scholarly journals Automated electronic medical record sepsis detection in the Emergency Department

2014 ◽  
Author(s):  
Su Nguyen ◽  
Edwin Mwakalindile ◽  
James S Booth ◽  
Vicki Hogan ◽  
Jordan Morgan ◽  
...  
Keyword(s):  
Emergency Department ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Respiratory Infections ◽  
Clinical Decision ◽  
Identification System ◽  
Decision Tools ◽  
Novel Strategy ◽  
Sepsis Detection ◽  
Identification Tool

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 non-sepsis alert records. We evaluated the diagnostic accuracy of the sepsis identification tool. Results : From January 1 through March 31, 2012, we analyzed 795 automated sepsis alerts and 300 non-alerts. The true prevalence of sepsis was 293/795 (37%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value was 36.9% (41.7-49.6). Respiratory infections (36.5%) and urinary tract infection (35.5%) were the most common infections among the 293 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (22.9%), traumatic (22.3%), and cardiovascular (17.5%). Conclusion : This ED EMR-based automated sepsis identification system was able to detect sepsis patients. Automated EMR-based detection may provide a viable strategy for identifying sepsis.

scholarly journals Automated electronic medical record sepsis detection in the Emergency Department

2014 ◽  
Author(s):  
Su Nguyen ◽  
Edwin Mwakalindile ◽  
James S Booth ◽  
Vicki Hogan ◽  
Jordan Morgan ◽  
...  
Keyword(s):  
Emergency Department ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Respiratory Infections ◽  
Clinical Decision ◽  
Identification System ◽  
Decision Tools ◽  
Novel Strategy ◽  
Sepsis Detection ◽  
Identification Tool

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 non-sepsis alert records. We evaluated the diagnostic accuracy of the sepsis identification tool. Results : From January 1 through March 31, 2012, we analyzed 795 automated sepsis alerts and 300 non-alerts. The true prevalence of sepsis was 293/795 (37%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value was 36.9% (41.7-49.6). Respiratory infections (36.5%) and urinary tract infection (35.5%) were the most common infections among the 293 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (22.9%), traumatic (22.3%), and cardiovascular (17.5%). Conclusion : This ED EMR-based automated sepsis identification system was able to detect sepsis patients. Automated EMR-based detection may provide a viable strategy for identifying sepsis.

scholarly journals Automated electronic medical record sepsis detection in the emergency department

PeerJ ◽  
2014 ◽  
Vol 2 ◽  
pp. e343 ◽  
Author(s):  
Su Q. Nguyen ◽  
Edwin Mwakalindile ◽  
James S. Booth ◽  
Vicki Hogan ◽  
Jordan Morgan ◽  
...  
Keyword(s):  
Emergency Department ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Sepsis Detection

scholarly journals Electronic medical record phenotyping using the anchor and learn framework

2016 ◽  
Vol 23 (4) ◽  
pp. 731-740 ◽  
Author(s):  
Yoni Halpern ◽  
Steven Horng ◽  
Youngduck Choi ◽  
David Sontag
Keyword(s):  
Emergency Department ◽  
Septic Shock ◽  
Nursing Home ◽  
Decision Support ◽  
Real Time ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

ABSTRACT Background Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient’s electronic medical record phenotype will play an important role in triggering clinical decision support systems that can deliver personalized recommendations in real time. Learning with anchors presents a method of efficiently learning statistically driven phenotypes with minimal manual intervention. Materials and Methods We developed a phenotype library that uses both structured and unstructured data from the EMR to represent patients for real-time clinical decision support. Eight of the phenotypes were evaluated using retrospective EMR data on emergency department patients using a set of prospectively gathered gold standard labels. Results We built a phenotype library with 42 publicly available phenotype definitions. Using information from triage time, the phenotype classifiers have an area under the ROC curve (AUC) of infection 0.89, cancer 0.88, immunosuppressed 0.85, septic shock 0.93, nursing home 0.87, anticoagulated 0.83, cardiac etiology 0.89, and pneumonia 0.90. Using information available at the time of disposition from the emergency department, the AUC values are infection 0.91, cancer 0.95, immunosuppressed 0.90, septic shock 0.97, nursing home 0.91, anticoagulated 0.94, cardiac etiology 0.92, and pneumonia 0.97. Discussion The resulting phenotypes are interpretable and fast to build, and perform comparably to statistically learned phenotypes developed with 5000 manually labeled patients. Conclusion Learning with anchors is an attractive option for building a large public repository of phenotype definitions that can be used for a range of health IT applications, including real-time decision support.

scholarly journals Doctors documenting: an ethnographic and informatics approach to understanding attending physician documentation in the pediatric emergency department

2020 ◽  
Author(s):  
Sarah D Fouquet ◽  
Laura Fitzmaurice ◽  
Y Raymond Chan ◽  
Evan M Palmer
Keyword(s):  
Emergency Department ◽  
Mixed Methods ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Pediatric Emergency ◽  
Second Phase ◽  
Pediatric Emergency Department ◽  
Mixed Methods Approach ◽  
Time And Motion ◽  
Attending Physician

Abstract Objective The pediatric emergency department is a highly complex and evolving environment. Despite the fact that physicians spend a majority of their time on documentation, little research has examined the role of documentation in provider workflow. The aim of this study is to examine the task of attending physician documentation workflow using a mixed-methods approach including focused ethnography, informatics, and the Systems Engineering Initiative for Patient Safety (SEIPS) model as a theoretical framework. Materials and Methods In a 2-part study, we conducted a hierarchical task analysis of patient flow, followed by a survey of documenting ED providers. The second phase of the study included focused ethnographic observations of ED attendings which included measuring interruptions, time and motion, documentation locations, and qualitative field notes. This was followed by analysis of documentation data from the electronic medical record system. Results Overall attending physicians reported low ratings of documentation satisfaction; satisfaction after each shift was associated with busyness and resident completion. Documentation occurred primarily in the provider workrooms, however strategies such as bedside documentation, dictation, and multitasking with residents were observed. Residents interrupted attendings more often but also completed more documentation actions in the electronic medical record. Discussion Our findings demonstrate that complex work processes such as documentation, cannot be measured with 1 single data point or statistical analysis but rather a combination of data gathered from observations, surveys, comments, and thematic analyses. Conclusion Utilizing a sociotechnical systems framework and a mixed-methods approach, this study provides a holistic picture of documentation workflow. This approach provides a valuable foundation not only for researchers approaching complex healthcare systems but also for hospitals who are considering implementing large health information technology projects.

scholarly journals A Rational Evaluation of the Syncope Patient: Optimizing the Emergency Department Visit

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 514
Author(s):  
Tarek Hatoum ◽  
Robert S. Sheldon
Keyword(s):  
Emergency Department ◽  
Diagnostic Yield ◽  
Clinical Decision ◽  
Emergency Department Visits ◽  
Decision Tools ◽  
Short And Long Term ◽  
Electrocardiogram Ecg ◽  
Structured Approach

Syncope accounts for up to 2% of emergency department visits and results in the hospitalization of 12–86% of patients. There is often a low diagnostic yield, with up to 50% of hospitalized patients being discharged with no clear diagnosis. We will outline a structured approach to the syncope patient in the emergency department, highlighting the evidence supporting the role of clinical judgement and the initial electrocardiogram (ECG) in making the preliminary diagnosis and in safely identifying the patients at low risk of short- and long-term adverse events or admitting the patient if likely to benefit from urgent intervention. Clinical decision tools and additional testing may aid in further stratifying patients and may guide disposition. While hospital admission does not seem to offer additional mortality benefit, the efficient utilization of outpatient testing may provide similar diagnostic yield, preventing unnecessary hospitalizations.

scholarly journals Evaluation of Electronic Medical Record Downtime in a Busy Emergency Department

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Dominic Jenkins ◽  
Raheel Sharfeen Qureshi ◽  
Jibin Moinudheen ◽  
Sameer A. Pathan ◽  
Stephen H. Thomas
Keyword(s):  
Emergency Department ◽  
Medical Record ◽  
Electronic Medical Record
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