Cargando…

Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study

BACKGROUND: The emergence of artificial intelligence (AI) in health care has impacted health care systems, including employment, training, education, and professional regulation. It is incumbent on health professional associations to assist their membership in defining and preparing for AI-related c...

Descripción completa

Detalles Bibliográficos
Autores principales: Gillan, Caitlin, Hodges, Brian, Wiljer, David, Dobrow, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173392/
https://www.ncbi.nlm.nih.gov/pubmed/34009136
http://dx.doi.org/10.2196/27340
_version_ 1783702714691092480
author Gillan, Caitlin
Hodges, Brian
Wiljer, David
Dobrow, Mark
author_facet Gillan, Caitlin
Hodges, Brian
Wiljer, David
Dobrow, Mark
author_sort Gillan, Caitlin
collection PubMed
description BACKGROUND: The emergence of artificial intelligence (AI) in health care has impacted health care systems, including employment, training, education, and professional regulation. It is incumbent on health professional associations to assist their membership in defining and preparing for AI-related change. Health professional associations, or the national groups convened to represent the interests of the members of a profession, play a unique role in establishing the sociocultural, normative, and regulative elements of health care professions. OBJECTIVE: The aim of this paper is to present a protocol for a proposed study of how, when faced with AI as a disruptive technology, health professional associations engage in sensemaking and legitimization of change to support their membership in preparing for future practice. METHODS: An exploratory multi-case study approach will be used. This study will be informed by the normalization process theory (NPT), which suggests behavioral constructs required for complex change, providing a novel lens through which to consider the agency of macrolevel actors in practice change. A total of 4 health professional associations will be studied, each representing an instrumental case and related fields selected for their early consideration of AI technologies. Data collection will consist of key informant interviews, observation of relevant meetings, and document review. Individual and collective sensemaking activities and action toward change will be identified using stakeholder network mapping. A hybrid inductive and deductive model will be used for a concurrent thematic analysis, mapping emergent themes against the NPT framework to assess fit and identify areas of discordance. RESULTS: As of January 2021, we have conducted 17 interviews, with representation across the 4 health professional associations. Of these 17 interviews, 15 (88%) have been transcribed. Document review is underway and complete for one health professional association and nearly complete for another. Observation opportunities have been challenged by competing priorities during COVID-19 and may require revisiting. A linear cross-case analytic approach will be taken to present the data, highlighting both guidance for the implementation of AI and implications for the application of NPT at the macro level. The ability to inform consideration of AI will depend on the degree to which the engaged health professional associations have considered this topic at the time of the study and, hence, what priority it has been assigned within the health professional association and what actions have been taken to consider or prepare for it. The fact that this may differ between health professional associations and practice environments will require consideration throughout the analysis. CONCLUSIONS: Ultimately, this protocol outlines a case study approach to understand how, when faced with AI as a disruptive technology, health professional associations engage in sensemaking and legitimization of change to support their membership in preparing for future practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/27340
format Online
Article
Text
id pubmed-8173392
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-81733922021-06-11 Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study Gillan, Caitlin Hodges, Brian Wiljer, David Dobrow, Mark JMIR Res Protoc Protocol BACKGROUND: The emergence of artificial intelligence (AI) in health care has impacted health care systems, including employment, training, education, and professional regulation. It is incumbent on health professional associations to assist their membership in defining and preparing for AI-related change. Health professional associations, or the national groups convened to represent the interests of the members of a profession, play a unique role in establishing the sociocultural, normative, and regulative elements of health care professions. OBJECTIVE: The aim of this paper is to present a protocol for a proposed study of how, when faced with AI as a disruptive technology, health professional associations engage in sensemaking and legitimization of change to support their membership in preparing for future practice. METHODS: An exploratory multi-case study approach will be used. This study will be informed by the normalization process theory (NPT), which suggests behavioral constructs required for complex change, providing a novel lens through which to consider the agency of macrolevel actors in practice change. A total of 4 health professional associations will be studied, each representing an instrumental case and related fields selected for their early consideration of AI technologies. Data collection will consist of key informant interviews, observation of relevant meetings, and document review. Individual and collective sensemaking activities and action toward change will be identified using stakeholder network mapping. A hybrid inductive and deductive model will be used for a concurrent thematic analysis, mapping emergent themes against the NPT framework to assess fit and identify areas of discordance. RESULTS: As of January 2021, we have conducted 17 interviews, with representation across the 4 health professional associations. Of these 17 interviews, 15 (88%) have been transcribed. Document review is underway and complete for one health professional association and nearly complete for another. Observation opportunities have been challenged by competing priorities during COVID-19 and may require revisiting. A linear cross-case analytic approach will be taken to present the data, highlighting both guidance for the implementation of AI and implications for the application of NPT at the macro level. The ability to inform consideration of AI will depend on the degree to which the engaged health professional associations have considered this topic at the time of the study and, hence, what priority it has been assigned within the health professional association and what actions have been taken to consider or prepare for it. The fact that this may differ between health professional associations and practice environments will require consideration throughout the analysis. CONCLUSIONS: Ultimately, this protocol outlines a case study approach to understand how, when faced with AI as a disruptive technology, health professional associations engage in sensemaking and legitimization of change to support their membership in preparing for future practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/27340 JMIR Publications 2021-05-19 /pmc/articles/PMC8173392/ /pubmed/34009136 http://dx.doi.org/10.2196/27340 Text en ©Caitlin Gillan, Brian Hodges, David Wiljer, Mark Dobrow. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 19.05.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.
spellingShingle Protocol
Gillan, Caitlin
Hodges, Brian
Wiljer, David
Dobrow, Mark
Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study
title Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study
title_full Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study
title_fullStr Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study
title_full_unstemmed Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study
title_short Health Care Professional Association Agency in Preparing for Artificial Intelligence: Protocol for a Multi-Case Study
title_sort health care professional association agency in preparing for artificial intelligence: protocol for a multi-case study
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173392/
https://www.ncbi.nlm.nih.gov/pubmed/34009136
http://dx.doi.org/10.2196/27340
work_keys_str_mv AT gillancaitlin healthcareprofessionalassociationagencyinpreparingforartificialintelligenceprotocolforamulticasestudy
AT hodgesbrian healthcareprofessionalassociationagencyinpreparingforartificialintelligenceprotocolforamulticasestudy
AT wiljerdavid healthcareprofessionalassociationagencyinpreparingforartificialintelligenceprotocolforamulticasestudy
AT dobrowmark healthcareprofessionalassociationagencyinpreparingforartificialintelligenceprotocolforamulticasestudy