SBIR Phase I: Adaptive E-Triage in Emergency Medicine

Period of Performance: 07/01/2016 - 06/30/2017


Phase 1 SBIR

Recipient Firm

Stocastic, LLC
629 S. Belnord Ave. Array
Baltimore, MD 21224
Firm POC, Principal Investigator


The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase 1 project is to drive safer and more cost-effective emergency department care pathways by improved risk stratification at patient presentation (triage) compared to the current standards of care. E-triage addresses the ED crowding crisis (136 million visits in US annually) that adversely affects patients' health outcomes and has led to a state of financial unsustainability in America's safety net. E-triage's approach supports new ED operational models to separate service streams for acutely ill and non-urgent patients. New streaming models are needed to mitigate ED crowding by: (1) conserving scarce ED resources for patients truly in need of emergency care, and (2) preventing unnecessary waiting and costly resource over-utilization for non-urgent patients. It does this by using local ED electronic health record (EHR) data to scientifically risk-stratify patients based on risk of critical events and severity of illness. E-triage meets a commercial opportunity to mitigate crowding, enhance ED operational performance, and improve the value of healthcare delivered to ED patients. The proposed project will transition E-triage to a scalable and commercially available platform under a business model that supports growth. The proposed project will yield a scaled and commercially available e-triage decision support platform that is currently being piloted in multiple emergency departments (EDs). E-triage deploys a novel combination of data-science methods and flexible information technology architecture that supports usability by diverse ED customers. The tool relies on advancements in machine learning methods, mechanisms to harness user feedback, and software technology that is flexible and interoperable with EHR systems. It must also securely transmit and store patient data and be computationally efficient to accommodate fast-paced ED environments. E-triage enables rapid data-driven prognostication of ED patients at presentation based on risk of critical events and severity of illness using common locally collected ED data. Compared to US triage practice standards, which relies heavily on provider subjective judgment, e-triage demonstrates improved identification of high- and low-risk patients based on evidence from retrospective and prospective evaluation. E-triage is disruptive in its design to support new ED operational models that separate service streams for acutely ill and non-urgent patients toward reducing the burden of ED crowding.