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      Physicians’ Perceptions of the Use of a Chatbot for Information Seeking: Qualitative Study

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          Abstract

          Background

          Seeking medical information can be an issue for physicians. In the specific context of medical practice, chatbots are hypothesized to present additional value for providing information quickly, particularly as far as drug risk minimization measures are concerned.

          Objective

          This qualitative study aimed to elicit physicians’ perceptions of a pilot version of a chatbot used in the context of drug information and risk minimization measures.

          Methods

          General practitioners and specialists were recruited across France to participate in individual semistructured interviews. Interviews were recorded, transcribed, and analyzed using a horizontal thematic analysis approach.

          Results

          Eight general practitioners and 2 specialists participated. The tone and ergonomics of the pilot version were appreciated by physicians. However, all participants emphasized the importance of getting exhaustive, trustworthy answers when interacting with a chatbot.

          Conclusions

          The chatbot was perceived as a useful and innovative tool that could easily be integrated into routine medical practice and could help health professionals when seeking information on drug and risk minimization measures.

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          Most cited references25

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          Conversational agents in healthcare: a systematic review

          Abstract Objective Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen’s kappa measured inter-coder agreement. Results The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.
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            Sharing decisions with patients: is the information good enough?

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              Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey

              Background Many potential benefits for the uses of chatbots within the context of health care have been theorized, such as improved patient education and treatment compliance. However, little is known about the perspectives of practicing medical physicians on the use of chatbots in health care, even though these individuals are the traditional benchmark of proper patient care. Objective This study aimed to investigate the perceptions of physicians regarding the use of health care chatbots, including their benefits, challenges, and risks to patients. Methods A total of 100 practicing physicians across the United States completed a Web-based, self-report survey to examine their opinions of chatbot technology in health care. Descriptive statistics and frequencies were used to examine the characteristics of participants. Results A wide variety of positive and negative perspectives were reported on the use of health care chatbots, including the importance to patients for managing their own health and the benefits on physical, psychological, and behavioral health outcomes. More consistent agreement occurred with regard to administrative benefits associated with chatbots; many physicians believed that chatbots would be most beneficial for scheduling doctor appointments (78%, 78/100), locating health clinics (76%, 76/100), or providing medication information (71%, 71/100). Conversely, many physicians believed that chatbots cannot effectively care for all of the patients’ needs (76%, 76/100), cannot display human emotion (72%, 72/100), and cannot provide detailed diagnosis and treatment because of not knowing all of the personal factors associated with the patient (71%, 71/100). Many physicians also stated that health care chatbots could be a risk to patients if they self-diagnose too often (714%, 74/100) and do not accurately understand the diagnoses (74%, 74/100). Conclusions Physicians believed in both costs and benefits associated with chatbots, depending on the logistics and specific roles of the technology. Chatbots may have a beneficial role to play in health care to support, motivate, and coach patients as well as for streamlining organizational tasks; in essence, chatbots could become a surrogate for nonmedical caregivers. However, concerns remain on the inability of chatbots to comprehend the emotional state of humans as well as in areas where expert medical knowledge and intelligence is required.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                November 2020
                10 November 2020
                : 22
                : 11
                : e15185
                Affiliations
                [1 ] Kap Code Paris France
                [2 ] CNRS PASSAGES Bordeaux France
                [3 ] Bordeaux Population Health Research Center University of Bordeaux Inserm Bordeaux France
                [4 ] Sanofi Aventis Gentilly Cedex France
                Author notes
                Corresponding Author: Khristina Fauvelle khristina.fauvelle@ 123456sanofi.com
                Author information
                https://orcid.org/0000-0002-1057-3481
                https://orcid.org/0000-0003-2679-8391
                https://orcid.org/0000-0003-2642-7726
                https://orcid.org/0000-0003-3749-254X
                https://orcid.org/0000-0002-2925-7585
                Article
                v22i11e15185
                10.2196/15185
                7685916
                33170134
                5f904487-f243-45ad-b67a-79bf72f17616
                ©Jason Koman, Khristina Fauvelle, Stéphane Schuck, Nathalie Texier, Adel Mebarki. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.11.2020.

                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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 27 June 2019
                : 21 October 2019
                : 9 March 2020
                : 31 March 2020
                Categories
                Original Paper
                Original Paper

                Medicine
                health,digital health,innovation,conversational agent,decision support system,qualitative research,chatbot,bot,medical drugs,prescription,risk minimization measures

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