scholarly journals Evaluating the Perceived Utility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment Using a Simulation Centre

2021 ◽  
Author(s):  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Christina Popescu ◽  
Tamara Perez ◽  
Colleen Rollins ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Treatment Options ◽  
Clinical Decision ◽  
Perceived Utility ◽  
Clinical Interactions

AbstractAifred is a clinical decision support system (CDSS) that uses artificial intelligence to assist physicians in selecting treatments for major depressive disorder (MDD) by providing probabilities of remission for different treatment options based on patient characteristics. We evaluated the utility of the CDSS as perceived by physicians participating in simulated clinical interactions. Twenty psychiatry and family medicine staff and residents completed a study in which each physician had three 10-minute clinical interactions with standardized patients portraying mild, moderate, and severe episodes of MDD. During these scenarios, physicians were given access to the CDSS, which they could use in their treatment decisions. The perceived utility of the CDSS was assessed through self-report questionnaires, scenario observations, and interviews. 60% of physicians perceived the CDSS to be a useful tool in their treatment-selection process, with family physicians perceiving the greatest utility. Moreover, 50% of physicians would use the tool for all patients with depression, with an additional 35% noting they would reserve the tool for more severe or treatment-resistant patients. Furthermore, clinicians found the tool to be useful in discussing treatment options with patients. The efficacy of this CDSS and its potential to improve treatment outcomes must be further evaluated in clinical trials.

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

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.

Artificial intelligence-based clinical decision-support system improves cancer treatment and patient satisfaction.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18303-e18303
Author(s):  
Zuochao Wang ◽  
Zhonghe Yu ◽  
Xuejing Zhang
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Treatment Options ◽  
Clinical Decision ◽  
Diagnostic Model ◽  
Consultation System

e18303 Background: Traditional diagnostic model for cancer heavily relies on physicians and their teams’ knowledge. However, under this diagnostic model, patients’ source of information is quite limited. Cancer patients usually fill with negative emotion. Lack of knowledge to the disease and treatment options further leads to less confidence to their treatment outcome. Methods: In order to improve their faith in getting proper treatment and the hope for surviving the deadly disease, we has introduced an artificial intelligence based clinical decision-support system, the Watson for Oncology (WFO), since May-2018. WFO is developed by IBM, it assesses information from a patient’s medical record, evaluates medical evidence, and displays potential treatment options. Our oncologist can then apply their own expertise to identify the most appropriate treatment options. We have generated a new 7-step consultation system with the help of WFO. That include 1: introduce the WFO to patients, 2: patients express their demands and expectations, 3: the oncologist presents patient’s medical condition, 4: discussion with other members in the consultation team, 5: input patients’ information into WFO system and review treatment options, 6: discuss and finalize treatment options with patients, 7: feedbacks form patients after consultation. 70 patients who were hospitalized from May-2018 to Dec-2018 were divided into two groups, 50 patients volunteered to be assigned to the new 7-step consultation system and 20 patients stayed with the traditional diagnostic method to find them treatment options. All patients were followed up by questionnaire. Results: The results showed that patients in the 7-step consultation group presented significantly higher satisfaction rate towards treatment options, confidence level to their health care workers, and willingness to follow the treatment option when compared to patients in the traditional diagnostic group. Conclusions: The WFO assisted 7-step consultation system not only provides a more efficient way to find treatment options, but also improves patients’ understanding to their disease and possible side effects towards the treatment. Most importantly, patients build stronger confidence with their health care team and are willing to believe they will benefit from the treatment plans.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered Clinical Decision Support System for Depression Treatment: A Longitudinal Feasibility Study (Preprint)

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

BACKGROUND Approximately two thirds of patients with major depressive disorder (MDD) do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence (AI)-powered clinical decision support systems (CDSS) to assist physicians in their treatment selection and management, improving personalization and use of best practices such as measurement-based care. Previous literature shows that in order for digital mental health tools to be successful, the tool must be easy to use for patients and physicians and feasible within existing clinical workflows. OBJECTIVE We examine the feasibility of an AI-powered clinical decision support system, 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 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. CLINICALTRIAL NCT04061642

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