Dataset Information

Automated electronic medical record sepsis detection in the emergency department.

ABSTRACT: 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 64,000 annual visits. 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. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. 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.

SUBMITTER: Nguyen SQ

PROVIDER: S-EPMC3994640 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Automated electronic medical record sepsis detection in the emergency department.

Nguyen Su Q SQ Mwakalindile Edwin E Booth James S JS Hogan Vicki V Morgan Jordan J Prickett Charles T CT Donnelly John P JP Wang Henry E HE

PeerJ 20140410

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 64,000 annual visits. The EMR system collected vital sign and labora ...[more]

PMID: 24765577

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Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Project description:INTRODUCTION:Smoking remains a major public health issue and a leading cause of death and disability in the United States. The objective of this study was to determine the effect of a simple intervention on smoking guidance, based on the electronic medical record (EMR), including providing discharge instructions and/or cessation counseling to emergency department (ED) patients who smoke. METHODS:This was an interventional before-and-after study in an ED with 70000 visits per year. A pre-intervention and post-intervention chart review was performed on a random sample of ED visits occurring in 2014 and 2016, identifying smokers and the frequency with which smokers received discharge instructions and/or cessation counseling. In the fall of 2015, our EMR was programmed to deploy smoking cessation discharge instructions automatically. RESULTS:In all, 28.7% (172/600; 95% CI: 25.2-32.4%) reported current smoking in the pre-intervention ED population and 27.6% (166/600; 95% CI: 24.2-31.4%) reported smoking in the post-intervention population. Smoking cessation guidance was provided to a total of 3.5% of self-reported smokers in the pre-intervention group (6/172; 95% CI: 1.4-7.6%); 1.2% (2/172; 95% CI: 0.3-4.1%) were informed of smoking cessation resources as part of their printed ED discharge instructions and 2.3% (4/172; 95% CI: 0.9-5.8%) received smoking cessation counseling by the ED provider. There was a statistically significant increase in the proportion of patients receiving any smoking cessation guidance after the intervention. All patients (166/166; 95% CI: 97-100% in this period received ED discharge instructions and a list of smoking cessation resources and 3.6% of smokers (6/166; 95% CI: 1.7-7.7%) received smoking cessation counseling by the ED provider. CONCLUSIONS:Automated deployment of smoking cessation discharge instructions in the EMR improves smoking cessation discharge instructions, and also has a positive impact on improving rates of in-person counseling by ED providers.

Dataset Information

Automated electronic medical record sepsis detection in the emergency department.

Publications

Automated electronic medical record sepsis detection in the emergency department.

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