Patient Location Prediction via Procedure Transition Matrices
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Solution Overview
Problem
Current hospital technologies lack the ability to provide informed predictions of a patient's next location and procedures within the healthcare facility, leading to a lack of transparency for patients and families and inefficient communication between caregivers and patients regarding the patient's in-hospital journey.
Innovation Solution
A method and system that utilize a location-procedure co-occurrence matrix and procedure transition matrix to predict the next location and procedures for a patient by analyzing patient data, producing a probability-weighted outcome vector, and transmitting this information to a display device, enhancing communication and knowledge transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional hospital information systems are used to track patient locations and procedures, then basic record-keeping is maintained, but patients and families lack transparency and understanding of the patient's in-hospital journey
Solution Approach 1:
The patent introduces an intermediary prediction system that acts as a mediator between traditional hospital information systems and patients/families. This system processes clinical data through location-procedure embedding models and prediction algorithms to generate understandable forecasts of patient journey, transforming complex medical data into transparent, actionable information for stakeholders without requiring them to directly interact with complex medical systems
Solution Approach 2:
The patent replaces traditional mechanical information retrieval methods (manual tracking, paper records, basic EHR systems) with an intelligent prediction system using machine learning models. The location-procedure embedding model and prediction algorithms automatically analyze clinical data patterns to generate forecasts, substituting manual or simple automated systems with sophisticated computational intelligence that provides deeper transparency
2Loss of information
If caregivers and administrators communicate detailed information to patients and families during clinical workflows, then patient understanding improves, but caregivers and administrators do not have sufficient time due to multiple clinical workflows
Solution Approach 1:
The prediction system enables patients and families to self-serve by providing them with automated, personalized predictions of their loved one's journey. The system generates and delivers prediction information directly to stakeholders without requiring caregivers to manually explain each step, allowing patients/families to access information independently while caregivers focus on clinical care
Solution Approach 2:
The system performs preliminary analysis and generates prediction information in advance of clinical events. By continuously processing clinical data and generating forecasts proactively, the system prepares communication materials before caregivers need to discuss them with patients, eliminating the need for time-consuming ad-hoc explanations during critical clinical moments
3Adaptability or versatility
If a comprehensive prediction system is implemented to provide holistic view of patient journey, then patient experience and communication improve, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex prediction system into distinct modular components: a location-procedure embedding model that processes spatial and procedural data, separate prediction modules for different aspects of patient journey, and individual processing streams for different data types. This segmentation allows each component to specialize in specific tasks, reducing overall system complexity while maintaining comprehensive predictive capability
Data Source
AI summary
A method and system for predicting the next location for a patient in a healthcare facility, including: defining a location-procedure co-occurrence matrix for the healthcare facility, wherein the location-procedure co-occurrence matrix define the probability that a procedure will be performed in a specific location; defining a procedure transition matrix, wherein the procedure transition matrix defines the probability of moving from a first procedure to a second procedure; defining a patient input vector based upon the patient condition and procedures performed on the patient; calculating an output vector based upon the patient input vector and the procedure transition matrix; producing a procedure vector by setting all values in the output vector to zero except for the N highest values in the output vector, where N is an integer; calculating a location prediction vector based upon the procedure vector and the location-procedure co-occurrence matrix; and transmitting information regarding the M most likely next locations for the patient to a display device.