An evaluation of emergency general surgery transfers and a call for standardization of practices

对急诊普通外科转诊情况的评估以及对操作规范的呼吁

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Abstract

BACKGROUND: There is an increasing trend toward regionalization of emergency general surgery, which burdens patients. The absence of a standardized, emergency general surgery transfer algorithm creates the potential for unnecessary transfers. The aim of this study was to evaluate clinical reasoning prompting emergency general surgery transfers and to initiate a discussion for optimal emergency general surgery use. METHODS: Consecutive emergency general surgery transfers (December 2018 to May 2019) to 2 tertiary centers were prospectively enrolled in an institutional review board-approved protocol. Clinical reasoning prompting transfer was obtained prospectively from the accepting/consulting surgeon. Patient outcomes were used to create an algorithm for emergency general surgery transfer. RESULTS: Two hundred emergency general surgery transfers (49% admissions, 51% consults) occurred with a median age of 59 (18 to 100) and body mass index of 30 (15 to 75). Insurance status was 25% private, 45% Medicare, 21% Medicaid, and 9% uninsured. Weekend transfers (Friday to Sunday) occurred in 45%, and 57% occurred overnight (6:00 pm to 6:00 am). Surgeon-to-surgeon communication occurred with 22% of admissions. Pretransfer notification occurred with 10% of consults. Common transfer reasons included no surgical coverage (20%), surgeon discomfort (24%), or hospital limitations (36%). A minority (36%) underwent surgery within 24 hours; 54% did not require surgery during the admission. Median length of stay was 6 (1 to 44) days. CONCLUSION: Conditions prompting emergency general surgery transfers are heterogeneous in this rural state review. There remains an unmet need to standardize emergency general surgery transfer criteria, incorporating patient and hospital factors and surgeon availability. Well-defined requirements for communication with the accepting surgeon may prevent unnecessary transfers and maximize resource allocation.

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