Healthcare Adoption Research Articles

Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review.

Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders. As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care. A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O'Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care. A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption. The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.

Establishing a Consensus-Based Framework for the Use of Wearable Activity Trackers in Health Care: Delphi Study.

Physical activity (PA) plays a crucial role in health care, providing benefits in the prevention and management of many noncommunicable diseases. Wearable activity trackers (WATs) provide an opportunity to monitor and promote PA in various health care settings. This study aimed to develop a consensus-based framework for the optimal use of WATs in health care. A 4-round Delphi survey was conducted, involving a panel (n=58) of health care professionals, health service managers, and researchers. Round 1 used open-response questions to identify overarching themes. Rounds 2 and 3 used 9-point Likert scales to refine participants' opinions and establish consensus on key factors related to WAT use in health care, including metrics, device characteristics, clinical populations and settings, and software considerations. Round 3 also explored barriers and mitigating strategies to WAT use in clinical settings. Insights from Rounds 1-3 informed a draft checklist designed to guide a systematic approach to WAT adoption in health care. In Round 4, participants evaluated the draft checklist's clarity, utility, and appropriateness. Participation rates for rounds 1 to 4 were 76% (n=44), 74% (n=43), 74% (n=43), and 66% (n=38), respectively. The study found a strong interest in using WATs across diverse clinical populations and settings. Key metrics (step count, minutes of PA, and sedentary time), device characteristics (eg, easy to charge, comfortable, waterproof, simple data access, and easy to navigate and interpret data), and software characteristics (eg, remote and wireless data access, access to multiple patients' data) were identified. Various barriers to WAT adoption were highlighted, including device-related, patient-related, clinician-related, and system-level issues. The findings culminated in a 12-item draft checklist for using WATs in health care, with all 12 items endorsed for their utility, clarity, and appropriateness in Round 4. This study underscores the potential of WATs in enhancing patient care across a broad spectrum of health care settings. While the benefits of WATs are evident, successful integration requires addressing several challenges, from technological developments to patient education and clinician training. Collaboration between WAT manufacturers, researchers, and health care professionals will be pivotal for implementing WATs in the health care sector.

Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care.

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. This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care. 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. 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. 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.

AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.

Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence-driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness. A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review. Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone. Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user's depression and anxiety is merited.

Adoption of health care and feeding management practices by the flood affected dairy farmers

Adoption of health care and feeding management practices by the flood affected dairy farmers

Barriers and Implications of 5G Technology Adoption for Hospitals in Western China: Integrated Interpretive Structural Modeling and Decision-Making Trial and Evaluation Laboratory Analysis.

5G technology is gaining traction in Chinese hospitals for its potential to enhance patient care and internal management. However, various barriers hinder its implementation in clinical settings, and studies on their relevance and importance are scarce. This study aimed to identify critical barriers hampering the effective implementation of 5G in hospitals in Western China, to identify interaction relationships and priorities of the above-identified barriers, and to assess the intensity of the relationships and cause-and-effect relations between the adoption barriers. This paper uses the Delphi expert consultation method to determine key barriers to 5G adoption in Western China hospitals, the interpretive structural modeling to uncover interaction relationships and priorities, and the decision-making trial and evaluation laboratory method to reveal cause-and-effect relationships and their intensity levels. In total, 14 barriers were determined by literature review and the Delphi method. Among these, "lack of policies on ethics, rights, and responsibilities in core health care scenarios" emerged as the fundamental influencing factor in the entire system, as it was the only factor at the bottom level of the interpretive structural model. Overall, 8 barriers were classified as the "cause group," and 6 as the "effect group" by the decision-making trial and evaluation laboratory method. "High expense" and "organizational barriers within hospitals" were determined as the most significant driving barrier (the highest R-C value of 1.361) and the most critical barrier (the highest R+C value of 4.317), respectively. Promoting the integration of 5G in hospitals in Western China faces multiple complex and interrelated barriers. The study provides valuable quantitative evidence and a comprehensive approach for regulatory authorities, hospitals, and telecom operators, helping them develop strategic pathways for promoting widespread 5G adoption in health care. It is suggested that the stakeholders cooperate to explore and solve the problems in the 5G medical care era, aiming to achieve the coverage of 5G medical care across the country. To our best knowledge, this study is the first academic exploration systematically analyzing factors resisting 5G integration in Chinese hospitals, and it may give subsequent researchers a solid foundation for further studying the application and development of 5G in health care.

Facilitators and Barriers for Telemedicine Systems in India from Multiple Stakeholder Perspectives and Settings: A Systematic Review.

Background: Telemedicine is viewed as a crucial tool for addressing the challenges of limited medical resources at health care facilities. However, its adoption in health care is not entirely realized due to perceived barriers. This systematic review outlines the critical facilitators and barriers that influence the implementation of telemedicine in the Indian health care system, observed at the infrastructural, sociocultural, regulatory, and financial levels, from the perspectives of health care providers, patients, patient caregivers, society, health organizations, and the government. Methods: This review complies with the current Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols 2015. A total of 2,706 peer-reviewed studies published from December 2016 to September 2023 in the PubMed, Cochrane, Scopus, Web of Science, CINAHL, MEDLINE, and PsycInfo databases were considered for the title and abstract screening, after which 334 articles were chosen for the full-text review. In the end, 46 studies were selected for data synthesis. Results: Analysis of the literature revealed key barriers such as data privacy and security concerns, doctor and patient resistance to information and communications technology (ICT), infrastructure issues, and ICT training gaps. Facilitators included reduced health care delivery costs, enhanced patient access to health care in remote areas, and shorter patient wait times. The real-world experiences of Indian telemedicine practitioners and pioneers are also explored to complement literature-based perspectives on telemedicine implementation. Both stress the need for reliable internet connectivity, technological adoption, comprehensive ICT training, positive sociocultural attitudes, stringent data privacy measures, and viable business models as crucial for effective telemedicine adoption, with experts emphasizing practical adaptability alongside the literature-recognized facilitators.

Empathy and Equity: Key Considerations for Large Language Model Adoption in Health Care.

The growing presence of large language models (LLMs) in health care applications holds significant promise for innovative advancements in patient care. However, concerns about ethical implications and potential biases have been raised by various stakeholders. Here, we evaluate the ethics of LLMs in medicine along 2 key axes: empathy and equity. We outline the importance of these factors in novel models of care and develop frameworks for addressing these alongside LLM deployment.

Examining the impact of cultural values on the adoption of big data analytics in healthcare organizations

PurposeThis study aims to examine the impact of cultural values [power distance (PD), uncertainty avoidance (UC), individualism vs collectivism (IC) and time orientation] and users’ perceptions on the adoption of big data analytics (BDA) within the context of the United Arab Emirates (UAE) health-care sector. It uses the unified theory of acceptance and use of technology (UTAUT) model as its theoretical foundation.Design/methodology/approachA cross-sectional survey involving 256 health-care organization users in major hospitals across the UAE was conducted. Smart partial least squares (PLS) structural equation modeling was used to assess users’ behavioral intentions (BI) to use BDA in the health-care context.FindingsResults indicate that performance expectancy, social influence, facilitating conditions and perceived trust significantly predicted respondents’ BI to use BDA. However, effort expectancy, perceived security and time orientation were found to have insignificant impacts on BI. Interestingly, the remaining cultural values (PD, UC and IC) did not significantly affect the relationship between social influence and BI in the context of BDA adoption in health care.Originality/valueThis study contributes to the literature by examining the role of cultural dimensions in BDA adoption within health-care organizations, particularly in the underrepresented UAE health-care context. Moreover, it extends the application of the UTAUT model to the BDA adoption in health care, providing insights into the factors affecting users’ BIs to use the technology.

Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence.

Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence.

Extended Reality for Health Care Systems: Recent Advances in Contemporary Research

Having worked with patient simulator systems, and at this point having used an oculus system to explore various virtual reality (VR) recordings for several months, this reviewer thought the above title might be worth reading and reviewing for the readers of IEEE Pulse. Per the publisher’s website advertising, the readership of this text might be “Extended reality technologists and engineers, students in biomedical engineering and extended reality, Instrumentation and measurement technologist and engineers, Computer Scientist; Health care professionals with an interest in using extended reality in surgery.” From the preface, we are promised an “elaborate introduction to extended reality,” a discussion of “future research, potential impacts, and benefits and benefits of XR adoption in health care” (XR is here defined as extended reality), as well as cases and case studies relating to the technology. The text comprises 15 chapters contributed by 34 individuals, two of whom are among the four named editors. A brief review of the chapters <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref> follows.

The Scope and Functions of Health Information Management in e-Health

e-Health adoption in health care leads to a clinical transformation, affecting the scope of health data, work processes, roles, interactions, and above all, the culture of the global health system as a whole. Advancements in the application of health information technology (HIT) have certainly reformed the health care landscape and how health data are being documented, processed, and disseminated This review paper aims to explore the expanding scope and functions of health information management in e-health. The study also intends to identify the evolving functions of health information management for the meaningful use and transmission of health data in e-health environment. The study reveals the challenges of transmitting health data in the e-health systems and the areas of e-health where meticulous health information management is required.

Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review.

BackgroundArtificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care.ObjectiveThis study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care.MethodsA systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted.ResultsOf the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study.ConclusionsThe perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.

Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review.

Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients. PubMed. A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review. In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients' engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care. Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

Using artificial intelligence for diabetic retinopathy screening: Policy implications.

Artificial intelligence (AI) has evolved over the last few years; its use in DR screening has been demonstrated in multiple evidences across the globe. However, there are concerns right from the data acquisition, bias in data, difficulty in comparing between different algorithm, challenges in machine learning, its application in different group of population, and human barrier to AI adoption in health care. There are also legal and ethical concerns related to AI. The tension between risks and concerns on one hand versus potential and opportunity on the other have driven a need for authorities to implement policies for AI in DR screening to address these issues. The policy makers should support and facilitate research and development of AI in healthcare, but at the same time, it has to be ensured that the use of AI in healthcare aligns with recognized standards of safety, efficacy, and equity. It is essential to ensure that algorithms, datasets, and decisions are auditable and when applied to medical care (such as screening, diagnosis, or treatment) are clinically validated and explainable. Policy frameworks should require design of AI systems in health care that are informed by real-world workflow and human-centric design. Lastly, it should be ensured that healthcare AI solutions align with all relevant ethical obligations, from design to development to use and to be delivered properly in the real world.

Barriers to blockchain adoption in health-care industry: an Indian perspective

PurposeThis paper aims to identify barriers toward the adoption of blockchain (BC) technology in Indian health-care industry and also examines the significant issues of BC applications in health-care industry.Design/methodology/approachThe barriers of the study are identified by two phases including the review of literature and semistructured interviews with hospital staff and administration operating in India. The experts (N = 15) are being taken from top-level management, IT experts and patients from the hospitals. The study implemented integrated total interpretative structural modeling-FUZZY-Cross-impact matrix multiplication applied to classification (TISM-FUZZY-MICMAC) methods for identifying the interrelationship among the barriers.FindingsA total of 15 barriers have been determined in the Indian health-care industry through discussion with the selected experts. TISM is applied to develop multilevel structure for BC barriers. Further, FUZZY-MICMAC has been used to compute driving and dependent barriers. The findings suggest that low awareness related to legal issues and low support from high level of management have maximum driving power.Research limitations/implicationsThe present study applies multicriterion approach to identify the limited barriers in BC adoption in health care. Future studies may develop the relationship and mark down the steps for implementation of BC in health-care setting of a developing economy. Empirical study can be conducted to verify the results along with selected case studies.Practical implicationsThe present study identifies the BC adoption barriers in health-care industry. The study examines the pertinent issues in context to major support required, bottlenecks in adoption, key benefits of adoption planning and activities. The technology adoption practices are expected to provide applications such as distributed, secured medical and clinical data and patient centric systems that will enhance the efficiency of the health-care industry.Originality/valueThe study is among few primary studies that identify and analyze the BC adoption in health-care industry.

What is needed to mainstream artificial intelligence in health care?

Artificial intelligence (AI) has become a mainstream technology in many industries, but not yet in health care. Although basic research and commercial investment are burgeoning across various clinical disciplines, AI remains relatively non-existent in most healthcare organisations. This is despite hundreds of AI applications having passed proof-of-concept phase, and scores receiving regulatory approval overseas. AI has considerable potential to optimise multiple care processes, maximise workforce capacity, reduce waste and costs, and improve patient outcomes. The current obstacles to wider AI adoption in health care and the pre-requisites for its successful development, evaluation and implementation need to be defined.

Implementation science and periodontal practice: Translation of evidence into periodontology.

The advent of evidence-based practice in the 1990s led to the development of processes and resources to support the use of high-quality research in the provision of health care. As the evidence-based approach to health care continues to evolve, it has become apparent that mere creation and access to scientific knowledge is not sufficient to facilitate its routine adoption in health care. Throughout any health care system, there are inherent barriers preventing the adoption and routine use of new evidence in patient care. These barriers include provider-level factors, such as knowledge and access to new evidence, as well as each provider's attitudes and beliefs around adopting and applying the evidence with their patients. Importantly, there are also health care system-level barriers that, even among willing providers, prevent the easy adoption of new evidence and routine application in patient care. In addition to barriers, there are facilitators that help promote adoption of evidence into practice. Understanding and addressing barriers and facilitators to promote adoption of evidence into practice has led to the growth of a new field known as implementation science. Successful application of implementation science in all areas of health care, including periodontology, will help bridge the gap between what are known from clinical research to be effective treatments and what treatments should be applied routinely in clinical practice. This article reviews key concepts in implementation science and how its application in periodontology can facilitate the translation of high-quality evidence into routine periodontal practice and improved patient outcomes.

The Influence of the COVID-19 Pandemic on Technology: Adoption in Health Care

SARS-CoV-2 (coronavirus disease 2019 [COVID-19]) has affected nearly every aspect of our lives. It has certainly changed how we deliver health care. In part, it has accelerated the development and use of technology solutions in health care. These technology tools were originally identified as mechanisms that would make future care easier or better; however, these tech solutions amidst COVID-19 are now viewed as essential. The use of technology discussed in this article has been accelerated during the current crisis because technology can provide distance, safety for health care professionals and patients, faster results reporting, virtual visits, and more. As we move forward, technology will continue to be positioned to best address public health needs and improve care efficiencies, as well as develop treatments and vaccines. Specifically, this article explores the adoption and growth of technology tools including: telehealth, artificial intelligence, and robotics over the first 90 days of the COVID-19 pandemic in the United States.

Artificial Intelligence in Health Care? Evidence from Online Job Postings

This paper documents a puzzle. Despite the numerous popular press discussions of artificial intelligence (AI) in health care, there has been relatively little adoption. Using data from Burning Glass Technologies on millions of online job postings, we find that AI adoption in health care remains substantially lower than in most other industries and that under 3 percent of the hospitals in our data posted any jobs requiring AI skills from 2015-2018. The low adoption rates mean any statistical analysis is limited. Nevertheless, the adoption we do observe shows that larger hospitals, larger counties, and integrated salary model hospitals are more likely to adopt.