Novel Use of Chatbot Technology to Educate Patients Before Breast Biopsy

Chetlen, Alison L.; Artrip, Rick; Drury, Brian; Arbaiza, Anthony; Moore, Michael J.

doi:10.1016/j.jacr.2019.05.050

Cited by 20 publications

(28 citation statements)

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“…Several studies focused on the development of virtual patients [10, 11, 13-18, 22, 25]. Another area chatbots have been used in is patients education [4,8,9,12,19,21,23,24]. Other uses have also been explored like course assistance by administering quizzes [20] and facilitating communication between students and instructors.…”

Section: Resultsmentioning

confidence: 99%

“…Interaction with the chatbots is primarily done through free text inputted through keyboard [4,10,16,17,22,24,25] and using speech [17,24]. Some chatbots, however, followed more linear flow with either pre-selected answers or specific commands that can be selected [9,11,12,20,23]. On the other hand, the logic behind the chatbots regarding understanding user input and the decision making primarily was done through some implementation of a natural language understanding system [4, 10, 13-15, 17-19, 21, 22, 25].…”

Section: Resultsmentioning

confidence: 99%

“…There is also a variety in the ways chatbots have been deployed, that shows the versatility of the technology. Some were used through smartphones or tablets [4,8,9,12,20,23,24], either through custom applications, using sms messages [9], or integrated in social media applications like Telegram [20] and Line [4]. Others were web-based [10,11,16,22], run as standalone applications on personal computers [14,17,25], and even integrations in Virtual Reality systems [18,19].…”

Section: Resultsmentioning

confidence: 99%

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An Overview of the Use of Chatbots in Medical and Healthcare Education

Frangoudes

Hadjiaros²,

Schiza³

et al. 2021

Lecture Notes in Computer Science

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Chatbots are becoming a trend in many fields such as medical, service industry and more recently in education. Especially in healthcare education, there is a growing interest in integrating chatbots in the learning and teaching processes mostly because of their portability and affordance. In this paper, we seek to explore the primary uses of chatbots in medical education, as well as how they are developed. We elaborate on current chatbot applications and research enacted in the domains of medical and healthcare education, We focus in the areas of virtual patients in medical education, patients' education related to healthcare matters but also chatbots as course assistance in for enhancing healthcare professionals' curricula. Additionally, we examine the metrics that have been used to evaluate these chatbots, which include subjective ones like the usability and acceptability by the users, and objectives ones, like their accuracy and users' skills evaluation. Overall, even though chatbots offer a flexible solution and a vast possibility to improve healthcare education, our literature review suggests that their efficacy has not been thoroughly tested. Also, limited examples of chatbots in European Healthcare curricula have been found. These call of the need for further research towards this direction.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of the Use of Chatbots in Medical and Healthcare Education

Frangoudes

Hadjiaros²,

Schiza³

et al. 2021

Lecture Notes in Computer Science

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show abstract

“…A "chatbot" is de ned as an "arti cial intelligence program designed to simulate human conversation via text or speech", as opposed to the current owchart model [20][21]. The use of chatbots has seen a growing development and usage within the medical literature [21][22], with successful application for recording key symptoms [23][24][25][26].…”

Section: Development Of a Chatbot As An Addition To The Copenhagen Emmentioning

confidence: 99%

Strategies to Handle Increased Demand in the COVID-19 Crisis a Corona Telephone Hotline and a Web-based Self-triage System

Jensen¹,

Holgersen

Jespersen

et al. 2020

Preprint

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BackgroundIn emergencies, such as the COVID-19 pandemic, there is an increased need for contact withemergency medical services (EMS), and call volume might surpass capacity. Thus, the Copenhagen EMS in Denmark implemented a separate coronavirus hotline followed by a web-based self-triage system to reduce nonemergency call volume. The aim of this paper is to present the two measures implemented to handle the increased call volume to the Copenhagen EMSfromthose with mild or no relevant COVID-19 symptoms.MethodsThis is a cross sectional observational study monitoring call volume in the first month of the COVID-19 pandemic in accumulated callnumbers, compared to the equivalent numbers during one month from the year before (2019). A coronavirus hotline and web-based self-triage system arepresented in absolute numbers of users.ResultsIn the first month of the COVID-19 pandemic in Copenhagen, emergency medical dispatch centers were extensively overloaded with more than 10.800 calls, resulting in significantly prolonged queue time (mean time in minutes:12:02; CI: 11:55-12:09)) compared to 2019 (mean time in minutes02:23; CI: 02:22-02:25) and thereby limiting access to emergency assistance and triage for citizens. The introduction of the coronavirus hotline showed reduced call volume and queue time to the EMS. The web-based self-triage system was used more than 107.000 times. However, no correlation between call volume and the use of a web-based self-triage systemwas observed.ConclusionsCreating a coronavirus hotlinestaffed by healthcare personnelseemed to have an impact on call volume and potentially relieved the strain in resources, while the web-based self-triage system was widely used and could be further developed to reach itsfull potential. Other EMS organizations can implement these measures to enhance capacity in a future epidemic.

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“…Natural language dialogue systems (chatbots or conversational agents) have been integrated in healthcare applications [17][18][19] and VP simulation environments. Interaction modules allow trainees to simulate history taking, mostly through constrained input-e.g.…”

mentioning

confidence: 99%

Lessons Learned from the Usability Evaluation of a Simulated Patient Dialogue System

Llanos

Thomas²,

Bilinski

et al. 2021

J Med Syst

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Simulated consultations through virtual patients allow medical students to practice history-taking skills. Ideally, applications should provide interactions in natural language and be multi-case, multi-specialty. Nevertheless, few systems handle or are tested on a large variety of cases. We present a virtual patient dialogue system in which a medical trainer types new cases and these are processed without human intervention. To develop it, we designed a patient record model, a knowledge model for the history-taking task, and a termino-ontological model for term variation and out-of-vocabulary words. We evaluated whether this system provided quality dialogue across medical specialities (n = 18), and with unseen cases (n = 29) compared to the cases used for development (n = 6). Medical evaluators (students, residents, practitioners, and researchers) conducted simulated history-taking with the system and assessed its performance through Likert-scale questionnaires. We analysed interaction logs and evaluated system correctness. The mean user evaluation score for the 29 unseen cases was 4.06 out of 5 (very good). The evaluation of correctness determined that, on average, 74.3% (sd = 9.5) of replies were correct, 14.9% (sd = 6.3) incorrect, and in 10.7% the system behaved cautiously by deferring a reply. In the user evaluation, all aspects scored higher in the 29 unseen cases than in the 6 seen cases. Although such a multi-case system has its limits, the evaluation showed that creating it is feasible; that it performs adequately; and that it is judged usable. We discuss some lessons learned and pivotal design choices affecting its performance and the end-users, who are primarily medical students.

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Novel Use of Chatbot Technology to Educate Patients Before Breast Biopsy

Cited by 20 publications

References 1 publication

An Overview of the Use of Chatbots in Medical and Healthcare Education

An Overview of the Use of Chatbots in Medical and Healthcare Education

Strategies to Handle Increased Demand in the COVID-19 Crisis a Corona Telephone Hotline and a Web-based Self-triage System

Lessons Learned from the Usability Evaluation of a Simulated Patient Dialogue System

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