Autonomous Vehicle-Integrated Emergency Medical Response using Salesforce Health Cloud: An AI-Driven Framework for Real-Time Health Crisis Detection and Hospital Routing

Autonomous vehicles open up a host of new possibilities to promote passenger safety through systems for intelligent health monitoring. This article describes a relatively simple implementation framework that connects real-time vital signs information from wearables with autonomous vehicle control so a passenger's emergency health status can be monitored and evaluated while traveling. The proposed system integrates with Salesforce Health Cloud (SHC) to provide approximately 70% of the required functionality through out-of-the-box capabilities, creating a robust care-coordination backbone. In this proposal, machine learning algorithms would continually analyze real-time data from multiple parameters of the passenger's physiological state, such as heart rate, blood oxygen levels, and motion patterns, to understand passive and active sickness or critical events (e.g., cardiac arrest or sudden loss of consciousness). If a critical health event is detected, the system would identify the original destination of the vehicle and then override it, navigating the car to the nearest medical facility (to the passenger) by the shortest route while simultaneously sending the patient's vital signs and estimated time of arrival to the hospital emergency departments in advance through Health Cloud's FHIR-aligned clinical data model and real-time eventing capabilities. The notification ahead of an imminent arrival gives medical personnel an advantage in planning for the patient's arrival for taking necessary action or invoking specialists, for example as medical professionals are often coupled to people's destinations and need to have personnel deployed before the patient arrives, to triage and save the person's life before the point of no return. The framework addresses important technical issues, such as processing real-time health data, minimizing false positives, securing communications protocols, and optimizing the travel route under variable traffic, while leveraging Health Cloud's native care-coordination workflows and compliance capabilities.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-15
DOI
https://doi.org/10.5281/zenodo.16883745
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
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article

Autonomous Vehicle-Integrated Emergency Medical Response using Salesforce Health Cloud: An AI-Driven Framework for Real-Time Health Crisis Detection and Hospital Routing

Zenodo (CERN European Organization for Nuclear Research)
ECG Monitoring and Analysis
article

Autonomous Vehicle-Integrated Emergency Medical Response using Salesforce Health Cloud: An AI-Driven Framework for Real-Time Health Crisis Detection and Hospital Routing

article en

Abstract

Autonomous vehicles open up a host of new possibilities to promote passenger safety through systems for intelligent health monitoring. This article describes a relatively simple implementation framework that connects real-time vital signs information from wearables with autonomous vehicle control so a passenger's emergency health status can be monitored and evaluated while traveling. The proposed system integrates with Salesforce Health Cloud (SHC) to provide approximately 70% of the required functionality through out-of-the-box capabilities, creating a robust care-coordination backbone. In this proposal, machine learning algorithms would continually analyze real-time data from multiple parameters of the passenger's physiological state, such as heart rate, blood oxygen levels, and motion patterns, to understand passive and active sickness or critical events (e.g., cardiac arrest or sudden loss of consciousness). If a critical health event is detected, the system would identify the original destination of the vehicle and then override it, navigating the car to the nearest medical facility (to the passenger) by the shortest route while simultaneously sending the patient's vital signs and estimated time of arrival to the hospital emergency departments in advance through Health Cloud's FHIR-aligned clinical data model and real-time eventing capabilities. The notification ahead of an imminent arrival gives medical personnel an advantage in planning for the patient's arrival for taking necessary action or invoking specialists, for example as medical professionals are often coupled to people's destinations and need to have personnel deployed before the patient arrives, to triage and save the person's life before the point of no return. The framework addresses important technical issues, such as processing real-time health data, minimizing false positives, securing communications protocols, and optimizing the travel route under variable traffic, while leveraging Health Cloud's native care-coordination workflows and compliance capabilities.

Zenodo (CERN European Organization for Nuclear Research)
Visvesvaraya Technological University (IN)
Openalex Percentile: Top 38%
ECG Monitoring and Analysis
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Autonomous Vehicle-Integrated Emergency Medical Response using Salesforce Health Cloud: An AI-Driven Framework for Real-Time Health Crisis Detection and Hospital Routing · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS