scholarly journals Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based, Clinical Decision Support System for Depression Treatment in Adults: A Longitudinal Feasibility Study (Preprint)

10.2196/31862 ◽  
2021 ◽  
Author(s):  
Christina Popescu ◽  
Grace Golden ◽  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Dominique Slowey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Feasibility Study ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Depression Treatment ◽  
Web Based

An artificial intelligence-based clinical decision support system for large kidney stone treatment

2019 ◽  
Vol 42 (3) ◽  
pp. 771-779 ◽  
Author(s):  
Tayyebe Shabaniyan ◽  
Hossein Parsaei ◽  
Alireza Aminsharifi ◽  
Mohammad Mehdi Movahedi ◽  
Amin Torabi Jahromi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Kidney Stone ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Stone Treatment

Guidance DS: A web-based clinical decision support system for antimicrobial stewardship in hospitals

2009 ◽  
Vol 42 (12) ◽  
pp. 354-358
Author(s):  
Karin Thursky ◽  
Marion Robertson ◽  
Susan Luu ◽  
James Black ◽  
Michael Richards ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Web Based

scholarly journals A Clinical Feasibility Study of an Artificial Intelligence-Powered Clinical Decision Support System

2021 ◽  
Author(s):  
Christina Popescu ◽  
Grace Golden ◽  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Dominique Slowey ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Session Length ◽  
Positive Effects ◽  
Significant Difference

Objective: We examine the feasibility of an Artificial Intelligence (AI)-powered clinical decision support system (CDSS), which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural-network based individualized treatment remission prediction. Methods: Due to COVID-19, the study was adapted to be completed entirely at a distance. Seven physicians recruited outpatients diagnosed with major depressive disorder (MDD) as per DSM-V criteria. Patients completed a minimum of one visit without the CDSS (baseline) and two subsequent visits where the CDSS was used by the physician (visit 1 and 2). The primary outcome of interest was change in session length after CDSS introduction, as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semi-structured interviews. Results: Seventeen patients enrolled in the study; 14 completed. There was no significant difference between appointment length between visits (introduction of the tool did not increase session length). 92.31% of patients and 71.43% of physicians felt that the tool was easy to use. 61.54% of the patients and 71.43% of the physicians rated that they trusted the CDSS. 46.15% of patients felt that the patient-clinician relationship significantly or somewhat improved, while the other 53.85% felt that it did not change. Conclusions: Our results confirm the primary hypothesis that the integration of the tool does not increase appointment length. Findings suggest the CDSS is easy to use and may have some positive effects on the patient-physician relationship. The CDSS is feasible and ready for effectiveness studies.

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