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      ‘AI gone mental’: engagement and ethics in data-driven technology for mental health

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      Journal of Mental Health
      Informa UK Limited

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          Ethnic variations in compulsory detention under the Mental Health Act: a systematic review and meta-analysis of international data

          Summary Background Evidence suggests that black, Asian and minority ethnic (BAME) groups have an increased risk of involuntary psychiatric care. However, to our knowledge, there is no published meta-analysis that brings together both international and UK literature and allows for comparison of the two. This study examined compulsory detention in BAME and migrant groups in the UK and internationally, and aimed to expand upon existing systematic reviews and meta-analyses of the rates of detention for BAME populations. Methods For this systematic review and meta-analysis, we searched five databases (PsychINFO, MEDLINE, Cochrane Controlled Register of Trials, Embase, and CINAHL) for quantitative studies comparing involuntary admission, readmission, and inpatient bed days between BAME or migrant groups and majority or native groups, published between inception and Dec 3, 2018. We extracted data on study characteristics, patient-level data on diagnosis, age, sex, ethnicity, marital status, and occupational status, and our outcomes of interest (involuntary admission to hospital, readmission to hospital, and inpatient bed days) for meta-analysis. We used a random-effects model to compare disparate outcome measures. We assessed explanations offered for the differences between minority and majority groups for the strength of the evidence supporting them. This study is prospectively registered with PROSPERO, number CRD42017078137. Findings Our search identified 9511 studies for title and abstract screening, from which we identified 296 potentially relevant full-text articles. Of these, 67 met the inclusion criteria and were reviewed in depth. We added four studies after reference and citation searches, meaning 71 studies in total were included. 1 953 135 participants were included in the studies. Black Caribbean patients were significantly more likely to be compulsorily admitted to hospital compared with those in white ethnic groups (odds ratio 2·53, 95% CI 2·03–3·16, p<0·0001). Black African patients also had significantly increased odds of being compulsorily admitted to hospital compared with white ethnic groups (2·27, 1·62–3·19, p<0·0001), as did, to a lesser extent, south Asian patients (1·33, 1·07–1·65, p=0·0091). Black Caribbean patients were also significantly more likely to be readmitted to hospital compared with white ethnic groups (2·30, 1·22–4·34, p=0·0102). Migrant groups were significantly more likely to be compulsorily admitted to hospital compared with native groups (1·50, 1·21–1·87, p=0·0003). The most common explanations for the increased risk of detainment in BAME populations included increased prevalence of psychosis, increased perceived risk of violence, increased police contact, absence of or mistrust of general practitioners, and ethnic disadvantages. Interpretation BAME and migrant groups are at a greater risk of psychiatric detention than are majority groups, although there is variation across ethnic groups. Attempts to explain increased detention in ethnic groups should avoid amalgamation and instead carry out culturally-specific, hypothesis-driven studies to examine the numerous contributors to varying rates of detention. Funding University College London Hospitals National Institute for Health Research (NIHR) Biomedical Research Centre, NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, King's College London, and NIHR Collaboration for Leadership in Applied Health Research and Care North Thames at Bart's Health NHS Trust.
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            Identifying research priorities for digital technology in mental health care: results of the James Lind Alliance Priority Setting Partnership

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              A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

              The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.
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                Author and article information

                Journal
                Journal of Mental Health
                Journal of Mental Health
                Informa UK Limited
                0963-8237
                1360-0567
                January 30 2020
                : 1-6
                Affiliations
                [1 ] Senior Fellow in Mental Health Policy, University of Birmingham, Edgbaston, Birmingham
                Article
                10.1080/09638237.2020.1714011
                32000544
                c5760d23-69b3-41c8-b538-db94ae789a5e
                © 2020
                History

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