Title: TheRapeutic Interactions in Psychological therapy Of anxiety and Depressive disorders (TRIPOD)
Funder: Health Education England

Abstract: Background Despite the well-established efficacy of psychological therapies for anxiety and depressive disorders, effectiveness varies greatly between individual therapists, especially in routine practice. The most effective therapists have recovery rates 10 times the average. Conversely, the least effective therapists can actually make their patients' problems worse. Differences between therapists grow where patients' problems are more severe or if therapy is short-term. Therapists also do not get more effective with time or experience. Overall, the field lacks a systematic means of measuring and monitoring the content of psychological therapy for effective delivery. All tools used in practice have lengthy, resource-intensive coding processes, which makes routine use prohibitive. Aims and objectives This project aims to develop an automated feedback tool to monitor and enhance the effectiveness of psychological therapies for anxiety and depressive disorders. The project builds on a reliable, published tool used to assess patient-therapist interactions at the first sessions of psychological therapy, which could predict a range of long-term health outcomes. In three stages this project will: Automate the process of rating therapeutic interactions. Validate the prognostic power of the interaction rating tool when automated. Translate these results into a usable feedback intervention for practising psychological therapists.Methods Automate: A type of artificial intelligence called Natural Language Processing will be used to automate the interaction rating process and improve the predictive validity. This has already started; current NIHR funding supports a multi-disciplinary team to use machine learning to begin the automated rating process and this will be refined during the clinical lectureship. Validate: Prognostic modelling will then be used to assess the predictive validity of automated ratings of 400 patients' first session interactions in Cognitive Behaviour Therapy (CBT) and counselling for depression. Translate: Using data from these prognostic models, a feasibility study will offer 20 practising NHS psychological therapists a training intervention where they are given feedback on the effectiveness of interactions in their psychological therapy sessions. The clinical outcomes they achieve prior to the intervention will be compared to those achieved after the intervention to gain initial data on clinical effectiveness. Retention and compliance will also be assessed alongside therapist experience interviews to inform implementation. Timelines for delivery The first 18 months will be split into an initial testing and refinement phase, followed by a validation modelling study. The final 18 months will be used for feasibility testing and dissemination with the aim of being ready to start a full trial of the intervention after the funding ends. Anticipated impact and disseminationThe project includes a stakeholder panel of patients, psychological therapy practitioners, managers, trainers and trainees to ensure that the study is designed in a way that is applicable to all these parties with strong potential for implementation. There will be annual dissemination events where results will be presented, which will begin implementation as study results are produced. Presentation at international conferences and peer reviewed publications will further support dissemination. Local psychological therapy services are collaborators in the study to inform implementation, where appropriate, after the study.

Lay abstract: BackgroundOne psychological therapist can be very different from another in how helpful they are for their patients. The most effective therapists can be 10 times better than average. However, the least effective therapists can make their patients' problems worse. These differences between therapists get bigger if patients' problems are more severe or if therapy is short-term. Therapists are also unlikely to get more effective with experience. This means patients may not be able to rely on the effectiveness of a psychological therapy, because of the vast differences amongst therapists they might see. This is not good for therapists either, as there are few evidence-based ways to help them improve. Health services may struggle with this problem too: How do they help failing therapists become more effective if they do not have evidence-based methods to do so? Most ways of helping therapists improve their skills need time-consuming rating processes and ratings do not necessarily link to the outcomes of therapy. AimsThis research will identify therapy interactions that are helpful or hindering for patient health using an automated tool. This will make it easier for therapists to become more effective for their patients. This information can then be fed back to therapists to help them understand where they could improve. This research will bring 'personalised medicine' to psychological therapy. Overall, this research aims to increase the amount of people that can be helped using psychological therapies by making therapists more able to improve their practice.Progress So FarA published, reliable tool has been used to rate 140 psychological therapy sessions by clinician raters, to identify helpful interaction-types that improve therapy outcomes. This tool showed that when more helpful and less hindering interactions happened in therapy, patients with anxiety disorders went on to have better quality of life, physical health and mental health. Over the coming year we are funded to make this rating process automatic using computerised artificial intelligence.Research PlanFirstly, we will see if this computerised rating tool can identify helpful therapy interactions in recordings of previous psychological therapy sessions from 400 patients with depression. Through this process the artificial intelligence will become more accurate in spotting helpful and hindering interactions, so it can give more sensitive feedback to therapists. This stage will also clarify what interactions are helpful where depression is the main problem (rather than anxiety, as this is the current research focus).Secondly, we will use this information to make the final, improved, automated tool to tell therapists when therapy is going well and when it is not. We will then do a first test of the tool with 20 therapists, over 12 months to see if it helps them and their patients, by checking whether their patients' outcomes improve when they start using the tool, compared to patient outcomes before they used it. Results will be published in scientific journals. If the tool is valuable to participating clinical services they can continue testing it after the study.Patient and Public InvolvementA stakeholder panel has been set up that includes all groups affected by this research, including three patients. Two stakeholder workshops have been held, which informed this application, and a separate patient and carer interview panel gave critical feedback on the application and the role of patients, carers and the public. An additional patient panel has been set up to facilitate the development of the automated tool currently under way, to ensure the tool is responsibly and ethically developed considering patients' views. We plan to have patients as permanent members of the research team throughout the project should this bid be successful.

Research activity codes: 6.6 PSYCHOLOGICAL AND BEHAVIOURAL;7.1 INDIVIDUAL CARE NEEDS
Health categories: MENTAL HEALTH