scholarly journals Peer Review #2 of "Automated electronic medical record sepsis detection in the emergency department (v0.1)"

2014 ◽  
Author(s):  
J Gerald
Keyword(s):  
Emergency Department ◽  
Peer Review ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Sepsis Detection

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 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 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 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

scholarly journals An intervention based on the Electronic Medical Record to improve smoking cessation guidance in an urban tertiary care center emergency department

2019 ◽  
Vol 5 (May) ◽  
Author(s):  
Michael Phelan ◽  
Balaji Nithianandam ◽  
Nathan Eikoff ◽  
Daniel Good ◽  
Fredric Hustey ◽  
...  
Keyword(s):  
Emergency Department ◽  
Smoking Cessation ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Care Center ◽  
Tertiary Care ◽  
Tertiary Care Center
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