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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.
Popescu, Christina; Golden, Grace; Benrimoh, David; Tanguay-Sela, Myriam; Slowey, Dominique; Lundrigan, Eryn; Williams, Jérôme; Desormeau, Bennet; Kardani, Divyesh; Perez, Tamara; Rollins, Colleen; Israel, Sonia; Perlman, Kelly; Armstrong, Caitrin; Baxter, Jacob; Whitmore, Kate; Fradette, Marie-Jeanne; Felcarek-Hope, Kaelan; Soufi, Ghassen; Fratila, Robert; Mehltretter, Joseph; Looper, Karl; Steiner, Warren; Rej, Soham; Karp, Jordan F; Heller, Katherine; Parikh, Sagar V; McGuire-Snieckus, Rebecca; Ferrari, Manuela; Margolese, Howard; Turecki, Gustavo.
  • Popescu C; Aifred Health Inc., Montreal, QC, Canada.
  • Golden G; University of Waterloo, Waterloo, ON, Canada.
  • Benrimoh D; Aifred Health Inc., Montreal, QC, Canada.
  • Tanguay-Sela M; Aifred Health Inc., Montreal, QC, Canada.
  • Slowey D; McGill University, Montreal, QC, Canada.
  • Lundrigan E; McGill University, Montreal, QC, Canada.
  • Williams J; McGill University, Montreal, QC, Canada.
  • Desormeau B; McGill University, Montreal, QC, Canada.
  • Kardani D; Aifred Health Inc., Montreal, QC, Canada.
  • Perez T; McGill University, Montreal, QC, Canada.
  • Rollins C; University of Cambridge, London, United Kingdom.
  • Israel S; Aifred Health Inc., Montreal, QC, Canada.
  • Perlman K; Aifred Health Inc., Montreal, QC, Canada.
  • Armstrong C; McGill University, Montreal, QC, Canada.
  • Baxter J; Aifred Health Inc., Montreal, QC, Canada.
  • Whitmore K; McGill University, Montreal, QC, Canada.
  • Fradette MJ; McGill University, Montreal, QC, Canada.
  • Felcarek-Hope K; McGill University, Montreal, QC, Canada.
  • Soufi G; McGill University, Montreal, QC, Canada.
  • Fratila R; McGill University, Montreal, QC, Canada.
  • Mehltretter J; Aifred Health Inc., Montreal, QC, Canada.
  • Looper K; McGill University, Montreal, QC, Canada.
  • Steiner W; McGill University, Montreal, QC, Canada.
  • Rej S; McGill University, Montreal, QC, Canada.
  • Karp JF; McGill University, Montreal, QC, Canada.
  • Heller K; University of Arizona, Tucson, AZ, United States.
  • Parikh SV; Duke University, Durham, NC, United States.
  • McGuire-Snieckus R; University of Michigan, Ann Arbor, MI, United States.
  • Ferrari M; Barts and the London School of Medicine, London, United Kingdom.
  • Margolese H; Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
  • Turecki G; McGill University, Montreal, QC, Canada.
JMIR Form Res ; 5(10): e31862, 2021 Oct 25.
Article in English | MEDLINE | ID: covidwho-1484964
ABSTRACT

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:

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:

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:

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; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.

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 ClinicalTrials.gov NCT04061642; http//clinicaltrials.gov/ct2/show/NCT04061642.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Clinical Practice Guide / Observational study / Prognostic study / Qualitative research / Randomized controlled trials / Risk factors Language: English Journal: JMIR Form Res Year: 2021 Document Type: Article Affiliation country: 31862

Full text: Available Collection: International databases Database: MEDLINE Type of study: Clinical Practice Guide / Observational study / Prognostic study / Qualitative research / Randomized controlled trials / Risk factors Language: English Journal: JMIR Form Res Year: 2021 Document Type: Article Affiliation country: 31862