Sorting Out the Virtual Patient

Author(s):  
Thomas B. Talbot ◽  
Kenji Sagae ◽  
Bruce John ◽  
Albert A. Rizzo

Since Dr. Howard Barrows (1964) introduced the human standardized patient in 1963, there have been attempts to game a computer-based simulacrum of a patient encounter; the first being a heart attack simulation using the online PLATO system (Bitzer, 1966). With the now ubiquitous use of computers in medicine, interest and effort have expended in the area of Virtual Patients (VPs). One problem in trying to understand VPs is that there are several quite distinct educational approaches that are all called a ‘virtual patient.’ This article is not a general review of virtual patients as current reviews of excellent quality exist (Poulton & Balasubramaniam, 2011; Cook & Triola, 2009). Also, research that demonstrates the efficacy of virtual patients is ample (Triola, et al., 2006). This article assesses the different kinds of things the authors call “virtual patients”, which are often mutually exclusive approaches, then analyzes their interaction structure or ‘game-play’, and considers the best use scenarios for that design strategy. This article also explores dialogue-based conversational agents as virtual patients and the technology approaches to creating them. Finally, the authors offer a theoretical approach that synthesizes several educational approaches over the course of a medical encounter and recommend the optimal technology for the type of encounter desired.

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Inga Hege ◽  
Isabel Kiesewetter ◽  
Martin Adler

Abstract Background The ability to compose a concise summary statement about a patient is a good indicator for the clinical reasoning abilities of healthcare students. To assess such summary statements manually a rubric based on five categories - use of semantic qualifiers, narrowing, transformation, accuracy, and global rating has been published. Our aim was to explore whether computer-based methods can be applied to automatically assess summary statements composed by learners in virtual patient scenarios based on the available rubric in real-time to serve as a basis for immediate feedback to learners. Methods We randomly selected 125 summary statements in German and English composed by learners in five different virtual patient scenarios. Then we manually rated these statements based on the rubric plus an additional category for the use of the virtual patients’ name. We implemented a natural language processing approach in combination with our own algorithm to automatically assess 125 randomly selected summary statements and compared the results of the manual and automatic rating in each category. Results We found a moderate agreement of the manual and automatic rating in most of the categories. However, some further analysis and development is needed, especially for a more reliable assessment of the factual accuracy and the identification of patient names in the German statements. Conclusions Despite some areas of improvement we believe that our results justify a careful display of the computer-calculated assessment scores as feedback to the learners. It will be important to emphasize that the rating is an approximation and give learners the possibility to complain about supposedly incorrect assessments, which will also help us to further improve the rating algorithms.


2021 ◽  
Vol 27 (1) ◽  
pp. 146045822199486
Author(s):  
Nicholas RJ Frick ◽  
Felix Brünker ◽  
Björn Ross ◽  
Stefan Stieglitz

Within the anamnesis, medical information is frequently withheld, incomplete, or incorrect, potentially causing negative consequences for the patient. The use of conversational agents (CAs), computer-based systems using natural language to interact with humans, may mitigate this problem. The present research examines whether CAs differ from physicians in their ability to elicit truthful disclosure and discourage concealment of medical information. We conducted an online questionnaire with German participants ( N = 148) to assess their willingness to reveal medical information. The results indicate that patients would rather disclose medical information to a physician than to a CA; there was no difference in the tendency to conceal information. This research offers a frame of reference for future research on applying CAs during the anamnesis to support physicians. From a practical view, physicians might gain better understanding of how the use of CAs can facilitate the anamnesis.


2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zalika Klemenc-Ketis ◽  
Branka Cagran ◽  
Dejan Dinevski

Introduction. A “virtual patient” is defined as a computer program which simulates real patients’ cases. The aim of this study was to determine whether the inclusion of virtual patients affects the level of factual knowledge of family medicine students at the undergraduate level. Methods. This was a case-controlled prospective study. The students were randomly divided into experimental (EG: N=51) and control (CG: N=48) groups. The students in the EG were asked to practice diagnosis using virtual patients instead of the paper-based clinical cases which were solved by the students in the CG. The main observed variable in the study was knowledge of family medicine, determined by 50 multiple choice questions (MCQs) about knowledge of family medicine. Results. There were no statistically significant differences in the groups’ initial knowledge. At the final assessment of knowledge, there were no statistically significant differences between the groups, but there was a statistically significant difference between their initial and final knowledge. Conclusions. The study showed that adding virtual patient cases to the curriculum, instead of paper clinical cases, did not affect the level of factual knowledge about family medicine. Virtual patients can be used, but a significant educational outcome is not expected.


MedEdPORTAL ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Anna Richmond ◽  
Anna Burgner ◽  
Jennifer Green ◽  
Greg Young ◽  
Jonathan Gelber ◽  
...  

2018 ◽  
Vol 108 (2) ◽  
pp. 145-150
Author(s):  
James M. Mahoney ◽  
Vassilios Vardaxis ◽  
Noreen Anwar ◽  
Jacob Hagenbucher

Background: This study examined the differences between faculty and trained standardized patient (SP) evaluations on student professionalism during a second-year podiatric medicine standardized simulated patient encounter. Methods: Forty-nine second-year podiatric medicine students were evaluated for their professionalism behavior. Eleven SPs performed an assessment in real-time, and one faculty member performed a secondary assessment after observing a videotape of the encounter. Five domains were chosen for evaluation from a validated professionalism assessment tool. Results: Significant differences were identified in the professionalism domains of “build a relationship” (P = .008), “gather information” (P = .001), and share information (P = .002), where the faculty scored the students higher than the SP for 24.5%, 18.9%, and 26.5% of the cases, respectively. In addition, the faculty scores were higher than the SP scores in all of the “gather information” subdomains; however, the difference in scores was significant only in the “question appropriately” (P = .001) and “listen and clarify” (P = .003) subdomains. Conclusions: This study showed that professionalism scores for second-year podiatric medical students during a simulated patient encounter varied significantly between faculty and SPs. Further consideration needs to be given to determine the source of these differences.


2017 ◽  
pp. 379-393
Author(s):  
Uno G. H. Fors ◽  
Olivier Courteille

Healthcare professionals need good communication skills to be able to communicate with patients. In such provider-patient communication, the professional needs to be well understood by the patient, but also be able to understand subtle parts of a medical history taking dialogue with worried, sick or mentally affected patients. Virtual Patients (VPs) – learning environments that simulate encounters between a patient and a physician – were used to prepare 26 immigrating professionals in Swedish for healthcare practitioners. The professionals were speaking nine different foreign languages and used two different VP systems to train patient communication. Almost all participants welcomed the use of VPs for training communication in healthcare Swedish and 19 of the 26 users indicated that they considered that VPs should be mandatory to use in future courses. Targeted individual training in provider-patient communication with Virtual Patients seems to be of great educational value and well accepted by immigrating healthcare professionals.


2014 ◽  
Vol 15 (3) ◽  
pp. 404-425 ◽  
Author(s):  
Marjorie McShane

Mental model ascription – also called mindreading – is the process of inferring the mental states of others, which happens as a matter of course in social interactions. But although ubiquitous, mindreading is presumably a highly variable process: people mindread to different extents and with different results. We hypothesize that human mindreading ability relies on a large number of personal and contextual features: the inherent abilities of specific individuals, their current physical and mental states, their knowledge of the domain of discourse, their familiarity with the interlocutor, the risks associated with an incorrect assessment of intent, and so on. This paper presents a theory of mindreading that models diverse artificial intelligent agents using an inventory of parameters and value sets that represent traits of humans and features of discourse contexts. Examples are drawn from Maryland Virtual Patient, a prototype system that will permit medical trainees to diagnose and treat cognitively modeled virtual patients with the optional assistance of a virtual tutor. Since real patients vary greatly with respect to physiological and cognitive features, so must a society of virtual patients. Modeling such variation is one of the goals of the overall OntoAgent program of research and development.


2020 ◽  
Vol 30 (1) ◽  
pp. 381-386
Author(s):  
Sherri A. Braksick ◽  
Yunxia Wang ◽  
Suzanne L. Hunt ◽  
William Cathcart-Rake ◽  
Jon P. Schrage ◽  
...  

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