Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.

artificial intelligence clinical decision support system feasibility major depressive disorder mobile phone usability

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
25 Oct 2021
Historique:
received: 07 07 2021
accepted: 23 08 2021
revised: 23 08 2021
entrez: 25 10 2021
pubmed: 26 10 2021
medline: 26 10 2021
Statut: epublish

Résumé

Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows. This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction. Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews. Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies. ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

Sections du résumé

BACKGROUND BACKGROUND
Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.
OBJECTIVE OBJECTIVE
This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.
METHODS METHODS
Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.
RESULTS RESULTS
Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F
CONCLUSIONS CONCLUSIONS
Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.
TRIAL REGISTRATION BACKGROUND
ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

Identifiants

pubmed: 34694234
pii: v5i10e31862
doi: 10.2196/31862
pmc: PMC8576598
doi:

Banques de données

ClinicalTrials.gov
['NCT04061642']

Types de publication

Journal Article

Langues

eng

Pagination

e31862

Informations de copyright

©Christina Popescu, Grace Golden, David Benrimoh, Myriam Tanguay-Sela, Dominique Slowey, Eryn Lundrigan, Jérôme Williams, Bennet Desormeau, Divyesh Kardani, Tamara Perez, Colleen Rollins, Sonia Israel, Kelly Perlman, Caitrin Armstrong, Jacob Baxter, Kate Whitmore, Marie-Jeanne Fradette, Kaelan Felcarek-Hope, Ghassen Soufi, Robert Fratila, Joseph Mehltretter, Karl Looper, Warren Steiner, Soham Rej, Jordan F Karp, Katherine Heller, Sagar V Parikh, Rebecca McGuire-Snieckus, Manuela Ferrari, Howard Margolese, Gustavo Turecki. Originally published in JMIR Formative Research (https://formative.jmir.org), 25.10.2021.

Références

Arch Intern Med. 2006 May 22;166(10):1092-7
pubmed: 16717171
Nature. 2016 Apr 7;532(7597):20-3
pubmed: 27078548
J Med Internet Res. 2009 Apr 24;11(2):e13
pubmed: 19403466
JMIR Mhealth Uhealth. 2014 Jan 21;2(1):e2
pubmed: 25098314
P T. 2014 May;39(5):356-64
pubmed: 24883008
Psychol Med. 2007 Jan;37(1):85-95
pubmed: 17094819
J Med Internet Res. 2016 Dec 20;18(12):e330
pubmed: 27998876
Behav Res Methods. 2007 May;39(2):175-91
pubmed: 17695343
Addict Behav. 1982;7(4):363-71
pubmed: 7183189
Am J Prev Med. 1998 May;14(4):245-58
pubmed: 9635069
J Gen Intern Med. 2001 Sep;16(9):606-13
pubmed: 11556941
BMC Med Res Methodol. 2018 Nov 16;18(1):137
pubmed: 30445910
BMJ Open. 2020 May 24;10(5):e035905
pubmed: 32448796
J Affect Disord. 2021 Feb 15;281:618-622
pubmed: 33248809
Can J Psychiatry. 2016 Sep;61(9):540-60
pubmed: 27486148
Schizophr Res. 2008 Mar;100(1-3):60-9
pubmed: 18255269
JMIR Ment Health. 2019 Apr 22;6(4):e12292
pubmed: 31008711
J Affect Disord. 2020 Mar 15;265:395-401
pubmed: 32090765
Focus (Am Psychiatr Publ). 2018 Jul;16(3):341-350
pubmed: 32015714
J Affect Disord. 2020 Feb 15;263:413-419
pubmed: 31969272
NPJ Digit Med. 2020 Feb 6;3:17
pubmed: 32047862
Acta Psychiatr Scand Suppl. 1987;334:1-100
pubmed: 2887090
Ann Fam Med. 2019 Jan;17(1):52-60
pubmed: 30670397
J Stud Alcohol. 1995 Jul;56(4):423-32
pubmed: 7674678
CMAJ. 2012 Feb 21;184(3):281-2
pubmed: 22231681
J Gen Intern Med. 2002 Jul;17(7):493-503
pubmed: 12133139
Curr Psychiatry Rep. 2007 Dec;9(6):449-59
pubmed: 18221624
BJPsych Open. 2021 Jan 06;7(1):e22
pubmed: 33403948
Eur Arch Psychiatry Clin Neurosci. 2020 Mar;270(2):139-152
pubmed: 30607530
J Psychiatr Pract. 2006 Mar;12(2):71-9
pubmed: 16728903
Schizophr Res. 2019 Jun;208:105-113
pubmed: 30979665
J Nerv Ment Dis. 2012 Aug;200(8):712-5
pubmed: 22850307
J Med Internet Res. 2011 Dec 31;13(4):e126
pubmed: 22209829
Focus (Am Psychiatr Publ). 2018 Oct;16(4):420-429
pubmed: 32021580
J Am Med Inform Assoc. 2001 Nov-Dec;8(6):527-34
pubmed: 11687560
Int J Neuropsychopharmacol. 2017 Sep 1;20(9):721-730
pubmed: 28645191
Front Artif Intell. 2020 Jan 21;2:31
pubmed: 33733120

Auteurs

Christina Popescu (C)

Aifred Health Inc., Montreal, QC, Canada.

Grace Golden (G)

University of Waterloo, Waterloo, ON, Canada.

David Benrimoh (D)

Aifred Health Inc., Montreal, QC, Canada.

Myriam Tanguay-Sela (M)

Aifred Health Inc., Montreal, QC, Canada.

Dominique Slowey (D)

McGill University, Montreal, QC, Canada.

Eryn Lundrigan (E)

McGill University, Montreal, QC, Canada.

Jérôme Williams (J)

McGill University, Montreal, QC, Canada.

Bennet Desormeau (B)

McGill University, Montreal, QC, Canada.

Divyesh Kardani (D)

Aifred Health Inc., Montreal, QC, Canada.

Tamara Perez (T)

McGill University, Montreal, QC, Canada.

Colleen Rollins (C)

University of Cambridge, London, United Kingdom.

Sonia Israel (S)

Aifred Health Inc., Montreal, QC, Canada.

Kelly Perlman (K)

Aifred Health Inc., Montreal, QC, Canada.
McGill University, Montreal, QC, Canada.

Caitrin Armstrong (C)

Aifred Health Inc., Montreal, QC, Canada.

Jacob Baxter (J)

McGill University, Montreal, QC, Canada.

Kate Whitmore (K)

McGill University, Montreal, QC, Canada.

Marie-Jeanne Fradette (MJ)

McGill University, Montreal, QC, Canada.

Kaelan Felcarek-Hope (K)

McGill University, Montreal, QC, Canada.

Ghassen Soufi (G)

McGill University, Montreal, QC, Canada.

Robert Fratila (R)

Aifred Health Inc., Montreal, QC, Canada.

Joseph Mehltretter (J)

McGill University, Montreal, QC, Canada.

Karl Looper (K)

McGill University, Montreal, QC, Canada.

Warren Steiner (W)

McGill University, Montreal, QC, Canada.

Soham Rej (S)

McGill University, Montreal, QC, Canada.

Jordan F Karp (JF)

University of Arizona, Tucson, AZ, United States.

Katherine Heller (K)

Duke University, Durham, NC, United States.

Sagar V Parikh (SV)

University of Michigan, Ann Arbor, MI, United States.

Rebecca McGuire-Snieckus (R)

Barts and the London School of Medicine, London, United Kingdom.

Manuela Ferrari (M)

Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.

Howard Margolese (H)

McGill University, Montreal, QC, Canada.

Gustavo Turecki (G)

Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.

Classifications MeSH