Transformative promise and implementation challenges: Health care managers' perceptions and experiences of artificial intelligence-A mixed-methods systematic review.
Oct 2026· Health Care Management Review· 0 citations· 35 references
Medicine
TL;DR
The thematic analysis identified five themes reflecting participants' perceptions and experiences: AI as a transformative mechanism, organizational enablers and outcomes of AI utilization, AI-supported operational and risk management efficiency, strategic financial benefits and long-term value creation through AI, and data-driven opportunities and challenges in AI utilization for health care management.
Abstract
Background
Artificial intelligence (AI) is a technological development that offers new opportunities for managing health care services.
Purpose
To identify and synthesize the best evidence on health care managers' and other professionals´ experiences and perceptions of the utilization of AI in management.
METHODOLOGY/APPROACH
A mixed-methods systematic review was conducted following the Joanna Briggs Institute methodology using a convergent integrated approach, including qualitative and quantitative data synthesis followed by thematic analysis. A systematic search was conducted in January 2025 in Ovid Medline, CINAHL, Scopus, ProQuest and Medic, limiting studies between 2016 and 2025 and written in English, Swedish, or Finnish.
Findings
The results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). A total of 16 studies from 10 countries were included, comprising 11 qualitative and 5 quantitative studies. Of the participants, 90% were health care managers, and 10% were other health care professionals, such as nurses, physicians, and developers. The thematic analysis identified five themes reflecting participants' perceptions and experiences: AI as a transformative mechanism, organizational enablers and outcomes of AI utilization, AI-supported operational and risk management efficiency, strategic financial benefits and long-term value creation through AI, and data-driven opportunities and challenges in AI utilization for health care management.
PRACTICE IMPLICATIONS
Health care managers should strengthen AI-related competencies, data governance, and organizational readiness to support effective AI implementation. Leadership practices that foster innovation, workforce engagement, and responsible data-driven decision-making may help organizations realize the operational and strategic benefits of AI.
Artificial intelligence has significant potential to enhance mental health nursing by supporting early identification of symptoms, improving access to care and strengthening clinical decision-making, with stronger evidence observed in reviews evaluating AI interventions for early detection and risk prediction.
J. Odame, Gabriel Obeng-Gyamfi, Dayeon Heo et al.· Journal of Psychiatric and M...· 0 citations
Background/Objectives: Artificial intelligence (AI) is increasingly being applied across healthcare, but empirical evidence on its use in home-care and community nursing remains fragmented across technologies, clinical contexts, nursing roles, and stages of implementation. This scoping review aimed to map current AI ap...
Hai-Ying Wang, Grace Y. Wang, Tao Wang et al.· Healthcare· 0 citations
The review found that AI tools—for example, selfreferral chatbots that reduced waiting times and increased treatment uptake—provide expanded 24/7 access, improved clinical efficiency, and potential for individualised personalisation, while Predictive models showed promise for treatment selection and risk stratification...
Shizal Nawaz, Laiba Nawaz, Hasnain Ali et al.· Digital Medicine· 0 citations
AI integration in nursing care may be supported by coordinated, ethically grounded, and context-sensitive strategies, and the proposed domains require further empirical evaluation across nursing settings.
D. Alrazeeni, Maryam Alharrasi, M. K. K. Rony et al.· International Nursing Review· 0 citations
Although stakeholders perceived AI to be useful for diabetes care, these qualitative findings do not demonstrate clinical effectiveness, safety, or improved patient outcomes.
Héctor Martínez-Martínez, Julia Martínez-Alfonso, Belén Sánchez-Rojo-Huertas et al.· Journal of Medical Internet...· 0 citations
GAI can effectively support early-stage qualitative analysis and enhance efficiency in health research; however, it cannot replace the interpretive and reflexive functions central to qualitative inquiry.
Raúl D. Gierbolini-Rivera, Milena Franco Silva, Alexandre Augusto de Paula da Silva et al.· Journal of Medical Internet...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.