Evidence map›Paper›PMID 35061037›Full record

ArticleJAMA network open2022

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

Gordon D Schiff, Mayya Volodarskaya, Elise Ruan, Andrea Lim, Adam Wright, Hardeep Singh, Harry Reyes Nieva

Abstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Gordon D SchiffDivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts.
Mayya VolodarskayaDepartment of Surgery, Rush University Medical Center, Chicago, Illinois.
Elise RuanDepartment of Medicine, Montefiore Medical Center, Bronx, New York.
Andrea LimDepartment of Internal Medicine, Kaiser Permanente, San Francisco, California.
Adam WrightDepartment of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee.
Hardeep SinghCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas.
Harry Reyes NievaDivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AdultAmbulatory CareDiagnostic ErrorsDiseaseFemaleHumansMaleMalpracticeMedical ErrorsMiddle AgedOutcome Assessment, Health CareQualitative ResearchQuality of Health CareRetrospective Studies

Identifiers

PMID35061037
PMCPMC8783262

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.