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Barriers and Enablers for Implementation of an Artificial Intelligence–Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0001-7610-0954
Cambio Healthcare Systems AB, Stockholm, Sweden.ORCID iD: 0009-0009-0678-4663
Halmstad University, School of Health and Welfare.ORCID iD: 0000-0002-3576-2393
Halmstad University, School of Business, Innovation and Sustainability.ORCID iD: 0000-0002-2513-3040
2023 (English)In: JMIR Formative Research, E-ISSN 2561-326X, Vol. 7, article id e47335Article in journal (Refereed) Published
Abstract [en]

Background: Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context.

Objective: This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system.

Methods: Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework.

Results: Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized.

Conclusions: Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process. © The Author(s) 2023.

Place, publisher, year, edition, pages
Toronto, ON: JMIR Publications, 2023. Vol. 7, article id e47335
Keywords [en]
implementation, AI systems, health care, interviews, decision support tool, readmission prediction, heart failure, digital tool
National Category
Medical and Health Sciences Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation; Health Innovation, IDC
Identifiers
URN: urn:nbn:se:hh:diva-51707DOI: 10.2196/47335PubMedID: 37610799Scopus ID: 2-s2.0-85170696488OAI: oai:DiVA.org:hh-51707DiVA, id: diva2:1800236
Funder
Knowledge Foundation
Note

This research is included in the CAISR Health research profile.

Available from: 2023-09-26 Created: 2023-09-26 Last updated: 2024-12-03Bibliographically approved

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Nair, MonikaNygren, Jens M.Lundgren, Lina E.

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