Template matching for benchmarking hospital performance in the veterans affairs healthcare system.

Comparing hospital performance in a health system is traditionally done with multilevel regression models that adjust for differences in hospitals' patient case-mix. In contrast, "template matching" compares outcomes of similar patients at different hospitals but has been used only in limited patient settings.Our objective was to test a basic template matching approach in the nationwide Veterans Affairs healthcare system (VA), compared with a more standard regression approach.We performed various simulations using observational data from VA electronic health records whereby we randomly assigned patients to "pseudo hospitals," eliminating true hospital level effects. We randomly selected a representative template of 240 patients and matched 240 patients on demographic and physiological factors from each pseudo hospital to the template. We varied hospital performance for different simulations such that some pseudo hospitals negatively impacted patient mortality.Electronic health record data of 460,213 hospitalizations at 111 VA hospitals across the United States in 2015.We assessed 30-day mortality at each pseudo hospital and identified lowest quintile hospitals by template matching and regression. The regression model adjusted for predicted 30-day mortality (as a measure of illness severity).Regression identified the lowest quintile hospitals with 100% accuracy compared with 80.3% to 82.0% for template matching when systematic differences in 30-day mortality existed.The current standard practice of risk-adjusted regression incorporating patient-level illness severity was better able to identify lower-performing hospitals than the simplistic template matching algorithm.

Veterans Affairs patient database (VAPD 2014-2017): building nationwide granular data for clinical discovery.

BACKGROUND: To study patient physiology throughout a period of acute hospitalization, we sought to create accessible, standardized nationwide data at the level of the individual patient-facility-day. This methodology paper summarizes the development, organization, and characteristics of the Veterans Affairs Patient Database 2014-2017 (VAPD 2014-2017). The VAPD 2014-2017 contains acute hospitalizations from all parts of the nationwide VA healthcare system with daily physiology including clinical data (labs, vitals, medications, risk scores, etc.), intensive care unit (ICU) indicators, facility, patient, and hospitalization characteristics. METHODS: The VA data structure and database organization represents a complex multi-hospital system. We define a single-site hospitalization as one or more consecutive stays with an acute treating specialty at a single facility. The VAPD 2014-2017 is structured at the patient-facility-day level, where every patient-day in a hospital is a row with separate identification variables for facility, patient, and hospitalization. The VAPD 2014-2017 includes daily laboratory, vital signs, and inpatient medication. Such data were validated and verified through lab value range and comparison with patient charts. Sepsis, risk scores, and organ dysfunction definitions were standardized and calculated. RESULTS: We identified 565,242 single-site hospitalizations (SSHs) in 2014; 558,060 SSHs in 2015; 553,961 SSHs in 2016; and 550,236 SSHs in 2017 at 141 VA hospitals. The average length of stay was four days for all study years. In-hospital mortality decreased from 2014 to 2017 (1.7 to 1.4%), 30-day readmission rates increased from 15.3% in 2014 to 15.6% in 2017; 30-day mortality also decreased from 4.4% in 2014 to 4.1% in 2017. From 2014 to 2017, there were 107,512 (4.8%) of SSHs that met the Center for Disease Control and Prevention's Electronic Health Record-based retrospective definition of sepsis. CONCLUSION: The VAPD 2014-2017 represents a large, standardized collection of granular data from a heterogeneous nationwide healthcare system. It is also a direct resource for studying the evolution of inpatient physiology during both acute and critical illness.