Augmented reality in healthcare education: an integrative review

Inclusion and exclusion criteria

In order to present a comprehensive overview of AR relative to healthcare education, we used broad inclusion criteria. An article was included in the review if it was a research paper (*), on AR (**), or on AR in healthcare education (***), and was written in English. The criteria of inclusion and exclusion were further defined as follows in Table 1:

Clarification of criteria terms:

• Research paper. There is no widespread accepted set of criteria with which to assess the quality of studies. Further, research paradigm is different across the various members of the academic community, such as developer, educator and doctor. We have not restricted the methodology and the writing style of the research papers but they should contain the following core information; clear description of the context, study aims, research question, study design, sampling, data collection and analysis, and findings. Papers were excluded if they did not describe the core information mentioned above.

• AR. Augmented reality, which sometimes is referred to as ‘mixed reality‘, or ‘blended reality,’ is a technology that allows a live real-time direct or indirect real-world environment to be augmented/enhanced by computer-generated virtual imagery information (Carmigniani & Furht, 2011; Lee, 2012). It is different from virtual reality that completely immerses the user in a computer-generated virtual environment. We did not make a clear distinction between augmented reality and augmented virtuality (AV) where AR is closer to the real world and AV is closer to a pure virtual environment (Milgram & Colquhoun, 1999). Studies focusing on enhancing the user’s perception of and interaction with the real world through virtual information were included. It would be excluded if it only discussed the virtual environment.

• Healthcare education. According to the glossary of medical education terms from AMEE, medical education is “the process of teaching, learning and training of students with an ongoing integration of knowledge, experience, skills, qualities, responsibility and values which qualify an individual to practice medicine” (Wojtczak, 2002, p 36). “With the growing understanding of the conditions for learning within medical care and health care, and the increasing focus on the ‘lifelong’ nature of medical education, medical education now, more so than in the past, needs to span three sectors: undergraduate, postgraduate and the continuing professional development of established clinicians” (Swanwick & Buckley, 2010, p 123). The two definitions represents to current established perspectives on medical education, the first with a process and outcome focus, while the second is acknowledging education as a lifelong continuum.

Search strategy and inclusion procedure

Agreement about the review protocol, and inclusion and exclusion criteria was reached through discussion between EZ, NZ and IM. Relevant computerized databases were searched for eligible studies, including: ERIC, CINAHL, Medline, Web of Science, PubMed and Springer-link. Separate searches were completed for each database with no date restrictions, no methodological filter, and the language limited to English. The searches were updated until November 2012. Word groups representing the key characteristics of our study were created and combined in several ways. The first group was ‘augmented reality’ and included terminology with similar meaning such as ‘mixed reality,’ or ‘blended reality.’ The second group was ‘medical education’ and included terms like ‘healthcare education,’ ‘health science education’ and so on. The two key groups of terms used the Boolean operator ‘(and)’ to combine with the terms one another when searching for papers to include. Also, we used symbols like ‘medic * education’ to include more related articles with potentially different endings.

EZ independently searched for eligible studies in the six databases using the methods above and identified each article meeting the inclusion criteria. ‘Medical education’ and synonyms were searched in ‘all areas’ in the six databases throughout the search procedure. We began by searching for ‘augmented reality,’ or synonyms, plus ‘medical education’ in all areas to get the overall data. Next, ‘augmented reality,’ or its’ synonyms, were searched within the title or abstract field, but with ‘medical education’ in ‘all areas.’ One reason for this is that we felt the focus terms should be placed in the title or abstract. Another reason is that the papers in which augmented reality was neither in the title nor abstract, were not studying augmented reality when we reviewed them. When the abstract contained insufficient information we sometimes referred to the full text to assess eligibility. This was then discussed with NZ and IM. After confirming that the paper’s title and abstract discussed augmented reality on medical education, the full text was downloaded and printed to re-read and analyze, if it met the review criteria. EZ examined and marked the full texts to select the articles that met the inclusion criteria. AH checked the excluded papers by EZ to ensure we did not leave out any papers that should include. NZ checked the full text and discussed with EZ. IM was involved in the discussions and selection process when necessary. The quality of the studies was then reviewed by all the co-authors for final inclusion.

Data extraction and analysis

We extracted information specifically on research, technology and learning from the included studies. The characteristics and the results of the included studies were recorded with a standardized data-extraction form. Data were extracted independently and in tripartite for all characteristics. Three main characteristics, including research, technology and learning, and eleven sub-characteristics were described through qualitative content analysis for each of the included studies (Appendix SI). Also, we used content analysis to describe the study design for each study (Appendix SI). Thematic analysis was used to identify the prominent themes that describe current use of AR in healthcare education. The themes are then presented in the result section in terms of strength and weakness of AR.

Identification of relevant studies

We found 2,529 papers on AR in medical education in the above-mentioned six databases. After screening the titles and abstracts, we found 270 citations in the titles and 179 in the abstracts that included ‘augmented reality’, ‘mixed reality’ or ‘blended reality.’ These terms were selected to keep focus on the key characteristics that we wanted to scrutinize and identify. After further reading of the title and abstracts, and removal of any duplicate papers, 77 full-text papers were retrieved and reviewed in more detail. Twenty-five articles met our inclusion criteria for data extraction and were analyzed. Figure 1 shows the selection process. Papers were mainly excluded if their research aim and context were not clearly described. Some articles which seemed to discuss medical education were later excluded because they only focused on medicine or treatment, and not on healthcare education (Bruellmann et al., 2012; Di Loreto et al., 2011; Pagador et al., 2011), and vice versa, one was excluded because it discussed education of another discipline that could contribute to the health of students (Hsiao, 2012).

From the included 25 research papers focusing on AR in healthcare education, 20 were based on quantitative research methods, 3 on qualitative research methods and 2 on mixed research methods. In these studies, AR was applied on 15 healthcare related subjects. Most of studies used their own AR system and 5 groups used the same system.

Methodological quality of the identified studies

We chose to apply a broad inclusion criteria and no restriction with regard to the papers’ methodology since research on AR is still in an early innovative phase. Methodological quality was presented adapting the Medical Education Research Study Quality Instrument (MERSQI) (Reed et al., 2007). Quality (Table 2) was assessed purely for descriptive purposes, not as grounds to exclude.

Use of augmented reality in healthcare education

The earliest study on AR in healthcare education was published in 2002 but publications in the field take off starting in 2008 (see Appendix SI). Fig. 2 was developed to map our results on AR in healthcare education, and to give us a clearer understanding of learning paradigms and the capabilities of AR offered in current research.

Across the studies we saw high variability in the research aims and also the role of AR in healthcare education (Fig. 2). Twelve studies focused on evidence that AR can improve learning. Seven studies were aimed at developing AR systems for healthcare education. Two studies investigated the user’s acceptance of AR as a learning technology. Six studies tested AR applications. The main use of AR for learning has been to provide feedback, and eight studies used AR as a means to provide feedback to students. Two studies used AR as an innovative interface and two studies used it for simulator practice. The other studies tried AR as navigation, regenerative concept, remote assessment and training, and as a meaningful information tool. One used it to reduce resources, while another group used it to offer immersion in a scenario, and one tried to give participatory reality.

The research results showed that learners can accept AR as a learning technology, and that AR can improve the learning effect by acquisition of skills and knowledge, understanding of spatial relationships and medical concepts, enhancing learning retention and performance on cognitive-psychomotor tasks, providing material in a convenient and timely manner that shortens the learning curve, giving subjective attractiveness, and simulating authentic experiences (see Appendix SI and Fig. 2).

Technical specifications

Most of the included papers (50%) employed mobile laptops. Four studies used smaller mobile devices such as smart phone, tablet, PDA and e-book readers. Seven papers used stationary desktop computers. Three papers did not mention which computing system they used in their studies.

Of the included papers, 68% used a camera and marker as a tracking device. Two papers used an electromagnetic tracker but different markers; one a radiographic marker and one used anatomical landmarks. Two papers used sensors. Other tracking systems, such as hybrid optical tracker and Wi-Fi signal, were found in at least one of the included papers. One paper described using a head-and-hand tracking system, but did not provide details on the technology (Yudkowsky et al., 2012). One did not use a tracking device because they projected the virtual picture on a manikin (Pretto et al., 2009).

Comparison with existing literature

Two pieces of literature relevant to healthcare education, focused on introducing several examples of using AR systems. Sherstyuk et al. (2011) introduced human manikins with augmented sensory input for medical education, while Botden & Jakimowicz (2009) compared three AR systems that allow the trainee to use the same instruments currently being used in the operating room for laparoscopic surgery. Al-Issa, Regenbrecht & Hale (2012) used systematic review to investigate the effectiveness of physical outcomes through use of AR in rehabilitation. AR is not currently included in rehabilitation training and the study also showed that research on AR in rehabilitation is still in its infancy. Rabbi, Ullah & Khan (2012) attempted a systematic review of AR tracking techniques but did not show a result. Carmigniani & Furht (2011) focused on analysis of the technical specifications of different types of AR and pointed out the advantages and disadvantages. They also discussed AR for use in medicine and education.

This review searched six different databases to determine the characteristics of AR in healthcare education, and to distinguish the strength and weakness found in current research. It particularly focused on including studies related to healthcare education. Most of the AR applications found in this review are based on mobile computing systems, especially on laptops. It is different with Carmigniani and Furht’s study where the medical AR application systems are fixed in-doors. While light mobile AR has been predicted to be feasible to develop as real-time AR applications that are locally processed, our findings show that there are still very few examples of light mobile AR (Carmigniani & Furht, 2011). In our review, we aimed to not only describe the research outcome and learning effect of included papers, but also to check which kind of learning theory was used and how they used it.

AR and educational theory

Although each study presented a clear research aim, few suggestions were given for choosing an AR model that is better for healthcare education. Moreover, there is not enough evidence to inform the design of suitable learning activities with AR system, where knowledge and skill development could be integrated into the learner’s world. Thus, further research in this area should be taken to clarify the appropriate AR model, instructional designs and how to effectively use AR for healthcare education.

Study strengths and limitations

To our knowledge, this is the first integrative review that specifically addresses AR in healthcare relative to education. We explored how AR was applied in healthcare education encompassing a broad range of learners, learning strategy, outcomes and study designs. Content analysis and thematic analysis were useful to provide a comprehensive understanding on AR in healthcare education.

This review tries to provide a comprehensive description of AR in healthcare education with no research methodology filter. However, it is possible that some studies were missed if the key words did not appear on the title or abstract. The studies were also limited by excluding any non-English studies. This was not only because most of papers were published in English, but also because the authors come from different countries, and English allowed them to reach a consensus on the articles to include in the analysis. It is useful to minimize bias, but we possibly excluded some important papers. Further, an interesting AR application could have been missed because it was not published.