Paper
Saturday, 22 July 2006
This presentation is part of : Acute Care Updates and Strategies
Improving Quality and Operational Effectiveness Using CQI Data
Joseph W. Dunn, RN, MSN, MPA and Debra R. Bamber, RNC, MSN. Children's Hospital Critical Care Transport Program, University of California, Davis Health System, Sacramento, CA, USA
Learning Objective #1: Recognize the value of utilizing data in improving clinical quality.
Learning Objective #2: Articulate the efficacy of data analysis to improve operational effectiveness.
Background: UC Davis Health System (UCDHS) is a large tertiary health system serving Northern California.  The UC Davis Children’s Hospital is part of the UCDHS.  The Children’s Hospital Critical Care Transport Program (CCTT) was established in October of 2004 to provide highly specialized interfacility transport of critically ill and injured neonates, infants and children.   

Purpose/Objective:  A collaborative multidisciplinary continuous quality improvement (CQI) program was implemented to obtain quantitative data to ensure evidence based clinical quality and to assist in decision making to facilitate operational effectiveness.  Initial areas of concern for quality were clinical expertise and effectiveness in stabilization of patient by CCTT registered nurses and response time of CCTT from activation of team to departure. 

Setting/Sample:  Large academic medical center.  The CCTT utilized ground ambulances, fixed wing air ambulance and helicopter air ambulance for the interfacility transport of neonates, infants and children.  CQI data was collected from October 2004 –October 2005 on all CCTT transports.  This resulted in a sample size of 654. 

Methods:  CQI review of 100% of transports was completed by CCTT.  Review include measuring response times, stabilization times, standardized procedures performed, standing orders initiated, significant changes in patients condition, and significant equipment or operational issues. 

Results:  The CQI process has driven our continuing education process and resulted in high percentage of success in management of patients on transport.  Based on departure time data the ground ambulance vendor was changed to decrease response time.  We currently are working on a contract for a dedicated ground ambulance located at UCDHS.  Data on increased fixed wing response time led to contract for fixed wing services with vendor.  Data on transport volumes had led to a shift in scheduling for transport RNs. 
Conclusion:  Data driven decision-making is critical in provision of quality clinical care and sound operational decision making.

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