Information Seeking and Sensemaking in Emergency Medical Service through Simulation Video Review.

Zhan Zhang, Karen Joy, Aastha S Bhadani, Tejas D Joshi, Kathleen Adelgais, Mustafa Ozkaynak

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
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

6 authors.

Zhan ZhangPace University, New York, NY, USA.

Karen JoyPace University, New York, NY, USA.

Aastha S BhadaniPace University, New York, NY, USA.

Tejas D JoshiPace University, New York, NY, USA.

Kathleen AdelgaisUniversity of Colorado, Aurora, CO, USA.

Mustafa OzkaynakUniversity of Colorado, Aurora, CO, USA.

Funding

AHRQ HHS R21 HS028104

6 · The paper itself

Abstract

Emergency medical services (EMS) providers often face significant challenges in their work, including collecting, integrating, and making sense of a variety of information. Despite their criticality, EMS work is one of the very few medical domains with limited technical support. To design and implement effective decision support, it is essential to examine and gain a holistic understanding of the fine-grained process of sensemaking in the field. To that end, we reviewed 25 video recordings of EMS simulations to understand the nuances of EMS sensemaking work, including 1) the types of information and situation that are collected and made sense of in the field; 2) the work practices and temporal patterns of EMS sensemaking work; and 3) the challenges in EMS sensemaking and decision-making process. Based on the results, we discuss implications for technology opportunities to support rapid information acquisition and sensemaking in time-critical, high-risk medical settings such as EMS.

Indexed as

Emergency Medical ServicesInformation Seeking BehaviorComputer SimulationHumansVideo Recording

Identifiers

PMID38222399

PMCPMC10785834

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.