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

Andrzej A KononowiczpersName, Nabil ZarypersName, Samuel EdelbringpersName, Janet CorralpersName, Inga HegepersName (2015date). “Virtual patients--what are we talking about? A framework to classify the meanings of the term in healthcare education..” BMC Medical Educationtitle·j.

Object of study — the meanings of the term virtual patientterm·dom in healthcare educationterm·key

Research question — how the use of the term virtual patientterm·dom has developed over time

Contribution — a virtual patientterm·dom classificationterm·key framework that makes the type of VP addressed explicit

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Virtual patients--what are we talking about? A framework to classify the meanings of the term in healthcare educationterm·key.

Abstract The term “virtual patientsterm·dom” (VPsterm·dom) 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 patientterm·dom” and then classify its use in healthcare educationterm·key.

A literature reviewterm·mth was conducted to determine all articles using the term “virtual patientterm·dom” 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 analysisterm approach.

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 VPsterm·dom in the form of Interactive Patient Scenariosterm·dom. VPsterm·dom in form of High Fidelity Software Simulationsterm·key (19%) and Virtual Standardized Patientsterm·dom (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 Scenariosterm·dom as the predominant form of VPsterm·dom in healthcare educationterm·key.

The main form of educational VPsterm·dom in the literature are Interactive Patient Scenariosterm·dom despite rapid technical advances that would support more complex applications. The adapted classificationterm·key provides a valuable model for VP developers and researchers in healthcare educationterm·key to more clearly communicate the type of VP they are addressing avoiding misunderstandings.

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

Background Virtual patientsterm·dom (VPsterm·dom) have been used for many years in various contexts [ 1 ]: in healthcare educationterm·key [ 2 - 4 ], electronic patient records [ 5 - 7 ], and clinical research [ 8 - 10 ]. Yet among these applications there exists a heterogeneous understanding about what virtual patientsterm·dom are and for what purposes they may be used. Particularly in the educational field the term “virtual patientterm·dom” is applied to many diverse approaches [ 11 - 13 ].

Similar heterogeneity exists among definitions that are used to specify the term “virtual patientterm·dom”. One recently proposed definition is quite general: “In the context of medical education, this term [VP] generally refers to any software that allows case-based training” [ 14 ].

An often-cited and more specific definition by the American Association of Medical Collegesterm·dom (AAMCterm·dom) delineates virtual patientsterm·dom as “A specific type of computer-based program that simulates real-life clinical scenarios; learners emulate the roles of health care providers to obtain a history, conduct a physical exam, and make diagnostic and therapeutic decisions” [ 15 ]. In contrast a definition by Kenny et al. describes VPsterm·dom as “virtual interactive agents who are trained to simulate a patient’s particular clinical presentation with a high degree of consistency and realism” [ 16 ].

There have been a few attempts in recent years to develop a taxonomy for VPsterm·dom.

While Bearman et al. categorized VPsterm·dom as either following a problem-solving design (focused on teaching clinical reasoningterm·key and diagnosing skills) or a narrative design (emphasis on teaching decision making) [ 17 ], Huwendiek et al. have developed a more extensive empirically derived VP typology framework [ 18 ] which is based on four categories of metadata (general, educational, instructional and technical) with 19 factors. However, these taxonomies focus on specific types of VPsterm·dom and suffer from the exclusion of other forms such as high-fidelity simulationsterm·key or manikins.

A similarly selective view can be seen in systematic reviewsterm·mth such as the one by Cook et al. [ 19 ] in which the authors excluded many articles using the virtual patientterm·dom definition suggested by the AAMCterm·dom. A more inclusive approach was provided by Talbot et al. in the overview of educational virtual patientsterm·dom identifying seven classes of VPsterm·dom, ranging from technically basic digital case presentationsterm·dom to advanced virtual reality applications and high-fidelity simulationsterm·key [ 20 ]. This classificationterm·key takes into account the following features: common names, teaching applications, learner skills evaluated, interactivity, consistency of experience & evaluation, flexibility to recover from learner errors, suitability for game-based approach, author challenge, core, and enabling technology (see Table 1 in http://www.igi-global.com/article/content/74790 ). However, it is based on experience and does not show the frequency of the terms. Table 1 Adapted virtual patientterm·dom classificationterm·key with two levels of description Class label Predominant competency Predominant technology Short description Case Presentationterm·dom Knowledge Multimedia systems Interactive multimedia presentation of a patient case to teach primarily basic medical knowledge Interactive Patient Scenarioterm·dom Clinical reasoningterm·key Multimedia systems Interactive multimedia presentation of a patient case to teach mainly clinical reasoningterm·key skills (e.g. VPsterm·dom created for the eViP project) VP Game Clinical reasoningterm·key or Team training Virtual worlds Virtual world to simulate high risk scenarios and team training situations (e.g. Second Life VPsterm·dom) High Fidelity Software Simulationterm·key Procedural or basic clinical skills Dynamic simulationsterm·key or mixed reality Real-time simulationterm·key of human physiology to teach mainly procedures or skills such as surgical simulationsterm·key. Non-standard devices (e.g. haptic technology) can be included. Human Standardized Patient Patient communication skills Multimedia systems Video-recorded actors who role-play a patient to train patient communication skills. High Fidelity Manikin Procedural and basic clinical skills, /Team training Manikins or Part Task Trainers Manikins with realistic anatomy to train complex procedures such as endoscopy. Virtual Standardized Patientterm·dom Patient communication skills Conversational characters A virtual representation of a human being using artificial intelligence technologies and natural language processing to train communication skills. Additional file 1 shows a more detailed description of the competencies and technologies.

Consequently, the term “virtual patientterm·dom” is used to describe a multitude of technologies and approaches, making effective communication difficult when educators, researchers and IT specialists share their experiences with VPsterm·dom.

For example Le Beux and Fieschi used the term “virtual patientterm·dom” in all three distinguished classes of educational tools: “Simulationsterm·key for training”, “Virtual reality, video and virtual classroom” and “Medical virtual universities” [ 21 ].

Another illustrative example is the article by Huang et al. [ 22 ] conducting a survey among US and Canadian medical schools to get an overview about VP activities. One of the results was that 85% of the VPsterm·dom cost more than $10,000 to develop, but it remained unclear for which kind of VPsterm·dom this is the case. Costs can vary tremendously between different types of VPsterm·dom. For example for creating a VP using an authoring tool there are few technical costs compared to programming a high-fidelity VP simulationterm·key, which is time-consuming and involves high costs for technical development and equipment.

Aim The aim of our project was to investigate and classify variations in the use of the term “virtual patientterm·dom” with a focus on the healthcare educationterm·key domain. We also outlined the development of the use of the term over time.

Methods Data extraction We systematically searched PubMed, Scopus, EMBASE, PsycINFO, CINAHL/EBSCO, and ERIC for citations on virtual patientsterm·dom. Search terms were: “virtual patientterm·dom” or “virtual patientsterm·dom” in title and/or abstract.

Exclusion criteria were the following: article in a language other than English short conference abstracts (less than one page) term “virtual patientterm·dom” not mentioned within the article (only in abstract or title) To ensure a comprehensive search in the literature, we did not use a beginning date cutoff, and the last date of inclusion was December 31st, 2013. All citations were indexed into a spreadsheet including keywords and year of publication. Finally, full-text versions of all included articles were downloaded, read and analyzed.

General categorization of articles We categorized the articles based on the proposal by Ellaway [ 1 ] as it provided the most comprehensive set of categories. It describes the usage of VPsterm·dom for educational purposes, electronic patient records and clinical research.

Classificationterm·key of educational articles We further classified the VPsterm·dom assigned to the educational category based on the model proposed by Talbot et al. [ 8 ]. We chose this framework because it provided the broadest approach as well as the necessary levels of description to classify VPsterm·dom into the following seven types: Case Presentationterm·dom, Interactive Patient Scenarioterm·dom, Virtual Patientterm·dom Game, High Fidelity Software Simulationsterm·key, Human Standardized Patients, High Fidelity Manikins and Virtual Standardized Patientsterm·dom. Each type is characterized by different descriptive levels such as enabling technology, evaluated learner skills, interactivity or authoring challenge.

Using a deductive content analysisterm approach [ 23 ], we decreased the number of levels in Talbot’s classificationterm·key to the two most fundamental: educational (“predominant competency”) and technological (“predominant technology”).

Two authors (AK and IH) independently read, categorized and classified each article according to the above described schemata. Conflicts were resolved by consensus with the other authors in case of disagreement. Once all articles were categorized, a descriptive statistical analysis was conducted using Microsoft Excel 2010. No ethical approval was needed, as the study did neither involve any human participants nor any sensitive data.

Results We identified 791 citations using our search strategy. We excluded 25 non-English articles, 162 short conference abstracts and 68 articles in which the term “virtual patientterm·dom” was absent in the body of the article. Overall we included 536 articles. The first paper using the term “virtual patientterm·dom” dated from 1991, the last were published in December 2013. Figure 1 shows the stages of the literature reviewterm·mth. Figure 1 Progress through the stages of the literature reviewterm·mth.

General categorization of articles The categories proposed by Ellaway needed further development in order to fit the body of articles. A new category “Clinical Procedures” was introduced under which we subsumed for example computer-assisted surgery (e.g. Kau [ 24 ]) or radiotherapy planning (e.g. Guo [ 25 ]). In addition we found the “Electronic Patient Records” category too narrow and broadened it to “E-Health” [ 26 ], which includes also the use of VPsterm·dom in the context of telemedicine (e.g. Sicotte et al. [ 27 ]) and healthcare information systems (e.g. Effken et al. [ 28 ]). Figure 1 shows the number of articles we assigned to each category. Additional file 1 presents a more detailed description of the applied categories.

Classificationterm·key of educational articles Within the largest category, educational articles, 67 (20%) articles were general ones such as reviewsterm·mth or overviews, and therefore classified as “Other”. From the remaining 263 articles, we were able to assign 80% ( n = 210) to the seven types suggested by Talbot et al. However, we encountered difficulties classifying 20% ( n = 53) articles for the following reasons: Discrepancy of evaluated learner skills and technology level. Talbot’s classificationterm·key implies by using just one dimension that certain attributes from different levels are bound together. This makes some combinations of attributes across levels impossible (e.g. articles focusing on teaching clinical reasoningterm·key but using a high fidelity software simulationterm·key, e.g. Kofránek et al. [ 29 ]). Missing characteristics at particular levels of description - for example we could not classify articles which concerned VPsterm·dom teaching basic clinical skills (e.g. Lehmann et al. [ 11 ]). No differentiation - for example the underlying technology of Talbot’s Case Presentationterm·dom and Interactive Patient Scenarioterm·dom is basically the same (HTML, Authoring tools).

It was not possible to reach consensus for articles having these issues, so we decided to modify the classificationterm·key and thereby improve the attribution. This modified classificationterm·key is displayed in Table 1 .

By applying this modified one-dimensional classificationterm·key we were able to classify 234 (89%) of the articles. However, 29 articles could only be assigned to independent technological and competency-based dimensions. Figure 2 shows the distribution of articles based on these two dimensions. Figure 2 Overview of VP classes with assigned number of articles. Grey cells represent the classes of the adapted framework.

Development over time The first article using the term “virtual patientterm·dom” in the educational category dates from 1991. It describes a simulationterm·key of hemodynamics to teach physiology by Davis et al. [ 30 ] Since then the number of published articles using the term “virtual patientterm·dom” has increased each year, reaching 41 in 2013. Figure 3 shows this development in healthcare educationterm·key over the last 22 years for the articles assigned to one of the seven classes. Figure 3 Development of virtual patientsterm·dom in healthcare educationterm·key since 1991.

The distribution over the past 20 years of those articles that could not be assigned to one of the seven classes shows no noticeable change, such as increase or decrease.

All articles included in the study are listed in Additional file 2 .

Discussion Categorization of VP literature The categorization of the literature shows that more than 60% of the 536 articles using the term “virtual patientterm·dom” originate from the healthcare educationterm·key domain. However, VPsterm·dom are also quite frequently mentioned in clinical research (25%), but less frequently in clinical procedures (e.g. as surgery planning tools) and as virtual patientterm·dom records in E-Health. As the focus of our reviewterm·mth is education, we did not further analyze the other categories. However, it might be interesting to look into the details of VP usage in these non-educational categories and investigate whether there are concepts or ideas transferable to healthcare educationterm·key.

Classificationterm·key of VP usage in healthcare educationterm·key articles When applying our modified classificationterm·key framework to the body of healthcare educationterm·key literature most of the articles - 234 (89%) of the 263 - fitted into the modified classificationterm·key system based on competency and technology. Most (37%) educational articles (excluding “Others”) describe Interactive Patient Scenariosterm·dom. The next most common classes are VPsterm·dom in the form of High Fidelity Software Simulationsterm·key to teach procedures and skills (19%) and Virtual Standardized Patientsterm·dom to train patient communication skills (16%).

In contrast, we only encountered ten (4%) studies using the term “virtual patientterm·dom” for Human Standardized Patients, eight studies (3%) for Case Presentationsterm·dom and three (1%) for High Fidelity Manikins.

An explanation for these rare occurrences may be that these forms are often not labeled as VPsterm·dom - for example Case Presentationsterm·dom are also often referred to as Case-based learningterm (CBLterm). Our assumption was to include any use of the term “virtual patientterm·dom” even though there might be more suitable terms (e.g. manikins are usually not called virtual patientsterm·dom). To comprehensively outline articles in these classes - including those describing VPsterm·dom that are not called VPsterm·dom within the article - future studies could extend the literature search and include additional search terms (e.g. “patient simulationterm·key”), similar to the reviewterm·mth done by Cook et al. [ 19 ].

Although we were able to classify 234 (89%) articles, we still had 29 (11%) that could not be assigned into our adapted one-dimensional model (see cells with white background in Figure 2 ).

For example ten articles mainly by Nirenburg et al. describe using dynamic simulationsterm·key to teach students clinical reasoningterm·key (e.g. [ 31 ]). Other examples are teaching clinical skills in multimedia systems (e.g. by Germanakis et al. [ 32 ]) and conversational characters to teach clinical reasoningterm·key (e.g. by Summons et al. [ 33 ]).

Development over time Looking at the development of VP-related educational articles over time there is an increase in the number of articles about Interactive Patient Scenariosterm·dom. At the same time the number of articles using VP denoting High Fidelity Simulationsterm·key and VP Games remained almost constant. This is surprising given the technical possibilities that have advanced rapidly over the past 20 years [ 34 ].

However, an explanation for this development could be that the cost of creating virtual patientsterm·dom using for example authoring tools is easier, faster and cheaper than creating high-fidelity applications or virtual worlds [ 35 ]. Also by using authoring tools teachers are able to create virtual patientsterm·dom without a team of technicians and instructional designers [ 36 , 37 ]. Further studies could look for cost statements over time and the number of virtual patientsterm·dom created in each classificationterm·key to verify this assumption.

The increase of Interactive Patient Scenariosterm·dom is coinciding with the publication of a reviewterm·mth article by Cook et al. [ 38 ] emphasizing the use of VPsterm·dom to teach clinical reasoningterm·key skills. Moreover the EU-funded project “eViP” (Electronic virtual patientsterm·dom) [ 35 , 39 , 40 ] published a series of freely available Interactive Patient Scenariosterm·dom and other resources, such as integration guidelines during the project duration from 2007 until 2010. It is likely that also this project had an influence on how VPsterm·dom are perceived in the healthcare educationterm·key community.

The 29 articles mentioning VP types that could not be assigned to one of the classes did not show a clear development over time, which makes it difficult to predict whether these types could be interesting innovations indicating the direction for future VP development and research. However, in case a VP does not fit into one of the classes we recommend that future researcher more explicitly explain which competency they are addressing and on which technology their VP is based on.

Classificationterm·key framework The developed virtual patientterm·dom classificationterm·key framework provides a valuable model for educators, VP developers and researchers in healthcare educationterm·key. It makes the conceptualization of VP use more explicit and supports a more precise communication about the specific type of VP addressed and allows a clearer focus of future research activities.

The use of the framework to classify virtual patientsterm·dom in medical education requires reflection on the predominant competency and technology of the virtual patientterm·dom. As our research has shown for most of the published research there exists an association between competency and technology (Figure 2 ). In these cases we recommend to use the class labels as proposed in Table 1 . However, we acknowledge that the competency and technology is a continuum. For instance some of the Interactive Patient Scenariosterm·dom might have incorporated elements from other virtual patientterm·dom classes (e.g. game-informed elements or outcomes of high fidelity simulationsterm·key). In such cases it is necessary to consider what is the base technology or main competency.

If no suitable class is present in our framework we propose to be specific in the description which type of predominant technology and competency is present. Further adaptions of the classificationterm·key might be necessary in the future, especially when new innovative forms of VPsterm·dom may arise during the next years. An advantage of the adapted classificationterm·key is that it is easy to present and apply, as it consists of seven classes with only two levels of description. It allows a quick division of virtual patientsterm·dom into substantially different groups. However, we expect that within each class features, methods, outcomes, and VP usage scenarios of the assigned articles may vary significantly, especially for classes containing many articles, such as Interactive Patient Scenarioterm·dom and High Fidelity Simulationterm·key. For these types of VP classes specific classificationterm·key frameworks can be elaborated. For instance the typology developed by Huwendiek et al. [ 18 ] describes the variations of Interactive Patient Scenariosterm·dom in more detail. Such more detailed typologies should be used in addition to our framework whenever available for the reporting of VPsterm·dom to allow for a better understanding of the used interventions. Future studies could consider the methodological quality of research reports published in individual classes of virtual patientsterm·dom, which has been already investigated for Interactive Patient Scenariosterm·dom e.g. by Cook et al. [ 19 ].

Limitations There are potential limitations to our study. The aim of our research was to classify the body of literature about virtual patientsterm·dom. Therefore we focused exclusively on the search term “virtual patientterm·dom”, not including other potentially related search terms, such as “patient simulationterm·key”. Although it would be interesting to include all those articles into the classificationterm·key, this was not the aim of our study. We are also aware that our approach solely focused on the usage of the term in research activities in healthcare educationterm·key and might not be entirely transferable to healthcare educationterm·key in general.

Conclusion Our research shows that there are several “communities” using the term “virtual patientterm·dom” for their activities. Within these communities it might be obvious what is meant by the term, but when communicating and exchanging research results between such communities and to non-VP experts a clear understanding is indispensable. The primary form of VPsterm·dom in the educational literature are Interactive Patient Scenariosterm·dom despite rapid technical advances that would nowadays support more complex applications. The adapted classificationterm·key provides a valuable model for VP developers, educators and researchers in healthcare educationterm·key to more clearly communicate about the VPsterm·dom they are using.