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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
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 tre...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576598/ https://www.ncbi.nlm.nih.gov/pubmed/34694234 http://dx.doi.org/10.2196/31862 |
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author | 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 |
author_facet | 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 |
author_sort | Popescu, Christina |
collection | PubMed |
description | 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; F(2,24)=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 |
format | Online Article Text |
id | pubmed-8576598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-85765982021-11-24 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 JMIR Form Res Original Paper 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; F(2,24)=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 JMIR Publications 2021-10-25 /pmc/articles/PMC8576598/ /pubmed/34694234 http://dx.doi.org/10.2196/31862 Text en ©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. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper 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 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576598/ https://www.ncbi.nlm.nih.gov/pubmed/34694234 http://dx.doi.org/10.2196/31862 |
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