Development of an Adverse Event Surveillance Model for Outpatient Surgery in the Veterans Health Administration.

ABSTRACT:

Objective

Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.

Data sources

Veterans Health Administration's Corporate Data Warehouse.

Study design

Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics

SUBMITTER: Mull HJ 

PROVIDER: S-EPMC6232409 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature