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 and Julien Cobert
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Samuel K. McGowan: Department of Internal Medicine, Division of Pulmonary and Critical Care, University of California, San Francisco, San Francisco, CA, USA
Maria-Jose Corrales-Martinez: Department of Anesthesia and Pain Medicine, The Cleveland Clinic, Cleveland, OH, USA
Teva Brender: Department of Internal Medicine, University of California, San Francisco, San Francisco, CA, USA
Alexander K. Smith: Department of Internal Medicine, Division of Geriatrics and Palliative Care, University of California, San Francisco, San Francisco, CA, USA
Shannen Kim: Department of Internal Medicine, University of California, San Francisco, San Francisco, CA, USA
Krista L. Harrison: Department of Internal Medicine, Division of Geriatrics and Palliative Care, University of California, San Francisco, San Francisco, CA, USA
Hunter Mills: Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
Albert Lee: Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
David Bamman: School of Information, University of California, Berkeley, CA, USA
Julien Cobert: Department of Anesthesia and Perioperative Medicine, University of California, San Francisco, San Francisco, CA, USA

Medical Decision Making, 2025, vol. 45, issue 1, 34-44

Abstract: Background Clinical 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. Design We 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. Results Nineteen 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. Conclusions Using 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.

Keywords: intensive care unit; uncertainty; natural language processing; palliative care; prognosis; complexity (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:1:p:34-44

DOI: 10.1177/0272989X241293446

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