Your browser doesn't support javascript.
loading
A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study.
Amland, Robert C; Lyons, Jason J; Greene, Tracy L; Haley, James M.
Afiliación
  • Amland RC; Population Health, Cerner Corporation, Kansas City, 64117 USA.
  • Lyons JJ; Pulmonary Division, Department of Medicine, Unity Hospital, Rochester, 14626 USA.
  • Greene TL; Business Intelligence and Long Term Care, Rochester Regional Health System; Rochester, 14626 USA.
  • Haley JM; Department of Medicine, Unity Hospital, Rochester, 14626 USA.
JRSM Open ; 6(10): 2054270415609004, 2015 Oct.
Article en En | MEDLINE | ID: mdl-26688744
ABSTRACT

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.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: JRSM Open Año: 2015 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: JRSM Open Año: 2015 Tipo del documento: Article