Sujan Mark, Thimbleby Harold, Habli Ibrahim, Cleve Andreas, Maaløe Lars, Rees Nigel
Human Factors Everywhere Ltd. ORCID iD: https://orcid.org/0000-0001-6895-946X.
Thimbleby Works.
Br Paramed J. 2022 Jun 1;7(1):36-42. doi: 10.29045/14784726.2022.06.7.1.36.
Early recognition of out-of-hospital cardiac arrest (OHCA) by ambulance service call centre operators is important so that cardiopulmonary resuscitation can be delivered immediately, but around 25% of OHCAs are not picked up by call centre operators. An artificial intelligence (AI) system has been developed to support call centre operators in the detection of OHCA. The study aims to (1) explore ambulance service stakeholder perceptions on the safety of OHCA AI decision support in call centres, and (2) develop a clinical safety case for the OHCA AI decision-support system.
The study will be undertaken within the Welsh Ambulance Service. The study is part research and part service evaluation. The research utilises a qualitative study design based on thematic analysis of interview data. The service evaluation consists of the development of a clinical safety case based on document analysis, analysis of the AI model and its development process and informal interviews with the technology developer.
AI presents many opportunities for ambulance services, but safety assurance requirements need to be understood. The ASSIST project will continue to explore and build the body of knowledge in this area.
救护服务呼叫中心操作员尽早识别院外心脏骤停(OHCA)很重要,这样才能立即进行心肺复苏,但约25%的院外心脏骤停未被呼叫中心操作员发现。已开发出一种人工智能(AI)系统,以支持呼叫中心操作员检测院外心脏骤停。本研究旨在:(1)探讨救护服务利益相关者对呼叫中心院外心脏骤停人工智能决策支持安全性的看法;(2)为院外心脏骤停人工智能决策支持系统制定临床安全案例。
本研究将在威尔士救护服务机构内进行。该研究兼具研究和服务评估的性质。该研究采用基于访谈数据主题分析的定性研究设计。服务评估包括基于文件分析、人工智能模型及其开发过程分析以及与技术开发者的非正式访谈来制定临床安全案例。
人工智能为救护服务带来了许多机遇,但需要了解安全保障要求。ASSIST项目将继续探索并积累该领域的知识体系。