Advanced Data Analytics for Improved Decision-Making at a Veterans Affairs Medical Center

Abstract

This article reports on a data-driven methodology for decision-making at a Veterans Affairs medical center (VAMC) to improve patient outcomes, specifically the 30-day standardized mortality ratio (SMR30). The quarterly strategic analytics for improvement and learning (SAIL) reports are used to visualize the data, study trends, provide actionable recommendations, and identify potential consequences.A case study using more than 4 years of data demonstrates the power of the methodology. After reviewing data and studying trends at other VAMCs, a decision is made to reduce the SMR30 value by 1%. In running correlation algorithms, in-hospital complications (IHC) are shown to be most closely correlated with SMR30. Modeling the results from 17 quarters' worth of data shows that a desired 1% change in SMR30 would require a targeted 18.6% decrease in IHC. This change, if accomplished, would yield good consequences on methicillin-resistant Staphylococcus aureus mitigation but potentially unintended consequences with catheter-associated urinary tract infections and patient safety indicators that would need to be monitored. This knowledge could enable healthcare leaders to make informed decisions of both potentially positive and unintended consequences that can be monitored and minimized. This study lays the groundwork for a healthy discussion among leaders, staff, and clinicians on the path forward, resources required, and-most importantly-a dashboard that reflects the progress each week rather than a quarterly SAIL report.