Traditional clinical training methods are expensive and non-standardized. They remove clinicians from the practice setting, and the ultimate impact is difficult to measure. We at TheraSim employ our interactive web-based simulation and data analysis program in which more than 130,000 practitioners from more than 180 countries during the past 6 years have managed about 500 virtual patients with more than 50 different medical conditions. These virtual cases have been available within approximately 100 CME programs that appeared in a variety of Internet and hospital venues throughout the globe, generating more than 6 million page views.

Our interactive virtual medical records interface allows clinicians to receive electronic mentoring and testing by reviewing histories, ordering tests, making diagnoses among hundreds of choices, and choosing treatments from more than 1,000 medications and other therapies. The simulations can allow or hide in-session diagnostic and therapeutic information that is produced by an expert system-based artificial intelligence engine (A.I.). The A.I. provides guidelines and evidence-based feedback on the appropriateness of choices. Finally, the simulation shows an explanation of reasonable choices for the case, a mini-review of the general topic, and the user’s errors, warnings and deviations from guideline, evidence and expert consensus-driven recommendations. All choices are recorded for analysis.

Using an analysis of very challenges cases within a neurology program involving 1,946 users and 2,642 sessions, all clinical guidance was turned off for more than 100 sessions in each of 3 patient simulation cases. Success in making a difficult diagnosis increased from 12% to 36% with guidance operative, an incorrect diagnosis was avoided in 74% with guidance vs. 48% without, and appropriate treatment was more likely with guidance turned on: 74% vs. 44%, 67% vs. 52%, and 42% vs. 23%.

In 5 HIV training simulation deployments during 2006-2007 in 3 African countries using WHO guidelines and involving 2,780 pre-/post-test simulations, 241 clinicians passed 71% of pre-tests. After clinical feedback was activated, scores increased by 35 points, resulting in a final pass rate of 93% (p<0.001 vs. pre-). Similar improvements were noted in 3 separate programs at 80 hospital sites in 2008 and 2009, the latter utilizing a competency-based model, which eliminated the need for formal post-testing.

Expert systems-based virtual patient simulation shows promise as a mechanism for assessing practitioner skill, detecting skill gaps and for electronic mentoring. These systems have the ability to extend the patient simulation process into chronic and infectious disease states, an area that has been primarily overlooked by mannequin-based simulators. In addition, user satisfaction remains positive for most users, including average scores of 4.2 of 5.0 using various questionnaires.