Skip to main content
Top
Published in: BMC Health Services Research 1/2024

Open Access 01-12-2024 | Artificial Intelligence | Research

Capturing artificial intelligence applications’ value proposition in healthcare – a qualitative research study

Authors: Jasmin Hennrich, Eva Ritz, Peter Hofmann, Nils Urbach

Published in: BMC Health Services Research | Issue 1/2024

Login to get access

Abstract

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.
Appendix
Available only for authorised users
Literature
2.
go back to reference Rai A, Constantinides P, Sarker S. Next-Generation Digital Platforms: Toward Human–AI Hybrid. Manag Inf Syst Q. 2019;43(1):iii–ix. Rai A, Constantinides P, Sarker S. Next-Generation Digital Platforms: Toward Human–AI Hybrid. Manag Inf Syst Q. 2019;43(1):iii–ix.
3.
go back to reference Russell S, Norvig P. Artificial Intelligence: A Modern Approach. Pearson Education Limited; 2016. Russell S, Norvig P. Artificial Intelligence: A Modern Approach. Pearson Education Limited; 2016.
4.
go back to reference He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6.PubMedPubMedCentralCrossRef He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6.PubMedPubMedCentralCrossRef
6.
go back to reference Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, et al. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017;8(7):10883–90.PubMedCrossRef Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, et al. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017;8(7):10883–90.PubMedCrossRef
7.
go back to reference Rezazade Mehrizi MH, van Ooijen P, Homan M. Applications of artificial intelligence (AI) in diagnostic radiology: a technography study. Eur Radiol. 2020;31:1805–11.PubMedPubMedCentralCrossRef Rezazade Mehrizi MH, van Ooijen P, Homan M. Applications of artificial intelligence (AI) in diagnostic radiology: a technography study. Eur Radiol. 2020;31:1805–11.PubMedPubMedCentralCrossRef
8.
go back to reference Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here. Popul Health Manag. 2019;22(3):229–42.PubMedPubMedCentralCrossRef Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here. Popul Health Manag. 2019;22(3):229–42.PubMedPubMedCentralCrossRef
9.
go back to reference Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in Healthcare. Elsevier; 2020. p. 25–60. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in Healthcare. Elsevier; 2020. p. 25–60.
10.
go back to reference Yu K-H, Beam AL, Kohane IS. Artificial Intelligence in Healthcare. Nat Biomed Eng. 2018;2(10):719–31.PubMedCrossRef Yu K-H, Beam AL, Kohane IS. Artificial Intelligence in Healthcare. Nat Biomed Eng. 2018;2(10):719–31.PubMedCrossRef
11.
go back to reference Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.PubMedPubMedCentralCrossRef Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.PubMedPubMedCentralCrossRef
12.
go back to reference Gilvary C, Madhukar N, Elkhader J, Elemento O. The Missing Pieces of Artificial Intelligence in Medicine. Trends Pharmacol Sci. 2019;40(8):555–64.PubMedCrossRef Gilvary C, Madhukar N, Elkhader J, Elemento O. The Missing Pieces of Artificial Intelligence in Medicine. Trends Pharmacol Sci. 2019;40(8):555–64.PubMedCrossRef
14.
go back to reference Topol EJ. Deep medicine: How artificial intelligence can make healthcare human again. 1st ed. New York: Basic Books; 2019. Topol EJ. Deep medicine: How artificial intelligence can make healthcare human again. 1st ed. New York: Basic Books; 2019.
15.
go back to reference Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262–73.PubMedCrossRef Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019;20(5):e262–73.PubMedCrossRef
16.
go back to reference Plastino E, Purdy M. Game changing value from artificial intelligence: eight strategies. Strategy Leadership. 2018;46(1):16–22.CrossRef Plastino E, Purdy M. Game changing value from artificial intelligence: eight strategies. Strategy Leadership. 2018;46(1):16–22.CrossRef
17.
go back to reference Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare J. 2019;6(2):94–8.CrossRef Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare J. 2019;6(2):94–8.CrossRef
20.
go back to reference Garbuio M, Lin N. Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models. Calif Manage Rev. 2019;61(2):59–83.CrossRef Garbuio M, Lin N. Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models. Calif Manage Rev. 2019;61(2):59–83.CrossRef
21.
go back to reference Väänänen A, Haataja K, Vehviläinen-Julkunen K, Toivanen P. AI in healthcare A narrative review. F1000Res. 2021;10:6.CrossRef Väänänen A, Haataja K, Vehviläinen-Julkunen K, Toivanen P. AI in healthcare A narrative review. F1000Res. 2021;10:6.CrossRef
22.
go back to reference Kim H-W, Chan HC, Gupta S. Value-based Adoption of Mobile Internet: An empirical investigation. Decis Support Syst. 2007;43(1):111–26.CrossRef Kim H-W, Chan HC, Gupta S. Value-based Adoption of Mobile Internet: An empirical investigation. Decis Support Syst. 2007;43(1):111–26.CrossRef
23.
go back to reference Lin TC, Wu S, Hsu JSC, Chou YC. The integration of value-based adoption and expectation–confirmation models An example of IPTV continuance intention. Decision Support Systems. 2012;54(1):63–75.CrossRef Lin TC, Wu S, Hsu JSC, Chou YC. The integration of value-based adoption and expectation–confirmation models An example of IPTV continuance intention. Decision Support Systems. 2012;54(1):63–75.CrossRef
24.
go back to reference Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–60.PubMedCrossRef Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–60.PubMedCrossRef
26.
27.
go back to reference Lidströmer N, Aresu F, Ashrafian H. Basic Concepts of Artificial Intelligence Primed for Clinicians In Artificial Intelligence in Medicine. Cham: Springer; 2022. p. 3–20. Lidströmer N, Aresu F, Ashrafian H. Basic Concepts of Artificial Intelligence Primed for Clinicians In Artificial Intelligence in Medicine. Cham: Springer; 2022. p. 3–20.
28.
go back to reference Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9(2):1–12. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9(2):1–12.
29.
go back to reference Lorenz U. Reinforcement Learning: Aktuelle Ansätze verstehen - mit Beispielen in Java und Greenfoot. Berlin, Heidelberg: Springer Berlin Heidelberg; 2020. Lorenz U. Reinforcement Learning: Aktuelle Ansätze verstehen - mit Beispielen in Java und Greenfoot. Berlin, Heidelberg: Springer Berlin Heidelberg; 2020.
30.
go back to reference Kunduru AR. Artificial intelligence advantages in cloud fintech application security. Central Asian J Mathematical Theory Comp Sci. 2023;4(8):48–53. Kunduru AR. Artificial intelligence advantages in cloud fintech application security. Central Asian J Mathematical Theory Comp Sci. 2023;4(8):48–53.
31.
go back to reference Zhai X, Chu X, Chai CS, Jong MSY, Istenic A, Spector M, et al. A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity. 2021;2021:1–18. Zhai X, Chu X, Chai CS, Jong MSY, Istenic A, Spector M, et al. A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity. 2021;2021:1–18.
32.
go back to reference Zhao Y, Li T, Zhang X, Zhang C. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renew Sustain Energy Rev. 2019;109:85–101.CrossRef Zhao Y, Li T, Zhang X, Zhang C. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renew Sustain Energy Rev. 2019;109:85–101.CrossRef
33.
go back to reference Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021;21(1):125.PubMedPubMedCentralCrossRef Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021;21(1):125.PubMedPubMedCentralCrossRef
34.
go back to reference Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E. Artificial intelligence a new tool in operating room management role of machine learning models in operating room optimization. J Med Syst. 2019;44(1):20.PubMedCrossRef Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E. Artificial intelligence a new tool in operating room management role of machine learning models in operating room optimization. J Med Syst. 2019;44(1):20.PubMedCrossRef
35.
go back to reference Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716–20.PubMedCrossRef Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018;24(11):1716–20.PubMedCrossRef
36.
go back to reference Roland T. Motion Artifact Suppression for Insulated EMG to Control Myoelectric Prostheses. Sensors (Basel). 2020;20(4):1031.PubMedCrossRef Roland T. Motion Artifact Suppression for Insulated EMG to Control Myoelectric Prostheses. Sensors (Basel). 2020;20(4):1031.PubMedCrossRef
37.
go back to reference Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.PubMedPubMedCentralCrossRef Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.PubMedPubMedCentralCrossRef
38.
go back to reference Hennrich J, Fuhrmann H, Eymann T. Accelerating the Adoption of Artificial Intelligence Technologies in Radiology: A Comprehensive Overview on Current Obstacles. Proceedings of the 57th Hawaii International Conference on System Sciences 2024. Hennrich J, Fuhrmann H, Eymann T. Accelerating the Adoption of Artificial Intelligence Technologies in Radiology: A Comprehensive Overview on Current Obstacles. Proceedings of the 57th Hawaii International Conference on System Sciences 2024.
39.
40.
go back to reference Schryen G. Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. Eur J Inf Syst. 2013;22(2):139–69.CrossRef Schryen G. Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. Eur J Inf Syst. 2013;22(2):139–69.CrossRef
41.
go back to reference Haddad P, Wickramasinghe N. Conceptualizing Business Value of IT in Healthcare to Design Sustainable e-Health Solutions. Proceedings of Americas Conference on Information Systems 2014. Haddad P, Wickramasinghe N. Conceptualizing Business Value of IT in Healthcare to Design Sustainable e-Health Solutions. Proceedings of Americas Conference on Information Systems 2014.
42.
go back to reference Strong DM, Volkoff O, Johnson SA, Pelletier LR, Tulu B, Bar-On I, et al. A Theory of Organization-EHR Affordance Actualization. J Assoc Inf Syst. 2014;15(2):53–85. Strong DM, Volkoff O, Johnson SA, Pelletier LR, Tulu B, Bar-On I, et al. A Theory of Organization-EHR Affordance Actualization. J Assoc Inf Syst. 2014;15(2):53–85.
43.
go back to reference Hofmann P, Oesterle S, Rust P, Urbach N. Machine Learning Approaches Along the Radiology Value Chain - Rethinking Value Propositions. Proceedings of the European Conference on Information Systems 2019. Hofmann P, Oesterle S, Rust P, Urbach N. Machine Learning Approaches Along the Radiology Value Chain - Rethinking Value Propositions. Proceedings of the European Conference on Information Systems 2019.
44.
go back to reference Badakhshan P, Wurm B, Grisold T, Geyer-Klingeberg J, Mendling J, Vom Brocke J. Creating business value with process mining. J Strateg Inf Syst. 2022;31(4):101745.CrossRef Badakhshan P, Wurm B, Grisold T, Geyer-Klingeberg J, Mendling J, Vom Brocke J. Creating business value with process mining. J Strateg Inf Syst. 2022;31(4):101745.CrossRef
45.
go back to reference Webster J, Watson RT. Analyzing the Past to Prepare for the Future Writing a Literature Review. MIS Quarterly. 2002;26(2):xiii–xxiii. Webster J, Watson RT. Analyzing the Past to Prepare for the Future Writing a Literature Review. MIS Quarterly. 2002;26(2):xiii–xxiii.
46.
go back to reference Wolfswinkel JF, Furtmueller E, Wilderom CPM. Using Grounded Theory as a Method for Rigorously Reviewing Literature. Eur J Inf Syst. 2013;22(1):45–55.CrossRef Wolfswinkel JF, Furtmueller E, Wilderom CPM. Using Grounded Theory as a Method for Rigorously Reviewing Literature. Eur J Inf Syst. 2013;22(1):45–55.CrossRef
47.
48.
go back to reference Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, et al. Machine Learning in Radiology: Applications Beyond Image Interpretation. J Am Coll Radiol. 2018;15(2):350–9.PubMedCrossRef Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, et al. Machine Learning in Radiology: Applications Beyond Image Interpretation. J Am Coll Radiol. 2018;15(2):350–9.PubMedCrossRef
49.
go back to reference Low S-K, Zembutsu H, Nakamura Y. Breast cancer: The translation of big genomic data to cancer precision medicine. Cancer Sci. 2018;109(3):497–506.PubMedCrossRef Low S-K, Zembutsu H, Nakamura Y. Breast cancer: The translation of big genomic data to cancer precision medicine. Cancer Sci. 2018;109(3):497–506.PubMedCrossRef
50.
go back to reference Altay EV, Alatas B. Association analysis of Parkinson disease with vocal change characteristics using multi-objective metaheuristic optimization. Med Hypotheses. 2020;141:109722.PubMedCrossRef Altay EV, Alatas B. Association analysis of Parkinson disease with vocal change characteristics using multi-objective metaheuristic optimization. Med Hypotheses. 2020;141:109722.PubMedCrossRef
52.
go back to reference Qin B, Quan Q, Wu J, Liang L, Li D. Diagnostic performance of artificial intelligence to detect genetic diseases with facial phenotypes: A protocol for systematic review and meta analysis. Medicine (Baltimore). 2020;99(27):e20989.PubMedCrossRef Qin B, Quan Q, Wu J, Liang L, Li D. Diagnostic performance of artificial intelligence to detect genetic diseases with facial phenotypes: A protocol for systematic review and meta analysis. Medicine (Baltimore). 2020;99(27):e20989.PubMedCrossRef
53.
go back to reference Zhavoronkov A, Mamoshina P, Vanhaelen Q, Scheibye-Knudsen M, Moskalev A, Aliper A. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res Rev. 2019;49:49–66.PubMedCrossRef Zhavoronkov A, Mamoshina P, Vanhaelen Q, Scheibye-Knudsen M, Moskalev A, Aliper A. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res Rev. 2019;49:49–66.PubMedCrossRef
54.
go back to reference Meystre SM, Friedlin FJ, South BR, Shen S, Samore MH. Automatic de-identification of textual documents in the electronic health record - a review of recent research. BMC Med Res Methodol. 2010;10(70):1–6. Meystre SM, Friedlin FJ, South BR, Shen S, Samore MH. Automatic de-identification of textual documents in the electronic health record - a review of recent research. BMC Med Res Methodol. 2010;10(70):1–6.
55.
go back to reference Rhine CL, Neil C, Glidden DT, Cygan KJ, Fredericks AM, Wang J, et al. Future directions for high-throughput splicing assays in precision medicine. Hum Mutat. 2019;40(9):1225–34.PubMedPubMedCentralCrossRef Rhine CL, Neil C, Glidden DT, Cygan KJ, Fredericks AM, Wang J, et al. Future directions for high-throughput splicing assays in precision medicine. Hum Mutat. 2019;40(9):1225–34.PubMedPubMedCentralCrossRef
56.
go back to reference Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med. 2018;36(8):1515–7.PubMedCrossRef Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med. 2018;36(8):1515–7.PubMedCrossRef
57.
go back to reference Kwon J-M, Kim K-H, Jeon K-H, Park J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography. 2019;36(2):213–8.PubMedCrossRef Kwon J-M, Kim K-H, Jeon K-H, Park J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography. 2019;36(2):213–8.PubMedCrossRef
58.
go back to reference Yang M, Li C, Shen Y, Wu Q, Zhao Z, Chen X. Hierarchical Human-Like Deep Neural Networks for Abstractive Text Summarization. IEEE Trans Neural Netw Learn Syst. 2021;32(6):2744–57. Yang M, Li C, Shen Y, Wu Q, Zhao Z, Chen X. Hierarchical Human-Like Deep Neural Networks for Abstractive Text Summarization. IEEE Trans Neural Netw Learn Syst. 2021;32(6):2744–57.
59.
go back to reference Hernandez JPT. Network diffusion and technology acceptance of a nurse chatbot for chronic disease self-management support a theoretical perspective. J Med Invest. 2019;66(1.2):24–30.PubMedCrossRef Hernandez JPT. Network diffusion and technology acceptance of a nurse chatbot for chronic disease self-management support a theoretical perspective. J Med Invest. 2019;66(1.2):24–30.PubMedCrossRef
60.
go back to reference Bhandari M, Zeffiro T, Reddiboina M. Artificial Intelligence and Robotic Surgery: Current Perspective and Future Directions. Curr Opin Urol. 2020;30(1):48–54.PubMedCrossRef Bhandari M, Zeffiro T, Reddiboina M. Artificial Intelligence and Robotic Surgery: Current Perspective and Future Directions. Curr Opin Urol. 2020;30(1):48–54.PubMedCrossRef
61.
go back to reference Padoy N. Machine and deep learning for workflow recognition during surgery. Minim Invasive Ther Allied Technol. 2019;28(2):82–90.PubMedCrossRef Padoy N. Machine and deep learning for workflow recognition during surgery. Minim Invasive Ther Allied Technol. 2019;28(2):82–90.PubMedCrossRef
62.
go back to reference Shepherd M, Abidi SSR, Gao Q, Chen Z, Qi Q, Finley GA. Information Systems and Health Care IX: Accessing Tacit Knowledge and Linking It to the Peer-Reviewed Literature. CAIS 2006; 17. Shepherd M, Abidi SSR, Gao Q, Chen Z, Qi Q, Finley GA. Information Systems and Health Care IX: Accessing Tacit Knowledge and Linking It to the Peer-Reviewed Literature. CAIS 2006; 17.
63.
go back to reference Wilson EV, Wang W, Sheetz SD. Underpinning a Guiding Theory of Patient-Centered E-Health. CAIS. 2014;34(1):16. Wilson EV, Wang W, Sheetz SD. Underpinning a Guiding Theory of Patient-Centered E-Health. CAIS. 2014;34(1):16.
64.
go back to reference Levy Y, Ellis TJ. A systems approach to conduct an effective literature review in support of information systems research. Informing Sci J. 2006;9:181–211.CrossRef Levy Y, Ellis TJ. A systems approach to conduct an effective literature review in support of information systems research. Informing Sci J. 2006;9:181–211.CrossRef
65.
go back to reference Corbin JM, Strauss AL. Basics of qualitative research: Techniques and procedures for developing grounded theory. Fourth edition. Thousand Oaks, Kalifornien: SAGE; 2015. Corbin JM, Strauss AL. Basics of qualitative research: Techniques and procedures for developing grounded theory. Fourth edition. Thousand Oaks, Kalifornien: SAGE; 2015.
66.
go back to reference Glaser BG, Strauss A. Discovery of grounded theory: Strategies for qualitative research. Routledge; 1967. Glaser BG, Strauss A. Discovery of grounded theory: Strategies for qualitative research. Routledge; 1967.
67.
go back to reference Feldman S, Horan T. The dynamics of information collaboration: a case study of blended IT value propositions for health information exchange in disability determination. J Assoc Inf Syst. 2011;12(2):189–207. Feldman S, Horan T. The dynamics of information collaboration: a case study of blended IT value propositions for health information exchange in disability determination. J Assoc Inf Syst. 2011;12(2):189–207.
68.
go back to reference Schultze U, Avital M. Designing interviews to generate rich data for information systems research. Inf Organ. 2011;21(1):1–16.CrossRef Schultze U, Avital M. Designing interviews to generate rich data for information systems research. Inf Organ. 2011;21(1):1–16.CrossRef
69.
go back to reference Myers MD, Newman M. The qualitative interview in IS research: examining the craft. Inf Organ. 2007;17(1):2–26.CrossRef Myers MD, Newman M. The qualitative interview in IS research: examining the craft. Inf Organ. 2007;17(1):2–26.CrossRef
70.
go back to reference Bhattacherjee A. Social Science Research: Principles, Methods, and Practices. Textbooks Collection 2012; 3. Bhattacherjee A. Social Science Research: Principles, Methods, and Practices. Textbooks Collection 2012; 3.
71.
go back to reference Moulaei K, Sheikhtaheri A, Fatehi F, Shanbehzadeh M, Bahaadinbeigy K. Patients’ perspectives and preferences toward telemedicine versus in-person visits: a mixed-methods study on 1226 patients. BMC Med Inform Decis Mak. 2023;23(1):261.PubMedPubMedCentralCrossRef Moulaei K, Sheikhtaheri A, Fatehi F, Shanbehzadeh M, Bahaadinbeigy K. Patients’ perspectives and preferences toward telemedicine versus in-person visits: a mixed-methods study on 1226 patients. BMC Med Inform Decis Mak. 2023;23(1):261.PubMedPubMedCentralCrossRef
72.
go back to reference Buck C, Hennrich J, Kauffmann A-L. Artificial Intelligence in Radiology – A Qualitative Study on Imaging Specialists’ Perspectives. Proceedings of the 42nd International Conference on Information Systems 2021. Buck C, Hennrich J, Kauffmann A-L. Artificial Intelligence in Radiology – A Qualitative Study on Imaging Specialists’ Perspectives. Proceedings of the 42nd International Conference on Information Systems 2021.
73.
go back to reference Degnan AJ, Ghobadi EH, Hardy P, Krupinski E, Scali EP, Stratchko L, et al. Perceptual and interpretive error in diagnostic radiology-causes and potential solutions. Acad Radiol. 2019;26(6):833–45.PubMedCrossRef Degnan AJ, Ghobadi EH, Hardy P, Krupinski E, Scali EP, Stratchko L, et al. Perceptual and interpretive error in diagnostic radiology-causes and potential solutions. Acad Radiol. 2019;26(6):833–45.PubMedCrossRef
74.
go back to reference Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35.PubMedPubMedCentralCrossRef Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35.PubMedPubMedCentralCrossRef
75.
go back to reference Schinkel M, Paranjape K, Nannan Panday RS, Skyttberg N, Nanayakkara PWB. Clinical applications of artificial intelligence in sepsis: A narrative review. Comput Biol Med. 2019;115:103488.PubMedCrossRef Schinkel M, Paranjape K, Nannan Panday RS, Skyttberg N, Nanayakkara PWB. Clinical applications of artificial intelligence in sepsis: A narrative review. Comput Biol Med. 2019;115:103488.PubMedCrossRef
77.
go back to reference Padoy N, Hager GD. Human-Machine Collaborative Surgery Using Learned Methods. Proceedings of IEEE International Conference on Robotics and Automation 2011:5285–92. Padoy N, Hager GD. Human-Machine Collaborative Surgery Using Learned Methods. Proceedings of IEEE International Conference on Robotics and Automation 2011:5285–92.
78.
go back to reference Nadarzynski T, Miles O, Cowie A, Ridge D. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digit Health. 2019;5:2055207619871808.PubMedPubMedCentral Nadarzynski T, Miles O, Cowie A, Ridge D. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digit Health. 2019;5:2055207619871808.PubMedPubMedCentral
79.
go back to reference Trevitt S, Simpson S, Wood A. Artificial Pancreas device systems for the closed-loop control of type 1 diabetes: what systems are in development? J Diabetes Sci Technol. 2016;10(3):714–23.PubMedCrossRef Trevitt S, Simpson S, Wood A. Artificial Pancreas device systems for the closed-loop control of type 1 diabetes: what systems are in development? J Diabetes Sci Technol. 2016;10(3):714–23.PubMedCrossRef
80.
go back to reference Luo L, Zhang F, Yao Y, Gong R, Fu M, Xiao J. Machine learning for identification of surgeries with high risks of cancellation. Health Informatics J. 2018;26(1):141–55.PubMedCrossRef Luo L, Zhang F, Yao Y, Gong R, Fu M, Xiao J. Machine learning for identification of surgeries with high risks of cancellation. Health Informatics J. 2018;26(1):141–55.PubMedCrossRef
81.
go back to reference Shollo A, Hopf K, Thiess T, Müller O. Shifting ML value creation mechanisms: a process model of ML value creation. J Strateg Inf Syst. 2022;31(3):101734.CrossRef Shollo A, Hopf K, Thiess T, Müller O. Shifting ML value creation mechanisms: a process model of ML value creation. J Strateg Inf Syst. 2022;31(3):101734.CrossRef
82.
go back to reference Buck C, Doctor E, Hennrich J, Jöhnk J, Eymann T. General Practitioners’ Attitudes Toward Artificial Intelligence-Enabled Systems: Interview Study. J Med Internet Res. 2022;24(1):e28916.PubMedPubMedCentralCrossRef Buck C, Doctor E, Hennrich J, Jöhnk J, Eymann T. General Practitioners’ Attitudes Toward Artificial Intelligence-Enabled Systems: Interview Study. J Med Internet Res. 2022;24(1):e28916.PubMedPubMedCentralCrossRef
83.
go back to reference Bennett SJ. Transmuting values in artificial intelligence: investigating the motivations and contextual constraints shaping the ethics of artificial intelligence practitioners; 2023. Bennett SJ. Transmuting values in artificial intelligence: investigating the motivations and contextual constraints shaping the ethics of artificial intelligence practitioners; 2023.
84.
go back to reference Baumgartner R, Arora P, Bath C, Burljaev D, Ciereszko K, Custers B, et al. Fair and equitable AI in biomedical research and healthcare: Social science perspectives. Artif Intell Med. 2023;144:102658.PubMedCrossRef Baumgartner R, Arora P, Bath C, Burljaev D, Ciereszko K, Custers B, et al. Fair and equitable AI in biomedical research and healthcare: Social science perspectives. Artif Intell Med. 2023;144:102658.PubMedCrossRef
Metadata
Title
Capturing artificial intelligence applications’ value proposition in healthcare – a qualitative research study
Authors
Jasmin Hennrich
Eva Ritz
Peter Hofmann
Nils Urbach
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2024
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-024-10894-4

Other articles of this Issue 1/2024

BMC Health Services Research 1/2024 Go to the issue