# Quantifying the Metrics of Surgical Mastery: An Exploration in Data Science

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $655,346

## Abstract

PROJECT SUMMARY
In surgery, it is accepted that there may be a ten to twenty-year learning curve to reach mastery for certain
procedures. We believe this timeline can and should be shortened to improve patient care. Our short-term goal
is to make a major contribution to the emerging field of Surgical Data Science by building a database of mastery
level surgical performance and generating a roadmap for multimodal data collection and analysis procedures.
Sharing our process, procedures and results broadly, will help to change measurement culture in healthcare.
Through our newly developed partnership (October 2019) with the American College of Surgeons, we have
already experienced early success in starting the conversation through the “Surgical Metrics Project”
(https://www.facs.org/education/surgical-metrics).
Using a standardized data collection platform (a mastery-level hernia simulation), we will deploy and synchronize
multiple data capture approaches (motion tracking, video, audio and validated surgical performance checklists)
to build our database. Data analysis will quantify surgical mastery and consist of new applications and
discoveries in machine learning.
Hypothesis: Using multiple, synchronized data capture approaches and machine learning, it is possible to
create a database of mastery level surgical strategies that can be translated into a value-added, surgical
navigation tool for surgeons.
To test this hypothesis, we will empirically investigate the following paraphrased aims:
SPECIFIC AIM 1: Quantify surgical mastery (cognitive and technical) during a simulated laparoscopic ventral
hernia (LVH) repair by using a post-procedure analysis of multi-modal performance metrics captured from
hospital credentialed surgeons (N~125).
SPECIFIC AIM 2: Establish validity evidence for surgical mastery metrics by comparing simulation-based LVH
performance with operating room LVH performance from the same surgeons (N~60).
SPECIFIC AIM 3: Empirically investigate the best implementation strategy for utilization of a surgical navigation
tool designed to deliver value-added information regarding mastery-level surgical performance strategies to a
new group of hospital credentialed surgeons (N~125).

## Key facts

- **NIH application ID:** 10457351
- **Project number:** 5R01DK123445-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** CARLA M PUGH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $655,346
- **Award type:** 5
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10457351

## Citation

> US National Institutes of Health, RePORTER application 10457351, Quantifying the Metrics of Surgical Mastery: An Exploration in Data Science (5R01DK123445-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10457351. Licensed CC0.

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