Evidence map›Paper›PMID 38190122›Full record

ArticleJAMA internal medicine2024

Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care.

Andrew D Auerbach, Tiffany M Lee, Colin C Hubbard, Sumant R Ranji, Katie Raffel, Gilmer Valdes, John Boscardin, Anuj K Dalal, Alyssa Harris, Ellen Flynn and 2 more

Abstract read
In one paragraph

Article in JAMA internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Andrew D AuerbachDivision of Hospital Medicine, Department of Medicine, University of California San Francisco.
Tiffany M LeeDivision of Hospital Medicine, Department of Medicine, University of California San Francisco.
Colin C HubbardDivision of Hospital Medicine, Department of Medicine, University of California San Francisco.
Sumant R RanjiDivision of Hospital Medicine, Zuckerberg San Francisco General Hospital, San Francisco, California.
Katie RaffelDepartment of Medicine, University of Colorado School of Medicine, Denver.
Gilmer ValdesDepartment of Radiation Oncology, University of California San Francisco.
John BoscardinDivision of Geriatrics, Department of Medicine, University of California San Francisco.
Anuj K DalalHospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
Alyssa HarrisVizient Inc, Irving, Texas.
Ellen FlynnVizient Inc, Irving, Texas.
Jeffrey L SchnipperHospital Medicine Unit, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
UPSIDE Research Group

Funding

Utility of Predictive Systems to identify Inpatient Diagnostic Errors: The UPSIDE StudyR01HS027369 · AHRQ · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AUERBACH, ANDREW D · 2019 to 2021
$1.5M
Rural Patient Risks and Exposures for Diabetes ConTrol (Rural PREDICT)K23MD016230 · NIMHD · MAYO CLINIC ROCHESTER · PI DUGANI, CHANDRASAGAR · 2021 to 2025
$763k
AHRQ HHS R01 HS027369NIMHD NIH HHS K23 MD016230
6 · The paper itself

Abstract

Importance: Diagnostic errors contribute to patient harm, though few data exist to describe their prevalence or underlying causes among medical inpatients. Objective: To determine the prevalence, underlying cause, and harms of diagnostic errors among hospitalized adults transferred to an intensive care unit (ICU) or who died. Design, Setting, and Participants: Retrospective cohort study conducted at 29 academic medical centers in the US in a random sample of adults hospitalized with general medical conditions and who were transferred to an ICU, died, or both from January 1 to December 31, 2019. Each record was reviewed by 2 trained clinicians to determine whether a diagnostic error occurred (ie, missed or delayed diagnosis), identify diagnostic process faults, and classify harms. Multivariable models estimated association between process faults and diagnostic error. Opportunity for diagnostic error reduction associated with each fault was estimated using the adjusted proportion attributable fraction (aPAF). Data analysis was performed from April through September 2023. Main Outcomes and Measures: Whether or not a diagnostic error took place, the frequency of underlying causes of errors, and harms associated with those errors. Results: Of 2428 patient records at 29 hospitals that underwent review (mean [SD] patient age, 63.9 [17.0] years; 1107 [45.6%] female and 1321 male individuals [54.4%]), 550 patients (23.0%; 95% CI, 20.9%-25.3%) had experienced a diagnostic error. Errors were judged to have contributed to temporary harm, permanent harm, or death in 436 patients (17.8%; 95% CI, 15.9%-19.8%); among the 1863 patients who died, diagnostic error was judged to have contributed to death in 121 (6.6%; 95% CI, 5.3%-8.2%). In multivariable models examining process faults associated with any diagnostic error, patient assessment problems (aPAF, 21.4%; 95% CI, 16.4%-26.4%) and problems with test ordering and interpretation (aPAF, 19.9%; 95% CI, 14.7%-25.1%) had the highest opportunity to reduce diagnostic errors; similar ranking was seen in multivariable models examining harmful diagnostic errors. Conclusions and Relevance: In this cohort study, diagnostic errors in hospitalized adults who died or were transferred to the ICU were common and associated with patient harm. Problems with choosing and interpreting tests and the processes involved with clinician assessment are high-priority areas for improvement efforts.

Indexed as

Critical CareIntensive Care UnitsAdultCohort StudiesDiagnostic ErrorsFemaleHumansMaleMiddle AgedRetrospective Studies

Identifiers

PMID38190122
PMCPMC10775080

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.