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CDA 13-270
Development of an Adverse Event Surveillance System for Outpatient Surgery
Hillary J Mull, PhD MPP VA Boston Healthcare System Jamaica Plain Campus, Jamaica Plain, MA Boston, MA Funding Period: October 2014 - September 2018 |
BACKGROUND/RATIONALE:
Background/Rationale: The focus of this project is to improve quality and safety in outpatient surgery through the development and implementation of an adverse event (AE) surveillance system. This system is based on using trigger tools (TTs) that screen outpatient surgical data from the VA Corporate Data Warehouse (CDW). Triggers flag outpatient surgeries with a high likelihood of patient safety problems enabling more efficient AE surveillance. I will carry out a comprehensive study of VA outpatient surgical quality, guided by a modified version of the Donabedian framework of structure, process, and outcomes to measure quality. OBJECTIVE(S): Objectives: 1) Develop a comprehensive database of VA outpatient surgeries from FY12-14, 2) Develop and validate an AE surveillance system for outpatient surgery, and (as of year 4 of the study) 3) Identify potential opportunities to improve quality and safety. METHODS: Methods: The CDA research will draw upon quantitative and qualitative methods. To achieve Aim 1, I will use input from surgical stakeholders (e.g., surgeons, anesthesiologists, nurses) to identify relevant variables to include in an AE surveillance system. I will then mine VA electronic data to identify and characterize outpatient surgeries from FY12-14 based on stakeholder input. I will aggregate information about procedures, providers, care settings, and same day pre- and post-procedure care from a variety of data sources including the VA Surgical Quality Improvement Program (VASQIP), administrative data files, and the CDW. In Aim 2, I will evaluate the predictive validity of TTs to detect AEs by reviewing a sample of trigger-flagged outpatient surgeries from patients' charts. The results of this effort will inform the development of a multivariate logistic regression model to identify outpatient surgeries with a high likelihood of having an AE. Lastly, in Aim 3, I will use analytical models to test whether antibiotics or opioids are associated with postoperative AEs in outpatient surgery; these results may lead to quality improvement opportunities. FINDINGS/RESULTS: Not yet available. IMPACT: Impact: Surgical quality in the outpatient setting has been largely neglected by researchers until recently and gaps in knowledge currently exist. This CDA developed and validated an AE surveillance model that predicted AEs with a high level of accuracy and showed that the outpatient surgical AE rate is higher than estimates in the literature (9% versus 1-5% in previous studies). The information generated in this study will inform quality measurement and improvement efforts in outpatient surgery. External Links for this ProjectNIH ReporterGrant Number: IK2HX001520-01A1Link: https://reporter.nih.gov/project-details/8780231 Dimensions for VA![]() If you have VA-Intranet access, click here for more information vaww.hsrd.research.va.gov/dimensions/ VA staff not currently on the VA network can access Dimensions by registering for an account using their VA email address. Search Dimensions for this project PUBLICATIONS:Journal Articles
DRA:
Health Systems
DRE: Technology Development and Assessment Keywords: none MeSH Terms: none |