Session ID: 
226

Combat Infusion Pump Shortages With RTLS and Network Dynamics

10:00am - 11:00am Thursday, March 12
Orlando - Orange County Convention Center
W311E

Description

What do taxi cabs and IV pumps have in common? You can never find one when you need one. Strategies to manage healthcare assets are varied, yet caregiver frustrations over finding resources persist. This session elevates the equipment availability conundrum by showing how infusion pump shortages spread like infections, and how data scientists at The Johns Hopkins Hospital use real-time locating system (RTLS) data and artificial intelligence to identify and combat asset inefficiencies. Initial findings inspired this team to study which traits of care communities and behaviors are more likely to result in shortages, and where modified distribution methods can increase pump availability. Exploring the science behind the data, this innovative presentation shares how machine-logic has helped increase pump availability across multiple areas by looking for efficiency gains that keep mobile equipment within care communities that display successful patterns of connectivity and sharing.

Learning Objectives

  • Demonstrate how shortages of infusion pumps spread like infections across hospital units
  • Describe how a machine learning-based system can harness RTLS data to increase infusion pump availability in hospital units
  • Explain how infusion pump movement in social networks impacts behaviors and increases fleet availability in the hospital floor

Speaker(s)

Graduate Research Assistant,
Johns Hopkins

Continuing Education Credits

ABPM
1.00
CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00

Audience

Clinical Engineering Professional
CNIO/CNO
Management Engineer and Process Improvement Prof.

Level

Intermediate