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).
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...
| Published in: | Joint Commission Journal on Quality & Patient Safety Vol. 46; no. 7; pp. 381 - 392 |
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| Main Authors: | , , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Elsevier B.V.
Jul2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143824110&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143824110 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15537250 17W5 jtl: Joint Commission Journal on Quality & Patient Safety issn: 15537250 maglogo: N pubinfo: dt: Jul2020 vid: 46 iid: 7 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 143824110 143824110 143824110 10.1016/j.jcjq.2020.04.006 143824110 ppf: 381 ppct: 11 formats: tig: atl: 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). aug: au: Perlin, Jonathan B. Jackson, Edmund Hall, Cody Mindick, Adam Berberich, Garrick Korwek, Kimberly Poland, Russell E. Roach, Jonathon Novak, Angela Guy, Jeffrey S. sug: subj: Sepsis Diagnosis Algorithms Program Development Patient Safety Quality of Health Care Awards and Honors Sepsis Prevention and Control Early Diagnosis Human International Agencies Disease Management United States Mortality Prevention and Control Survival Professional Recognition Outcomes (Health Care) ab: 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. 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. 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. 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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