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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.

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cam.issuedOnline2021-10-25
dc.contributor.authorPopescu, Christina
dc.contributor.authorGolden, Grace
dc.contributor.authorBenrimoh, David
dc.contributor.authorTanguay-Sela, Myriam
dc.contributor.authorSlowey, Dominique
dc.contributor.authorLundrigan, Eryn
dc.contributor.authorWilliams, Jérôme
dc.contributor.authorDesormeau, Bennet
dc.contributor.authorKardani, Divyesh
dc.contributor.authorPerez, Tamara
dc.contributor.authorRollins, Colleen
dc.contributor.authorIsrael, Sonia
dc.contributor.authorPerlman, Kelly
dc.contributor.authorArmstrong, Caitrin
dc.contributor.authorBaxter, Jacob
dc.contributor.authorWhitmore, Kate
dc.contributor.authorFradette, Marie-Jeanne
dc.contributor.authorFelcarek-Hope, Kaelan
dc.contributor.authorSoufi, Ghassen
dc.contributor.authorFratila, Robert
dc.contributor.authorMehltretter, Joseph
dc.contributor.authorLooper, Karl
dc.contributor.authorSteiner, Warren
dc.contributor.authorRej, Soham
dc.contributor.authorKarp, Jordan F
dc.contributor.authorHeller, Katherine
dc.contributor.authorParikh, Sagar V
dc.contributor.authorMcGuire-Snieckus, Rebecca
dc.contributor.authorFerrari, Manuela
dc.contributor.authorMargolese, Howard
dc.contributor.authorTurecki, Gustavo
dc.contributor.orcidPopescu, Christina [0000-0003-1738-4680]
dc.contributor.orcidGolden, Grace [0000-0002-8771-0210]
dc.contributor.orcidBenrimoh, David [0000-0002-1452-4791]
dc.contributor.orcidTanguay-Sela, Myriam [0000-0002-8056-1697]
dc.contributor.orcidSlowey, Dominique [0000-0003-2128-5140]
dc.contributor.orcidLundrigan, Eryn [0000-0001-5896-9752]
dc.contributor.orcidWilliams, Jérôme [0000-0002-8871-2509]
dc.contributor.orcidDesormeau, Bennet [0000-0003-3624-8641]
dc.contributor.orcidKardani, Divyesh [0000-0002-7214-1780]
dc.contributor.orcidPerez, Tamara [0000-0002-9892-3043]
dc.contributor.orcidRollins, Colleen [0000-0002-0291-0038]
dc.contributor.orcidIsrael, Sonia [0000-0002-2213-4179]
dc.contributor.orcidPerlman, Kelly [0000-0002-2716-0712]
dc.contributor.orcidArmstrong, Caitrin [0000-0002-4375-7471]
dc.contributor.orcidBaxter, Jacob [0000-0002-3295-0225]
dc.contributor.orcidWhitmore, Kate [0000-0001-9427-9417]
dc.contributor.orcidFradette, Marie-Jeanne [0000-0002-5036-4000]
dc.contributor.orcidFelcarek-Hope, Kaelan [0000-0002-1643-057X]
dc.contributor.orcidSoufi, Ghassen [0000-0001-6790-6083]
dc.contributor.orcidFratila, Robert [0000-0002-3292-6233]
dc.contributor.orcidMehltretter, Joseph [0000-0003-4689-4436]
dc.contributor.orcidLooper, Karl [0000-0003-4258-3286]
dc.contributor.orcidSteiner, Warren [0000-0003-2984-1770]
dc.contributor.orcidRej, Soham [0000-0002-3908-9124]
dc.contributor.orcidKarp, Jordan F [0000-0002-5171-5028]
dc.contributor.orcidHeller, Katherine [0000-0002-4848-7466]
dc.contributor.orcidParikh, Sagar V [0000-0003-4817-3042]
dc.contributor.orcidMcGuire-Snieckus, Rebecca [0000-0003-1464-0749]
dc.contributor.orcidFerrari, Manuela [0000-0002-7530-6210]
dc.contributor.orcidMargolese, Howard [0000-0002-3912-0566]
dc.contributor.orcidTurecki, Gustavo [0000-0003-4075-2736]
dc.date.accessioned2022-01-06T11:50:45Z
dc.date.available2022-01-06T11:50:45Z
dc.date.issued2021-10-25
dc.date.updated2022-01-06T11:50:44Z
dc.description.abstractBACKGROUND: 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.
dc.identifier.doi10.17863/CAM.79604
dc.identifier.eissn2561-326X
dc.identifier.issn2561-326X
dc.identifier.otherPMC8576598
dc.identifier.other34694234
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332158
dc.languageeng
dc.language.isoeng
dc.publisherJMIR Publications Inc.
dc.publisher.urlhttp://dx.doi.org/10.2196/31862
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2561-326X
dc.sourcenlmid: 101726394
dc.subjectartificial intelligence
dc.subjectclinical decision support system
dc.subjectfeasibility
dc.subjectmajor depressive disorder
dc.subjectmobile phone
dc.subjectusability
dc.titleEvaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.
dc.typeArticle
dcterms.dateAccepted2021-08-23
prism.issueIdentifier10
prism.publicationNameJMIR Form Res
prism.volume5
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.2196/31862

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