Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care.
Application development
Artificial intelligence
Patient engagement
Patient perspective
Qualitative research
Journal
BMC health services research
ISSN: 1472-6963
Titre abrégé: BMC Health Serv Res
Pays: England
ID NLM: 101088677
Informations de publication
Date de publication:
26 Oct 2023
26 Oct 2023
Historique:
received:
23
05
2023
accepted:
01
10
2023
medline:
30
10
2023
pubmed:
27
10
2023
entrez:
26
10
2023
Statut:
epublish
Résumé
Artificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care. We conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis. We found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development. Given the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.
Sections du résumé
BACKGROUND
BACKGROUND
Artificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care.
METHODS
METHODS
We conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis.
RESULTS
RESULTS
We found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development.
CONCLUSION
CONCLUSIONS
Given the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.
Identifiants
pubmed: 37884940
doi: 10.1186/s12913-023-10098-2
pii: 10.1186/s12913-023-10098-2
pmc: PMC10605984
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1163Informations de copyright
© 2023. The Author(s).
Références
J Med Internet Res. 2021 Aug 26;23(8):e26162
pubmed: 34236994
BMC Health Serv Res. 2014 Feb 26;14:89
pubmed: 24568690
Digit Health. 2019 Aug 21;5:2055207619871808
pubmed: 31467682
BMJ. 2021 Mar 15;372:n304
pubmed: 33722847
Nature. 2017 Jun 28;546(7660):686
pubmed: 28658222
JMIR Ment Health. 2018 Dec 13;5(4):e64
pubmed: 30545815
J Nurs Adm. 2016 Mar;46(3 Suppl):S11-8
pubmed: 26906687
Health Expect. 2018 Dec;21(6):1075-1084
pubmed: 30062858
Health Res Policy Syst. 2018 Feb 7;16(1):5
pubmed: 29415734
CJC Open. 2019 Mar 29;1(2):43-46
pubmed: 32159082
EGEMS (Wash DC). 2016 Mar 07;4(1):1163
pubmed: 27141516
Biomed Instrum Technol. 2012 Fall;Suppl:49-56
pubmed: 23039777
JMIR Nurs. 2021 Jan 28;4(1):e23933
pubmed: 34345794
Science. 2017 Jul 7;357(6346):19
pubmed: 28684481
Pac Symp Biocomput. 2021;26:351-355
pubmed: 33691033
Lancet Digit Health. 2020 Mar;2(3):e111-e112
pubmed: 33328081
Pak J Med Sci. 2020 Jul-Aug;36(5):857-859
pubmed: 32704252
Health Expect. 2015 Oct;18(5):1151-66
pubmed: 23731468
JMIR Cancer. 2019 May 02;5(1):e12856
pubmed: 31045505
BMC Health Serv Res. 2017 Aug 7;17(1):539
pubmed: 28784138
Implement Sci. 2014 Feb 20;9:24
pubmed: 24555508
Patterns (N Y). 2022 Apr 13;3(6):100489
pubmed: 35755876
Res Involv Engagem. 2015 Jul 31;1:6
pubmed: 29062495
NPJ Digit Med. 2020 Jun 19;3:86
pubmed: 32577533
Health Expect. 2020 Feb;23(1):5-18
pubmed: 31489988
JAMA Netw Open. 2018 Aug 3;1(4):e181018
pubmed: 30646095
Health Expect. 2014 Oct;17(5):637-50
pubmed: 22809132
Res Involv Engagem. 2019 Jun 04;5:18
pubmed: 31183162
Nat Med. 2018 Sep;24(9):1342-1350
pubmed: 30104768
Qual Saf Health Care. 2010 Oct;19 Suppl 3:i68-74
pubmed: 20959322
J Med Educ Curric Dev. 2021 Sep 6;8:23821205211036836
pubmed: 34778562