Computer Modeling to Evaluate the Impact of Technology Changes on Resident Procedural Volume

Author:

Grenda Tyler R.1,Ballard Tiffany N. S.1,Obi Andrea T.1,Pozehl William1,Seagull F. Jacob1,Chen Ryan1,Cohn Amy M.1,Daskin Mark S.1,Reddy Rishindra M.1

Affiliation:

1. All authors are with the University of Michigan, Ann Arbor. Tyler R. Grenda, MD, is General Surgery Resident, Department of Surgery; Tiffany N. S. Ballard, MD, is Plastic Surgery Resident, Department of Surgery; Andrea T. Obi, MD, is Vascular Surgery Fellow, Department of Surgery; William Pozehl, MSE, is Research Assistant, Center for Healthcare Engineering and Patient Safety; F. Jacob Seagull, P

Abstract

ABSTRACT Background  As resident “index” procedures change in volume due to advances in technology or reliance on simulation, it may be difficult to ensure trainees meet case requirements. Training programs are in need of metrics to determine how many residents their institutional volume can support. Objective  As a case study of how such metrics can be applied, we evaluated a case distribution simulation model to examine program-level mediastinoscopy and endobronchial ultrasound (EBUS) volumes needed to train thoracic surgery residents. Methods  A computer model was created to simulate case distribution based on annual case volume, number of trainees, and rotation length. Single institutional case volume data (2011–2013) were applied, and 10 000 simulation years were run to predict the likelihood (95% confidence interval) of all residents (4 trainees) achieving board requirements for operative volume during a 2-year program. Results  The mean annual mediastinoscopy volume was 43. In a simulation of pre-2012 board requirements (thoracic pathway, 25; cardiac pathway, 10), there was a 6% probability of all 4 residents meeting requirements. Under post-2012 requirements (thoracic, 15; cardiac, 10), however, the likelihood increased to 88%. When EBUS volume (mean 19 cases per year) was concurrently evaluated in the post-2012 era (thoracic, 10; cardiac, 0), the likelihood of all 4 residents meeting case requirements was only 23%. Conclusions  This model provides a metric to predict the probability of residents meeting case requirements in an era of changing volume by accounting for unpredictable and inequitable case distribution. It could be applied across operations, procedures, or disease diagnoses and may be particularly useful in developing resident curricula and schedules.

Publisher

Journal of Graduate Medical Education

Subject

General Medicine

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