There were several interesting articles in the August issueof Academic Medicine.
The first was a retrospective study by Norcini, et al (1) that actually tries to connect performance on a high-stakes examination (USMLEStep 2 CK) with some real patient outcomes. The authors looked at about 61,000 patients who were hospitalized in Pennsylvania for Congestive Heart Failure (CHF) or Myocardial Infarction (MI). They were looking at admitting physicians who were graduates of an international medical school and had taken the Step 2 Clinical Knowledge (CK) examination. The authors found that an increase of one point on the examination was associated with 0.2% decrease in the mortality of their patients (95% CI: 0.1—0.4%). The authors recommended using the Step 2 CK as part of the licensure process but that seems premature. It would also be interesting to look at physicians who were graduates of US Allopathic and Osteopathic medical schools.
The second study, by Nixon, et al (2), evaluated students on the Internal Medicine clerkship at the University of Minnesota. Students were instructed on using educational prescriptions to create PICO-formatted questions (Patient-Intervention-Comparison-Outcome) and then answers to those questions for a bedside case presentation. The content and quality of the questions and answers was then analyzed by the authors. They found that 59% (112/190) of the questions were about therapy, and 19% (37/190) were related to making a diagnosis. They also saw that 61% (116/190) were scored 7/8 - 8/8 on the PICO conformity scale. The quality of answers was pretty high with 37% (71/190) meeting all criteria for high quality.
And finally, a really cool study by Watson (3) that analyzed hand motion patterns using an inertial measurement unit. The author looked at 14 surgical attendings and 10 first- and second-year surgical residents. They were asked to do a simulated surgical procedure while wearing an inertial measurement unit on their dominant hand. They used the pattern of movements to train a classification algorithm with expert and novice patterns. The classification algorithm (which is similar to an artificial neural network) is good at identifying patterns. In this case, when the authors gave the classification algorithm blinded hand motion patterns, it did a pretty good job of classifying them as expert or novice. Its accuracy was 83%, with a sensitivity of 86% and specificity of 80%. The classification algorithm was able to reliably classify surgical hand motion patterns as expert or novice. This could be used in the future to make an objective assessment of procedural or surgical proficiency.
This was a good month in Academic Medicine. Some pretty good studies!
(1) Norcini J, et al. The Relationship Between Licensing Examination Performance and the Outcomes of Care by International Medical School Graduates. Acad Med 2014; 89(8): 1157-1162.
(2) Nixon J, et al. SNAPPS-Plus: An Educational Prescription for Students to Facilitate Formulating and Answering Clinical Questions. Acad Med 2014; 89(8): 1174-1179.
(3) Watson R. Use of a Machine Learning Algorithm to Classify Expertise: Analysis of Hand Motion Patterns During a Simulated Surgical Task. Acad Med 2014; 89(8): 1163-1167.