Evidence map›Paper›PMID 34561251›Full record

ArticleBMJ open2021

Understanding diagnostic processes in emergency departments: a mixed methods case study protocol.

Michelle Daniel, SunYoung Park, Colleen M Seifert, P Paul Chandanabhumma, Michael D Fetters, Eric Wilson, Hardeep Singh, Kalyan Pasupathy, Prashant Mahajan

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

9 authors.

Michelle DanielEmergency Medicine, University of California San Diego School of Medicine, La Jolla, California, USA midaniel@health.ucsd.edu.ORCID 0000-0001-8961-7119
SunYoung ParkSchool of Art and Design and School of Information, University of Michigan, Ann Arbor, Michigan, USA.
Colleen M SeifertPsychology, University of Michigan, Ann Arbor, Michigan, USA.
P Paul ChandanabhummaFamily Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Michael D FettersFamily Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Eric WilsonEmergency Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Hardeep SinghHouston Veterans Affairs Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, USA.
Kalyan PasupathyMayo Clinic Department of Health Sciences Research, Rochester, Minnesota, USA.
Prashant MahajanEmergency Medicine and Pediatrics, University of Michigan, Ann Arbor, Michigan, USA.

Funding

Improving Diagnosis in Emergency and Acute Care: A Learning Laboratory (IDEA-LL)R18HS026622 · AHRQ · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MAHAJAN, PRASHANT, PASUPATHY, KALYAN S · 2018 to 2021
$2.5M
Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety EventsR01HS027363 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2019 to 2021
$1.5M
AHRQ HHS R01 HS027363AHRQ HHS R18 HS026622
6 · The paper itself

Abstract

introductionDiagnostic processes in the emergency department (ED) involve multiple interactions among individuals who interface with information systems to access and record information. A better understanding of diagnostic processes is needed to mitigate errors. This paper describes a study protocol to map diagnostic processes in the ED as a foundation for developing future error mitigation strategies. METHODS AND ANALYSIS: This study of an adult and a paediatric academic ED uses a prospective mixed methods case study design informed by an ED-specific diagnostic decision-making model (the modified ED-National Academies of Sciences, Engineering and Medicine (NASEM) model) and two cognitive theories (dual process theory and distributed cognition). Data sources include audio recordings of patient and care team interactions, electronic health record data, observer field notes and stakeholder interviews. Multiple qualitative analysis methods will be used to explore diagnostic processes in situ, including systems information flow, human-human and human-system interactions and contextual factors influencing cognition. The study has three parts. Part 1 involves prospective field observations of patients with undifferentiated symptoms at high risk for diagnostic error, where each patient is followed throughout the entire care delivery process. Part 2 involves observing individual care team providers over a 4-hour window to capture their diagnostic workflow, team coordination and communication across multiple patients. Part 3 uses interviews with key stakeholders to understand different perspectives on the diagnostic process, as well as perceived strengths and vulnerabilities, in order to enrich the ED-NASEM diagnostic model. ETHICS AND DISSEMINATION: The University of Michigan Institutional Review Board approved this study, HUM00156261. This foundational work will help identify strengths and vulnerabilities in diagnostic processes. Further, it will inform the future development and testing of patient, provider and systems-level interventions for mitigating error and improving patient safety in these and other EDs. The work will be disseminated through journal publications and presentations at national and international meetings.

Indexed as

CommunicationEmergency Service, HospitalAdultChildHumansPatient SafetyProspective Studiesaccident & emergency medicinehealth & safetypaediatric A&E and ambulatory carequalitative researchrisk management

Identifiers

PMID34561251
PMCPMC8475137

What OpenQuestion holds

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LicenceCC BY-NC
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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.