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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4601128 | PMC |
| http://dx.doi.org/10.1177/2054270415609004 | DOI Listing |
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