Activation of a Medical Emergency Team Using an Electronic Medical Recording–Based Screening System*

Huh, Jin Won; Lim, Chae‐Man; Koh, Younsuck; Lee, Ju-Ry; Jung, Youn-Kyung; Seo, H; Hong, Sang‐Bum

doi:10.1097/ccm.0000000000000031

Cited by 56 publications

(52 citation statements)

References 27 publications

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“…Huh and colleagues demonstrated a reduction in ICU transfers using a single parameter activation criteria system (18). And Bellomo et al demonstrated a reduction in mortality in a multicenter heterogeneous intervention (19).…”

Section: Discussionmentioning

confidence: 99%

Real-Time Risk Prediction on the Wards: A Feasibility Study

Kang

Churpek

Zadravecz

et al. 2016

Critical Care Medicine

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Objective Failure to detect clinical deterioration in the hospital is common and associated with poor patient outcomes and increased healthcare costs. Our objective was to evaluate the feasibility and accuracy of real-time risk-stratification using the electronic Cardiac Arrest Risk Triage score version 1 (eCART), an electronic health record based early warning score. Design We conducted a prospective black-box validation study. Data were transmitted via HL7 feed in real-time to an integration engine and database server wherein the scores were calculated and stored without visualization for clinical providers. The high-risk threshold was set a priori. Timing and sensitivity of eCART activation were compared to standard of care Rapid Response Team (RRT) activation for patients who experienced a ward cardiac arrest or intensive care unit (ICU) transfer. Setting Three general care wards at an academic medical center Patients 3,889 adult inpatients Measurements and Main Results The system generated 5,925 segments during 5,751 admissions. The area under the receiver operating characteristic curve for eCART was 0.88 for cardiac arrest and 0.80 for ICU transfer, consistent with previously published derivation results. During the study period, 8/10 patients with a cardiac arrest had high-risk eCART scores, while the RRT was activated on 2 of these patients (p<0.05). Further, eCART identified 52% (n=201) of the ICU transfers compared to 34% (n=129) by the current system (p<0.001). Patients met the high-risk eCART threshold a median of 30 hours prior to cardiac arrest or ICU transfer versus 1.7 hours for standard RRT activation. Conclusions eCART identified significantly more cardiac arrests and ICU transfers than standard RRT activation and did so many hours in advance.

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Section: Discussionmentioning

confidence: 99%

Real-Time Risk Prediction on the Wards: A Feasibility Study

Kang

Churpek

Zadravecz

et al. 2016

Critical Care Medicine

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“…One of the unique aspects of some EHRs is the ability to capture dense longitudinal information on patients. These data may consist of, for example, respiratory measurements from an intensive care unit 1 or heart rate measurements from a surgical procedure 2 . Such data present the opportunity to observe and analyze a patient’s changing vital signs over time.…”

Section: Introductionmentioning

confidence: 99%

Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment

Goldstein

Chang

Winkelmayer

2015

Journal of Biomedical Informatics

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Electronic Health Records (EHRs) present the opportunity to observe serial measurements on patients. While potentially informative, analyzing these data can be challenging. In this work we present a means to classify individuals based on a series of measurements collected by an EHR. Using patients undergoing hemodialysis, we categorized people based on their intradialytic blood pressure. Our primary criteria were that the classifications were time dependent and independent of other subjects. We fit a curve of intradialytic blood pressure using regression splines and then calculated first and second derivatives to come up with four mutually exclusive classifications at different time points. We show that these classifications relate to near term risk of cardiac events and are moderately stable over a succeeding two-week period. This work has general application for analyzing dense EHR data.

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“…There is some debate whether plasma cTn is an independent prognostic marker in sepsis or simply a surrogate for overall severity of illness (4,6). cTn abnormalities are also common in the postoperative setting (14), but some clinicians have argued that routine testing may simply increase the risk of harm from inappropriate treatment for myocardial infarction (15). However, the work by Landesberg et al suggests that there may be value in investigating the cause of cTn abnormalities in sepsis using echocardiography to identify and assess potential targets for therapy.…”

mentioning

confidence: 97%

“…In this issue of Critical Care Medicine, Huh et al (14) attempted to overcome afferent limb RRS failure through modifying human computer interaction, using EHR as a screening tool linked to automated RRT activation. After predefined criteria were met, automatic activation of RRT through EHR screening (named electronic medical record…”

mentioning

confidence: 99%

Early Detection of Deteriorating Patients

Bassily‐Marcus¹

2014

Critical Care Medicine

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Activation of a Medical Emergency Team Using an Electronic Medical Recording–Based Screening System*

Cited by 56 publications

References 27 publications

Real-Time Risk Prediction on the Wards: A Feasibility Study

Real-Time Risk Prediction on the Wards: A Feasibility Study

Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment

Early Detection of Deteriorating Patients

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