| SYMPOSIUM: SIMULATION IN MEDICAL EDUCATION |
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| Year : 2017 | Volume
: 3
| Issue : 1 | Page : 78-83 |
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Clinical decision support systems: From medical simulation to clinical practice
Scott M Pappada1, Thomas J Papadimos2
1 Department of Anesthesiology, University of Toledo College of Medicine and Life Sciences; Department of Bioengineering, University of Toledo College of Engineering; Department of Anesthesiology, Medical Director of the Lloyd Jacobs Simulation Center, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
2 Department of Anesthesiology, University of Toledo College of Medicine and Life Sciences; Department of Bioengineering, University of Toledo College of Engineering, Toledo, OH, USA
Correspondence Address:
Thomas J Papadimos
3000 Arlington Avenue, Toledo, OH 43614
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/IJAM.IJAM_34_17
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Medical simulation has become an integral part of the training of medical students, residents, and faculty. While our traditional sense of medical simulation involves scenarios, manikins, resuscitation, and procedures, we must now expand this vision. With the development of the electronic medical record and the resultant large data sets (“Big Data”) that encompass each patient's record, there is an opportunity to engage in the modeling and prediction of individual patient outcomes and trajectories of care. Here, medical simulation delves into the arena of clinical decision support systems (CDSSs) which leverage advanced algorithms, analytics, and machine learning approaches. The advent and continued adoption of health information technologies such as CDSSs into real-world health-care operations can lead to improved patient care, safety, and cost savings.
The following core competencies are addressed in this article: System-based practice, Practice-based learning, and Patient care.
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