Virtual patients - what are we talking about? A framework to classify the meanings of the term in healthcare education

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Abstract

Background

The term “virtual patients” (VPs) has been used for many years in academic publications, but its meaning varies, leading to confusion. Our aim was to investigate and categorize the use of the term “virtual patient” and then classify its use in healthcare education.

Methods

A literature review was conducted to determine all articles using the term “virtual patient” in the title or abstract. These articles were categorized into: Education, Clinical Procedures, Clinical Research and E-Health. All educational articles were further classified based on a framework published by Talbot et al. which was further developed using a deductive content analysis approach.

Results

536 articles published between 1991 and December 2013 were included in the study. From these, 330 were categorized as educational. Classifying these showed that 37% articles used VPs in the form of Interactive Patient Scenarios. VPs in form of High Fidelity Software Simulations (19%) and Virtual Standardized Patients (16%) were also frequent. Less frequent were other forms, such as VP Games.

Analyzing the literature across time shows an overall trend towards the use of Interactive Patient Scenarios as the predominant form of VPs in healthcare education.

Conclusions

The main form of educational VPs in the literature are Interactive Patient Scenarios despite rapid technical advances that would support more complex applications. The adapted classification provides a valuable model for VP developers and researchers in healthcare education to more clearly communicate the type of VP they are addressing avoiding misunderstandings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12909-015-0296-3) contains supplementary material, which is available to authorized users.

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Most cited references 43

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The qualitative content analysis process.

Satu Elo, Helvi Kyngäs (2008)

This paper is a description of inductive and deductive content analysis. Content analysis is a method that may be used with either qualitative or quantitative data and in an inductive or deductive way. Qualitative content analysis is commonly used in nursing studies but little has been published on the analysis process and many research books generally only provide a short description of this method. When using content analysis, the aim was to build a model to describe the phenomenon in a conceptual form. Both inductive and deductive analysis processes are represented as three main phases: preparation, organizing and reporting. The preparation phase is similar in both approaches. The concepts are derived from the data in inductive content analysis. Deductive content analysis is used when the structure of analysis is operationalized on the basis of previous knowledge. Inductive content analysis is used in cases where there are no previous studies dealing with the phenomenon or when it is fragmented. A deductive approach is useful if the general aim was to test a previous theory in a different situation or to compare categories at different time periods.

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Virtual patients: a critical literature review and proposed next steps.

Marc M Triola, Bruce Cook (2009)

The opposing forces of increased training expectations and reduced training resources have greatly impacted health professions education. Virtual patients (VPs), which take the form of interactive computer-based clinical scenarios, may help to reconcile this paradox. We summarise research on VPs, highlight the spectrum of potential variation and identify an agenda for future research. We also critically consider the role of VPs in the educational armamentarium. We propose that VPs' most unique and cost-effective function is to facilitate and assess the development of clinical reasoning. Clinical reasoning in experts involves a non-analytical process that matures through deliberate practice with multiple and varied clinical cases. Virtual patients are ideally suited to this task. Virtual patients can also be used in learner assessment, but scoring rubrics should emphasise non-analytical clinical reasoning rather than completeness of information or algorithmic approaches. Potential variations in VP design are practically limitless, yet few studies have rigorously explored design issues. More research is needed to inform instructional design and curricular integration. Virtual patients should be designed and used to promote clinical reasoning skills. More research is needed to inform how to effectively use VPs.

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The history of simulation in medical education and possible future directions.

Paul Bradley (2006)

Clinical simulation is on the point of having a significant impact on health care education across professional boundaries and in both the undergraduate and postgraduate arenas. The use of simulation spans a spectrum of sophistication, from the simple reproduction of isolated body parts through to complex human interactions portrayed by simulated patients or high-fidelity human patient simulators replicating whole body appearance and variable physiological parameters. After a prolonged gestation, recent advances have made available affordable technologies that permit the reproduction of clinical events with sufficient fidelity to permit the engagement of learners in a realistic and meaningful way. At the same time, reforms in undergraduate and postgraduate education, combined with political and societal pressures, have promoted a safety-conscious culture where simulation provides a means of risk-free learning in complex, critical or rare situations. Furthermore, the importance of team-based and interprofessional approaches to learning and health care can be promoted. However, at the present time the quantity and quality of research in this area of medical education is limited. Such research is needed to enable educators to justify the cost and effort involved in simulation and to confirm the benefit of this mode of learning in terms of the outcomes achieved through this process.

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Author and article information

Contributors

Andrzej A Kononowicz: andrzej.kononowicz@ki.se

Nabil Zary: Nabil.Zary@ki.se

Samuel Edelbring: Samuel.Edelbring@ki.se

Janet Corral: jktworek@gmail.com

Inga Hege: inga.hege@med.uni-muenchen.de

Journal

Journal ID (nlm-ta): BMC Med Educ

Journal ID (iso-abbrev): BMC Med Educ

Title: BMC Medical Education

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6920

Publication date (Electronic): 1 February 2015

Publication date PMC-release: 1 February 2015

Publication date Collection: 2015

Volume: 15

Electronic Location Identifier: 11

Affiliations

[ ]Department of Learning, Informatics Management and Ethics, Karolinska Institutet, Stockholm, Sweden

[ ]Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Krakow, Poland

[ ]School of Medicine at the University of Colorado Denver in Denver, Colorado, USA

[ ]Institut für Didaktik und Ausbildungsforschung in der Medizin am Klinikum der Universität München, Ziemssenstr 1, 80336 München, Germany

Article

Publisher ID: 296

DOI: 10.1186/s12909-015-0296-3

PMC ID: 4318546

PubMed ID: 25638167

SO-VID: 71d31ba5-2ff1-490b-9228-a607d2cb16e8

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 28 July 2014

Date accepted : 19 January 2015

Custom metadata

ScienceOpen disciplines: Education

Keywords: virtual patients,healthcare education,classification

Data availability:

ScienceOpen disciplines: Education

Keywords: virtual patients, healthcare education, classification

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Virtual patients - what are we talking about? A framework to classify the meanings of the term in healthcare education

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Abstract

Background

Methods

Results

Conclusions

Electronic supplementary material

Related collections

Nonpartisan Education Review

Most cited references 43

The qualitative content analysis process.

Virtual patients: a critical literature review and proposed next steps.

The history of simulation in medical education and possible future directions.

Author and article information

Contributors

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Affiliations

Article

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Cited by 53

Most referenced authors 244