Resilience in children of parents with mental illness, alcohol or substance misuse-An integrative review.

AIM: The aim of this integrative review was to investigate how resilience has been researched and explore experiences of resilience, in children of parents with mental illness or alcohol or substance misuse. DESIGN: An integrative review. METHOD: The search included three major electronic databases, PubMed, Scopus and PsycINFO with the aim of identifying peer-reviewed studies where the concept of resilience was explored as resilience, coping, adaptation or protective factors. RESULTS: Out of 4016 studies, 14 were included after meeting predetermined criteria and methodological quality evaluation. The findings are presented in five categories: characteristics of the studies, operationalization and interpretation of resilience, individual resources, family resources and resources outside the family. PATIENT OR PUBLIC CONTRIBUTION: Resilience in children of parents with mental illness or substance misuse refers to coping strategies, protective factors and absence of symptoms or risk behaviour despite being exposed to risk. We suggest a three-level approach for mapping of resilience resources in the target group: the individual level, family level and outside of the family that includes both non-professionals and professionals. The use of disengagement or avoidance strategies implies poor resilience but may be necessary in absence of support, as acts of self-preservation during chaotic periods or harmful situations.

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare.

Background: Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this. Challenges with the three components of EBP: Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources. AI assistance for the three components of EBP: AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue. Conclusion: This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.

SoCap YMH - youth mental health, social capital and help-seeking: a study protocol.

Background: The increase in adolescents reporting mental health problems presents a major public health challenge. The complex association between mental health and social capital motivates further investigation of social capital as a crucial aspect in shaping adolescents' help-seeking knowledge, attitudes, and behaviours. Aim: This protocol presents a project that aims to investigate social capital in relation to help-seeking and mental health in close collaboration with adolescents and key stakeholders in the school setting, in the southern part of Sweden. Methods: A mixed-method design with three interconnected work packages (WP) will be undertaken with an emphasis on co-production where adolescents are involved throughout the process. WP1 is a development and validation of two questionnaire instruments for assessing social capital and help-seeking in adolescence. WP2 is a longitudinal quantitative study involving 1,500 adolescents from two regions representing rural and suburban/urban settings. Adolescents aged 15 will be asked to complete questionnaires concerning social capital, mental health, and help-seeking in a baseline and one-year follow-up, allowing for investigation of the role of social capital for help-seeking. WP3 is designed to elucidate experiences and knowledge of adolescents and key stakeholders via collaborative World Café workshops. These will be held along the project to evolve the generated knowledge and maximize it's applicability during and after the project is finalized. Conclusion: The results are expected to further the understanding of the relationship between adolescents' social capital, mental health, and help-seeking, to contribute to a deeper understanding of the mechanisms behind the paradoxical help-seeking patterns among adolescents today and to narrow the gap between research and practice to produce sustainable and efficient strategies, which may facilitate help-seeking and improve the mental health of adolescents within existing organizational structures.

Sex Differences in Children with Uncomplicated Attention Deficit/Hyperactivity Disorder and Sleep Problems.

BACKGROUND: Approximately 7.6% of children are diagnosed with attention deficit/hyperactivity disorder (ADHD), and sleep impairments affect 25-85%. There is a noticeable lack of research on girls and sex differences. The aim of this study was to examine sex differences in children with uncomplicated ADHD and sleep problems. METHODS: Cross-sectional baseline data were retrieved from a randomized controlled trial with weighted blankets (55 boys and 41 girls, 6-14 years) on a cohort recently diagnosed with uncomplicated ADHD and sleep problems. Differences between boys and girls in ADHD symptoms, objectively and subjectively measured sleep, anxiety, and functioning were examined via parent- or self-reported validated instruments. RESULTS: Girls reported significantly lower (worse) satisfaction with well-being, life overall, and school, but not for family. Parents reported more sleep anxiety and night-time wakings among boys, but no sex differences in other measures and also not in self-reported measures or objective sleep measures. Children who reported worry, sadness, or unhappiness had more sleep problems. CONCLUSIONS: Boys with ADHD and sleep problems may need support with sleep-related anxiety and night-time wakings, while girls may require support with overall functioning. Additionally, children who express feelings of worry, sadness, or unhappiness alongside their ADHD symptoms should have attention given to their sleep.

Validity and reliability of the Swedish version of the Children's Sleep Habits Questionnaire (CSHQ-SWE).

BACKGROUND: To translate and culturally adapt the Children's Sleep Habits Questionnaire (CSHQ) to a Swedish version, CSHQ-SWE, and to assess its validity and reliability for use with children with attention deficit hyperactivity disorder (ADHD). METHODS: A total of 84 children with ADHD (51 boys and 33 girls; 6-12 years) and parents (7 men and 77 women; 28-51 years) were included in the study. CSHQ was translated and culturally adapted to Swedish, and assessed for concurrent validity with sleep actigraphy (analyzed by Kendall's Tau) and for reliability by internal consistency (analyzed by McDonald's Omega H). Face and content validity was evaluated by parents (n = 4) and healthcare professionals (n = 6) qualitatively (comprehensiveness, relevance, and comprehensibility assessed by interviews and analyzed by thematic analysis) and quantitatively (analyzed by content validity ratio and content validity index for 33 items and four non-scored inquiries). RESULTS: Parent-reported sleep problems (CSHQ-SWE total score) were moderately correlated with less "Sleep Efficiency" (Tau = -0.305; p < 0.001) measured by sleep actigraphy. Parent-reported problems with "Sleep Onset Delay" was moderately correlated with measured time for "Sleep Onset Latency" (Tau = 0.433; p < 0.001). Parent-reported problems with "Night Wakings" were weakly correlated with measured time for "Wake After Sleep Onset" (Tau = 0.282; p < 0.001). Parents estimation of "Total daily sleep duration" was moderately correlated with measured "Total Sleep Time" (Tau = 0.386; p < 0.001). Five of the seven subscales reached an acceptable level for internal consistency (McDonald's Omega H > 0.700). Comprehensiveness, relevance, and comprehensibility of CSHQ-SWE were satisfactory overall. Content validity ratio was 0.80 to 1.00 for six items, 0.00 to 0.60 for 22 items, and < 0.00 for nine items. Content validity index was 0.22. CONCLUSIONS: CSHQ-SWE demonstrated acceptable concurrent validity with objectively measured sleep and internal consistency, whereas the overall results of face and content validity assessment varied. The instrument needs to be further evaluated regarding construct validity, responsiveness, test-retest reliability, and its generalization to other populations.

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.

Adult survivors' perceptions of their childhood and the influences of being treated for acute lymphoblastic leukaemia with allogeneic hematopoietic stem cell transplantation as a child: A phenomenographic study.

PURPOSE: Adults who had acute lymphoblastic leukaemia (ALL) as children and were treated with allogeneic hematopoietic stem cell transplantation (aHSCT) may have been affected in their lives due to several long-term complications. From a clinical point of view, it is of interest to study how survivors describe their perceptions of their childhood today. The aim was therefore to describe how adults perceived their childhood and the influences of being treated for ALL with aHSCT as a child. METHOD: Semi-structured telephone interviews were undertaken with 18 adults who had been treated for childhood ALL with aHSCT and were included in a national cohort of childhood ALL survivors, diagnosed between 1985 and 2007 at an age between 0 and 17 years. A phenomenographic analysis was used. RESULTS: Three categories emerged: Feeling different, Feeling security and Feeling guilty. The informants felt that they had been different from other children but had felt security with the healthcare professionals and in care. They felt guilty because both their siblings' and parents' lives had been affected, but at the same time many perceived that they and their family members had become closer to one another. CONCLUSIONS: The results emphasised that adults who had been treated for childhood ALL with aHSCT were affected both in negative and positive ways during their childhood. This indicates the importance for early psychosocial care interventions directed to children during their treatment, but also the need for person-centred psychological care in long-term outpatient clinics.

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.

BACKGROUND: Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). OBJECTIVE: This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system's outputs to analyze usability aspects and obtain insights related to future implementation. METHODS: A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. RESULTS: The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. CONCLUSIONS: This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52744.

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.

The effects of interpersonal development programmes with sport coaches and parents on youth athlete outcomes: A systematic review and meta-analysis.

Interpersonal coach-and parent development programmes (CDP and PDP, respectively), have the goal to foster positive youth sport experiences through high-quality relations between coaches, parents, and youth athletes. In this paper we systematically reviewed the extant literature and estimate the overall magnitude of such programmes and how they can inform future interventions. Specifically, we aimed to: (a) conduct a systematic review on the literature of interpersonal CDPs and PDPs within the youth sport context; (b) examine the effects of such interventions on youth athlete outcomes via a meta-analysis. English written peer-reviewed publications and grey literature was identified through electronic search in databases and manual searches of reference lists. By utilising a priori criteria for inclusion and exclusion, 33 studies describing interpersonal CDPs, and PDPs were identified in the systematic review. Studies that presented required data for estimation of Hedge's g effect sizes were included in the meta-analysis (k = 27). By and large, the included studies used a quasi-experimental design (58%), sampled from team sports (79%), and reported several delivery methods (e.g., workshops, audio feedback, observations, peer group discussions) and outcome measures (e.g., anxiety, autonomous motivation, self-confidence). Some interventions were based on the same delivery protocols (e.g., Coach Effectiveness Training, Mastery Approach to Coaching) or theoretical frameworks (e.g., Achievement Goal Theory, Self-Determination Theory). The meta-analysis showed statistically significant small, and medium, effect sizes on a subsample of youth athlete outcomes (e.g., task-related climate, fun and enjoyment, anxiety), indicating that coach interpersonal skills can contribute to positive youth sport experiences. Theory-based interpersonal CDPs and PDPs are recommended to expand the knowledge in this field of research.

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.

A Pragmatic Mapping of Perceptions and Use of Digital Information Systems in Primary Care in Sweden: Survey Study.

BACKGROUND: Electronic health records and IT infrastructure in primary care allow for digital documentation and access to information, which can be used to guide evidence-based care and monitor patient safety and quality of care. Quality indicators specified by regulatory authorities can be automatically computed and presented to primary care staff. However, the implementation of digital information systems (DIS) in health care can be challenging, and understanding factors such as relative advantage, compatibility, complexity, trialability, and observability is needed to improve the success and rate of adoption and diffusion. OBJECTIVE: This study aims to explore how DIS are used and perceived by health care professionals in primary care. METHODS: This study used quantitative assessment to gather survey data on the use and potential of DIS in health care in Sweden from the perspectives of primary care personnel in various roles. The digital questionnaire was designed to be short and contained 3 sections covering respondent characteristics, current use of platforms, and perceptions of decision support tools. Data were analyzed using descriptive statistics, nonparametric hypothesis testing, ordinal coefficient α, and confirmatory factor analysis. RESULTS: The study collected responses from participants across 10 regions of Sweden, comprising 31.9% (n=22) from private clinics and 68.1% (n=47) from public clinics. Participants included administrators (18/69, 26.1%), a medical strategist (1/69, 1.4%), and physicians (50/69, 72.5%). Usage frequency varied as follows: 11.6% (n=8) used DIS weekly, 24.6% (n=17) monthly, 27.5% (n=19) a few times a year, 26.1% (n=18) very rarely, and 10.1% (n=7) lacked access. Administrators used DIS more frequently than physicians (P=.005). DIS use centered on quality improvement and identifying high-risk patients, with differences by role. Physicians were more inclined to use DIS out of curiosity (P=.01). Participants desired DIS for patient follow-up, lifestyle guidance, treatment suggestions, reminders, and shared decision-making. Administrators favored predictive analysis (P<.001), while physicians resisted immediate patient identification (P=.03). The 5 innovation attributes showed high internal consistency (α>.7). These factors explained 78.5% of questionnaire variance, relating to complexity, competitive advantage, compatibility, trialability, and observability. Factors 2, 3, and 4 predicted intention to use DIS, with factor 2 alone achieving the best accuracy (root-mean-square=0.513). CONCLUSIONS: Administrators and physicians exhibited role-based DIS use patterns highlighting the need for tailored approaches to promote DIS adoption. The study reveals a link between positive perceptions and intention to use DIS, emphasizing the significance of considering all factors for successful health care integration. The results suggest various directions for future studies. These include refining the trialability and observability questions for increased reliability and validity, investigating a larger sample with more specific target groups to improve generalization, and exploring the relevance of different groups' perspectives and needs in relation to decisions about and use of DIS.

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.

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.

Barriers and Enablers for Implementation of an Artificial Intelligence-Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews.

BACKGROUND: Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context. OBJECTIVE: This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system. METHODS: Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework. RESULTS: Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized. CONCLUSIONS: Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.

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.

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.

Interdisciplinary Human-Centered AI for Hospital Readmission Prediction of Heart Failure Patients.

The evolution of clinical decision support (CDS) tools has been improved by usage of new technologies, yet there is an increased need to develop user-friendly, evidence-based, and expert-curated CDS solutions. In this paper, we show with a use-case how interdisciplinary expertise can be combined to develop CDS tool for hospital readmission prediction of heart failure patients. We also discuss how to make the tool integrated in clinical workflow by understanding end-user needs and have clinicians-in-the-loop during the different development stages.

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.