Changed sleep according to weighted blanket adherence in a 16-week sleep intervention among children with attention-deficit/hyperactivity disorder.

STUDY OBJECTIVES: To examine differences in sample characteristics and longitudinal sleep outcomes according to weighted blanket adherence. METHODS: Children with attention-deficit/hyperactivity disorder (ADHD) (n =94), mean age 9.0 (sd 2.2, range 6-14) participated in a 16-week sleep intervention with weighted blankets (WB). Children were classified as WB adherent (use of WB ≥ 4 nights/week) or non-adherent (use of WB ≤ 3 nights/week). Changes in objectively measured sleep by actigraphy, parent-reported sleep problems (Children's Sleep Habits Questionnaire (CSHQ)) and child-reported Insomnia Severity Index (ISI) were evaluated according to adherence with mixed effect models. Gender, age, and ADHD subtype were examined as potential moderators. RESULTS: Children adherent to WBs (48/94) showed an early response in sleep outcomes and an acceptance of the WB after four weeks of use as well as a decrease in parent- (CSHQ) (-5.73, P = .000) and child-reported sleep problems (ISI) (-4.29, P = .005) after 16 weeks. The improvement in sleep was larger among WB adherent vs. non-adherent (between-group difference: CSHQ: -2.09, P = .038; ISI: -2.58, P =.007). Total sleep time was stable for children adherent to WB but decreased for non-adherent (between-group difference: +16.90, P = .019). CONCLUSIONS: An early response in sleep and acceptance of the WB predicted later adherence to WBs. Improvements in sleep were more likely among WB adherents vs. non-adherents. Children with ADHD may thus benefit from using WBs to handle their sleep problems.

The efficacy of weighted blankets for sleep in children with attention-deficit/hyperactivity disorder-A randomized controlled crossover trial.

Weighted blankets are a non-pharmacological intervention for treating sleep and anxiety problems in children with attention-deficit/hyperactivity disorder. However, research on the efficacy of weighted blankets is sparse. The aim of this randomized controlled trial with a crossover design (4 + 4 weeks) was to evaluate the efficacy of weighted blankets on sleep among children with attention-deficit/hyperactivity disorder and sleeping problems. Children diagnosed with uncomplicated Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition attention-deficit/hyperactivity disorder with verified sleep problems were randomized to start with either a weighted blanket or a lighter control blanket. Data collection was performed at weeks 0, 4 and 8 using actigraphy, questionnaires and a daily sleep diary. T-tests were used to evaluate efficacy. The study included 94 children with attention-deficit/hyperactivity disorder (mean age 9.0 [sd 2.2] years; 54 [57.4%] boys). Weighted blankets had a significant effect on total sleep time (mean diff. 7.72 min, p = 0.027, Cohen's d = 0.24), sleep efficiency (mean diff. 0.82%, p = 0.038, Cohen's d = 0.23) and wake after sleep onset (mean diff. -2.79 min, p = 0.015, Cohen's d = -0.27), but not on sleep-onset latency (p = 0.432). According to our exploratory subgroup analyses, weighted blankets may be especially beneficial for improving total sleep time in children aged 11-14 years (Cohen's d = 0.53, p = 0.009) and in children with the inattentive attention-deficit/hyperactivity disorder subtype (Cohen's d = 0.58, p = 0.016). Our results suggest that weighted blankets may improve children's sleep and could be used as an alternative to pharmacological sleep interventions.

A Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project.

BACKGROUND: Artificial intelligence (AI) has the potential in health care to transform patient care and administrative processes, yet health care has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge and tools to guide such processes when implementing AI-based applications in health care. OBJECTIVE: The aim of this protocol is to describe the development, testing, and evaluation of a framework, "Artificial Intelligence-Quality Implementation Framework" (AI-QIF), intended to guide decisions and activities related to the implementation of various AI-based applications in health care. METHODS: The paper outlines the development of an AI implementation framework for broad use in health care based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope, and detailed guidance concerning important activities and considerations for successful implementation. The development will proceed in 5 phases with primarily qualitative methods being used. The process starts with phase I, in which an AI-adapted version of QIF is created (AI-QIF). Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to usability testing of the prototype in health care environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Cocreation is a guiding principle for the project and is an important aspect in 4 of the 5 development phases. The cocreation process will enable the use of both on research-based and practice-based knowledge. RESULTS: The project is being conducted within the frame of a larger research program, with the overall objective of developing theoretically and empirically informed frameworks to support AI implementation in routine health care. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide the implementation of AI-based applications in health care will draw on knowledge and experience acquired from these activities. The framework is being developed over 2 years, from January 2023 to December 2024. It is under continuous development and refinement. CONCLUSIONS: The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in health care based on the premise that implementation processes benefit from being well-prepared and structured. The framework will be coproduced to enhance its relevance, validity, usefulness, and potential value for application in practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50216.

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice.

Background: Artificial intelligence (AI) is predicted to be a solution for improving healthcare, increasing efficiency, and saving time and recourses. A lack of ethical principles for the use of AI in practice has been highlighted by several stakeholders due to the recent attention given to it. Research has shown an urgent need for more knowledge regarding the ethical implications of AI applications in healthcare. However, fundamental ethical principles may not be sufficient to describe ethical concerns associated with implementing AI applications. Objective: The aim of this study is twofold, (1) to use the implementation of AI applications to predict patient mortality in emergency departments as a setting to explore healthcare professionals' perspectives on ethical issues in relation to ethical principles and (2) to develop a model to guide ethical considerations in AI implementation in healthcare based on ethical theory. Methods: Semi-structured interviews were conducted with 18 participants. The abductive approach used to analyze the empirical data consisted of four steps alternating between inductive and deductive analyses. Results: Our findings provide an ethical model demonstrating the need to address six ethical principles (autonomy, beneficence, non-maleficence, justice, explicability, and professional governance) in relation to ethical theories defined as virtue, deontology, and consequentialism when AI applications are to be implemented in clinical practice. Conclusions: Ethical aspects of AI applications are broader than the prima facie principles of medical ethics and the principle of explicability. Ethical aspects thus need to be viewed from a broader perspective to cover different situations that healthcare professionals, in general, and physicians, in particular, may face when using AI applications in clinical practice.

Developing sustainable service user involvement practices in mental health services in Sweden: the "Userinvolve" research program protocol.

Background: The purpose of this paper is to outline the protocol for the research program "UserInvolve," with the aim of developing sustainable, service user involvement practices in mental health services in Sweden. Methods: This protocol outlines the knowledge gap and aim of the UserInvolve-program. It further provides an overview of the research infrastructure, with specific focus on the organization and management of the program as well as the design of the six underlying research projects. These six research projects form the core of the UserInvolve-program and will be carried out during a six-year period (2022-2027). The projects are focused on examining articulations of experiential knowledge in user collectives, on four specific user involvement interventions (shared decision-making, peer support, user-focused monitoring, and systemic involvement methods) and on developing theory and method on co-production in mental health research and practice. Results or conclusion: The knowledge gained through the co-production approach will be disseminated throughout the program years, targeting service users, welfare actors and the research community. Based on these research activities, our impact goals relate to strengthening the legitimacy of and methods for co-production in the mental health research and practice field.

Implementing AI in healthcare-the relevance of trust: a scoping review.

Background: The process of translation of AI and its potential benefits into practice in healthcare services has been slow in spite of its rapid development. Trust in AI in relation to implementation processes is an important aspect. Without a clear understanding, the development of effective implementation strategies will not be possible, nor will AI advance despite the significant investments and possibilities. Objective: This study aimed to explore the scientific literature regarding how trust in AI in relation to implementation in healthcare is conceptualized and what influences trust in AI in relation to implementation in healthcare. Methods: This scoping review included five scientific databases. These were searched to identify publications related to the study aims. Articles were included if they were published in English, after 2012, and peer-reviewed. Two independent reviewers conducted an abstract and full-text review, as well as carrying out a thematic analysis with an inductive approach to address the study aims. The review was reported in accordance with the PRISMA-ScR guidelines. Results: A total of eight studies were included in the final review. We found that trust was conceptualized in different ways. Most empirical studies had an individual perspective where trust was directed toward the technology's capability. Two studies focused on trust as relational between people in the context of the AI application rather than as having trust in the technology itself. Trust was also understood by its determinants and as having a mediating role, positioned between characteristics and AI use. The thematic analysis yielded three themes: individual characteristics, AI characteristics and contextual characteristics, which influence trust in AI in relation to implementation in healthcare. Conclusions: Findings showed that the conceptualization of trust in AI differed between the studies, as well as which determinants they accounted for as influencing trust. Few studies looked beyond individual characteristics and AI characteristics. Future empirical research addressing trust in AI in relation to implementation in healthcare should have a more holistic view of the concept to be able to manage the many challenges, uncertainties, and perceived risks.

Social Capital in Relation to Mental Health-The Voices of Adolescents in Sweden.

The social environment that adolescents interact in has undoubtedly changed over the past decades. The latent constructs of social capital that have been described in theory may be universal, but it is necessary to reveal sociocultural specific pathways and manifestation in order to validly operationalize social capital for adolescents. There is a call for qualitative data to enhance our understanding of social capital for adolescents today and the specific sociocultural context they live in. The aim of this study was to explore social capital from the perspective of adolescents in relation to mental health. Twenty-three semi-structured interviews were conducted in a school setting with a sample of adolescents aged 11 and 15 years. Qualitative content analysis was applied, and analysis remained on a manifest level. From having adolescents describe their social relations and networks in relation to mental health, three main categories were formed: accessing a safe space, with sub-categories of trusting enough to share, having someone close to you, and being part of an inclusive and honest environment; feeling connected to others, with sub-categories of hanging out and having things in common; and maintaining control, with sub-categories of deciding for yourself, dealing with change, and having social skills. Having access to a safe space is vital for adolescents' mental health, by providing resources such as mutual trust, honesty, and unconditional access. Feeling connected to others is important in close relationships and reveals the glue that holds networks together, but also links to sociability in a wider sense. Predictability in adolescents' social relationships and networks, influenced by internal and external factors, may be a resource of increasing importance in todays' society and an interesting subject for intervention and future research on social capital and adolescent mental health.

Innovation in healthcare: leadership perceptions about the innovation characteristics of artificial intelligence-a qualitative interview study with healthcare leaders in Sweden.

BACKGROUND: Despite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation. METHODS: The study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare. RESULTS: Participants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation. CONCLUSION: Healthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI's opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare.

Strengthening Digital Transformation and Innovation in the Health Care System: Protocol for the Design and Implementation of a Multidisciplinary National Health Innovation Research School.

BACKGROUND: Digital health technologies have the potential to transform health care services to be more cost-effective, coordinated, and accessible on equal terms for entire populations. In the future, people will be assisted by such technologies to monitor their health status, take preventive measures, and have more control of their health situation. An increase in digital supplementation or substitution of physical care visits can potentially add value to patients and care providers by increasing accessibility, safety, and quality of care. However, health care organizations struggle with the challenges of developing and implementing digital health technologies and services in practice. As a response to this, we have developed a national multidisciplinary research school to increase competence and capacity for research on the development, implementation, and dissemination of digital health technology solutions. The overall aim of the research school is to increase national competence and capacity for the development, implementation, and dissemination of digital health technology to increase the preparedness to support and facilitate the ongoing digital transformation in the health care system. OBJECTIVE: The purpose of this paper is to outline the protocol for the development and implementation of a national multidisciplinary doctoral education program of health innovation supporting digital transformation in the health care system. METHODS: A national multidisciplinary research school for health innovation was planned in collaboration between 7 Swedish universities and their partners from industry and the public sector. The research school will run over 6 years, of which 5 years are dedicated for the doctoral education program and 1 year for the project start-up and closing. In this paper, we outline the methodological approach of the research school; the combining of knowledge and expertise of the universities that are important to run the research school; the jointly formulated research-oriented and societally relevant research focus, goals, and objectives for the research school; the established and developed relationships with partners from industry and the public sector for joint research training projects; the forms of collaboration in the research school; and the format of the doctoral education process. RESULTS: The research school was funded in December 2021 and started in March 2022. The research school starts with an initiation period from March 2022 to December 2022 where the infrastructure and the action plans to run the school are set up. The PhD projects start in January 2023, and these projects will be completed in 5 years. Additional activities within the research program are doctoral courses, networking activities, and dissemination of results. CONCLUSIONS: The network of several partners from industry, public sector, and academia enables the research school to pose research questions that can contribute to solving relevant societal problems related to the development, evaluation, implementation, and dissemination of methods and processes assisted by digital technologies. Ultimately, this will promote innovation to improve health outcomes, quality of care, and prioritizations of resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46595.

What Are We Talking About When We Talk About Information-Driven Care? A Delphi-Study on a Definition.

In Sweden, the term information-driven care has recently been put forward by healthcare organizations and researchers as a means for taking a comprehensive approach to the introduction of Artificial Intelligence (AI) in healthcare. The aim of this study is to systematically generate a consensus definition of the term information-driven care. To this end, we are conducting a Delphi study utilizing literature and experts' opinions. The definition is needed to enable knowledge exchange on information-driven care and operationalize its introduction into healthcare practice.

Ethical Perspectives on Implementing AI to Predict Mortality Risk in Emergency Department Patients: A Qualitative Study.

Artificial intelligence (AI) is predicted to improve health care, increase efficiency and save time and recourses, especially in the context of emergency care where many critical decisions are made. Research shows the urgent need to develop principles and guidance to ensure ethical AI use in healthcare. This study aimed to explore healthcare professionals' perceptions of the ethical aspects of implementing an AI application to predict the mortality risk of patients in emergency departments. The analysis used an abductive qualitative content analysis based on the principles of medical ethics (autonomy, beneficence, non-maleficence, and justice), the principle of explicability, and the new principle of professional governance, that emerged from the analysis. In the analysis, two conflicts and/or considerations emerged tied to each ethical principle elucidating healthcare professionals' perceptions of the ethical aspects of implementing the AI application in emergency departments. The results were related to aspects of sharing information from the AI application, resources versus demands, providing equal care, using AI as a support system, trustworthiness to AI, AI-based knowledge, professional knowledge versus AI-based information, and conflict of interests in the healthcare system.

Healthcare Leaders' Perceptions of the Usefulness of AI Applications in Clinical Work: A Qualitative Study.

Artificial intelligence (AI) is often presented as a technology that changes healthcare and is useful in clinical work in disease prediction, diagnosis, treatment effectiveness, and precision health. This study aimed to explore healthcare leaders' perceptions of the usefulness of AI applications in clinical work. The study was based on qualitative content analysis. Individual interviews were conducted with 26 healthcare leaders. The usefulness of AI applications in clinical care was described in terms of expected benefits for 1) patients as supporting individualized self-management and person-centered information support tools 2) healthcare professionals in terms of providing decision-support in diagnostics, risk assessments, treatment recommendations, warning systems, and as a new colleague supporting the clinical work, and 3) organizations as providing patient safety and decision-support in prioritizing healthcare resources in organizing healthcare.

Experiences of Using Weighted Blankets among Children with ADHD and Sleeping Difficulties.

Introduction: Sleeping difficulties are common in children with attention deficit hyperactivity disorder (ADHD). A sleep intervention with weighted blankets was designed to increase current understanding of using weighted blankets to target children's individual needs in connection with sleep and daytime functioning. Aim: To explore how children with ADHD and sleeping difficulties experience the use of weighted blankets. Methods: An explorative qualitative design in which 26 children with ADHD and sleeping difficulties, 6-15 years old, were interviewed about a sleep intervention with weighted blankets. Four categories emerged from qualitative content analysis. Results: Children's experiences revealed that the use of weighted blankets 1) requires a commitment, by adjusting according to needs and preferences and adapting to the environment; 2) improves emotional regulation by feeling calm and feeling safe; 3) changes sleeping patterns by creating new routines for sleep and improving sleep quality; and 4) promotes everyday participation by promoting daily function and balancing activity and sleep. Conclusions: Using weighted blankets promoted children's management of daily life with ADHD and sleeping difficulties. Occupational therapists can improve the assessment and delivery of weighted blankets tailored to individual needs based on increased knowledge from the children themselves.

Co-design Process of a Digital Return-to-Work Solution for People With Common Mental Disorders: Stakeholder Perception Study.

BACKGROUND: Service users and other stakeholders have had few opportunities to influence the design of their mental health and return-to-work services. Likewise, digital solutions often fail to align with stakeholders' needs and preferences, negatively impacting their utility. mWorks is a co-design initiative to create a digital return-to-work solution for persons with common mental disorders that is acceptable and engaging for those receiving and delivering the intervention. OBJECTIVE: This study aimed to describe stakeholder perceptions and the involvement of a design process during the prototype development of mWorks. METHODS: A co-design approach was used during the iterative development of mWorks. Overall, 86 stakeholders were recruited using a combination of purposeful and convenience sampling. Five stakeholder groups represented service users with experience of sick leave and common mental disorders (n=25), return-to-work professionals (n=19), employers (n=1), digital design and system developers (n=4), and members of the public (n=37). Multiple data sources were gathered using 7 iterations, from March 2018 to November 2020. The rich material was organized and analyzed using content analysis to generate themes and categories that represented this study's findings. RESULTS: The themes revealed the importance of mWorks in empowering service users with a personal digital support solution that engages them back in work. The categories highlighted that mWorks needs to be a self-management tool that enables service users to self-manage as a supplement to traditional return-to-work services. It was also important that content features helped to reshape a positive self-narrative, with a focus on service users' strengths and resources to break the downward spiral of ill health during sick leave. Additional crucial features included helping service users mobilize their own strategies to cope with thoughts and feelings and formulate goals and a plan for their work return. Once testing of the alpha and beta prototypes began, user engagement became the main focus for greater usability. It is critical to facilitate the comprehension and purpose of mWorks, offer clear guidance, and enhance motivational and goal-setting strategies. CONCLUSIONS: Stakeholders' experience-based knowledge asserted that mWorks needs to empower service users by providing them with a personal support tool. To enhance return-to-work prospects, users must be engaged in a meaningful manner while focusing on their strengths and resources.

Psychosocial Predictors of Drop-Out from Organised Sport: A Prospective Study in Adolescent Soccer.

In recent years an increased drop-out rate in adolescents' soccer participation has been observed. Given the potentially adverse consequences of drop-out from soccer, more information about risk factors for drop-out is warranted. In the current study, Classification and Regression Tree (CRT) analysis was used to investigate demographic and motivational factors associated with an increased risk of drop-out from adolescent soccer. The results of this study indicate that older age, experiencing less autonomy support from the coach, less intrinsic motivation, being female, and lower socioeconomic status are factors associated with an increased risk of drop-out. An interpretation of the results of this study is that coaches play a central part in creating a sports context that facilitates motivation and continued soccer participation. Based on the findings of the current study we propose that soccer clubs implement theoretically informed coach education programs to help coaches adopt autonomy-supportive coaching strategies.

Exploring psychosocial risk factors for dropout in adolescent female soccer.

OBJECTIVES: We examined the manner in which age, participation in other sports, socioeconomic status, perceived sport competence, achievement goal orientations, and perceived motivational climate may interact to predict the risk of dropout among adolescent female soccer players. METHODS: Self-reported data from 519 female soccer players between 10 and 19 years of age (M = 13.41, SD = 1.77) were analysed using a person-centred approach to uncover the interactions among risk factors and their relative predictability of dropout. RESULTS: Perceived motivational climate was identified as the main predictor, where relatively lower levels of mastery climate were associated with a higher dropout tendency (absolute risk reduction [ARR] = 12.2% ±6.1% [95% CL]). If combined with relatively lower levels of mastery climate, then relatively lower levels of perceived sport competence were related to higher dropout risks (ARR = 16.5% ±9.5%), whereas, in combination with relatively higher levels of mastery climate, then relatively lower levels of ego-orientated achievement goals were associated with higher dropout rates (ARR = 10.8% ±12.6%). CONCLUSIONS: Our findings afford novel insights into the interactions between, and the relative importance of, various risk factors for dropout in adolescent female soccer. This knowledge may be useful for soccer associations, clubs, and coaches when developing guidelines and strategies that aim to foster young females' sustained participation in organised soccer.

Current Conceptualization and Operationalization of Adolescents' Social Capital: A Systematic Review of Self-Reported Instruments.

There is a great heterogeneity in the conceptualization and operationalization of social capital in empirical research targeting adolescents. There has not yet been an attempt to systematically map and psychometrically evaluate the existing instruments for measuring social capital that have been developed and validated for adolescent samples. The aim of this systematic review was to identify and evaluate the design and psychometric properties of self-reported instruments for social capital, specifically developed and validated for use among adolescents. The design of this study was a systematic review guided by the COSMIN methodology for systematic reviews of Patient Reported Outcome Measures. The search included six electronic databases and no time frame was applied. Twenty studies were identified as describing the development and validation of a social capital instrument for adolescent samples. The results reveal common denominators, but also great variation in the design and validation of the instruments. Adolescents were only involved in the development procedures of four instruments. There is a lack of social capital instruments that cover both the multidimensionality of social capital and contextual relevance in relation to adolescents. Careful examination of instruments should thus precede a decision when designing studies and further instrument development involving the target group is encouraged.

Parents' Experiences of Direct and Indirect Implications of Sleep Quality on the Health of Children with ADHD: A Qualitative Study.

Sleep problems represent a significant challenge for children with ADHD. However, lack of knowledge about how sleep affects children with ADHD in terms of their health and everyday life prevents the development and implementation of interventions to promote sleep. The aim of this study was to explore parents' experiences of direct and indirect implications of sleep quality on the health of children with ADHD. The study used an abductive qualitative design, with Tengland's two-dimensional theory of health as a deductive analysis framework. Semi-structured interviews were conducted with 21 parents of children aged 6-13 with ADHD and sleep problems. The parents experienced that sleep influenced their children's abilities to control emotional behaviour related to ADHD and to manage everyday life. Sleep also had an impact on the children's well-being, in relation to both vitality and self-esteem. In conclusion, the results show important direct and indirect implications of sleep quality on the health of children with ADHD. This implies a need for greater focus on sleep, to target both abilities and well-being in promoting health among children with ADHD.

Artificial Intelligence Applications in Health Care Practice: Scoping Review.

BACKGROUND: Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. OBJECTIVE: The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? METHODS: A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. RESULTS: Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. CONCLUSIONS: Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.

Legitimizing user knowledge in mental health services: Epistemic (in)justice and barriers to knowledge integration.

Including the voices and knowledge of service users is essential for developing recovery-oriented and evidence-based mental health services. Recent studies have however, suggested that challenges remain to the legitimization of user knowledge in practice. To further explore such challenges, a co-production study was conducted by a team of researchers and representatives from user organizations in Sweden. The aim of the study was to explore the barriers and facilitators to the legitimacy of user knowledge, as a central factor in sustainably implementing user influence in mental health practice. A series of workshops, with representatives of mental health services and user organizations were conducted by the research team to explore these issues. The analysis built on the theoretical framework of epistemic injustice, and the underlying aspects, testimonial, hermeneutic and participation-based injustice, were utilized as a framework for a deductive analysis. Results suggest that this is a useful model for exploring the complex dynamics related to the legitimacy of user knowledge in mental health systems. The analysis suggests that the legitimacy of user knowledge is related to the representativeness of the knowledge base, the systematic formulation of this knowledge in applicable methods, access to resources and positions within the mental health system and participation in the process of integrating this knowledge-base in mental health contexts. Legitimizing user knowledge in practice additionally challenges mental health systems to support readiness for change in working environments and to address the power and role issues that these changes involve.