Doi 10.7717/peerj.343 published 2014 04 10 accepted 2014 03 25 received 2013 10 31 academic editor jonathan eisen subject areas emergency and critical care. Licence this is an open access article distributed under the terms of the creative commons attribution license. Which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Cite this article nguyen sq, mwakalindile e, booth js, hogan v, morgan j, prickett ct, donnelly jp, wang he. 2014 automated electronic medical record sepsis detection in the emergency department.

E343 background. while often first treated in the emergency department ed , identification of sepsis is difficult. Electronic medical record emr clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an emr based, automated sepsis identification system. methods. we tested an emr based sepsis identification tool at a major academic, urban ed with 64,0 annual visits.

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The emr system collected vital sign and laboratory test information on all ed patients, triggering a sepsis alert for those with ≥2 sirs systemic inflammatory response syndrome criteria fever, tachycardia, tachypnea, leukocytosis plus ≥1 major organ dysfunction sbp ≤ 90 mm hg, lactic acid ≥2.0 mg/dl. We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ed cases that did not trigger a sepsis alert. results. from january 1 through march 31, 2012, there were 795 automated sepsis alerts.

The true prevalence of sepsis was 355/795 44.7% among alerts and 0/300 0% among non alerts. The positive predictive value of the sepsis alert was 44.7% 95% ci 41.2–48.2% . Pneumonia and respiratory infections 38% and urinary tract infection 32.7% were the most common infections among the 355 patients with true sepsis true positives. Among false positive sepsis alerts, the most common medical conditions were gastrointestinal 26.1% , traumatic 25.7% , and cardiovascular 20.0% conditions. Rates of hospital admission were: true positive sepsis alert 91.0%, false positive alert 83.0%, no sepsis alert 5.7%.

conclusions. this ed emr based automated sepsis identification system was able to detect cases with sepsis. Automated emr based detection may provide a viable strategy for identifying sepsis in the ed. Sepsis is the syndrome of microbial infection complicated by systematic inflammation which may subsequently lead to organ dysfunction, shock, and death levy et al. Sepsis is a major public health problem, accounting for more than 750,0 hospital admissions, 500,0 emergency department ed visits and 200,0 deaths annually angus et al. Early aggressive therapy is essential for optimizing outcomes from sepsis rivers et al. In recent years, physicians have increasingly utilized electronic medical records emr systems to aid clinical decision making levy amp heyes, 2012 .

By collecting and organizing clinical data, emr systems have strong potential to improve the detection of conditions where symptoms or laboratory findings are difficult to discern. Diagnosis of sepsis is difficult because clinicians may not recognize the constellation of clinical, physiologic and laboratory abnormalities that comprise the syndrome. Several efforts have attempted to use emr systems for sepsis detection, albeit with marginal results jaimes et al. A prominent limitation of these prior efforts was the absence of data for hypotension or lactic acidosis, which are often prominent features of sepsis and may indicate the need for aggressive protocolized resuscitation rivers et al. In this study we sought to evaluate the accuracy of an automated emr sepsis detection system in the ed. We conducted a retrospective analysis of automated clinical data collected by an ed emr system. The study was approved via a written application by the institutional review board of the university of alabama at birmingham approval x120409014.

This study utilized ed data from the university of alabama at birmingham uab hospital, an urban academic tertiary care referral medical center in birmingham, alabama, united states. The ed treats over 64,0 patients annually and is the only level i trauma center in alabama. While the ed does not restrict the age of treated patients, the ed population is predominantly gt 99% adult. Uab hospital has over 900 inpatient beds, including more than 180 critical care beds. The firstnet system collects comprehensive demographic and clinical information for all patients presenting receiving care in the ed, including patient demographics, location and status in the ed, care time points, laboratory and other test results, nursing and physician documentation, and patient education. Access to the master database is facilitated using a proprietary database language cerner command language patterned after structured query language sql.