Event Response Recommendation System for Patient Transfer
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Solution Overview
Problem
Current patient transfer processes in healthcare facilities during events like natural disasters or cyberattacks are manual, time-consuming, and inefficient, especially when multiple patients need to be transferred, which can be critical and lengthy, especially when time is of the essence.
Innovation Solution
An event response recommendation system identifies affected healthcare facilities and patients needing transfer, generates recommendations for optimal patient relocation, and automates the transfer process by interfacing with other healthcare systems to ensure patients are moved to facilities with matching care levels and availability, using machine-learning models for efficient decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual patient transfer processes are used during healthcare events, then staff can assess each patient individually, but the process becomes time-consuming and inefficient when multiple patients need transfer
Solution Approach 1:
The system segments the patient transfer process into distinct functional modules: event detection module, patient identification module, facility matching module, and transfer coordination module. Each module handles specific tasks independently, allowing parallel processing of multiple patients while maintaining individualized assessment quality through specialized algorithms for each segment.
Solution Approach 2:
An automated recommendation system acts as an intermediary between healthcare facilities and patients during transfers. This intermediary uses machine learning models to rapidly match patients with appropriate receiving facilities based on care levels, availability, and event conditions, dramatically reducing transfer time while maintaining assessment accuracy through sophisticated matching algorithms.
2Productivity
If automated systems are used to speed up patient transfers, then transfer time is reduced, but system complexity increases
Solution Approach 1:
The automated recommendation system is designed as a universal platform that can handle multiple patient transfer scenarios simultaneously - natural disasters, cyberattacks, resource shortages, and other healthcare events. The system integrates multiple functions including event detection, patient triage, facility matching, and transfer coordination into a single multi-functional architecture, reducing overall system complexity despite handling diverse situations.
Solution Approach 2:
The system incorporates continuous feedback loops where transfer outcomes, facility availability updates, and event status changes are fed back into the machine learning models. This feedback mechanism allows the system to self-optimize and adapt to changing conditions without requiring complex manual reconfiguration, maintaining high transfer speed while managing system complexity through automated learning and adjustment.
3Reliability
If comprehensive patient assessment is performed manually, then transfer appropriateness is ensured, but the process becomes lengthy and critical delays occur
Solution Approach 1:
The system performs preliminary actions by pre-establishing care level classifications, facility capability profiles, and transfer criteria before events occur. Machine learning models are pre-trained on historical transfer data, and facility databases are pre-populated with capacity and service information. When an event occurs, the system rapidly queries these pre-prepared resources instead of creating assessments from scratch, ensuring reliable transfer decisions are made quickly during critical events.
Data Source
AI summary
One embodiment provides a method, the method including: identifying, at an event response recommendation system, an event affecting a healthcare facility; identifying, using the event response recommendation system, at least one patient within the healthcare facility needing transferred due to the event; and generating, using the event response recommendation system, a recommendation for transferring the at least one patient. Other aspects are described and claimed.