BackgroundIt is challenging to engage repeat users of unscheduled healthcare with severe health anxiety in psychological help and high service costs are incurred. We investigated whether clinical and economic outcomes were improved by offering remote cognitive behaviour therapy (RCBT) using videoconferencing or telephone compared to treatment as usual (TAU).MethodsA single-blind, parallel group, multicentre randomised controlled trial was undertaken in primary and general hospital care. Participants were aged ≥18 years with ≥2 unscheduled healthcare contacts within 12 months and scored >18 on the Health Anxiety Inventory. Randomisation to RCBT or TAU was stratified by site, with allocation conveyed to a trial administrator, research assessors masked to outcome. Data were collected at baseline, 3, 6, 9 and 12 months. The primary outcome was change in HAI score from baseline to six months on an intention-to-treat basis. Secondary outcomes were generalised anxiety, depression, physical symptoms, function and overall health. Health economics analysis was conducted from a health service and societal perspective.ResultsOf the 524 patients who were referred and assessed for trial eligibility, 470 were eligible and 156 (33%) were recruited; 78 were randomised to TAU and 78 to RCBT. Compared to TAU, RCBT significantly reduced health anxiety at six months, maintained to 9 and 12 months (mean change difference HAI –2.81; 95% CI –5.11 to –0.50; P = 0.017). Generalised anxiety, depression and overall health was significantly improved at 12 months, but there was no significant change in physical symptoms or function. RCBT was strictly dominant with a net monetary benefit of £3,164 per participant at a willingness to pay threshold of £30,000. No treatment-related adverse events were reported in either group.ConclusionsRCBT may reduce health anxiety, general anxiety and depression and improve overall health, with considerable reductions in health and informal care costs in repeat users of unscheduled care with severe health anxiety who have previously been difficult to engage in psychological treatment. RCBT may be an easy-to-implement intervention to improve clinical outcome and save costs in one group of repeat users of unscheduled care.Trial registrationThe trial was registered at ClinicalTrials.gov on 19 Nov 2014 with reference number NCT02298036Electronic supplementary materialThe online version of this article (10.1186/s12916-019-1253-5) contains supplementary material, which is available to authorized users.

Plain english summary Members of the public share their views with researchers to improve health and social care research. Lay assessing is one way of doing this. This is where people, drawing upon personal and general life experience, comment on material, such as grant applications and patient information, to highlight strengths and weaknesses and to suggest improvements. This paper reports on setting up a training programme for lay assessors. Meetings were held between interested public and staff from research organisations. People discussed what lay assessing is, why they want to do it, skills and support needed and if training was wanted. They were invited to form a group to develop the training together. Training was delivered in the East Midlands. People who attended gave their thoughts about it by completing questionnaires and joining a feedback event. The group developed the structure of the training programme together and it oversaw the development of the training content by individual members. People who attended training reported feeling more confident about lay assessing. This was particularly so for those who had not done lay assessing before. They indicated how valuable it was to talk with others at the training. Our findings support the National Institute for Health Research recommendations for improving learning and development for public involvement in research. This project has created a solid base for local research organisations to work together in public involvement training. Lay assessor training is now part of a wider programme of shared resources called the Sharebank. Abstract Background Involving members of the public in research can improve its quality and incorporate the needs and views of patients. One method for doing this is lay assessing, where members of the public are consulted to improve research materials. This paper documents the establishment of a pilot training programme for lay assessors. It describes a way of working that embodies a regional, cross-organisational approach to co-producing training with members of the public. Methods Open meetings, led by AH, were held for existing and aspiring lay assessors to define lay assessing, motivations for doing it, skills required, associated learning and development needs, and to gauge interest for training. Those who attended meetings, including members of the public and staff, were invited to form a working group to co-produce the training programme. Training was delivered in modules at two centres in the East Midlands and evaluated through participant feedback at the end of each module and at an evaluation event. Feedback was through a mix of Likert scale scoring, open text and verbal responses. Results Discussions from the open meetings informed the development of the training by the working group. Led by AH, the working group, as a whole, co-produced the structure ...

Background Patient activation is defined as a patient’s confidence and perceived ability to manage their own health. Patient activation has been a consistent predictor of long-term health and care costs, particularly for people with multiple long-term health conditions. However, there is currently no means of measuring patient activation from what is said in health care consultations. This may be particularly important for psychological therapy because most current methods for evaluating therapy content cannot be used routinely due to time and cost restraints. Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. Objective This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. Methods With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. Results The k-nearest neighbors classifier obtained 73% accuracy (82% precision and 80% recall) in a test data set. The bagged trees classifier obtained 81% accuracy for test data (87% precision and 75% recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. Conclusions Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions.

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