A Study to Evaluate Strategies for Teaching Effective Use of Diagnostic Tests

Instructional Methods Clinical Trial

Official title:

A Study to Evaluate Strategies for Teaching Effective Use of Diagnostic Tests

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04130607
• Other study ID #: 18-04-EX-0062
• Status: Completed
• Phase: N/A
• Start date: May 15, 2018
• Est. completion date: October 15, 2019

Study information

• Verified date: October 2019
• Source: Sentara Norfolk General Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

A recent Institute of Medicine monograph brought attention to high rates of diagnostic error and called for better educational efforts to improve diagnostic accuracy.1 Educational methods, however, are rarely tested and some educational efforts may be ineffective and wasteful.2 In this study, we plan to examine whether explicit instruction on diagnostic methods will have an effect on diagnostic accuracy of 2nd-year medical students and internal medicine residents.

Description:

Research has shown that expert diagnosticians use a two-step process to confirm a diagnosis:

hypothesis generation to generate diagnostic possibilities, followed by hypothesis verification to confirm the most likely diagnostic possibility.3-5 The first step appears to be non-analytical, related to pattern recognition. The second step could be calculated using analytical reasoning, however, physicians rarely make an overt calculation of conditional probabilities. Instead, experienced clinicians typically use an implicit habit or heuristic called "anchoring and adjusting" to incorporate diagnostic testing information into their thinking.6,7 Cognitive psychologists have postulated that anchoring and adjusting provides a way that probability estimates can be updated based on additional new evidence. Most of the discussion in the literature focuses on how this heuristic can lead to biased thinking because of base-rate neglect or anchoring.6 Very little discussion is on how this heuristic could be improved to yield more accurate probability estimates and whether proper use of the heuristic could be taught.

The degree to which a diagnostic test should lead to an adjustment of a probability estimate depends on the operating characteristics of a test, that is, the sensitivity and specificity. Likelihood ratios, once understood, are easier to incorporate into one's thinking, and thus could be used to calibrate the anchoring and adjusting heuristic.7

In this randomized trial, we tested whether explicit conceptual instruction on Bayesian reasoning and likelihood ratios would improve Bayesian updating, compared with a second intervention where we provided multiple (27) examples of clinical problem solving. The third arm provided minimal teaching about diagnosis, but no explicit teaching or examples.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 65
• Est. completion date: October 15, 2019
• Est. primary completion date: January 1, 2019
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Medical Student at McMaster University or Eastern Virginia Medical School

• Completed 18 months of coursework

Related Conditions & MeSH terms

• Instructional Methods

Intervention

Other:
• Conceptual teaching
The present study is designed to contrast two instructional methods - explicit instruction in likelihood ratios and pretest/posttest probabilities versus implicit instruction based on presentation of multiple cases. These will be compared to a "no intervention" control group.

Locations

Country	Name	City	State
United States	Sentara Norfolk General Hospital	Norfolk	Virginia

Sponsors (2)

Lead Sponsor	Collaborator
Sentara Norfolk General Hospital	McMaster University

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy of participants probability revisions were compared to posttest probability revisions that were calculated using Bayes Rule. An effect size was calculated to measure how close students matched the calculated revision.	To perform the effect size analysis, two transformations were performed. First, the difference between the subjective estimate and the Bayesian calculation of post-test probability was squared to remove negative differences and permit combining of the effects of positive and negative test results. Second, a correction based on the intrinsic error of a probability estimate was applied by dividing each squared difference by p(1-p). In this manner, we transformed each raw difference to a squared effect size (difference / error of difference). Finally, the square root was computed, to transform the data back to an effect size. The resulting effect size was then used for statistical analysis. For this primary analysis, a mixed model ANOVA was used.	Post-test was taken within 72 hours of instructional phase completion.