Literature DB >> 24739532

A balance beam aid for instruction in clinical diagnostic reasoning.

Robert M Hamm1, William Howard Beasley1,2, William Jay Johnson3.   

Abstract

We describe a balance beam aid for instruction in diagnosis (BBAID) and demonstrate its potential use in supplementing the training of medical students to diagnose acute chest pain. We suggest the BBAID helps students understand the process of diagnosis because the impact of tokens (weights and helium balloons) attached to a beam at different distances from the fulcrum is analogous to the impact of evidence to the relative support for 2 diseases. The BBAID presents a list of potential findings and allows students to specify whether each is present, absent, or unknown. It displays the likelihood ratios corresponding to a positive (LR+) or negative (LR-) observation for each symptom, for any pair of diseases. For each specified finding, a token is placed on the beam at a location whose distance from the fulcrum is proportional to the finding's log(LR): a downward force (a weight) if the finding is present and a lifting force (a balloon) if it is absent. Combining the physical torques of multiple tokens is mathematically identical to applying Bayes' theorem to multiple independent findings, so the balance beam is a high-fidelity metaphor. Seven first-year medical students and 3 faculty members consulted the BBAID while diagnosing brief patient case vignettes. Student comments indicated the program is usable, helpful for understanding pertinent positive and negative findings' usefulness in particular situations, and welcome as a reference or self-test. All students attended the effect of the tokens on the beam, although some stated they did not use the numerical statistics. Faculty noted the BBAID might be particularly helpful in reminding students of diseases that should not be missed and identifying pertinent findings to ask for.
© The Author(s) 2014.

Entities:  

Keywords:  Bayes’ theorem; balance beam metaphor; chest pain diagnosis; computer tutorial; differential diagnosis; medical education

Mesh:

Year:  2014        PMID: 24739532     DOI: 10.1177/0272989X14529623

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  1 in total

1.  Effectiveness of a biopsychosocial e-learning intervention on the clinical judgements of medical students and GP trainees regarding future risk of disability in patients with chronic lower back pain: study protocol for a randomised controlled trial.

Authors:  Christopher P Dwyer; Hannah Durand; Pádraig MacNeela; Bronagh Reynolds; Robert M Hamm; Christopher J Main; Laura L O'Connor; Sinéad Conneely; Darragh Taheny; Brian W Slattery; Ciaran O'Neill; Saoirse NicGabhainn; Andrew W Murphy; Thomas Kropmans; Brian E McGuire
Journal:  BMJ Open       Date:  2016-05-26       Impact factor: 2.692

  1 in total

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