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Rony MKK, Numan SM, Johra FT, Akter K, Akter F, Debnath M, Mondal S, Wahiduzzaman M, Das M, Ullah M, Rahman MH, Das Bala S, Parvin MR. Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care. Health Sci Rep 2024; 7:e70006. [PMID: 39175600 PMCID: PMC11339127 DOI: 10.1002/hsr2.70006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 07/31/2024] [Accepted: 08/06/2024] [Indexed: 08/24/2024] Open
Abstract
Background With the ever-increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in-depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology. Objectives This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care. Methods This qualitative research employed a descriptive and phenomenological approach using in-depth interviews. Data were collected through a semi-structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data. Results The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient-centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care. Conclusions This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.
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Affiliation(s)
| | - Sharker Md. Numan
- School of Science and TechnologyBangladesh Open UniversityGazipurBangladesh
| | - Fateha tuj Johra
- Masters in Disaster ManagementUniversity of DhakaDhakaBangladesh
| | - Khadiza Akter
- Master of Public HealthDaffodil International UniversityDhakaBangladesh
| | - Fazila Akter
- Dhaka Nursing Collegeaffiliated with the University of DhakaDhakaBangladesh
| | - Mitun Debnath
- Master of Public HealthNational Institute of Preventive and Social MedicineDhakaBangladesh
| | - Sujit Mondal
- Master of Science in NursingNational Institute of Advanced Nursing Education and Research MugdaDhakaBangladesh
| | - Md. Wahiduzzaman
- School of Medical SciencesShahjalal University of Science and TechnologySylhetBangladesh
| | - Mousumi Das
- Master of Public HealthLeading UniversitySylhetBangladesh
| | - Mohammad Ullah
- College of NursingInternational University of Business Agriculture and TechnologyDhakaBangladesh
| | | | - Shuvashish Das Bala
- College of NursingInternational University of Business Agriculture and TechnologyDhakaBangladesh
| | - Mst. Rina Parvin
- Bangladesh Army (AFNS Officer)Combined Military Hospital DhakaDhakaBangladesh
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Ho A, Bavli I, Mahal R, McKeown MJ. Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson's Disease. AJOB Empir Bioeth 2024; 15:178-191. [PMID: 37889210 DOI: 10.1080/23294515.2023.2274582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Artificial intelligence (AI) has garnered tremendous attention in health care, and many hope that AI can enhance our health system's ability to care for people with chronic and degenerative conditions, including Parkinson's Disease (PD). This paper reports the themes and lessons derived from a qualitative study with people living with PD, family caregivers, and health care providers regarding the ethical dimensions of using AI to monitor, assess, and predict PD symptoms and progression. Thematic analysis identified ethical concerns at four intersecting levels: personal, interpersonal, professional/institutional, and societal levels. Reflecting on potential benefits of predictive algorithms that can continuously collect and process longitudinal data, participants expressed a desire for more timely, ongoing, and accurate information that could enhance management of day-to-day fluctuations and facilitate clinical and personal care as their disease progresses. Nonetheless, they voiced concerns about intersecting ethical questions around evolving illness identities, familial and professional care relationships, privacy, and data ownership/governance. The multi-layer analysis provides a helpful way to understand the ethics of using AI in monitoring and managing PD and other chronic/degenerative conditions.
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Affiliation(s)
- Anita Ho
- Centre for Applied Ethics, School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Itai Bavli
- Centre for Applied Ethics, School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Ravneet Mahal
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
| | - Martin J McKeown
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada
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Cerda IH, Zhang E, Dominguez M, Ahmed M, Lang M, Ashina S, Schatman ME, Yong RJ, Fonseca ACG. Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management. Curr Pain Headache Rep 2024:10.1007/s11916-024-01279-7. [PMID: 38836996 DOI: 10.1007/s11916-024-01279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of the current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders. RECENT FINDINGS Through machine learning and natural language processing approaches, AI offers unprecedented opportunities to identify patterns within complex and voluminous datasets, including brain imaging data. This technology has demonstrated promise in optimizing diagnostic approaches to headache disorders and automating their classification, an attribute particularly beneficial for non-specialist providers. Furthermore, AI can enhance headache disorder management by enabling the forecasting of acute events of interest, such as migraine headaches or medication overuse, and by guiding treatment selection based on insights from predictive modeling. Additionally, AI may facilitate the streamlining of treatment efficacy monitoring and enable the automation of real-time treatment parameter adjustments. VR technology, on the other hand, offers controllable and immersive experiences, thus providing a unique avenue for the investigation of the sensory-perceptual symptomatology associated with certain headache disorders. Moreover, recent studies suggest that VR, combined with biofeedback, may serve as a viable adjunct to conventional treatment. Addressing challenges to the widespread adoption of AI and VR in headache medicine, including reimbursement policies and data privacy concerns, mandates collaborative efforts from stakeholders to enable the equitable, safe, and effective utilization of these technologies in advancing headache disorder care. This review highlights the potential of AI and VR to support precise diagnostics, automate classification, and enhance management strategies for headache disorders.
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Affiliation(s)
| | - Emily Zhang
- Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Moises Dominguez
- Department of Neurology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA
| | | | - Min Lang
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Sait Ashina
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Anesthesiology, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael E Schatman
- Department of Anesthesiology, Perioperative Care, and Pain Medicine, NYU Grossman School of Medicine, New York, NY, USA
- Department of Population Health-Division of Medical Ethics, NYU Grossman School of Medicine, New York, NY, USA
| | - R Jason Yong
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA
| | - Alexandra C G Fonseca
- Harvard Medical School, Boston, MA, USA.
- Brigham and Women's Hospital, Department of Anesthesiology, Perioperative, and Pain Medicine, 75 Francis Street, Boston, MA, 02115, USA.
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Racine N, Chow C, Hamwi L, Bucsea O, Cheng C, Du H, Fabrizi L, Jasim S, Johannsson L, Jones L, Laudiano-Dray MP, Meek J, Mistry N, Shah V, Stedman I, Wang X, Riddell RP. Health Care Professionals' and Parents' Perspectives on the Use of AI for Pain Monitoring in the Neonatal Intensive Care Unit: Multisite Qualitative Study. JMIR AI 2024; 3:e51535. [PMID: 38875686 PMCID: PMC11041412 DOI: 10.2196/51535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/24/2023] [Accepted: 12/17/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The use of artificial intelligence (AI) for pain assessment has the potential to address historical challenges in infant pain assessment. There is a dearth of information on the perceived benefits and barriers to the implementation of AI for neonatal pain monitoring in the neonatal intensive care unit (NICU) from the perspective of health care professionals (HCPs) and parents. This qualitative analysis provides novel data obtained from 2 large tertiary care hospitals in Canada and the United Kingdom. OBJECTIVE The aim of the study is to explore the perspectives of HCPs and parents regarding the use of AI for pain assessment in the NICU. METHODS In total, 20 HCPs and 20 parents of preterm infants were recruited and consented to participate from February 2020 to October 2022 in interviews asking about AI use for pain assessment in the NICU, potential benefits of the technology, and potential barriers to use. RESULTS The 40 participants included 20 HCPs (17 women and 3 men) with an average of 19.4 (SD 10.69) years of experience in the NICU and 20 parents (mean age 34.4, SD 5.42 years) of preterm infants who were on average 43 (SD 30.34) days old. Six themes from the perspective of HCPs were identified: regular use of technology in the NICU, concerns with regard to AI integration, the potential to improve patient care, requirements for implementation, AI as a tool for pain assessment, and ethical considerations. Seven parent themes included the potential for improved care, increased parental distress, support for parents regarding AI, the impact on parent engagement, the importance of human care, requirements for integration, and the desire for choice in its use. A consistent theme was the importance of AI as a tool to inform clinical decision-making and not replace it. CONCLUSIONS HCPs and parents expressed generally positive sentiments about the potential use of AI for pain assessment in the NICU, with HCPs highlighting important ethical considerations. This study identifies critical methodological and ethical perspectives from key stakeholders that should be noted by any team considering the creation and implementation of AI for pain monitoring in the NICU.
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Affiliation(s)
- Nicole Racine
- School of Psychology, University of Ottawa, Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Cheryl Chow
- Department of Psychology, York University, Toronto, ON, Canada
| | - Lojain Hamwi
- Department of Psychology, York University, Toronto, ON, Canada
| | - Oana Bucsea
- Department of Psychology, York University, Toronto, ON, Canada
| | - Carol Cheng
- Department of Nursing, Mount Sinai Hospital, Toronto, ON, Canada
| | - Hang Du
- Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Lorenzo Fabrizi
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Sara Jasim
- Department of Psychology, York University, Toronto, ON, Canada
| | | | - Laura Jones
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Maria Pureza Laudiano-Dray
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Judith Meek
- Neonatal Care Unit, University College London Hospitals, London, United Kingdom
| | - Neelum Mistry
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Vibhuti Shah
- Department of Pediatrics, Mount Sinai Hospital, Toronto, ON, Canada
| | - Ian Stedman
- School of Public Policy and Administration, York University, Toronto, ON, Canada
| | - Xiaogang Wang
- Department of Mathematics and Statistics, York University, Toronto, ON, Canada
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Rony MKK, Parvin MR, Wahiduzzaman M, Debnath M, Bala SD, Kayesh I. "I Wonder if my Years of Training and Expertise Will be Devalued by Machines": Concerns About the Replacement of Medical Professionals by Artificial Intelligence. SAGE Open Nurs 2024; 10:23779608241245220. [PMID: 38596508 PMCID: PMC11003342 DOI: 10.1177/23779608241245220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/08/2024] [Accepted: 03/15/2024] [Indexed: 04/11/2024] Open
Abstract
Background The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown. Objective This qualitative research aimed to investigate healthcare workers' concerns about artificial intelligence replacing medical professionals. Methods A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis. Results The analysis revealed seven key themes reflecting healthcare workers' concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient-provider relationships; healthcare policy and regulation. Conclusions This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient-provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.
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Affiliation(s)
- Moustaq Karim Khan Rony
- Master of Public Health, Bangladesh Open University, Gazipur, Bangladesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
| | - Mst. Rina Parvin
- Armed Forces Nursing Service, Major at Bangladesh Army (AFNS Officer), Combined Military Hospital, Dhaka, Bangladesh
| | - Md. Wahiduzzaman
- School of Medical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Mitun Debnath
- Master of Public Health, National Institute of Preventive and Social Medicine, Dhaka, Bangladesh
| | - Shuvashish Das Bala
- College of Nursing, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Ibne Kayesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
- Faculty of Graduate Studies, University of Kelaniya, Colombo, Sri Lanka
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Khavandi S, Zaghloul F, Higham A, Lim E, de Pennington N, Celi LA. Investigating the Impact of Automation on the Health Care Workforce Through Autonomous Telemedicine in the Cataract Pathway: Protocol for a Multicenter Study. JMIR Res Protoc 2023; 12:e49374. [PMID: 38051569 PMCID: PMC10731565 DOI: 10.2196/49374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/17/2023] [Accepted: 09/18/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND While digital health innovations are increasingly being adopted by health care organizations, implementation is often carried out without considering the impacts on frontline staff who will be using the technology and who will be affected by its introduction. The enthusiasm surrounding the use of artificial intelligence (AI)-enabled digital solutions in health care is tempered by uncertainty around how it will change the working lives and practices of health care professionals. Digital enablement can be viewed as facilitating enhanced effectiveness and efficiency by improving services and automating cognitive labor, yet the implementation of such AI technology comes with challenges related to changes in work practices brought by automation. This research explores staff experiences before and after care pathway automation with an autonomous clinical conversational assistant, Dora (Ufonia Ltd), that is able to automate routine clinical conversations. OBJECTIVE The primary objective is to examine the impact of AI-enabled automation on clinicians, allied health professionals, and administrators who provide or facilitate health care to patients in high-volume, low-complexity care pathways. In the process of transforming care pathways through automation of routine tasks, staff will increasingly "work at the top of their license." The impact of this fundamental change on the professional identity, well-being, and work practices of the individual is poorly understood at present. METHODS We will adopt a multiple case study approach, combining qualitative and quantitative data collection methods, over 2 distinct phases, namely phase A (preimplementation) and phase B (postimplementation). RESULTS The analysis is expected to reveal the interrelationship between Dora and those affected by its introduction. This will reveal how tasks and responsibilities have changed or shifted, current tensions and contradictions, ways of working, and challenges, benefits, and opportunities as perceived by those on the frontlines of the health care system. The findings will enable a better understanding of the resistance or susceptibility of different stakeholders within the health care workforce and encourage managerial awareness of differing needs, demands, and uncertainties. CONCLUSIONS The implementation of AI in the health care sector, as well as the body of research on this topic, remain in their infancy. The project's key contribution will be to understand the impact of AI-enabled automation on the health care workforce and their work practices. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/49374.
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Affiliation(s)
- Sarah Khavandi
- Ufonia, Oxford, United Kingdom
- Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, United Kingdom
- Imperial College School of Medicine, Imperial College London, London, United Kingdom
| | - Fatema Zaghloul
- Operations and Management Science, Healthcare and Innovation, University of Bristol, Bristol, United Kingdom
| | - Aisling Higham
- Ufonia, Oxford, United Kingdom
- Royal Berkshire NHS Foundation Trust, Reading, United Kingdom
| | - Ernest Lim
- Ufonia, Oxford, United Kingdom
- Department of Computer Science, University of York, York, United Kingdom
| | | | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States
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