Implementing a Digital Tool to Support Shared Care Planning in Community-Based Mental Health Services: Qualitative Evaluation.

BACKGROUND: Mental health services aim to provide recovery-focused care and facilitate coproduced care planning. In practice, mental health providers can find supporting individualized coproduced care with service users difficult while balancing administrative and performance demands. To help meet this aim and using principles of coproduction, an innovative mobile digital care pathway tool (CPT) was developed to be used on a tablet computer and piloted in the West of England. OBJECTIVE: The aim of this study was to examine mental health care providers' views of and experiences with the CPT during the pilot implementation phase and identify factors influencing its implementation. METHODS: A total of 20 in-depth telephone interviews were conducted with providers participating in the pilot and managers in the host organization. Interviews were audio recorded, transcribed, anonymized, and thematically analyzed guided by the Consolidated Framework for Implementation Research. RESULTS: The tool was thought to facilitate coproduced recovery-focused care planning, a policy and organizational as well as professional priority. Internet connectivity issues, system interoperability, and access to service users' health records affected use of the tool during mobile working. The organization's resources, such as information technology (IT) infrastructure and staff time and IT culture, influenced implementation. Participants' levels of use of the tool were dependent on knowledge of the tool and self-efficacy; perceived service-user needs and characteristics; and perceptions of impact on the therapeutic relationship. Training and preparation time influenced participants' confidence in using the tool. CONCLUSIONS: Findings highlight the importance of congruence between staff, organization, and external policy priorities and digital technologies in aiding intervention engagement, and the need for ongoing training and support of those intended to use the technology during and after the end of implementation interventions.

Pilot implementation of co-designed software for co-production in mental health care planning: a qualitative evaluation of staff perspectives.

Background: Mental health policies advocate service user participation in care planning. However, service users often feel they're not fully involved and direct access to users' own electronic care plans in the community can be an obstacle. To address this, an electronic care pathway tool (CPT) was co-designed by service users, staff and software developers, to facilitate co-production of care and crisis plans. Aims: To investigate the feasibility and acceptability of the pilot implementation of the CPT in professionals' practice to co-produce care plans and enable efficient working. Method: Qualitative interviews with fifteen mental health practitioners, and five service development/management staff. Normalisation process theory, which outlines the social processes involved in implementing technology, and co-production theory, informed interviews and data analysis. Results: Multiple factors influenced CPT usage, including people's views of technology, practitioners' relationships with service users, service users' mental health needs, and their capacity for reflective thinking. The CPT's visual and interactive features could enable co-production of care plans. The CPT supported practitioners' efficiency, but its features did not easily streamline with electronic patient records. Conclusions: CPT interactive touchpoints supported service users' therapeutic reflection and facilitated care planning involvement. Information technology system interoperability was an obstacle.

Models to predict relapse in psychosis: A systematic review.

BACKGROUND: There is little evidence on the accuracy of psychosis relapse prediction models. Our objective was to undertake a systematic review of relapse prediction models in psychosis. METHOD: We conducted a literature search including studies that developed and/or validated psychosis relapse prediction models, with or without external model validation. Models had to target people with psychosis and predict relapse. The key databases searched were; Embase, Medline, Medline In-Process Citations & Daily Update, PsychINFO, BIOSIS Citation Index, CINAHL, and Science Citation Index, from inception to September 2016. Prediction modelling studies were assessed for risk of bias and applicability using the PROBAST tool. RESULTS: There were two eligible studies, which included 33,088 participants. One developed a model using prodromal symptoms and illness-related variables, which explained 14% of relapse variance but was at high risk of bias. The second developed a model using administrative data which was moderately discriminative (C = 0.631) and associated with relapse (OR 1.11 95% CI 1.10, 1.12) and achieved moderately discriminative capacity when validated (C = 0.630). The risk of bias was low. CONCLUSIONS: Due to a lack of high quality evidence it is not possible to make any specific recommendations about the predictors that should be included in a prognostic model for relapse. For instance, it is unclear whether prodromal symptoms are useful for predicting relapse. The use of routine data to develop prediction models may be a more promising approach, although we could not empirically compare the two included studies.