Health services across the world made rapid adjustments to the direct and indirect consequences of covid-19 with varying success. 1 The World Health Organization's initial recommendations were based on system adaptations in China, focusing predominantly on secondary care and public health. 2 3 Primary care received less policy attention both globally and in the UK.

Background Cognitive–behavioural therapy aims to increase quality of life by changing cognitive and behavioural factors that maintain problematic symptoms. A previous overview of cognitive–behavioural therapy systematic reviews suggested that cognitive–behavioural therapy was effective for many conditions. However, few of the included reviews synthesised randomised controlled trials. Objectives This project was undertaken to map the quality and gaps in the cognitive–behavioural therapy systematic review of randomised controlled trial evidence base. Panoramic meta-analyses were also conducted to identify any across-condition general effects of cognitive–behavioural therapy. Data sources The overview was designed with cognitive–behavioural therapy patients, clinicians and researchers. The Cochrane Library, MEDLINE, EMBASE, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, Child Development & Adolescent Studies, Database of Abstracts of Reviews of Effects and OpenGrey databases were searched from 1992 to January 2019. Review methods Study inclusion criteria were as follows: (1) fulfil the Centre for Reviews and Dissemination criteria; (2) intervention reported as cognitive–behavioural therapy or including one cognitive and one behavioural element; (3) include a synthesis of cognitive–behavioural therapy trials; (4) include either health-related quality of life, depression, anxiety or pain outcome; and (5) available in English. Review quality was assessed with A MeaSurement Tool to Assess systematic Reviews (AMSTAR)-2. Reviews were quality assessed and data were extracted in duplicate by two independent researchers, and then mapped according to condition, population, context and quality. The effects from high-quality reviews were pooled within condition groups, using a random-effect panoramic meta-analysis. If the across-condition heterogeneity was I 2 < 75%, we pooled across conditions. Subgroup analyses were conducted for age, delivery format, comparator type and length of follow-up, and a sensitivity analysis was performed for quality. Results A total of 494 reviews were mapped, representing 68% (27/40) of the categories of the International Classification of Diseases, Eleventh Revision, Mortality and Morbidity Statistics. Most reviews (71%, 351/494) were of lower quality. Research on older adults, using cognitive–behavioural therapy preventatively, ethnic minorities and people living outside Europe, North America or Australasia was limited. Out of 494 reviews, 71 were included in the primary panoramic meta-analyses. A modest effect was found in favour of cognitive–behavioural therapy for health-related quality of life (standardised mean difference 0.23, 95% confidence interval 0.05 to 0.41, prediction interval –0.05 to 0.50, I 2 = 32%), anxiety (standardised mean difference 0.30, 95% confidence interval 0.18 to 0.43, prediction interval –0.28 to 0.88, I 2 = 62%) and pain (standardised mean difference 0.23, 95% confidence interval 0.05 to 0.41, prediction interval –0.28 to 0.74, I 2 = 64%) outcomes. All condition, subgroup and sensitivity effect estimates remained consistent with the general effect. A statistically significant interaction effect was evident between the active and non-active comparator groups for the health-related quality-of-life outcome. A general effect for depression outcomes was not produced as a result of considerable heterogeneity across reviews and conditions. Limitations Data extraction and analysis were conducted at the review level, rather than returning to the individual trial data. This meant that the risk of bias of the individual trials could not be accounted for, but only the quality of the systematic reviews that synthesised them. Conclusion Owing to the consistency and homogeneity of the highest-quality evidence, it is proposed that cognitive–behavioural therapy can produce a modest general, across-condition benefit in health-related quality-of-life, anxiety and pain outcomes. Future work Future research should focus on how the modest effect sizes seen with cognitive–behavioural therapy can be increased, for example identifying alternative delivery formats to increase adherence and reduce dropout, and pursuing novel methods to assess intervention fidelity and quality. Study registration This study is registered as PROSPERO CRD42017078690. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 9. See the NIHR Journals Library website for further project information.

No abstract

IntroductionCognitive–behavioural therapy (CBT) is a psychological therapy that has been used to improve patient well-being across multiple mental and physical health problems. Its effectiveness has been examined in thousands of randomised control trials that have been synthesised into hundreds of systematic reviews. The aim of this overview is to map, synthesise and assess the reliability of evidence generated from these systematic reviews of the effectiveness of CBT across all health conditions, patient groups and settings.Methods and analysisWe will run our search strategy, to identify systematic reviews of CBT, within the Database of Abstracts of Reviews of Effects, the Cochrane Library of Systematic Reviews, MEDLINE, Embase, PsycInfo, CINAHL, Child Development and Adolescent Studies, and OpenGrey between January 1992 and 25 April 2018. Independent reviewers will sift, perform data extraction in duplicate and assess the quality of the reviews using the Assessing the Methodological Quality of Systematic Reviews (V.2) tool. The outcomes of interest include: health-related quality of life, depression, anxiety, psychosis and physical/physiological outcomes prioritised in the individual reviews. The evidence will be mapped and synthesised where appropriate by health problem, patient subgroups, intervention type, context and outcome.Ethics and disseminationEthical approval is not required as this is an overview of published systematic reviews. We plan to publish results in peer-reviewed journals and present at international and national academic, clinical and patient conferences.Trial registration numberCRD42017078690.

BackgroundAccessing specialist secondary mental health care in the NHS in England requires a referral, usually from primary or acute care. Community mental health teams triage these referrals deciding on the most appropriate team to meet patients’ needs. Referrals require resource-intensive review by clinicians and often, collation and review of the patient’s history with services captured in their electronic health records (EHR). Triage processes are, however, opaque and often result in patients not receiving appropriate and timely access to care that is a particular concern for some minority and under-represented groups. Our project, funded by the National Institute of Health Research (NIHR) will develop a clinical decision support tool (CDST) to deliver accurate, explainable and justified triage recommendations to assist clinicians and expedite access to secondary mental health care.MethodsOur proposed CDST will be trained on narrative free-text data combining referral documentation and historical EHR records for patients in the UK-CRIS database. This high-volume data set will enable training of end-to-end neural network natural language processing (NLP) to extract ‘signatures’ of patients who were (historically) triaged to different treatment teams. The resulting algorithm will be externally validated using data from different NHS trusts (Nottinghamshire Healthcare, Southern Health, West London and Oxford Health). We will use an explicit algorithmic fairness framework to mitigate risk of unintended harm evident in some artificial intelligence (AI) healthcare applications. Consequently, the performance of the CDST will be explicitly evaluated in simulated triage team scenarios where the tool augments clinician’s decision making, in contrast to traditional “human versus AI” performance metrics.DiscussionThe proposed CDST represents an important test-case for AI applied to real-world process improvement in mental health. The project leverages recent advances in NLP while emphasizing the risks and benefits for patients of AI-augmented clinical decision making. The project’s ambition is to deliver a CDST that is scalable and can be deployed to any mental health trust in England to assist with digital triage.