Literature Collection

ObjectivesCentral government has been promoting closer integration between the National Health Service (NHS) and local government social services in England for more than five decades. Improved coordination between primary, hospital, community health and social services has been advocated as a cost-effective response to growing care needs in an ageing population. This paper concentrates on one of the principal local care coordination mechanisms: community-based multidisciplinary teams (MDTs) involving NHS and social services staff. It reports local leaders' perceptions of MDTs' current and future contributions to more coordinated care and support systems in two integrated care Pioneer sites.MethodsThirty-two qualitative semi-structured interviews with 25 local system leaders and operational managers in two contrasting Integrated Care and Support Pioneer areas were conducted between October 2018 and April 2021, as part of a wider evaluation of the Integrated Care and Support Pioneer Programme. Eight of those interviews took place after the start of the COVID-19 pandemic and between lockdowns. Interviews were analysed thematically.ResultsLocal leaders in both areas broadly shared a vision of integrated care in which MDTs were essential mechanisms for coordinating improvements in health and wellbeing, especially for older people who are frail, experience falls and have long-term health conditions. Organisational differences between and within sites influenced local decisions about the purpose and structure of MDTs, but, despite such variations, interviewees identified similar challenges to implementation. Staff turnover, often linked to funding uncertainties, and the lack of shared information systems, were among the most frequent operational challenges noted. System leaders valued national policy frameworks as potential enablers of integrated care but also recognised the role of local contexts in shaping local implementation decisions. Interviewees highlighted benefits emerging from multidisciplinary working, including its potential to deliver more holistic care, fewer instances of work duplication, speedier access to care and enhanced home care provision. However, they were concerned such benefits were not always captured by commonly used performance indicators and thus the value of MDTs could be under-estimated.ConclusionsLocal contextual variables and local understandings of these variables appeared to be the main influences on variations in local responses to national expectations of improvements in care integration. Local leaders in both areas broadly shared a vision of integrated care in which MDTs provided essential mechanisms for securing interdependent improvements in both the health and wellbeing of local populations and improvements in workforce job satisfaction.

Objective: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication and repetitive behaviors. This systematic review examines the application of artificial intelligence (AI) in diagnosing ASD, focusing on pediatric populations aged 0-18 years. Materials and methods: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Inclusion criteria encompassed studies applying AI techniques for ASD diagnosis, primarily evaluated using metriclike accuracy. Non-English articles and studies not focusing on diagnostic applications were excluded. The literature search covered PubMed, ScienceDirect, CENTRAL, ProQuest, Web of Science, and Google Scholar up to November 9, 2024. Bias assessment was performed using the Joanna Briggs Institute checklist for critical appraisal. Results: The review included 25 studies. These studies explored AI-driven approaches that demonstrated high accuracy in classifying ASD using various data modalities, including visual (facial, home videos, eye-tracking), motor function, behavioral, microbiome, genetic, and neuroimaging data. Key findings highlight the efficacy of AI in analyzing complex datasets, identifying subtle ASD markers, and potentially enabling earlier intervention. The studies showed improved diagnostic accuracy, reduced assessment time, and enhanced predictive capabilities. Conclusion: The integration of AI technologies in ASD diagnosis presents a promising frontier for enhancing diagnostic accuracy, efficiency, and early detection. While these tools can increase accessibility to ASD screening in underserved areas, challenges related to data quality, privacy, ethics, and clinical integration remain. Future research should focus on applying diverse AI techniques to large populations for comparative analysis to develop more robust diagnostic models.