This call, led by Elvira Perez Vallejos, is the fourth of nine from the EPSRC Network Plus in Human Data Interaction (HDI).
The discovery, interpretation and sharing of meaningful data patterns in mental health could drive diagnosis, monitoring and treatment. However, for this, some profound privacy and security concerns among patients and clinicians will need to be addressed. Issues of information governance and data ethics will be especially to the forefront.
This theme will consider the implications of data regulations on such things as the personalisation of complex interventions. However, regulation alone will not deal with concerns arising concerning some of the most private data assets, such as in-home video cameras used for activity recognition. We will therefore focus on technical responses, such as system architectures that permit compartmentalised and truly private data analytics.
There was a very successful workshop at the Institute of Mental Heath in Nottingham where the call, done jointly with enurture was launched. One large project was funded by this call.
ExTRA-PPOLATE: (Explainable Therapy Related Annotations: Patient & Practitioner Oriented Learning Assisting Trust & Engagement)
The research team includes Mat Rawsthorne and Jacob Andrews (Principal Investigators) from MindTech, and a multidisciplinary research team including Sam Malins (Clinical Psychologist), Dan Hunt (Linguist), Jeremie Clos (Computer Scientist) from Nottingham University, Tahseen Jilani (Data Analyst) from Health Data Research UK and Yunfei Long (Computer Scientist) from Essex University.
This project aims to examine the key components of trust in algorithm-mediated digital mental health (DMH) through a participatory design and dissemination study. We will co-create a collaborative machine learning decision support tool to help mental health practitioners and patients classify key processes in therapy transcripts. This will speed up rating of therapist fidelity and assessment of patient activation, thereby providing evidence for improving practice. The bigger question is: does the engagement of patients and practitioners in the design process make the AI application more credible and trustworthy?
To answer this question, ExTRA-PPOLATE will:
- Adapt responsible research and innovation (RRI) methods to manage the coproduction of an interdisciplinary mental health data science initiative.
- Prototype a person-centred semi-supervised model training process to refine definitions, expose and explore tacit and latent knowledge in assessment of psychotherapy.
- Identify the key factors contributing to trust in the model pipeline (data, processing, deployment) by examining domain expert requirements for the qualities of an engaging, interactive feedback interface and eliciting wider concerns about its acceptability.
- Assess whether patient and practitioner involvement in the development of a digital mental health decision support tool increases trust in it.
- Foster a community of interest in HDI approaches applied to mental health.
This is a very exciting project that could have significant impact on the process of quality-assuring therapeutic interventions. We are looking forward to seeing progress and research outputs.