Lineberry, Matthew

Abstract Purpose Assessing expertise using psychometric models usually yields a measure of ability that is difficult to generalize to the complexity of diagnoses in clinical practice. However, using an item response modeling framework, it is possible to create a decision-aligned response model that captures a clinician’s decision-making behavior on a continuous scale that fully represents competing diagnostic possibilities. In this proof-of-concept study, the authors demonstrate the necessary statistical conceptualization of this model using a specific electrocardiogram (ECG) example. Method The authors collected a range of ECGs with elevated ST segments due to either ST-elevation myocardial infarction (STEMI) or pericarditis. Based on pilot data, 20 ECGs were chosen to represent a continuum from “definitely STEMI” to “definitely pericarditis,” including intermediate cases in which the diagnosis was intentionally unclear. Emergency medicine and cardiology physicians rated these ECGs on a 5-point scale (“definitely STEMI” to “definitely pericarditis”). The authors analyzed these ratings using a graded response model showing the degree to which each participant could separate the ECGs along the diagnostic continuum. The authors compared these metrics with the discharge diagnoses noted on chart review. Results Thirty-seven participants rated the ECGs. As desired, the ECGs represented a range of phenotypes, including cases where participants were uncertain in their diagnosis. The response model showed that participants varied both in their propensity to diagnose one condition over another and in where they placed the thresholds between the 5 diagnostic categories. The most capable participants were able to meaningfully use all categories, with precise thresholds between categories. Conclusions The authors present a decision-aligned response model that demonstrates the confusability of a particular ECG and the skill with which a clinician can distinguish 2 diagnoses along a continuum of confusability. These results have broad implications for testing and for learning to manage uncertainty in diagnosis.

Because tests that do not alter management (i.e., influence decisions and actions) should not be performed, data on the consequences of assessment constitute a critical source of validity evidence. Consequences validity evidence is challenging for many educators to understand, perhaps because it has no counterpart in the older framework of content, criterion, and construct validity. The authors’ purpose is to explain consequences validity evidence and propose a framework for organizing its collection and interpretation. Both clinical and educational assessments can be viewed as interventions. The act of administering or taking a test, the interpretation of scores, and the ensuing decisions and actions influence those being assessed (e.g., patients or students) and other people and systems (e.g., physicians, teachers, hospitals, schools). Consequences validity evidence examines such impacts of assessments. Despite its importance, consequences evidence is reported infrequently in health professions education (range 5%–20% of studies in recent systematic reviews) and is typically limited in scope and rigor. Consequences validity evidence can derive from evaluations of the impact on examinees, educators, schools, or the end target of practice (e.g., patients or health care systems); and the downstream impact of classifications (e.g., different score cut points and labels). Impact can result from the uses of scores or from the assessment activity itself, and can be intended or unintended and beneficial or harmful. Both quantitative and qualitative research methods are useful. The type, quantity, and rigor of consequences evidence required will vary depending on the assessment and the claims for its use.