Characteristics of Disease-Specific and Generic Diagnostic Pitfalls: A Qualitative Study.

Importance: Progress in understanding and preventing diagnostic errors has been modest. New approaches are needed to help clinicians anticipate and prevent such errors. Delineating recurring diagnostic pitfalls holds potential for conceptual and practical ways for improvement. Objectives: To develop the construct and collect examples of "diagnostic pitfalls," defined as clinical situations and scenarios vulnerable to errors that may lead to missed, delayed, or wrong diagnoses. Design, Setting, and Participants: This qualitative study used data from January 1, 2004, to December 31, 2016, from retrospective analysis of diagnosis-related patient safety incident reports, closed malpractice claims, and ambulatory morbidity and mortality conferences, as well as specialty focus groups. Data analyses were conducted between January 1, 2017, and December 31, 2019. Main Outcomes and Measures: From each data source, potential diagnostic error cases were identified, and the following information was extracted: erroneous and correct diagnoses, presenting signs and symptoms, and areas of breakdowns in the diagnostic process (using Diagnosis Error Evaluation and Research and Reliable Diagnosis Challenges taxonomies). From this compilation, examples were collected of disease-specific pitfalls; this list was used to conduct a qualitative analysis of emerging themes to derive a generic taxonomy of diagnostic pitfalls. Results: A total of 836 relevant cases were identified among 4325 patient safety incident reports, 403 closed malpractice claims, 24 ambulatory morbidity and mortality conferences, and 355 focus groups responses. From these, 661 disease-specific diagnostic pitfalls were identified. A qualitative review of these disease-specific pitfalls identified 21 generic diagnostic pitfalls categories, which included mistaking one disease for another disease (eg, aortic dissection is misdiagnosed as acute myocardial infarction), failure to appreciate test result limitations, and atypical disease presentations. Conclusions and Relevance: Recurring types of pitfalls were identified and collected from diagnostic error cases. Clinicians could benefit from knowledge of both disease-specific and generic cross-cutting pitfalls. Study findings can potentially inform educational and quality improvement efforts to anticipate and prevent future errors.

Medical Student Research in Surgery: From Abstract Presentation to Publication.

OBJECTIVE: Early career mentorship in surgical research often begins in medical school, and scholarly activity in the forms of abstract presentations and publications is seen as a critical criterion in residency applications. The goal of this study was to examine how often medical student abstract presentations at the American College of Surgeons (ACS) Clinical Congress are eventually published as peer-reviewed publications. DESIGN: Medical student abstract presentations from ACS Clinical Congress 2014 to 2018 were reviewed. Abstract information was cross-referenced for companion peer-reviewed publication in the PubMed and Google Scholar databases. RESULTS: In total, 219 students presented abstracts at the ACS Clinical Congress between 2014 and 2018. Of these, 101 (46%) led to publications in 61 different journals. The percentage of presentations that were published was 63% from 2014, 51% from 2015, 56% from 2016, 39% from 2017, and 25% from 2018. Medical students were named as first authors on 54%, second authors on 19%, and third authors on 13% of publications. The basic science presentation category had the greatest conversion to publications (54%), followed by clinical research (48%) and outcomes (45%). CONCLUSIONS: Forty-six percent of medical student abstract presentations at the ACS Clinical Congress were converted to peer-reviewed publications. While it is encouraging that the ACS Clinical Congress is a productive forum to showcase medical student scholarly activity, more can be done to encourage full translation of research activity to peer-reviewed work. Further studies should be performed to look at influential factors amongst medical students, faculty mentors, and medical schools.

Screening for medication errors using an outlier detection system.

Objective: The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening. Materials and Methods: Five years of clinical data were extracted from an electronic health record system for 747 985 patients who had at least one visit during 2012-2013 at practices affiliated with 2 academic medical centers. Data were screened using the system to detect outliers suggestive of potential medication errors. A sample of 300 charts was selected for review from the 15 693 alerts generated. A coding system was developed and codes assigned based on chart review to reflect the accuracy, validity, and clinical value of the alerts. Results: Three-quarters of the chart-reviewed alerts generated by the screening system were found to be valid in which potential medication errors were identified. Of these valid alerts, the majority (75.0%) were found to be clinically useful in flagging potential medication errors or issues. Discussion: A clinical decision support (CDS) system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated potentially useful alerts with a modest rate of false positives. The performance of such a surveillance and alerting system is critically dependent on the quality and completeness of the underlying data. Conclusion: The screening system was able to generate alerts that might otherwise be missed with existing CDS systems and did so with a reasonably high degree of alert usefulness when subjected to review of patients' clinical contexts and details.