A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study
BackgroundTo cope with the enormous burdens placed on health care systems around the world, from the strains and stresses caused by longer life expectancy to the large-scale emergency relief actions required by pandemics like COVID-19, many health care companies have been usi...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2024-11-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2024/1/e53986 |
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| _version_ | 1846151187083034624 |
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| author | Marlene Blaß Henner Gimpel Philip Karnebogen |
| author_facet | Marlene Blaß Henner Gimpel Philip Karnebogen |
| author_sort | Marlene Blaß |
| collection | DOAJ |
| description |
BackgroundTo cope with the enormous burdens placed on health care systems around the world, from the strains and stresses caused by longer life expectancy to the large-scale emergency relief actions required by pandemics like COVID-19, many health care companies have been using artificial intelligence (AI) to adapt their services. Nevertheless, conceptual insights into how AI has been transforming the health care sector are still few and far between. This study aims to provide an overarching structure with which to classify the various real-world phenomena. A clear and comprehensive taxonomy will provide consensus on AI-based health care service offerings and sharpen the view of their adoption in the health care sector.
ObjectiveThe goal of this study is to identify the design characteristics of AI-based health care services.
MethodsWe propose a multilayered taxonomy created in accordance with an established method of taxonomy development. In doing so, we applied 268 AI-based health care services, conducted a structured literature review, and then evaluated the resulting taxonomy. Finally, we performed a cluster analysis to identify the archetypes of AI-based health care services.
ResultsWe identified 4 critical perspectives: agents, data, AI, and health impact. Furthermore, a cluster analysis yielded 13 archetypes that demonstrate our taxonomy’s applicability.
ConclusionsThis contribution to conceptual knowledge of AI-based health care services enables researchers as well as practitioners to analyze such services and improve their theory-led design. |
| format | Article |
| id | doaj-art-7dc11dd6f68143b18fe0266e824f6182 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-7dc11dd6f68143b18fe0266e824f61822024-11-27T19:15:32ZengJMIR PublicationsJournal of Medical Internet Research1438-88712024-11-0126e5398610.2196/53986A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative StudyMarlene Blaßhttps://orcid.org/0009-0009-3551-5980Henner Gimpelhttps://orcid.org/0000-0003-1730-2614Philip Karnebogenhttps://orcid.org/0000-0001-7314-9023 BackgroundTo cope with the enormous burdens placed on health care systems around the world, from the strains and stresses caused by longer life expectancy to the large-scale emergency relief actions required by pandemics like COVID-19, many health care companies have been using artificial intelligence (AI) to adapt their services. Nevertheless, conceptual insights into how AI has been transforming the health care sector are still few and far between. This study aims to provide an overarching structure with which to classify the various real-world phenomena. A clear and comprehensive taxonomy will provide consensus on AI-based health care service offerings and sharpen the view of their adoption in the health care sector. ObjectiveThe goal of this study is to identify the design characteristics of AI-based health care services. MethodsWe propose a multilayered taxonomy created in accordance with an established method of taxonomy development. In doing so, we applied 268 AI-based health care services, conducted a structured literature review, and then evaluated the resulting taxonomy. Finally, we performed a cluster analysis to identify the archetypes of AI-based health care services. ResultsWe identified 4 critical perspectives: agents, data, AI, and health impact. Furthermore, a cluster analysis yielded 13 archetypes that demonstrate our taxonomy’s applicability. ConclusionsThis contribution to conceptual knowledge of AI-based health care services enables researchers as well as practitioners to analyze such services and improve their theory-led design.https://www.jmir.org/2024/1/e53986 |
| spellingShingle | Marlene Blaß Henner Gimpel Philip Karnebogen A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study Journal of Medical Internet Research |
| title | A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study |
| title_full | A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study |
| title_fullStr | A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study |
| title_full_unstemmed | A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study |
| title_short | A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study |
| title_sort | taxonomy and archetypes of ai based health care services qualitative study |
| url | https://www.jmir.org/2024/1/e53986 |
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