Measurement Based Care in a first episode psychosis program: Development of an algorithm of care based on the Clinical Global Impressions Scale.


Journal

Journal of psychiatric research
ISSN: 1879-1379
Titre abrégé: J Psychiatr Res
Pays: England
ID NLM: 0376331

Informations de publication

Date de publication:
06 2022
Historique:
received: 28 11 2021
revised: 26 01 2022
accepted: 07 03 2022
pubmed: 28 3 2022
medline: 18 5 2022
entrez: 27 3 2022
Statut: ppublish

Résumé

Adherence to therapeutic guidelines in psychiatry is anchored and facilitated by rating scales. However, they are rarely used in routine care, particularly for psychotic disorders. Consequently, adherence to treatment guidelines are not ideal and patient outcomes are often sub-optimal. In this study, we used the clinician-rated Clinical Global Impressions Scale (CGI) to implement a measurement-based care (MBC) approach and derive indices of quality of care at a first episode psychosis (FEP) program. At the individual level, an algorithm was created using CGI scores and their changes over time to define the concept of Patient Requiring Clinical Attention (PRCA) that encompasses several categories (e.g. episode of severity, treatment inertia, or treatment resistance). At the service level, CGI scores were used to derive several indices of quality of care: severity of illness and its change over time, conformity to the use of low doses of antipsychotic medications, and clozapine offer index. 135 Patients were included in this study of whom 19 patients were identified as PRCA. Of these, 12 (63%) received timely medication, and 7 (37%) were suspected cases of therapeutic inertia. Additionally, 15 patients met criteria for treatment resistance of whom 7 were offered clozapine (47%). At the service level, the average CGI improved by 2 points from baseline to month 1 and average doses of antipsychotic medications prescribed were in line with prescription guidelines for FEP patients. The proposed CGI-based treatment algorithm and service evaluation strategy can help to optimize quality care and services for patients.

Identifiants

pubmed: 35339740
pii: S0022-3956(22)00133-9
doi: 10.1016/j.jpsychires.2022.03.012
pii:
doi:

Substances chimiques

Antipsychotic Agents 0
Clozapine J60AR2IKIC

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

8-16

Informations de copyright

Copyright © 2022 Elsevier Ltd. All rights reserved.

Auteurs

Michelle Khau (M)

Integrated Program of Neuroscience, McGill University, Montreal, Qc, Canada; Douglas Mental Health University Institute, Verdun, Qc, Canada.

Karim Tabbane (K)

Douglas Mental Health University Institute, Verdun, Qc, Canada; Department of Psychiatry, McGill University, Montreal, Qc, Canada.

David Bloom (D)

Douglas Mental Health University Institute, Verdun, Qc, Canada.

Sherezad Abadi (S)

Douglas Mental Health University Institute, Verdun, Qc, Canada.

Celine Villemus (C)

Douglas Mental Health University Institute, Verdun, Qc, Canada.

Mark Rabinovitch (M)

Douglas Mental Health University Institute, Verdun, Qc, Canada; Department of Psychiatry, McGill University, Montreal, Qc, Canada.

Jai L Shah (JL)

Integrated Program of Neuroscience, McGill University, Montreal, Qc, Canada; Douglas Mental Health University Institute, Verdun, Qc, Canada; Department of Psychiatry, McGill University, Montreal, Qc, Canada.

Alexandre Veillette (A)

Douglas Mental Health University Institute, Verdun, Qc, Canada.

Srividya N Iyer (SN)

Douglas Mental Health University Institute, Verdun, Qc, Canada; Department of Psychiatry, McGill University, Montreal, Qc, Canada.

Patricia Boksa (P)

Douglas Mental Health University Institute, Verdun, Qc, Canada; Department of Psychiatry, McGill University, Montreal, Qc, Canada.

Ridha Joober (R)

Douglas Mental Health University Institute, Verdun, Qc, Canada; Department of Psychiatry, McGill University, Montreal, Qc, Canada. Electronic address: ridha.joober@mcgill.ca.

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