Evidence map›Paper›PMID 38286969›Full record

ReviewJournal of general internal medicine2024

Navigating Uncertainty in Clinical Practice: A Structured Approach.

Badar Patel, Galina Gheihman, Joel T Katz, Arabella Simpkin Begin, Sonja R Solomon

Abstract readReview
In one paragraph

Review in Journal of general internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. "With Great Responsibility Comes Great Uncertainty".Journal of general internal medicine · 2025
    Article
  12. Article
  13. Article
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

5 authors.

Badar PatelDivision of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Galina GheihmanHarvard Medical School, Boston, MA, USA.
Joel T KatzHarvard Medical School, Boston, MA, USA.
Arabella Simpkin BeginHarvard Medical School, Boston, MA, USA.
Sonja R SolomonHarvard Medical School, Boston, MA, USA. ssolomon5@bwh.harvard.edu.ORCID 0000-0002-8416-4592

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The practice of clinical medicine is imbued with uncertainty. The ways in which clinicians and patients think about, communicate about, and act within situations of heightened uncertainty can have significant implications for the therapeutic alliance and for the trajectory and outcomes of clinical care. Despite this, there is limited guidance about the best methods for physicians to recognize, acknowledge, communicate about, and manage uncertainty in clinical settings. In this paper, we propose a structured approach for discussing and managing uncertainty within the context of a clinician-patient relationship. The approach involves four steps: Recognize, Acknowledge, Partner, and Seek Support (i.e., the RAPS framework). The approach is guided by existing literature on uncertainty as well as our own experience as clinicians working at different stages of career. We define each component of the approach and present sample language and actions for how to implement it in practice. Our aim is to empower clinicians to regard situations of high uncertainty as an opportunity to deepen the therapeutic alliance with the patient, and simultaneously to grow and learn as practitioners.

Indexed as

Physician-Patient RelationsCommunicationHumansUncertaintycommunicationframeworkshared decision-makinguncertainty

Identifiers

PMID38286969
PMCPMC11043270

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.