheduling problem by processing data from multiple operating entities separately while maintaining an integrated view. The AI model analyzes each entity's specific characteristics, staff pool, and operational requirements independently, then combines these analyses to create optimized schedules that consider the broader multi-entity context, enabling both localized customization and global optimization.
3Measurement precision
If binary staffing allocation is used, then ease of operation is maintained, but measurement precision of staffing needs deteriorates
Solution Approach 1:
The patent transforms the binary staffing allocation approach into a multi-parameter optimization system. The AI model considers numerous factors including staff qualifications, client demographics, operational status, historical data, and predicted demands to continuously adjust staffing allocations. This multi-parameter approach enables precise measurement and assessment of staffing needs by analyzing the impact of various parameters on service delivery and outcomes, rather than relying on simple binary decisions.
Solution Approach 2:
The system implements continuous feedback loops where the AI model analyzes actual performance data, service delivery metrics, and operational outcomes to refine its staffing predictions and allocations. Historical data from past scheduling decisions and their results are fed back into the model to improve its accuracy over time, enabling precise measurement of staffing needs through iterative learning and adjustment based on real-world performance.
Data Source
AI summary
Described herein are systems and methods for allocating staffing resources across multiple operating entities. In some examples, the method includes: (a) retrieving data from a plurality of operating entities; (b) sorting the retrieved data into data groups of retrieved data subsets; (c) providing the data group with a data group identifier; (d) comparing each of the data group identifiers with stored data group identifiers of stored data groups which are stored on at least one memory device; (e) adding that retrieved data subset to one of the stored data groups or generating a new stored data group; (f) identifying a staffing resource surplus for a particular time frame; (g) determining a staffing demand for the particular time frame for at least one of the plurality of operating entities; and (h) allocating the staffing resource surplus to the operating entity having the greatest staffing demand.