Evidence map›Paper›PMID 28367397›Full record

ArticleDiagnosis (Berlin, Germany)2014

A unified conceptual model for diagnostic errors: underdiagnosis, overdiagnosis, and misdiagnosis.

David E Newman-Toker

Abstract read
In one paragraph

Article in Diagnosis (Berlin, Germany), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 pooled it
–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

40 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Strategies to reduce diagnostic errors: a systematic review.BMC medical informatics and decision making · 2019
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  18. Providers' and Patients' Perspectives on Diagnostic Errors in the Acute Care Setting.Joint Commission journal on quality and patient safety · 2023
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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

1 author.

David E Newman-Toker1Associate Professor, Department of Neurology, The Johns Hopkins University School of Medicine, The Johns Hopkins Hospital Meyer Building 8-154 600 North Wolfe Street, Baltimore, MD 21287, USA.

Funding

A Multiyear Grant to Support the Diagnostic Error in Medicine (DEM) Annual ConferR13HS019252 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI NEWMAN-TOKER, DAVID · 2010 to 2012
$265k
AHRQ HHS R13 HS019252
6 · The paper itself

Abstract

Progress in diagnostic error research has been hampered by a lack of unified terminology and definitions. This article proposes a novel framework for considering diagnostic errors, offering a unified conceptual model for underdiagnosis, overdiagnosis, and misdiagnosis. The model clarifies the critical separation between 'diagnostic process failures' (incorrect workups) and 'diagnosis label failures' (incorrect diagnoses). By dividing processes into those that are substandard, suboptimal, or optimal, important distinctions are drawn between 'preventable', 'reducible,' and 'unavoidable' diagnostic errors. The new model emphasizes the importance of mitigating diagnosis-related harms, regardless of whether the solutions require traditional safety strategies (preventable errors), more effective evidence dissemination (reducible errors; harms from overtesting and overdiagnosis), or new scientific discovery (currently unavoidable errors). Doing so maximizes our ability to prioritize solving various diagnosis-related problems from a societal value perspective. This model should serve as a foundation for developing consensus terminology and operationalized definitions for relevant diagnostic-error categories.

Indexed as

diagnosisdiagnostic errorsmisdiagnosisoverdiagnosispatient safetyunderdiagnosis

Identifiers

PMID28367397
PMCPMC5373075

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.