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Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine

August 20, 2026 · via Europe PMC

From the Oracle of Delphi to Intensive Care Unit Guidelines: Consensus Building Using the Delphi Method.

Schultz MJ, Nasa P.

research-articleOpen access

AI summary — for doctors, generated from the abstract only

This is a viewpoint article, not an original research study; it argues that the Delphi method's structured, anonymous, iterative process can improve transparency and rigor in building expert consensus for ICU guidelines, compared with less structured consensus approaches.

What was studied

Not applicable: this is an opinion/viewpoint article discussing the epistemological principles of the Delphi method (anonymous voting, iteration, controlled feedback) and their application to intensive care unit guideline development, rather than a study collecting new data.

Key findings

  • The authors argue that Delphi methodology is designed to minimize group convergence and peer pressure that can occur in face-to-face consensus discussions.
  • They note that despite efforts to improve reporting standards for Delphi studies, comprehensive and uniform guidance for their design and conduct is still lacking.
  • The article examines limitations of current consensus methods used in guideline development and proposes how Delphi methodology could enhance transparency and methodological rigor.

Clinical relevance

This may be relevant to guideline developers and critical care societies considering how to structure consensus processes, but as an opinion piece it reflects the authors' analysis and recommendations rather than empirical evidence of improved guideline quality or patient outcomes.

Who should care

Critical Care MedicineEmergency MedicinePediatric Critical Care

Limitations

This is a viewpoint/commentary article without original data, a systematic literature review, or a formal evaluation of outcomes; its conclusions reflect the authors' interpretation rather than empirical testing.

This viewpoint argues that using the structured Delphi method more rigorously could make ICU guideline consensus processes more transparent and less prone to bias.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Delphi methodology is increasingly employed in health care, particularly in areas where evidence is limited, ambiguous, or conflicting. Consensus-based approaches are integral to guideline development; however, the processes underpinning consensus are often inadequately described or lack transparency, particularly in relation to mitigating cognitive biases within expert panels. The epistemological principles of the Delphi method-anonymous voting, iteration, and controlled feedback-are designed to minimize group convergence and peer pressure commonly observed in face-to-face discussions. While efforts have been made to improve reporting standards for Delphi studies, there remains a lack of comprehensive guidance and uniform standards for their design and conduct. This viewpoint examines the limitations of consensus methods used in guideline development and highlights how the Delphi methodology can enhance transparency, methodological rigor, and robustness in this process.

How to cite this article

Schultz MJ, Nasa P. From the Oracle of Delphi to Intensive Care Unit Guidelines: Consensus Building Using the Delphi Method. Indian J Crit Care Med 2026;30(8):623-627.

DOI

10.5005/jp-journals-10071-25261

PMID

42757371

PMCID

PMC13585103

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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