Background: The integration of artificial intelligence (AI) systems for automating medical history taking and triage can significantly enhance patient flow in health care systems. Despite the promising performance of numerous AI studies, only a limited number of these systems have been successfully integrated into routine health care practice. To elucidate how AI systems can create value in this context, it is crucial to identify the current state of knowledge, including the readiness of these systems, the facilitators of and barriers to their implementation, and the perspectives of various stakeholders involved in their development and deployment. Objective: This study aims to map and summarize empirical research on AI systems designed for automating medical history taking and triage in health care settings. Methods: The study was conducted following the framework proposed by Arksey and O’Malley and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search of 5 databases—PubMed, CINAHL, PsycINFO, Scopus, and Web of Science—was performed. A detailed protocol was established before the review to ensure methodological rigor. Results: A total of 1248 research publications were identified and screened. Of these, 86 (6.89%) met the eligibility criteria. Notably, most (n=63, 73%) studies were published between 2020 and 2022, with a significant concentration on emergency care (n=32, 37%). Other clinical contexts included radiology (n=12, 14%) and primary care (n=6, 7%). Many (n=15, 17%) studies did not specify a clinical context. Most (n=31, 36%) studies used retrospective designs, while others (n=34, 40%) did not specify their methodologies. The predominant type of AI system identified was the hybrid model (n=68, 79%), with forecasting (n=40, 47%) and recognition (n=36, 42%) being the most common tasks performed. While most (n=70, 81%) studies included patient populations, only 1 (1%) study investigated patients’ views on AI-based medical history taking and triage, and 2 (2%) studies considered health care professionals’ perspectives. Furthermore, only 6 (7%) studies validated or demonstrated AI systems in relevant clinical settings through real-time model testing, workflow implementation, clinical outcome evaluation, or integration into practice. Most (n=76, 88%) studies were concerned with the prototyping, development, or validation of AI systems. In total, 4 (5%) studies were reviews of several empirical studies conducted in different clinical settings. The facilitators and barriers to AI system implementation were categorized into 4 themes: technical aspects, contextual and cultural considerations, end-user engagement, and evaluation processes. Conclusions: This review highlights current trends, stakeholder perspectives, stages of innovation development, and key influencing factors related to implementing AI systems in health care. The identified literature gaps regarding stakeholder perspectives and the limited research on AI systems for automating medical history taking and triage indicate significant opportunities for further investigation and development in this evolving field. ©Elin Siira, Hanna Johansson, Jens Nygren.

Background: Acute lymphoblastic leukemia (ALL) is the most prevalent childhood malignancy. To improve long-term health-related quality of life (HRQOL) in adult survivors of childhood ALL, more longitudinal studies are needed to assess outcomes and risk factors throughout treatment and survivorship. The aim of this study was to examine the long-term changes in HRQOL, self-efficacy, and social support among adult survivors of childhood ALL and to explore the temporal relationship between HRQOL, self-efficacy, and social support. Procedure: The study includes 148 adult childhood ALL survivors who responded to a questionnaire assessing HRQOL (SF36), self-efficacy (General Self-Efficacy Scale, GSE), and quantitative and qualitative social support (AVSI and AVAT in SS13) in 2012 and 2021. Changes in the HRQOL, GSE, and social support were calculated using paired t-tests. Bayesian path models were specified, and separate models were estimated for each relationship between GSE and AVSI, and AVAT and HRQOL. Cross-sectional associations, autoregressive effects within constructs over time, and cross-lagged effects between two variables over time were specified within each model. Results: The mean of six of the eight HRQOL dimensions, as well as quantitative and qualitative social support, deteriorated during the 9-year follow-up. Self-efficacy was unchanged. Temporal positive relationships were found between baseline GSE and the HRQOL dimension of social functioning, as well as between social support and the HRQOL dimensions of physical functioning, vitality, and mental health at follow-up. Conclusion: The findings highlight the importance of self-efficacy and social support as potential buffering factors for HRQOL in adult survivors of childhood ALL over time. © 2025 The Author(s). Pediatric Blood & Cancer published by Wiley Periodicals LLC.

Background:Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.

Objective:This study aims to explore the use of AI systems in mental health to support decision-making, focusing on 3 key areas: the characteristics of research on AI systems in mental health; the current applications, decisions, end users, and user flow of AI systems to support decision-making; and the evaluation of AI systems for the implementation of decision-making support, including elements influencing the long-term use.

Methods:A scoping review of empirical evidence was conducted across 5 databases: PubMed, Scopus, PsycINFO, Web of Science, and CINAHL. The searches were restricted to peer-reviewed articles published in English after 2011. The initial screening at the title and abstract level was conducted by 2 reviewers, followed by full-text screening based on the inclusion criteria. Data were then charted and prepared for data analysis.

Results:Of a total of 1217 articles, 12 (0.99%) met the inclusion criteria. These studies predominantly originated from high-income countries. The AI systems were used in health care, self-care, and hybrid care contexts, addressing a variety of mental health problems. Three types of AI systems were identified in terms of decision-making support: diagnostic and predictive AI, treatment selection AI, and self-help AI. The dynamics of the type of end-user interaction and system design were diverse in complexity for the integration and use of the AI systems to support decision-making in care processes. The evaluation of the use of AI systems highlighted several challenges impacting the implementation and functionality of the AI systems in care processes, including factors affecting accuracy, increase of demand, trustworthiness, patient-physician communication, and engagement with the AI systems.

Conclusions:The design, development, and implementation of AI systems to support decision-making present substantial challenges for the sustainable use of this technology in care processes. The empirical evidence shows that the evaluation of the use of AI systems in mental health is still in its early stages, with need for more empirically focused research on real-world use. The key aspects requiring further investigation include the evaluation of the use of AI-supported decision-making from human-AI interaction and human-computer interaction perspectives, longitudinal implementation studies of AI systems in mental health to assess the use, and the integration of shared decision-making in AI systems.

©Hassan Auf, Petra Svedberg, Jens Nygren, Monika Nair, Lina E. Lundgren.

Few educational programs to nurture coach need-supportive behaviors have been delivered by sport governing bodies (Evans et al., 2015). Consequently, the potential for such programs to meaningfully change coaches’ interpersonal behaviors requires further investigation (Cushion et al., 2010). Grounded in self-determination theory, we hypothesized that participation in an educational program would increase youth ice hockey coaches’ self-reported beliefs (e.g., effectiveness; Hypothesis 1) and application (Hypothesis 2) of need-supportive coaching styles. The study comprised 52 intervention coaches and 40 wait-list control group coaches enrolled in a 2-day regular education. Data were collected before the education with follow-up assessments 1½and 3 weeks later. We used multigroup multilevel growth models to analyze the change trajectories of the outcomes. A significant group difference was shown for competence support, for which the intervention group exhibited a greater increase than the control group (Δ = 0.14, SE = 0.05, p =.004). Further, the findings revealed significant increases in the intervention group’s effectiveness (slope mean = 0.11, p =.013) and easy-to-implement beliefs (slope mean = 0.18, p =.026); both conditions significantly increased in autonomy support (intervention group: slope mean = 0.25, p =.006; control group: slope mean = 0.11, p =.006). We found no significant change in the normative beliefs or relatedness support in any condition. The study demonstrates the benefits of a self-determination theory-based coach intervention advocating the collaboration between researchers and sport governing bodies in designing, implementing, and evaluating such endeavors. © 2024 The Author(s)

Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study’s objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content analysis of 69 interviews with healthcare leaders and healthcare professionals confirmed and added results from the literature review. Thirty-eight empirical cases from the six identified scoping and literature reviews met the inclusion and exclusion criteria. Barriers to and strategies for AI implementation were grouped under three phases of implementation (planning, implementing, and sustaining the use) and were categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance and human resources, Legal, Training, Data, Evaluation and monitoring, Maintenance. Ethics emerged as a twelfth concept through qualitative analysis of the interviews. This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice. Future research should explore various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians’ liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process. Copyright: © 2024 Nair et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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. © 2024 The Authors. Published by Elsevier Ltd.

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.

© 2024 American Academy of Sleep Medicine

Background: Acute lymphoblastic leukemia (ALL) is the most common malignancy in childhood, but the prognosis has remarkably improved over the last 50 years in high-income countries, and thus, there is a focus on long-term health outcomes following survival and how to best provide health care support to adult long-term survivors of childhood ALL to prevent and handle potential health problems. Digital health interventions are promising to deliver feasible health promotion and prevention programs. This is particularly relevant for ensuring long-term follow-up in cases where continuous contact with oncology care may be disrupted. Moreover, these interventions are beneficial in reaching geographically dispersed target groups and overcoming the time constraints of everyday life that often hinder participation in such programs. Objective: This study aimed to fill the gaps in existing research on adult long-term survivors of childhood ALL and provide formative data that can inform the development of formalized follow-up services designed to meet the needs of these survivors in ways that align with their preferences for digital health interventions. Methods: In this cross-sectional national study, adult survivors (aged ≥18 years) of childhood ALL for over 10 years after diagnosis were compared to their siblings in terms of mental and physical health-related factors, including sleep, stress, anxiety, and depression (Depression Anxiety and Stress Scale 21 [DASS-21]); several dimensions of fatigue (Multidimensional Fatigue Inventory 20 [MFI-20]); work ability (Work Ability Index); chronic pain; and prevalences of diabetes, cardiovascular disease, headache or migraine, and rheumatic disease. Results: Overall, 426 of 855 eligible ALL survivors responded (mean age 30.9, SD 7.7 years), and they participated at an average of 24 (SD 6.9) years after ALL diagnosis. Siblings (n=135; mean age 31.5, SD 7.7 years) acted as controls. Sleep quality, sleep quantity, and mean work ability scores were significantly lower, and physical fatigue, reduced motivation, and reduced activity scores were higher in ALL survivors than in siblings. There were no significant differences between the groups in terms of BMI and prevalence of chronic pain, depression, anxiety, or stress. Physical and psychological complications were more frequent among adult ALL survivors who had received hematopoietic stem cell transplantation (HSCT) than among those who had not received HSCT. Conclusions: Our nationwide cross-sectional study addressed the scarcity of knowledge regarding the self-reported health outcomes of adult long-term survivors of childhood ALL. We highlighted significant disparities within this population and emphasized the potential of comprehensive digital interventions that target vitality, sleep quality, fatigue, and psychosocial well-being to enhance well-being and bolster the capacity for managing chronic health conditions in this target group. Such an intervention would align with the needs of this target group, which is a prerequisite for successfully incorporating technology into the daily lives of survivors of childhood ALL. © 2024 JMIR Publications Inc.. All rights reserved.

Employing a design science research approach building on four modes of inquiry, this study presents a Clinical Decision Support System for predicting heart failure readmissions, combining machine learning, inpatient care process analysis, and user experience design. It introduces three key design principles: contextual integration, actionable insights, and adaptive explanation levels, to support the design of decision support in clinical settings. The research, while focused on a specific healthcare context, offers a model for integrating technical precision and user-centric design in inpatient care processes, suggesting broader applications and future research directions in diverse healthcare environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Background: Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders regarding the barriers to implementing an AI application for automating medical history-taking and triage in Swedish primary care, as well as the actions they took to overcome these barriers. Furthermore, the study seeks to provide insights that can inform the development of AI implementation strategies for healthcare.

Methods: We adopted an inductive qualitative approach, conducting semi-structured interviews with 13 healthcare leaders representing seven primary care units across three regions in Sweden. The collected data were subsequently analysed utilizing thematic analysis. Our study adhered to the Consolidated Criteria for Reporting Qualitative Research to ensure transparent and comprehensive reporting.

Results: The study identified implementation barriers encountered by healthcare leaders across three domains: (1) healthcare professionals, (2) organization, and (3) technology. The first domain involved professional scepticism and resistance, the second involved adapting traditional units for digital care, and the third inadequacies in AI application functionality and system integration. To navigate around these barriers, the leaders took steps to (1) address inexperience and fear and reduce professional scepticism, (2) align implementation with digital maturity and guide patients towards digital care, and (3) refine and improve the AI application and adapt to the current state of AI application development.

Conclusion: The study provides valuable empirical insights into the implementation of AI for automating medical history-taking and triage in primary care as experienced by healthcare leaders. It identifies the barriers to this implementation and how healthcare leaders aligned their actions to overcome them. While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding. This underscores the necessity of a holistic approach for the successful implementation of AI in healthcare. © The Author(s) 2024.