HSR&D Home » Research » IIR 18-034 – HSR&D Study
How Can We Make Invasive Non-Surgical Procedures Safer? Using Big Data to Identify Adverse Events and Opportunities to Mitigate Harm
Hillary J Mull, PhD MPP
VA Boston Healthcare System Jamaica Plain Campus, Jamaica Plain, MA
Funding Period: April 2020 - March 2024
AbstractBackground: This is the second submission of an HSR&D IIR proposal to transition Dr. Hillary Mull, Ph.D. from her HSR&D Career Development Award (CDA) project toward an independent VA health services research career. The proposed work seeks to build on Dr. Mull's successful CDA project by adapting her approach to developing and validating a surveillance model for outpatient surgery to invasive procedures in non-surgical clinical specialties: interventional cardiology, interventional radiology and gastrointestinal endoscopy procedures. This informatics-based approach relies on combining text and structured data fields in the VA Corporate Data Warehouse (CDW). Dr. Mull's CDA-funded surveillance research identified an adverse event rate of 9% and had a positive predictive value of 85%, dramatically improving adverse event detection. Significance/Impact: Presently, there is no active surveillance of invasive procedures and preliminary analyses and conversations with frontline staff suggest adverse events occur with some frequency and impose significant patient harm. Prior work found invasive procedures in these three specialties result in post- procedure emergency room visits or hospitalizations exceeding 50,000 cases annually. Non-VA literature suggests half of this utilization may be preventable with improvements in clinical care (e.g., adherence to antibiotic prescribing guidelines). This field of research will become even more important as care increasingly transitions outside the operating room. Detecting and monitoring adverse events in understudied settings using existing data in the VA CDW is consistent with HSR&D funding priority C-Healthcare Informatics. Innovation: Together with experts from COINs around the country and the support of operational partners from each clinical specialty, Pharmacy Benefits Management and VA Informatics and Computing Infrastructure, Dr. Mull proposes to apply her CDA expertise to build a surveillance system to identify invasive non-surgical procedures with preventable adverse events; these procedures are not subject to any VA surveillance activities. A second gap this work addresses is the lack of a nationally available dataset capturing procedural anesthesia use. We will use chart review and text-query data mining methods to obtain this information. The culmination of our IIR work will be a comprehensive database of adverse events and potentially modifiable contributing factors, including procedural anesthesia data, available to VA researchers. Specific Aims: 1) develop and validate surveillance models using FY17-20 data; 2) test the surveillance system (apply model coefficients, perform limited chart review on a monthly basis) from FY21-22, and refine the system using additional CDW variables; 3) test hypotheses related to modifiable processes including whether a trained anesthesia provider was involved or patients received inappropriate antibiotics. Methodology: Our sample includes non-surgical invasive procedures defined by expert clinician co- investigators. We will follow the methods outlined in Dr. Mull's CDA work to aggregate patient, procedure, provider and facility data from the CDW. Next, we will review cases to determine whether a preventable event occurred and use chart review data to estimate logistic regression models predicting the likelihood of an adverse event. Model coefficients will be applied on an ongoing fashion to identify cases likely to have an adverse event to target chart review. Surveillance data will be used to test study hypotheses. Next Steps/Implementation: Through this IIR, and in a subsequent partner-funded QUERI proposal, Dr. Mull and her team will establish an adverse event surveillance system designed for invasive non-surgical procedures that can be used to assess modifiable processes of care to prevent patient harm. By identifying risk factors for preventable adverse events, we can determine where we, with our operational partners, should focus QUERI-funded QI work to improve patient safety. Study results will provide much needed information to the research and clinical communities as they continue to measure and improve the quality of VA care.
External Links for this Project
NIH ReporterGrant Number: I01HX002694-01A2
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PUBLICATIONS:None at this time.
DRA: Digestive Diseases, Cardiovascular Disease
DRE: Treatment - Implementation, TRL - Applied/Translational, Prevention
Keywords: Adverse Event Monitoring, Healthcare Algorithms, Safety Measurement Development
MeSH Terms: None at this time.