Automated Care Provider Assignment Engine
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Solution Overview
Problem
Manual assignment of healthcare providers to patients in hospitals often results in inconsistent and suboptimal matches due to lack of systematic tools, leading to potential misqualification and infectious exposure risks, as well as workload imbalances.
Innovation Solution
A system and method for automatically generating evidence-based assignments using a rules-based engine that compares patient needs with provider factors, including qualifications, workload, and infectious exposure, to optimize matches and prevent contraindications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual assignment methods are used, then ease of operation is maintained, but assignment quality and consistency deteriorate
Solution Approach 1:
The system enables self-service through automated provider-to-patient assignment where the system independently evaluates provider qualifications, patient needs, and matching criteria without requiring manual intervention. The automated system performs the entire assignment process by comparing provider skill sets against patient requirements and assigning matches based on predefined clinical guidelines, thereby maintaining ease of operation while significantly improving assignment quality and consistency.
Solution Approach 2:
The patent replaces the mechanical manual assignment process with an automated computer-based system that uses algorithms to match providers with patients. The system substitutes human decision-making with computational processes that systematically evaluate multiple criteria including provider certifications, patient acuity levels, and workload balances, eliminating the inconsistencies inherent in manual methods while preserving operational simplicity through automated rule-based logic.
2Reliability
If automated assignment systems are implemented, then assignment quality and consistency improve, but device complexity increases
Solution Approach 1:
The system segments the complex assignment process into distinct modular components: provider qualification verification module, patient needs assessment module, matching algorithm module, and assignment validation module. Each component handles a specific aspect of the assignment process independently, making the overall system more manageable and easier to implement despite the automated nature. This segmentation reduces the perceived complexity by breaking down the assignment task into discrete, manageable functions.
Solution Approach 2:
The patent employs parameter changes by using adjustable weighting factors and thresholds in the matching algorithm. The system can modify the importance of different criteria such as provider experience level, patient acuity, or workload balance by changing parameter values rather than restructuring the entire system. This flexibility allows the system to adapt to different hospital policies and priorities without increasing structural complexity, as parameters can be tuned through configuration rather than requiring system redesign.
3Ease of operation
If manual assignment processes are used, then operational simplicity is maintained, but workload balance and safety deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor provider workloads and patient assignments in real-time. The system receives feedback on current assignment status, provider availability, and patient condition changes, and automatically adjusts assignments to maintain optimal workload balance and safety. This feedback loop ensures that the system responds dynamically to changing conditions, preventing workload imbalances and safety issues before they arise, while maintaining operational simplicity through automated real-time adjustments.
Solution Approach 2:
The patent implements preliminary action by pre-evaluating provider qualifications and patient needs before assignments are made. The system performs advance screening of provider certifications, skill sets, and availability, as well as assessment of patient acuity and specific care requirements, before the actual assignment occurs. This preliminary evaluation phase identifies potential mismatches and safety risks in advance, allowing the system to prevent harmful assignments before they happen, thereby maintaining operational simplicity while eliminating safety concerns associated with manual assignment processes.
Data Source
AI summary
Care provider assignments to a patient may be automatically generated based on clinical evidence, documentation, workload, infectious status and other factors. The patient's chart may be accessed by a rules-based engine configured with rules to relate a patient's clinical status and needs to qualifications, certifications, capabilities and skills of care providers to select the care provider best qualified to assign to the patient. For instance, care providers having specialized training may be identified for assignment to patients presenting with specialized needs. Graphical displays of available providers may be displayed to and overridden by a manager. Because patient needs are automatically aligned with provider capabilities, availability and other factors, the errors, oversights and inefficiencies of manual or informal assignment systems are avoided and better health care delivery can be realized.