2019 John M. Eisenberg Patient Safety and Quality Awards: SPOTting Sepsis to Save Lives: A Nationwide Computer Algorithm for Early Detection of Sepsis: Innovation in Patient Safety and Quality at the National Level (Eisenberg Award)

Background: In recognition of the potential of data to drive care and the need for early identification of patients with sepsis, HCA Healthcare developed an automated sepsis detection algorithm-SPOT (Sepsis Prediction and Optimization of Therapy). The algorithm was deployed at scale and served as a mechanism to reduce the time to detection and improve sepsis mortality in 173 hospitals across the United States.

Methods: The SPOT algorithm was designed as a rules-based detection of defined criteria that would interpret available electronic and basic laboratory data in near real time. Working from an organizational recognition of the need to construct a national clinical data warehouse to allow for the aggregation and analysis of data streams, HCA Healthcare designed and deployed SPOT and delivered the alert from the algorithm to the bedside to initiate existing clinical workflows for patients with sepsis.

Results: SPOT improved the timeliness of sepsis detection by providing alerts when signals of sepsis become available, triggering initiation of sepsis screens. This gave an advantage of about six hours over the legacy practice of sepsis screening at shift change. When deployed alongside existing sepsis improvement initiatives, SPOT was associated with an acceleration of improvement in mortality-particularly in the not-present-on-admission (NPOA) septic shock population, the patients at greatest risk for mortality. This population had seen little improvement with prior initiatives, but mortality improved 3.9 percentage points from 2018 to 2019. When accounting for seasonal variation, there was a decline in mortality rate following the deployment of SPOT, as compared to the year prior, of 9.9% for NPOA severe sepsis and 5.1% for NPOA septic shock.

Conclusion: Development of the SPOT algorithm for the detection of sepsis from data available in the electronic health record resulted in more timely recognition, faster initiation of treatment, and improved survival for patients.