Literature DB >> 33258359

Organizational readiness for artificial intelligence in health care: insights for decision-making and practice.

Hassane Alami1,2,3, Pascale Lehoux1,2, Jean-Louis Denis2,4, Aude Motulsky2,4, Cecile Petitgand2,4, Mathilde Savoldelli5, Ronan Rouquet6, Marie-Pierre Gagnon7,8, Denis Roy3, Jean-Paul Fortin8,9.   

Abstract

PURPOSE: Artificial intelligence (AI) raises many expectations regarding its ability to profoundly transform health care delivery. There is an abundant literature on the technical performance of AI applications in many clinical fields (e.g. radiology, ophthalmology). This article aims to bring forward the importance of studying organizational readiness to integrate AI into health care delivery. DESIGN/METHODOLOGY/APPROACH: The reflection is based on our experience in digital health technologies, diffusion of innovations and healthcare organizations and systems. It provides insights into why and how organizational readiness should be carefully considered.
FINDINGS: As an important step to ensure successful integration of AI and avoid unnecessary investments and costly failures, better consideration should be given to: (1) Needs and added-value assessment; (2) Workplace readiness: stakeholder acceptance and engagement; (3) Technology-organization alignment assessment and (4) Business plan: financing and investments. In summary, decision-makers and technology promoters should better address the complexity of AI and understand the systemic challenges raised by its implementation in healthcare organizations and systems. ORIGINALITY/VALUE: Few studies have focused on the organizational issues raised by the integration of AI into clinical routine. The current context is marked by a perplexing gap between the willingness of decision-makers and technology promoters to capitalize on AI applications to improve health care delivery and the reality on the ground, where it is difficult to initiate the changes needed to realize their full benefits while avoiding their negative impacts. © Emerald Publishing Limited.

Entities:  

Keywords:  Artificial intelligence; Decision-making; Health care delivery; Healthcare organizations; Organizational readiness

Year:  2020        PMID: 33258359     DOI: 10.1108/JHOM-03-2020-0074

Source DB:  PubMed          Journal:  J Health Organ Manag        ISSN: 1477-7266


  5 in total

1.  Exploring healthcare professionals' perceptions of artificial intelligence: Validating a questionnaire using the e-Delphi method.

Authors:  Lucy Shinners; Christina Aggar; Sandra Grace; Stuart Smith
Journal:  Digit Health       Date:  2021-03-23

2.  Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study.

Authors:  Luisa Pumplun; Mariska Fecho; Nihal Wahl; Felix Peters; Peter Buxmann
Journal:  J Med Internet Res       Date:  2021-10-15       Impact factor: 5.428

3.  Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients.

Authors:  Wei-Chun Tsai; Chung-Feng Liu; Hung-Jung Lin; Chien-Chin Hsu; Yu-Shan Ma; Chia-Jung Chen; Chien-Cheng Huang; Chia-Chun Chen
Journal:  Healthcare (Basel)       Date:  2022-08-09

4.  Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden.

Authors:  Lena Petersson; Ingrid Larsson; Jens M Nygren; Per Nilsen; Margit Neher; Julie E Reed; Daniel Tyskbo; Petra Svedberg
Journal:  BMC Health Serv Res       Date:  2022-07-01       Impact factor: 2.908

5.  A survey of extant organizational and computational setups for deploying predictive models in health systems.

Authors:  Sehj Kashyap; Keith E Morse; Birju Patel; Nigam H Shah
Journal:  J Am Med Inform Assoc       Date:  2021-10-12       Impact factor: 4.497

  5 in total

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