Assessing minimal medical statistical literacy using the Quick Risk Test: a prospective observational study in...

To assess minimal medical statistical literacy in medical students and senior educators using the 10-item Quick Risk Test; to assess whether deficits in statistical literacy are stable or can be reduced by training.

Prospective observational study on the students, observational study on the university lecturers.

Charité University Medicine medical curriculum for students and a continuing medical education (CME) course at a German University for senior educators.

169 students taking part in compulsory final-year curricular training in medical statistical literacy (63% female, median age 25 years). Sixteen professors of medicine and other senior educators attending a CME course on medical statistical literacy (44% female, age range=30-65 years).

Students completed a 90 min training session in medical statistical literacy. No intervention for the senior educators.

Primary outcome measure was the number of correct answers out of four multiple-choice alternatives per item on the Quick Risk Test.

Final-year students answered on average half (median=50%) of the questions correctly while senior educators answered three-quarters correctly (median=75%). For comparison, chance performance is 25%. A 90 min training session for students increased the median percentage correct from 50% to 90%. 82% of participants improved their performance.

Medical students and educators do not master all basic concepts in medical statistics. This can be quickly assessed with the Quick Risk Test. The fact that a 90 min training session on medical statistical literacy improves students' understanding from 50% to 90% indicates that the problem is not a hard-wired inability to understand statistical concepts. This gap in physicians' education has long-lasting effects; even senior medical educators could answer only 75% of the questions correctly on average. Hence, medical students and professionals should receive enhanced training in how to interpret risk-related medical statistics.