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eICU STUDY: A Proof of Concept

eICU STUDY: A Proof of Concept

Ajit Dhakal (Northside Medical Center, Youngstown, Ohio, USA), Binod Dhakal (Manipal, Medical College of Wisconsin, Milwaukee, WI, USA), Lakshmi Kant Pathak (Saint Francis Hospital, Evanston, IL, USA), Shadi Marina (University of Damascus, Syria), and Vimala Vijayaraghavan (Caribbean Medical University, Curacao)
Copyright: © 2014 |Pages: 5
DOI: 10.4018/ijudh.2014040101
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Abstract

The authors' objective is to study the effect of eICU (electronic ICU) as a supplementary remote intensive care program on improvement in clinical outcomes. The experiment is designed to determine the clinical usefulness before and after evaluation of the effects of eICU program. The setting is two adult ICU of a large 375 bed teaching community hospital in US. The study was undertaken by group of resident physicians after eICU was started to study the impact on few selected parameters in a teaching hospital. A total of 2537Patients admitted to ICU between June 2006 to June 2008 (n= 1310 before and n= 1227 after implementation of the eICU) The eICU Program used intensivists and other healthcare providers to give 24x7 supplemental monitoring and management from a remote location. Supporting software and computer based decision support tool were available. The outcome is to study and compare rate of falls, mortalities, incidence of code blues and length of stay between the two periods before and after the implementation of eICU. In the results no statistical difference was observed in the studied parameters thus showing contrary results to other previous studies. The incidence of code blue (39 vs. 54 with P value of 0.36), length of stay (3.0 vs. 3.1 P value 0.36), mortality rates (77 vs. 90 P value 0.28) and incidence of fall (0 vs. 1 P value 0.28) all show no improve outcomes before and after the implementation of eICU.
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Methods

Design

It was a prospective, controlled, study before and after eICU system implementation at a university hodpital.35 beds in 2 intensive care units were assigned. All patients admitted to the ICU from June 2006 to June 2008 (n = 1310 before and n=1227 after implementation of eICU) were included in the study.

When the patient was admitted to ICU he or she was first registered electronically into eICU system. His laboratory data, vitals and admitting diagnosis are then entered along with complete medical notes.

Interventions

e ICU system was introduced in June 2007 at this facility. Intensivists and specially trained nurses use early warning software, video monitoring, physician note and order-writing applications and a computer based decision support tool for monitoring and management of critical care patients 24 hours a day, seven days a week.

Outcomes

Primary clinical outcomes studied were incidence of code blues, falls, mortality rates and mean length of stay.

Statistical Analysis

Chi-square test is used for categorical parameter like mortality and fall by analyzing a 2x2 contingency table and computing the P value by Fischer exact test. Student t test is used for ICU mean length of stay and code blues.

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Results

A total of 2537 patients were admitted to ICU over a period of 2 years. Of these 1310 patients were without eICU monitoring and 1227 were monitored with eICU besides the in house monitoring by the physician and the nurses. Specific outcomes measured by patients with normal vs. eICU monitoring were; code blues 54 vs. 39(p=0.36), falls 1 vs.0 (p=0.28) and overall mortality 90 vs. 77(p=0.28). The median length of stay was 3.1 days in those without eICU monitoring and 3 days with eICU monitoring (p=0.36) (Table 1).

Table 1.
Results
Total no of admission in ICUTotal no of code blueIncidence rate of code blueTotal no of fallIncidence rate of fallMean length of stayTotal no of deathsMortality rate
12 month before eICU13103929.77003.00610277.86 per1000
12 month after eICU12275444.0010.813.15411190.47 per1000

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