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Civilian-Military Trauma Partnerships and the Visiting Surgeon Model for Maintaining Medical Readiness.

OBJECTIVE: The main objective of this paper is to create a model to predict the amount of trauma experience at a level 1 trauma center a visiting surgeon can expect to obtain with near certainty, in a specific amount of time, to maintain trauma skills.

DESIGN: The trauma database of level 1 trauma center (Saint Louis University Hospital, a military civilian partnership site) was examined to identify all urgent trauma cases between 1 October 2015 and 30 September 2017. Using retrospective data, a prospective hypothesis of a mixture of various case exposures a visiting surgeon may experience was made using Monte Carlo statistical methods, various probabilities for wartime relevant specialties were examined.

SETTING: Saint Louis University Hospital, a level 1 trauma and tertiary referral center.

PARTICIPANTS: Trauma patients between the dates October 1, 2015 and September 30, 2017 that underwent an operation at Saint Louis University Hospital.

RESULTS: Orthopedics and general/trauma surgery had the largest number of urgent trauma cases with an average daily amount of 1.03 and 0.49 cases, respectively. Using Monte Carlo methods, various scenarios and probabilities were tabulated. For example, a general surgeon on shift for 10days could expect to experience 4.9 (95% confidence interval 1-11) urgent cases or a visiting surgeon would require twenty-six 24-hour shifts in the summer to have a 95% certainty to experience at least 10 cases.

CONCLUSIONS: Other than for orthopedics, prolonged training timelines would be required to expose a visiting surgeon to multiple operative trauma cases. Though a specific number of cases to achieve "readiness" is undefined, a visiting-surgeon model may be unacceptable if a large number of cases are required prior to military deployment. This predictive model could be extrapolated to other centers and assist in identifying adequate settings and durations of trauma training sites.

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