Towards value creation with artificial intelligence in healthcare: a qualitative study on user requirements
- In recent years, artificial intelligence (AI) has emerged as a promising technology for healthcare. As a result, incumbents and startups entering the healthcare market with AI-enabled products need to explore the user requirements of medical professionals to enable reliable and satisfactory solutions. To overcome barriers and channel AI for value creation, we examine how startups and incumbents in the healthcare industry are innovating their business models to create value with AI. We conducted a qualitative interview study in which we investigate medical professionals from a provider perspective and representatives of medical companies. In the first round of data collection, we developed a holistic view of the specific requirements for AI-enabled medical devices in radiology (organizational, regulatory, product, communicative, and financial). In the second round, we plan to explore how companies can respond to the previously identified requirements through business model innovationIn recent years, artificial intelligence (AI) has emerged as a promising technology for healthcare. As a result, incumbents and startups entering the healthcare market with AI-enabled products need to explore the user requirements of medical professionals to enable reliable and satisfactory solutions. To overcome barriers and channel AI for value creation, we examine how startups and incumbents in the healthcare industry are innovating their business models to create value with AI. We conducted a qualitative interview study in which we investigate medical professionals from a provider perspective and representatives of medical companies. In the first round of data collection, we developed a holistic view of the specific requirements for AI-enabled medical devices in radiology (organizational, regulatory, product, communicative, and financial). In the second round, we plan to explore how companies can respond to the previously identified requirements through business model innovation and identified risks. We contribute to research and practice.…
| Author: | Maximilian Grüning, Dennis Renee MetzlerGND, Manuel TrenzGND |
|---|---|
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/108437 |
| URL: | https://aisel.aisnet.org/pacis2023/119 |
| Parent Title (English): | Pacific Asia Conference on Information Systems (PACIS), July 8-12th, 2023, Nanchang, China |
| Publisher: | AISeL |
| Place of publication: | New York, NY |
| Editor: | Patrick Chau, Jack Jing, Mikko Siponen, Andrew Burton-Jones, Chuan-Hoo Tan, Bo Sophia Xiao |
| Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2023 |
| Release Date: | 2023/10/17 |
| First Page: | 119 |
| Institutes: | Wirtschaftswissenschaftliche Fakultät |
| Wirtschaftswissenschaftliche Fakultät / Institut für Betriebswirtschaftslehre | |
| Wirtschaftswissenschaftliche Fakultät / Institut für Betriebswirtschaftslehre / Lehrstuhl für Betriebswirtschaftslehre mit dem Schwerpunkt Financial Data Analytics | |
| Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
