Evidence map›Paper›PMID 38774830›Full record

ArticleAEM education and training2024

Precision medicine within health professions education: Defining a research agenda for emergency medicine using a foresight and strategy technique (FaST) review.

Teresa M Chan, Brent Thoma, John T Finnell, Bradley D Gordon, Susan Farrell, Martin Pusic, Daniel Cabrera, Michael A Gisondi, Holly A Caretta-Weyer, Christopher Stave and 1 more

Abstract read
In one paragraph

Article in AEM education and training, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Focusing on "Precision" in Precision Education.Academic medicine : journal of the Association of American Medical Colleges · 2025
    Article
  3. Review
  4. [Precision surgical education].Revista colombiana de obstetricia y ginecologia · 2024
    Article
  5. Article
  6. 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

11 authors.

Teresa M ChanSchool of Medicine Toronto Metropolitan University Toronto Ontario Canada.ORCID https://orcid.org/0000-0001-6104-462X
Brent ThomaSchool of Medicine Toronto Metropolitan University Toronto Ontario Canada.ORCID https://orcid.org/0000-0003-1124-5786
John T FinnellClinical Emergency Medicine Indiana University Bloomington Indiana USA.ORCID https://orcid.org/0000-0002-6281-6238
Bradley D GordonEmergency Medicine Regions Hospital Saint Paul Minnesota USA.ORCID https://orcid.org/0000-0002-5026-5854
Susan FarrellEmergency Medicine Harvard Medical School Boston Massachusetts USA.
Martin PusicPediatrics and Emergency Medicine Harvard Medical School Boston Massachusetts USA.ORCID https://orcid.org/0000-0001-5236-6598
Daniel CabreraEmergency Medicine Mayo Clinic Rochester Minnesota USA.ORCID https://orcid.org/0000-0002-5891-0459
Michael A GisondiDepartment of Emergency Medicine Stanford School of Medicine Stanford California USA.ORCID https://orcid.org/0000-0002-6800-3932
Holly A Caretta-WeyerDepartment of Emergency Medicine Stanford University School of Medicine Palo Alto California USA.ORCID https://orcid.org/0000-0002-9783-5797
Christopher StaveStanford University Stanford California USA.ORCID https://orcid.org/0000-0001-8232-3994
Felix AnkelEmergency Medicine Regions Hospital Saint Paul Minnesota USA.ORCID https://orcid.org/0000-0002-7329-5095

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Precision medicine, sometimes referred to as personalized medicine, is rapidly changing the possibilities for how people will engage health care in the near future. As technology to support precision medicine exponentially develops, there is an urgent need to proactively improve our understanding of precision medicine and pose important research questions (RQs) related to its inclusion in the education and training of future emergency physicians. Methods: A seven-step process was employed to develop a research agenda exploring the intersection of precision and emergency medicine education/training. A literature search of articles about precision medicine was conducted first, which informed the creation of future four scenarios in which trainees and practicing physicians regularly discuss and incorporate precision medicine tools into their discussions and work. Based on these futurist narratives, potential education RQs were generated by an expert panel. A total of 59 initial questions were subsequently categorized and refined to a priority list through a nominal group voting method. The top/priority questions were presented at the 2023 SAEM Consensus Conference on Precision Medicine, Austin, Texas, for further input. Results: Eight high-value education RQs were developed, reflecting a holistic view of the challenges and opportunities for precision medicine education in the knowledge, skills, and attitudes relevant to emergency medicine. These questions contend with topics such as most effective pedagogical methods; intended resulting outcomes and behaviors; the generational differences between practicing emergency physicians, educators, and future trainees; and the desires and expectations of patients. Conclusions: Emergency medicine and emergency physicians must be prepared to understand precision medicine and incorporate this information into their "toolbox" of thinking, problem solving, and communication with patients and colleagues. This research agenda on how best to educate future emergency physicians in the use of personalized data to provide optimal health care is the focus of this article.

Identifiers

PMID38774830
PMCPMC11102948

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.