Automation of a Paper-based Screening Tool for Early Sepsis Risk Detection in the Emergency Department. Issue 2 (March 2018)

• Record Type: Journal Article
• Authors: Lloyd, Julia K.; Ahrens, Erin A.; Clark, Donnie; Nuss, Kathryn E.
• Is Part Of: Pediatric quality & safety. Volume 3:Issue 2(2018)
• Publication Date: 2018-03
• Subjects: Sepsis -- Screening tool -- Emergency department; Pediatric nursing -- Periodicals; Pediatrics -- Periodicals; Patients -- Safety measures -- Periodicals; Children -- Hospital care -- Periodicals; 618.92
• DOI: 10.1097/pq9.0000000000000070
• Languages: English
• ISSNs: 2472-0054

Abstract

Abstract : Background: Presentation of sepsis is dependent on synthesis of varied clinical information, making identification of septic patients challenging. Common practice is to identify sepsis through a manual screening tool, which may miss opportunities for early sepsis detection. Electronic screening efforts requiring additional documentation by providers may not integrate easily into provider workflow. Objectives: To develop an automated sepsis risk screening tool in the electronic health record that would accurately identify patients at risk for sepsis without requiring additional documentation. Methods: Criteria in the manual screening tool were mapped to standard documentation routinely entered in the electronic health record (Epic Systems, Corp.). Data elements were scored electronically at arrival and every 15 minutes during their encounter from the medical history, medication record, vital signs, and physical assessment (Fig. 1 ). Scores that exceeded a predefined sepsis risk threshold triggered a Best Practice Advisory, which alerted bedside staff to perform sepsis huddles and consider appropriate interventions. Statistical comparison of the automated tool to the manual process was completed by two-tail paired t test. Results: In an 8-week testing period, the automated sepsis risk screening tool identified 100% of patients flagged by the manual process (N = 29) (Table 1 ). The electronic tool identified sepsis patients, on average, 68 minutes earlier. This was statistically significant ( P < 0.001). Conclusions/Implications: The automated sepsis risk screening tool is as accurate as a validated manual process and alerted bedside clinicians earlier. Deployment has potential to improve timely sepsis detection and management of patients without requiring additional documentation by provider.