Effect of Automated Real-time Feedback on Early Sepsis Care

Sepsis is the leading cause of death among US hospitals, accounting for 6% of all hospitalizations and 35% of all inpatient deaths. International guidelines and the CMS SEP-1 bundle stress the importance of adhering to specific steps in the diagnosis and management of sepsis. This can be very difficult, especially in the setting of a busy ED, ward, or ICU where there are multiple simultaneous demands on providers' attention and time. Critical steps can be missed or delayed. The CMS SEP-1 bundle is a measure of compliance with sepsis care that is being tracked nationally across hospitals. Unfortunately, a recent study demonstrated that every hour of delay to the completion of a sepsis bundle, including antibiotic administration, was associated with a 4% increase in risk-adjusted hospital mortality. One strategy to improve the care and outcomes of patients with sepsis is the use of information technology to support our providers in a targeted manner. Technology has already been developed and deployed to help with the early identification of patients with sepsis using a Best Practice Alert (BPA), which has been in place at our hospital since 2017. This pop-up window alerts the team to the possibility of sepsis based on data within the medical record. However, once the alert is accepted or declined, the BPA does not offer ongoing support to clinicians, leaving the clinician to track and execute multiple time-based and inter-dependent sepsis bundle measures in a busy, hectic environment. To augment this existing tool, here we propose to study the efficacy of a novel technology called the Sepsis Care Tracking Platform (SCTP) to provide ongoing support at the bedside to providers, thus improving the care we deliver to patients. SCTP is a monitoring and notification platform that aims to increase the timely delivery of key elements of evidence-based sepsis care. This platform, which was built by clinicians for clinicians, leverages the electronic medical record (EMR) to