Sage Journals: Discover world-class research

Abstract

Virtual patient simulation (VPS) has begun to gather interest as a method to train and assess clinical reasoning. However, the lack of published materials illustrating how to develop patient simulation scenarios coupled with concerns over the best way to assess clinical reasoning has slowed the adoption of VPS as an education platform. To remedy these concerns, we describe the use of a model of medical diagnosis to assist with the development of a diagnosis-based VPS and test the ability of VPS to administer measures of situation awareness as an assessment of clinician reasoning during the simulation scenario. A case example of one clinician's participation in the diagnosis-based VPS suggests the model-directed VPS provides a unique opportunity to observe clinical reasoning as the interplay between information collection and the participant's report of a presumptive diagnosis extracted from measures of situation awareness. Suggestions for future directions and implications of this research are provided.

Get full access to this article

View all access options for this article.

References

Candler

(2007). Effective use of educational technology in medical education. In Colloquium on Educational Technology: Recommendations and Guidelines for Medical Educators. Washington, DC: AAMC Institute for Improving Medical Education.

Cook

D. A.

, & Triola

M. M.

(2009). Virtual patients: a critical literature review and proposed next steps. Medical education, 43, 303–311.

DecisionSimulation. (2015). What is decisionsim? Retrieved from http://decisionsimulation.com/what-is-decisionsim/ on May,15 2015

Endsley

M. R.

(1995a). Measurement of situation awareness in dynamic systems. Human Factors, 37(1), 65–84.

Endsley

M. R.

(1995b). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64.

Kononowicz

A. A.

, Zary

, Edelbring

, Corral

, & Hege

(2015). Virtual patients - what are we talking about? A framework to classify the meanings of the term in healthcare education. BMC medical education, 15(1), 11.

Lintern

(2011). Tutorial: Work Domain Analysis. Retrieved from http://www.cognitivesystemsdesign.net/

Manning

(2015) Clinical manifestations and diagnosis of aortic dissection. Retrieved from http://www.uptodate.com/contents/clinical-manifestations-and-diagnosis-of-aortic-dissection on May,15 2015

Nystrom

, Williams

, Paull

, & Graber

(2014, June). Work domain analysis applied to medical diagnosis. In Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare 2014 (pp. 157–161). SAGE Publications.

10.

Nystrom

D. T.

, Williams

, Paull

D. E.

, & Graber

M. L.

(2015) A theory integrated model of medical diagnosis. Manuscript submitted for publication.

11.

Peterson

M. C.

, Holbrook

J. H.

, Hales

, Smith

N. L.

, & Staker

L. V.

(1992). Contributions of the history, physical examination, and laboratory investigation in making medical diagnoses. Western journal of medicine, 156(2), 163.

12.

Rosen

K. R.

(2008). The history of medical simulation. Journal of Critical Care, 23(2), 157–166.

13.

Salas

, Wilson

K. A.

, Burke

C. S.

, & Priest

H. A.

(2005). Using simulation-based training to improve patient safety: what does it take?. Joint Commission Journal on Quality and Patient Safety, 31(7), 363–371.

14.

Singh

, Petersen

L. A.

, & Thomas

E. J.

(2006). Understanding diagnostic errors in medicine: A lesson from aviation. Quality and Safety in Health Care, 15(3), 159–164.

15.

Vicente

K. J.

(1999). Cognitive work analysis: Toward safe, productive, and healthy computer-based work. Mahwah, NJ: Lawrence Erlbaum Associates.

Investigating Medical Diagnosis: Qualitative Results from a Virtual Patient Simulation Pilot Study

Abstract

Get full access to this article

References