Risk reduction training for UK care home staff is limited, not standardised and challenging to implement. Virtual reality (VR) is an immersive, engaging method of education delivery that is being adopted in health and social care. VR may be an effective education tool in care homes, but this research has yet to be conducted.

The VR-CARE project aims to create a new VR risk reduction training programme for care homes that combines hand hygiene and falls prevention modules, and to evaluate this through a pilot trial to inform a future randomised controlled trial (RCT).

There are two research phases with patient and public involvement and engagement (PPIE) activities embedded throughout. Care home stakeholders are collaborating to design the training and toolkit, oversee methods, review resources for accessibility, support recruitment and ensure the project meets the needs of the workforce and positively impacts resident care.

In phase 1, we will use a mixed-methods and user-centred design approach to develop the VR training and an accompanying implementation toolkit needed to deliver it. The training will be developed and tested by 15 care home staff across three rounds to identify and inform changes that maximise usability and acceptability. We will conduct up to 20 interviews with staff from VR companies and care homes to support toolkit development.

Phase 2 is a mixed-methods pilot cluster RCT, with a waitlist control and process evaluation with up to 80 unregistered staff members from six North England care homes, to develop the measures and methods to inform a future trial. The process evaluation will generate knowledge about VR as a training mechanism in care homes. This phase will focus on the practicality of using VR, broader impacts (eg, on residents), contextual considerations and how it might be scaled up.

The University of Manchester Proportionate University Research Ethics Committee has approved phase 1 (Reference: 2025-24416-44642). We will obtain further approval before commencing phase 2.

Outputs will include user-friendly and acceptable VR risk reduction training for care homes, accompanied by an implementation toolkit adaptable for other VR training in social care settings. Materials (eg, training overviews, infographics and videos) will be developed to support uptake. Findings will be presented at conferences and published in journals. Lay summaries will be co-created with our PPIE group, and additional dissemination methods will be co-developed to broaden reach.

There is interest in using predictive models to address non-attendance of healthcare appointments without prior notification. Although several National Health Service (NHS) hospital trusts have piloted predictive models for non-attendance, there is a lack of published evidence in clinical settings.

This mixed-methods evaluation of the pilot of a predictive model intervention in outpatient services aimed to examine (1) the effect of the intervention on patient non-attendance and (2) staff engagement in the delivery of the intervention.

A mixed-methods study across two pilot phases. Quantitative data explored the use and impact of the predictive model on non-attendance. Z-tests were conducted to assess changes to non-attendance rates prepilot and in the two phases. Qualitative ethnographic work included 30 periods of observation and interviews with staff.

Nine outpatient services in an NHS Foundation Trust that volunteered to pilot the predictive model intervention. Qualitative participants were NHS clerical and administrative staff delivering the intervention and service managers.

An off-the-shelf predictive model, consisting of a cloud-based, random forest algorithm, produced a risk score of non-attendance by drawing on information from patients’ electronic health records. Staff in the pilot sites attempted to phone patients with a risk score to remind them of upcoming appointments.

Quantitative analysis showed that in phase 1, there were low volumes of intervention calls made across services, but three of nine outpatient services significantly reduced their non-attendance rate. There was a lower overall call rate in phase 2 among the four remaining participating services. One significantly reduced its non-attendance rate from 20.4% to 19.1% (p

The predictive model intervention was positioned as a simple solution to address a complex problem; however, there were challenges inherent in deployment within a dynamic healthcare setting. The sustainability of the intervention and its impact on patient experience warrants further exploration.