A Multidisciplinary Sepsis Program Enabled by a Two-Stage Clinical Decision Support System: Factors That Influence Patient Outcomes.

Sepsis is an inflammatory response triggered by infection, with risk of in-hospital mortality fueled by disease progression. Early recognition and intervention by multidisciplinary sepsis programs may reverse the inflammatory response among at-risk patient populations, potentially improving outcomes. This retrospective study of a sepsis program enabled by a 2-stage sepsis Clinical Decision Support (CDS) system sought to evaluate the program's impact, identify early indicators that may influence outcomes, and uncover opportunities for quality improvement. Data encompassed 16 527 adult hospitalizations from 2014 and 2015. Of 2108 non-intensive care unit patients screened-in by sepsis CDS, 97% patients were stratified by 177 providers. Risk of adverse outcome improved 30% from baseline to year end, with gains materializing and stabilizing at month 7 after sepsis program go-live. Early indicators likely to influence outcomes include patient age, recent hospitalization, electrolyte abnormalities, hypovolemic shock, hypoxemia, patient location when sepsis CDS activated, and specific alert patterns.

A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study.

OBJECTIVE: To examine the diagnostic accuracy of a two-stage clinical decision support system for early recognition and stratification of patients with sepsis. DESIGN: Observational cohort study employing a two-stage sepsis clinical decision support to recognise and stratify patients with sepsis. The stage one component was comprised of a cloud-based clinical decision support with 24/7 surveillance to detect patients at risk of sepsis. The cloud-based clinical decision support delivered notifications to the patients' designated nurse, who then electronically contacted a provider. The second stage component comprised a sepsis screening and stratification form integrated into the patient electronic health record, essentially an evidence-based decision aid, used by providers to assess patients at bedside. SETTING: Urban, 284 acute bed community hospital in the USA; 16,000 hospitalisations annually. PARTICIPANTS: Data on 2620 adult patients were collected retrospectively in 2014 after the clinical decision support was implemented. MAIN OUTCOME MEASURE: 'Suspected infection' was the established gold standard to assess clinical decision support clinimetric performance. RESULTS: A sepsis alert activated on 417 (16%) of 2620 adult patients hospitalised. Applying 'suspected infection' as standard, the patient population characteristics showed 72% sensitivity and 73% positive predictive value. A postalert screening conducted by providers at bedside of 417 patients achieved 81% sensitivity and 94% positive predictive value. Providers documented against 89% patients with an alert activated by clinical decision support and completed 75% of bedside screening and stratification of patients with sepsis within one hour from notification. CONCLUSION: A clinical decision support binary alarm system with cross-checking functionality improves early recognition and facilitates stratification of patients with sepsis.