Machine Learning Patient Placement System
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Solution Overview
Problem
Current patient transfer systems between medical facilities are inefficient and prone to errors due to manual processes and lack of real-time optimization, failing to effectively analyze complex scenarios and evaluate multiple facilities simultaneously.
Innovation Solution
A machine learning system that receives patient data and facility metrics to match patients with appropriate treatment facilities based on capacity, location, and capabilities, determining likelihoods of acceptance and providing user-friendly visual indicators for selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes and simple rule sets are used for patient transfer, then the system is easy to operate, but the productivity and efficiency of patient transfer are poor
Solution Approach 1:
The patent replaces manual mechanical processes (phone calls, human workflow) with an automated machine learning system that uses algorithms to match patients with appropriate facilities. The system automatically processes patient data, evaluates facility capabilities, and determines placement decisions, substituting human labor with computational processes that dramatically improve productivity while maintaining ease of operation through automated interfaces.
Solution Approach 2:
The machine learning system performs self-service by autonomously evaluating patient data against facility metrics, automatically generating match recommendations, and making placement decisions without requiring manual intervention. The system independently processes complex scenarios, optimizes resource allocation, and continuously learns from outcomes, enabling the transfer process to serve itself rather than relying on human operators.
2Device complexity
If manual processes and simple rule sets are used for patient transfer, then the device complexity is low, but the reliability of patient transfer is poor
Solution Approach 1:
The patent replaces error-prone manual processes with a reliable machine learning system that systematically evaluates patient data against comprehensive facility metrics. The automated system eliminates human errors in data processing, consistently applies evaluation criteria, and provides auditable decision-making processes, thereby significantly improving reliability while managing complexity through standardized algorithms and data structures.
Solution Approach 2:
The machine learning system incorporates feedback mechanisms that continuously monitor transfer outcomes, facility capacity changes, and patient placement success rates. This feedback loop allows the system to learn from past decisions, adjust its matching algorithms, and improve reliability over time. The system uses historical data to refine its predictions and prevent recurring errors, creating a self-improving reliability mechanism.
3Device complexity
If current disparate systems are used for each building, then the device complexity is low, but the loss of time in patient transfer is high
Solution Approach 1:
The patent merges previously disparate building-level systems into a unified machine learning platform that handles patient transfers across the entire network. By consolidating data processing, facility evaluation, and placement decision-making into a single centralized system, the patent eliminates redundant manual processes at each building, enabling real-time optimization across all facilities and dramatically reducing transfer time while managing complexity through integration.
Solution Approach 2:
The machine learning system performs preliminary actions by pre-evaluating facility capabilities, maintaining real-time capacity metrics, and pre-establishing matching criteria before patient transfers are needed. The system proactively monitors facility status and prepares match recommendations in advance, so when a transfer is required, the process can be completed rapidly using pre-computed information rather than requiring time-consuming real-time assessments.
4Ease of operation
If manual calling and communication processes are used, then the ease of operation is maintained, but the loss of time and productivity are poor
Solution Approach 1:
The patent replaces manual calling and communication processes with automated electronic data exchange between facilities. The machine learning system electronically transmits patient data, receives facility responses, and coordinates transfers through digital communications, eliminating the time-consuming phone call process while maintaining operational simplicity through automated interfaces that require minimal user input.
Solution Approach 2:
The machine learning system enables continuous operation of the patient transfer process by eliminating interruptions inherent in manual communication. The system continuously processes patient data, maintains real-time connections with facilities, and can immediately initiate transfers without the breaks, delays, and human interruptions that characterize manual phone-based processes, thereby dramatically reducing transfer time while keeping the system easy to operate through automated workflows.
Data Source
AI summary
The present disclosure relates to systems and methods for determining one or more appropriate treatment facilities for patient placement. A machine learning system may perform operations. The operations may receive a patient data set including patient attributes and patient location metrics; access a plurality of data sets for a plurality of medical facilities, the plurality of data sets include facility capacity, location, and capability metrics for each of the plurality of medical facilities; apply a model to match the patient data set with at least one of the plurality of medical facility data sets based on at least one of the patient attributes and patient location metrics and at least one of the facility capacity, location, and capability metrics; determine, based on the application of the model, one or more likelihoods of acceptance associated with each match.