The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.
Virtual patients: a critical literature review and proposed next steps
Abstract
Context 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. Methods 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. Results 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. Conclusions Virtual patients should be designed and used to promote clinical reasoning skills. More research is needed to inform how to effectively use VPs.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“maintaining their utility requires continuous updates to reflect evolving clinical standards17–20.”
From A large language model digital patient system enhances ophthalmology history taking skills (2025) · cites this paper as [17]
In the citing paper
“While VPs effectively foster intrinsic motivation and baseline clinical reasoning competence”
From A large language model digital patient system enhances ophthalmology history taking skills (2025) · cites this paper as [17]
In the citing paper
“Moreover, it is possible to find systematic reviews that include studies where VP has been compared with some of those strategies and where it showed positive results against traditional lectures and even standardized patients, but evidence is not [7] conclusive and heterogeneity of the data suggest that more research is needed [7, 26, 29].”
From The virtual patient as a learning tool: a mixed quantitative qualitative study (2018) · cites this paper as [29]
In the citing paper
“The educational use of digital patients can complement learners’ clinical experiences through experiential learning theory, providing a mechanism for information gathering and clinical decision-making in a safe environment [37].”
From Developing Medical Education Curriculum Reform Strategies to Address the Impact of Generative AI: Qualitative Study (2023) · cites this paper as [37]
Citations
Cites
Barry Issenberg, S., Mcgaghie, W. C., Petrusa, E. R., Lee Gordon, D., & Scalese, R. J. (2005) · Medical Teacher · doi:10.1080/01421590500046924 · Cited by 3,966
Ericsson, K. A. (2004) · Academic Medicine · doi:10.1097/00001888-200410001-00022 · Cited by 2,988 · cited 2×
“Not only can VP cases illustrate multiple distinct presentations, 46 but deliberate practice 4 can be facilitated by sequencing cases to ensure an ideal case mix, structuring learning to develop constituent skills and reinforce knowledge structures 45 (i. e. considering alternative options, 47…”
BackgroundThe Cambridge handbook of multimedia learning
Mayer, R. E. (2006) · Choice Reviews Online · doi:10.5860/choice.43-5643 · Cited by 2,321
Internet-Based Learning in the Health Professions
Cook, D., Levinson, A. J., Garside, S., Dupras, D. M., Erwin, P. J., & Montori, V. M. (2008) · JAMA · doi:10.1001/jama.300.10.1181 · Cited by 1,603
Self-citationThe Cambridge Handbook of Multimedia Learning
(2005) · doi:10.1017/cbo9780511816819 · Cited by 1,595
Research in clinical reasoning: past history and current trends
Norman, G. (2005) · Medical Education · doi:10.1111/j.1365-2929.2005.02127.x · Cited by 1,018
Blueprints for complex learning: The 4C/ID-model
van Merriënboer, J. J. G., Clark, R. E., & de Croock, M. B. M. (2002) · Educational Technology Research and Development · doi:10.1007/bf02504993 · Cited by 708
How expertise develops in medicine: knowledge encapsulation and illness script formation
Schmidt, H. G., & Rikers, R. M. J. P. (2007) · Medical Education · doi:10.1111/j.1365-2923.2007.02915.x · Cited by 619
Scripts and clinical reasoning
Charlin, B., Boshuizen, H. P. A., Custers, E. J. F. M., & Feltovich, P. J. (2007) · Medical Education · doi:10.1111/j.1365-2923.2007.02924.x · Cited by 444
The Research We Still Are Not Doing: An Agenda for the Study of Computer-Based Learning
Cook, D. A. (2005) · Academic Medicine · doi:10.1097/00001888-200506000-00005 · Cited by 289
Self-citation
Research works printed but not linked: 6
- Association of American Medical Colleges, 2007, Effective Use of Educational Technology in Medical Education: Summary Report of the 2006 AAMC Colloquium on Educational Technology
- 10.1097/00001888-199311000-00018 · DOI
- Harless WG, 1971, CASE: a computer‐aided simulation of the clinical encounter, J Med Educ, 46, 443
- Krahn CG, 1986, Using an advance organiser to improve knowledge application by medical students in computer‐based clinical simulations, J Comput Base Instruct, 13, 71
- Kumta SM, 2003, Fostering critical thinking skills through a web‐based tutorial programme for final year medical students – a randomised, controlled study, J Educ Multimed Hypermed, 12, 267
- Case SM, 2001, Constructing Written Test Questions for the Basic and Clinical Sciences
Cited by
Sorted by citations
Items 601–623 of 623
Khare, S. (2025) · Journal of Anatomical Sciences · doi:10.5005/jas-11049-0007
Hu, Y., Xiong, Q., Qin, Z., Watanabe, B., Wang, Y., Prpa, M., & Yoon, I. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536351 · Cited by 2
Innovative technologies in psychiatry education: a systematic review
Mokhtari, S., Mokhtari, A., Shariat, S. V., Astaneh, A. N., & Shalbafan, M. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09573-1
MediMate: Co-Crafting Patient-Centered Medical Explanations Using LLMs as a Rehearsal Partner
Jin, Y., Zhang, S., Chen, D., Ouyang, Y., Shao, Y., Jiang, C., Li, H., & Li, Q. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812891
Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology
Baschet, L., Marque, S., Locret, S., Jensen, J., Barbet, V., & Patrick Jourdain, M. (2026) · JMIR Cardio · doi:10.2196/92930
MedLearn: Empowering medical students
Mane, S. B., Dhoble, R., & Londhe, G. (2026) · AIP conference proceedings · doi:10.1063/5.0330123
Wang, H., Xu, F., Li, R., Pan, T., Li, Z., Wiley, J., Tang, S., & Sun, M. (2026) · Midwifery · doi:10.1016/j.midw.2026.104906
Brecheisen, H., Backhaus, J., Zerban, N. L., Mühling, T., Hann, A., & Koenig, S. (2026) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205261446855
Liu, Y., Zhu, Y., Shi, C., Lu, X., Wu, L., Yue, M., Hong, X., Xia, O., & Zhang, W. (2026) · JMIR Medical Education · doi:10.2196/101425
Xiaoyuan, W., Tang, Y., Lin, Y., Huang, Z., Huang, X., & Cai, J. (2026) · doi:10.2196/preprints.106057
Hung, C., Liu, S., Wu, S., & Liu, F. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09759-7
Alinasab, Z., Shafizad, M., Soltani, S. A., & YAZDANI-CHARATI, J. (2026) · Research Square · doi:10.21203/rs.3.rs-10056258/v1
Hu, Y., Wang, Y., Watanabe, B., Gao, M., Qin, Z., Shi, K., Zhang, Z., Shambanna, S., Xue, Z., Zou, T., et al. (2026) · doi:10.2196/preprints.106505
Artificial intelligence generated virtual patients in health professions education: a scoping review
Fang, E., Boon, Y., Ho, J. Y., Tey, N., & Low, M. (2026) · BMC Medical Education · doi:10.1186/s12909-026-10028-w · Cited by 2
Wu, W., Sr., Li, J., Wu, T., Liu, P., He, B., Han, Z., Xu, Q., & Qiu, Y. (2026) · doi:10.2196/preprints.108572
Çetinkaya, L., Yildirim, S., Keser, İ., & Keser, H. (2026) · Journal of Spinal Cord Medicine · doi:10.1080/10790268.2026.2702191
Gandhi, B., Sheth, U., McCarthy, J., & Sibbald, M. (2026) · International Medical Education · doi:10.3390/ime5030085
Wang, X., Sang, A., Zhang, J., Tang, H., Liu, Y., Zhao, Z., Liu, J., Xu, M., & Li, X. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1927674
Technology-Based Vestibular Education
Kotwal, S., Omron, R., Edlow, J. A., & Bhandari, A. (2026) · Neurologic Clinics · doi:10.1016/j.ncl.2026.07.019
Dudziński, Ł., Kubiak, T., Madej-Węgier, K., & Czyżewski, Ł. (2026) · Pielegniarstwo XXI wieku / Nursing in the 21st Century · doi:10.12923/pielxxiw-2026-0032
Bostraeten, P. V., Mertens, L., Jaeken, J., Bossen, J. K. J., Toelen, J., Bekkering, G. E., Delvaux, N., & Aertgeerts, B. (2026) · JMIR Formative Research · doi:10.2196/102449
Deng, Y. (2026) · International Conference on Intelligent Education and Computer Technology (IECT) · doi:10.1145/3845354.3845545
Chen, R. (2013) · MacSphere (McMaster University) · doi:10.71548/20548
Metadata
- Authors
- Cook, D. A., & Triola, M. M.
- Affiliations
- Mayo Clinic (US)
- New York University (US)
- Corresponding author
- Cook, David A
- Venue
- Medical Education
- Publisher
- Wiley
- Published
- 2009-03-18
- Volume
- 43
- Issue
- 4
- Pages
- 303-311
- ISSN
- 0308-0110 (ISSN-L)
- 1365-2923
- OpenAlex
- W2036488299
- Cited by
- 624
- References
- 65
- Citations by year
- 2026: 49 · 2025: 39 · 2024: 37 · 2023: 28 · 2022: 33 · 2021: 34 · 2020: 33 · 2019: 37 · 2018: 41 · 2017: 31 · 2016: 45 · 2015: 35 · 2014: 39 · 2013: 34 · 2012: 45
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Education (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Education › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Education › Innovations in Medical Education
- Keywords
- virtual patients, clinical reasoning, clinical reasoning assessment, student assessment, rubric development, instructional design, curriculum integration, health professions education, deliberate practice, training materials
- MeSH terms
- Clinical Competence, Computer-Assisted Instruction, Education, Medical, Undergraduate, Educational Measurement, Humans, Patient Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
