Real-world implementation of precision psychiatry: Transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis

Dominic Oliver; Giulia Spada; Craig Colling; Matthew Broadbent; Helen Baldwin; Rashmi Patel; Robert Stewart; Daniel Stahl; Richard Dobson; Philip McGuire; Paolo Fusar-Poli

doi:10.1016/j.schres.2020.05.007

Real-world implementation of precision psychiatry: Transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis

Schizophr Res. 2021 Jan:227:52-60. doi: 10.1016/j.schres.2020.05.007. Epub 2020 Jun 19.

Authors

Affiliations

¹ Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
² National Institute for Health Research, Maudesley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom.
³ Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; National Institute for Health Research, Maudesley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom.
⁴ Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
⁵ Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; South London and Maudsley Foundation Trust, London, United Kingdom.
⁶ Department of Biostatistics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
⁷ National Institute for Health Research, Maudesley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Institute of Health Informatics Research, University College London, London, United Kingdom; Health Data Research UK London, University College London, London, United Kingdom.
⁸ Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; OASIS Service, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom.
⁹ Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; National Institute for Health Research, Maudesley Biomedical Research Centre, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom; OASIS Service, South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. Electronic address: paolo.fusar-poli@kcl.ac.uk.

Abstract

Background: Risk estimation models integrated into Electronic Health Records (EHRs) can deliver innovative approaches in psychiatry, but clinicians' endorsement and their real-world usability are unknown. This study aimed to investigate the real-world feasibility of implementing an individualised, transdiagnostic risk calculator to automatically screen EHRs and detect individuals at-risk for psychosis.

Methods: Feasibility implementation study encompassing an in-vitro phase (March 2018 to May 2018) and in-vivo phase (May 2018 to April 2019). The in-vitro phase addressed implementation barriers and embedded the risk calculator (predictors: age, gender, ethnicity, index cluster diagnosis, age*gender) into the local EHR. The in-vivo phase investigated the real-world feasibility of screening individuals accessing secondary mental healthcare at the South London and Maudsley NHS Trust. The primary outcome was adherence of clinicians to automatic EHR screening, defined by the proportion of clinicians who responded to alerts from the risk calculator, over those contacted.

Results: In-vitro phase: implementation barriers were identified/overcome with clinician and service user engagement, and the calculator was successfully integrated into the local EHR through the CogStack platform. In-vivo phase: 3722 individuals were automatically screened and 115 were detected. Clinician adherence was 74% without outreach and 85% with outreach. One-third of clinicians responded to the first email (37.1%) or phone calls (33.7%). Among those detected, cumulative risk of developing psychosis was 12% at six-month follow-up.

Conclusion: This is the first implementation study suggesting that combining precision psychiatry and EHR methods to improve detection of individuals with emerging psychosis is feasible. Future psychiatric implementation research is urgently needed.

Keywords: Feasibility; Implementation; Precision psychiatry; Psychosis;transdiagnostic; Risk calculator.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electronic Health Records
Feasibility Studies
Humans
London
Psychiatry*
Psychotic Disorders* / diagnosis

Abstract

Publication types

MeSH terms

Grants and funding