July 28, 2021 by Philips
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A critical solution for critical care at Rush University System for Health

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Standardized patient monitoring facilitates the quadruple aim


Critical care units account for a disproportionate share of inpatient charges and patient errors.1 That makes effective monitoring of patients critically important—not only to alert clinicians when a patient’s condition deteriorates, but also to acquire, organize and present physiological data to support good care decisions


But safely monitoring patients across numerous units and facilities is a daunting task, especially when units rely on a variety of point solutions with different interfaces that don’t exchange data. That’s why standardizing with a single advanced monitoring system is so crucial—especially when units rely on a variety of solutions that don’t work together in unison.


This paper includes examples from the Rush University System for Health in the Chicago area. In 2017, Rush partnered with Philips to standardize its patient monitoring system across its large campus, which included over 700 beds and seven critical care units in three hospitals. Taking just 18 months, the project was completed on time and on budget, helping Rush to enhance care, improve staff work lives, simplify operations and reduce costs.

In this whitepaper


  • Boosting health outcomes—delivering virtually gap-free patient data with anywhere, anytime access to enable rapid, informed care decisions.
  • Improving providers’ work lives—reducing stress and increasing the time spent on patient care.
  • Enriching patient and family experiences—creating a quieter environment and improving health outcomes.2
  • Improving operational and financial performance— increasing productivity and patient throughput, cutting costs and saving administrative time3

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  1. www.hcup-us.ahrq.gov/reports/statbriefs/sb185-Hospital-Intensive-Care-Units-2011.pdf 
  2. www.ncbi.nlm.nih.gov/pmc/articles/PMC6069923/
  3.  Based on results from Hirslanden Klinik Aarau, Switzerland, a Philips customer. Results may not be predictive of results for other customers. 

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