Evidence map›Paper›PMID 39559986›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2025

Unclear Trajectory and Uncertain Benefit: Creating a Lexicon for Clinical Uncertainty in Patients with Critical or Advanced Illness Using a Delphi Consensus Process.

Samuel K McGowan, Maria-Jose Corrales-Martinez, Teva Brender, Alexander K Smith, Shannen Kim, Krista L Harrison, Hunter Mills, Albert Lee, David Bamman, Julien Cobert

Abstract readConsensus Statement
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Samuel K McGowanDepartment of Internal Medicine, Division of Pulmonary and Critical Care, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0003-0453-2648
Maria-Jose Corrales-MartinezDepartment of Anesthesia and Pain Medicine, The Cleveland Clinic, Cleveland, OH, USA.
Teva BrenderDepartment of Internal Medicine, University of California, San Francisco, San Francisco, CA, USA.
Alexander K SmithDepartment of Internal Medicine, Division of Geriatrics and Palliative Care, University of California, San Francisco, San Francisco, CA, USA.
Shannen KimDepartment of Internal Medicine, University of California, San Francisco, San Francisco, CA, USA.
Krista L HarrisonDepartment of Internal Medicine, Division of Geriatrics and Palliative Care, University of California, San Francisco, San Francisco, CA, USA.
Hunter MillsBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Albert LeeBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
David BammanSchool of Information, University of California, Berkeley, CA, USA.
Julien CobertDepartment of Anesthesia and Perioperative Medicine, University of California, San Francisco, San Francisco, CA, USA.

Funding

VARC CoreP30AG044281 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Louise C. Walter · 2013 to 2026
$19.9M
RESEARCH TRAINING IN GERIATRIC MEDICINET32AG000212 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Alexander Keliimoeanu Smith · 1991 to 2026
$7.7M
Mentoring Researchers in Prognosis Research in Alzheimer’s Disease and Related DementiasK24AG068312 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Alexander Keliimoeanu Smith · 2020 to 2026
$1.2M
NIA NIH HHS K24 AG068312NIA NIH HHS P30 AG044281NIA NIH HHS T32 AG000212
6 · The paper itself

Abstract

backgroundClinical uncertainty is associated with increased resource utilization, worsened health-related quality of life for patients, and provider burnout, particularly during critical illness. Existing data are limited, because determining uncertainty from notes typically requires manual, qualitative review. We sought to develop a consensus list of descriptors of clinical uncertainty and then, using a thematic analysis approach, describe how respondents consider their use in intensive care unit (ICU) notes, such that future work can extract uncertainty data at scale.

designWe conducted a Delphi consensus study with physicians across multiple institutions nationally who care for critically ill patients or patients with advanced illnesses. Participants were given a definition for clinical uncertainty and collaborated through multiple rounds to determine which words represent uncertainty in clinician notes. We also administered surveys that included open-ended questions to participants about clinical uncertainty. Following derivation of a consensus list, we analyzed participant responses using thematic analysis to understand the role of uncertainty in clinical documentation.

resultsNineteen physicians participated in at least 2 of the Delphi rounds. Consensus was achieved for 44 words or phrases over 5 rounds of the Delphi process. Clinicians described comfort with using uncertainty terms and used them in a variety of ways: documenting and processing the diagnostic thinking process, enlisting help, identifying incomplete information, and practicing transparency to reflect uncertainty that was present.

conclusionsUsing a consensus process, we created an uncertainty lexicon that can be used for uncertainty data extraction from the medical record. We demonstrate that physicians, particularly in the ICU, are comfortable with uncertainty and document uncertainty terms frequently to convey the complexity and ambiguity that is pervasive in critical illness. HIGHLIGHTS: Question: What words do physicians caring for critically ill patients use to document clinical uncertainty, and why?Findings: A consensus list of 44 words or phrases was identified by a group of experts. Physicians expressed comfort with using these words in the electronic health record.Meaning: Physicians are comfortable with uncertainty words and document them frequently to convey the complexity and ambiguity that is pervasive in critical illness.

Indexed as

Critical IllnessDelphi TechniqueFemaleHumansIntensive Care UnitsMalePhysiciansUncertaintycomplexityintensive care unitnatural language processingpalliative careprognosisuncertainty

Identifiers

PMID39559986
PMCPMC11645224

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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.