scholarly journals Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician–patient interaction

BJPsych Open ◽  
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Kelly Perlman ◽  
Sonia Israel ◽  
Joseph Mehltretter ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Major Depression ◽  
Clinical Decision Support ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Patient Interaction ◽  
Simulation Centre ◽  
Standardised Patients ◽  
Physician Patient

Background Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction. Aims Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction. Method Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback. Results All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction. Conclusions The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

scholarly journals Using a Simulation Centre to Evaluate the Effect of anArtificial Intelligence-Powered Clinical Decision Support System for Depression Treatment on the Physician-Patient Interaction

2020 ◽  
Author(s):  
David Benrimoh ◽  
Myriam Tanguay-Sela ◽  
Kelly Perlman ◽  
Sonia Israel ◽  
Joseph Mehltretter ◽  
...  
Keyword(s):  
Decision Support ◽  
Major Depression ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Standardized Patients ◽  
Clinical Decision ◽  
Patient Interaction ◽  
Simulation Centre ◽  
Physician Patient

ABSTRACTObjectiveAifred is an artificial intelligence (AI)-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction.MethodsTwenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-hour study at a clinical interaction simulation centre with standardized patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-minute clinical scenarios with standardized patients portraying mild, moderate, and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews, and standardized patient feedback.ResultsAll twenty participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the AI’s predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool’s impact on the physician-patient interaction.ConclusionsThe simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction prior to clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

scholarly journals A Mixed-Methods Feasibility Study of a Novel AI-Enabled, Web-Based, Clinical Decision Support System for the Treatment of Major Depression in Adults

2022 ◽  
Author(s):  
Sabrina Qassim ◽  
Grace L Golden ◽  
Dominique Slowey ◽  
Mary Sarfas ◽  
Kate Whitmore ◽  
...  
Keyword(s):  
Decision Support ◽  
Major Depression ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Utility ◽  
Clinical Decision ◽  
Treatment Decisions ◽  
Physician Patient

The objective of this paper is to discuss perceived clinical utility and impact on physician-patient relationship of a novel, artificial-intelligence (AI) enabled clinical decision support system (CDSS) for use in the treatment of adults with major depression. Patients had a baseline appointment, followed by a minimum of two appointments with the CDSS. For both physicians and patients, study exit questionnaires and interviews were conducted to assess perceived clinical utility, impact on patient-physician relationship, and understanding and trust in the CDSS. 17 patients consented to participate in the study, of which 14 completed. 86% of physicians (6/7) felt the information provided by the CDSS provided a more comprehensive understanding of patient situations and 71% (5/7) felt the information was helpful. 86% of physicians (6/7) reported the AI/predictive model was useful when making treatment decisions. 62% of patients (8/13) reported improvement in their care as a result of the tool. 46% of patients (6/13) felt the app significantly or somewhat improved their relationship with their physicians; 54% felt it did not change. 71% of physicians (5/7) and 62% of patients (8/13) rated they trusted the tool. Qualitative results are analyzed and presented. Findings suggest physicians perceived the tool as useful in conducting appointments and used it while making treatment decisions. Physicians and patients generally found the tool trustworthy, and it may have positive effects on physician-patient relationships.

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 in Outpatient Care in Times of Covid-19: A Multidisciplinary Comment on the Impact of a Notional Artificial Intelligence Based Clinical Decision Support System on Outpatient Care for Kidney Transplant Recipients (Preprint)

2020 ◽  
Author(s):  
Bilgin Osmanodja ◽  
Matthias Braun ◽  
Aljoscha Burchardt ◽  
Wiebke Duettmann ◽  
Michelle Fiekens ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Kidney Transplant ◽  
Clinical Decision Support ◽  
Support System ◽  
Outpatient Care ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Transplant Recipients ◽  
Kidney Transplant Recipients

UNSTRUCTURED The Covid-19 pandemic has put new demands on the medical systems worldwide. The pressure of taking far-reaching decisions within multiply limited resources under the constraint that personal contact must be minimized has evoked the question if technical support in the form of Artificial Intelligence (AI) could help leverage these challenges. At the same time, AI comes with its own issues such as limited transparency that cannot be neglected especially in a medical context. We will deliberate this in the domain of specialized outpatient care of kidney transplant recipients. In order to improve long-term care for these patients, we implemented a telemedicine functionality monitoring vital signs, medication adherence and symptoms at Charité – Universitätsmedizin Berlin. This paper seeks to combine this established telemonitoring approach with methods from Artificial Intelligence proposing an AI-based clinical decision support system (AI-CDSS) that aims to detect Covid-19 and other severe diseases in this high-risk population. After analyzing medical needs and difficulties and suggesting possible technical solutions, we argue that AI-supported telemonitoring in outpatient care can play a valuable role in managing resources and risks in kidney transplant patients in times of Covid-19 and beyond. Additionally, regarding the multitude of ethical and legal questions arising when integrating AI into workflows, we exemplarily discuss the concept of meaningful human control and whether it is achievable with the proposed AI-CDSS.

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.

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