No abstract

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.

BackgroundThere 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.MethodWe 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.ResultsThere 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.ConclusionsDue 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.

No abstract

AimsThe Management and Supervision Tool (MaST) helps NHS mental health care professionals identify patients who are most likely to need psychiatric hospital admission or home treatment, due to severe mental illness, through a Risk of Crisis (RoC) algorithm driven by electronic health record (EHR) data analytics. We describe the derivation and validation of the MaST RoC algorithm, and its implementation to support preventative mental healthcare in the NHS.MethodsThe RoC algorithm was developed and evaluated with EHR data from six UK NHS trusts using Ordered Predictor List propensity scores informed by a priori weightings from pre-existing literature, as well as real-world evidence evaluating the associations of clinical risk factors with mental health crisis using NHS EHR data. Mental health crisis was defined as admission to a psychiatric hospital or acceptance to a community crisis service within a 28-day period. Predictor variables included age, gender, accommodation status, employment status, Mental Health Act (MHA) status (under section or Community Treatment Order), and previous mental health service contacts (including hospital admissions and crisis services). Data were analysed using Ordered Predictor List propensity scores. The algorithm was derived using structured EHR data from 2,620 patients in a single NHS trust and externally validated using data from 107,879 patients in five other NHS trusts. Qualitative and quantitative data on feasibility, acceptability and system efficiency impacts of MaST implementation were obtained through staff surveys and local audits.ResultsThe factors associated with greatest propensity for mental health crisis included recent previous crisis, multiple previous crises, higher number of mental health service contacts in recent weeks, MHA section, accommodation status and employment status. The RoC algorithm identified 64% and 80% crises in its top quintile. Sentiment analysis of staff surveys suggested that the use of MaST improved productivity by reducing time taken to access patient information to support caseload management that was previously difficult to obtain through manual review of EHRs. The systems efficiency audit revealed a reduction in duration of crisis and inpatient admissions following MaST implementation.ConclusionThe MaST RoC algorithm supports the identification of people more likely to use crisis services in NHS mental health trusts, is feasible to implement, and improves systems efficiency. EHR-derived algorithms can support real-world clinical practice to improve outcomes in people receiving NHS mental healthcare.