Automated Emergency Call Triage System
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Solution Overview
Problem
Current emergency response systems often rely on limited information during emergency calls, leading to inefficient allocation of resources and potential overutilization of services, as decisions are made without access to comprehensive patient data, which may change over time.
Innovation Solution
A computer-implemented method and system that extracts features from emergency call speech and combines them with patient medical data to predict acuity levels and determine appropriate actions, including dispatching responders or transporting patients, using predictive models and automated triaging processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If decisions are made based on limited information during emergency calls, then the emergency response system operates with simple processes, but resource allocation becomes inefficient and overutilization occurs
Solution Approach 1:
The system performs preliminary actions by gathering and analyzing patient data before the emergency call decision-making process. Electronic health records, medication lists, and previous medical history are retrieved and integrated with call information in advance, enabling more informed triage decisions without delaying the emergency response.
Solution Approach 2:
The system implements feedback mechanisms where the automated triage system continuously receives updates from multiple sources (call information, EHR data, sensor data) and refines its acuity level predictions. This feedback loop ensures that decisions are based on the most complete and current information available, improving resource allocation efficiency.
2Productivity
If automated triaging systems are implemented to improve decision-making, then resource allocation efficiency improves, but system complexity increases
Solution Approach 1:
The automated triage system is designed as a multi-functional platform that handles multiple tasks: speech processing from emergency calls, electronic health record retrieval, sensor data integration, acuity level prediction, and action recommendation. This universal system consolidates what would otherwise require multiple separate systems, managing complexity through integration rather than proliferation of components.
Solution Approach 2:
The system performs self-service by automatically gathering data from multiple sources, processing information through predictive models, and generating triage recommendations without requiring manual intervention from dispatchers. This automation reduces the operational complexity burden on human operators while maintaining high resource allocation efficiency.
3Measurement precision
If multiple assessments are performed over time, then patient acuity level prediction accuracy improves, but time consumption increases
Solution Approach 1:
The system maintains continuous assessment by continuously monitoring and integrating new information as it becomes available during the emergency response process. Rather than performing discrete, time-consuming assessments, the system continuously updates acuity level predictions based on incoming data from calls, EHR updates, and sensor readings, providing accurate predictions without significant time delays.
Data Source
AI summary
According to an aspect, there is provided an apparatus (500; FIG. 5) for determining an action to be taken in response to an emergency call, the apparatus comprising: a processor (502; FIG. 5) configured to receive an indication that an emergency call has been initiated; extract, from speech transmitted as part of the emergency call, features indicative of a medical condition or event relating to a subject; obtain medical data relating to the subject; predict, based on the extracted features and the medical data, an acuity level of the subject; determine, based on the acuity level, an action to be taken in respect of the subject; and provide an indication of the determined action for presentation to a recipient.