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Capturing Artificial Intelligence Applications’ Value Proposition in Healthcare : A Qualitative Research Study

Title data

Hennrich, Jasmin ; Ritz, Eva ; Hofmann, Peter ; Urbach, Nils:
Capturing Artificial Intelligence Applications’ Value Proposition in Healthcare : A Qualitative Research Study.
In: BMC Health Services Research. Vol. 24 (2024) . - 420.
ISSN 1472-6963
DOI: https://doi.org/10.1186/s12913-024-10894-4

Official URL: Volltext

Project information

Project title:
Project's official title
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Projektgruppe WI Künstliche Intelligenz
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Abstract in another language

Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications’ potential.

We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.

Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.

We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Artificial Intelligence; Value propositions; Business objectives; Healthcare
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration VII - Information Systems Management and Digital Society
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration VII - Information Systems Management and Digital Society > Chair Business Administration VII - Information Systems Management and Digital Society - Univ.-Prof. Dr. Torsten Eymann
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT
Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 10 Apr 2024 12:02
Last Modified: 10 Apr 2024 12:02
URI: https://eref.uni-bayreuth.de/id/eprint/89288