@article{2999,
  keywords = {cloud-based computerized clinical decision support (CDS) system, early recognition and detection of sepsis, electronic health record (EHR), patient safety and prevention},
  author = {Robert C. Amland and Kristin E. Hahn-Cover},
  title = {Clinical decision support for early recognition of sepsis},
  abstract = {<p>Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient's infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours. </p>},
  year = {2016},
  journal = { Am J Med Qual},
  volume = {31},
  pages = {103-10},
  month = {12/2016},
  issn = {1555-824X},
  doi = {10.1177/1062860614557636},
  language = {eng},
}