Evidence map›Paper›PMID 38797012›Full record

ArticleApplied ergonomics2024

Eye tracking and audio sensors to evaluate surgeon's non-technical skills: An empirical study.

Shraddhaa Narasimha, Marian Obuseh, Nicholas Eric Anton, Haozhi Chen, Raunak Chakrabarty, Dimitrios Stefanidis, Denny Yu

Abstract read
In one paragraph

Article in Applied ergonomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. Development of simulation scenarios for surgeons' non-technical skills evaluation.Global surgical education : journal of the Association for Surgical Education · 2025
    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

7 authors.

Shraddhaa NarasimhaSchool of Industrial Engineering, Purdue University, USA. Electronic address: narasi15@purdue.edu.
Marian ObusehSchool of Industrial Engineering, Purdue University, USA.
Nicholas Eric AntonSchool of Industrial Engineering, Purdue University, USA; School of Medicine, Indiana University, USA.
Haozhi ChenSchool of Industrial Engineering, Purdue University, USA.
Raunak ChakrabartySchool of Industrial Engineering, Purdue University, USA.
Dimitrios StefanidisSchool of Medicine, Indiana University, USA.
Denny YuSchool of Industrial Engineering, Purdue University, USA.

Funding

AHRQ HHS R01 HS028026
6 · The paper itself

Abstract

Non-Technical Skills (NTS) of medical teams are currently measured using subjective and resource-intensive ratings given by experts. This study explores if objective NTS assessment approaches with eye-tracking and audio sensors can measure teamwork and communication skills in surgery. Eight surgeons participated in a simulated two-phase surgical scenario developed to assess their NTS. Sensor-based audio, eye tracking and video data were collected and analyzed along with rating from the NOTSS scale. Different levels of communication were detected by the sensor data during the two phases of the simulated surgery. Sensor data detected leadership qualities among surgeons based on speech metrics, and eye tracking offered additional evidence about gaze patterns related to NTS. This objective approach to NTS measurement captured differences in communication in greater detail as opposed to a single collective rating obtained using current assessment tools.

Indexed as

Clinical CompetenceCommunicationEye-Tracking TechnologyLeadershipSurgeonsAdultEmpirical ResearchFemaleHumansMalePatient Care TeamSimulation TrainingVideo RecordingHealthcareNontechnical skillsSensor-based data

Identifiers

PMID38797012
PMCPMC11863474

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.