RESEARCH LIBRARY

Diagnosis: Volume 10, Issue 1, Pages 54-60

This op-ed discusses the advantages of leveraging natural language processing (NLP) in the assessment of clinical reasoning. It also provides an overview of INCITE, the Intelligent Clinical Text Evaluator, a scalable NLP-based computer-assisted scoring system that was developed to measure clinical reasoning ability as assessed in the written documentation portion of the now-discontinued USMLE Step 2 Clinical Skills examination. 

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - p 2880–2886

This paper presents a corpus of 43,985 clinical patient notes (PNs) written by 35,156 examinees during the high-stakes USMLE® Step 2 Clinical Skills examination.

Academic Medicine: Volume 97 - Issue 4 - Pages 476-477

Response to to emphasize that although findings support a relationship between multiple USMLE attempts and increased likelihood of receiving disciplinary actions, the findings in isolation are not sufficient for proposing new policy on how many attempts should be allowed.

Academic Medicine: Volume 96 - Issue 9 - Pages 1319-1323

This study examined the relationship between USMLE attempts and the likelihood of receiving disciplinary actions from state medical boards.

Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 314-316

The United States Medical Licensing Examination Step 2 Clinical Skills (CS) exam uses physician raters to evaluate patient notes written by examinees. In this Invited Commentary, the authors describe the ways in which the Step 2 CS exam could benefit from adopting a computer-assisted scoring approach that combines physician raters’ judgments with computer-generated scores based on natural language processing (NLP).

The authors examined the extent to which USMLE scores relate to the odds of receiving a disciplinary action from a U.S. state medical board.