AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism

Background Hospital Acquired Venous Thromboembolism (HA-VTE) remains the leading cause of death in hospitalized patients in the US. Approximately 900,000 people experience VTE each year, with incidence-based medical costs estimated between $7 and $10 billion per year. The second leading cause of disability-adjusted life-years, HA-VTE causes significant morbidity and mortality in adult and pediatric patients. Roughly 1 in 3 people experience long-term complications (i.e., post-thrombotic syndrome) following VTE. Reducing HA-VTE presents a major diagnostic challenge.

Despite numerous published prognostic models of HA-VTE, no single model outperforms the rest. And HA-VTE affects groups inequitably, which means models might reflect or worsen healthcare disparities if they are not deployed in the context of responsible, algorithmovigilant systems. Integrating scalable AI for HA-VTE prevention into effective clinical decision support (CDS) might effectively reduce HA-VTE incidence while aiding the realization of the potential for AI in high-value clinical practice. Recently, a Vanderbilt team of clinicians and biostatisticians validated a regression risk score called "VTE-AI" to prognosticate risk of HA-VTE on admission.

The urban-rural divide has long caused healthcare disparities in morbidity and mortality. These differences might not result from rurality itself, but from "the effects of socio-economic disadvantage, ethnicity, poorer service availability, higher levels of personal risk and more hazardous environmental, occupational and transportation conditions." AI implementation will be no different without close attention to differences in both deployment settings. Studying multiple simultaneous implementations of AI in both urban and rural setting with adult and pediatric patients will yield unprecedented insights for AI-driven CDS.

Rationale and Specific Aims This study will rigorously study a novel AI approach to predicting risk of HA-VTE and guiding prevention. This trial will produce strong evidence for the pragmatic use of novel AI-CDS to prevent HA-VTE across diverse sites and populations.

The goal of this study is to evaluate the effectiveness of AI-driven CDS to reduce the incidence of HA-VTE across the Vanderbilt Health System including urban and rural sites: Vanderbilt Adult Hospital (VUH) and the Vanderbilt Regional Health System (VRHS) including Vanderbilt Tullahoma Harton Hospital (VTHH), Vanderbilt Bedford County Hospital (VBCH), and Vanderbilt Wilson County Hospital (VWCH).