ASSESSING THE POTENTIAL VALUE OF AUTONOMOUS VEHICLES IN EMERGENCY MEDICAL SERVICES USING THE KNOWLEDGE VALUE ADDED METHODOLOGY
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Authors
Hillhouse, Joseph S.
Subjects
emergency medical service
EMS
advanced life support
ALS
basic life support
BLS
emergency medical technician
EMT
paramedic
fire service
multi-role responder
single role-responder
staffing levels
staffing shortages
National Fire Protection Association
NFPA
driver operator
emergency vehicle operations
knowledge value added
KVA
Lean Six Sigma
balanced scorecard
autonomous vehicles
hardware
software
EMS
advanced life support
ALS
basic life support
BLS
emergency medical technician
EMT
paramedic
fire service
multi-role responder
single role-responder
staffing levels
staffing shortages
National Fire Protection Association
NFPA
driver operator
emergency vehicle operations
knowledge value added
KVA
Lean Six Sigma
balanced scorecard
autonomous vehicles
hardware
software
Advisors
Housel, Thomas J.
Bergin, Richard D., IV
Date of Issue
2019-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Directors and fire chiefs throughout the emergency services are facing staffing shortages as emergency medical technicians and paramedics migrate to higher-paying, less-hazardous jobs in the medical field or emergency management environment. These shortages are compounded by a continually increasing service demand. This research compares the current “As Is” model in the multi-tiered, fire-based, advanced-life-support emergency medical system with the “To Be” model, which incorporates autonomous vehicle technologies. The two models were assessed using a knowledge value added (KVA) methodology to determine whether autonomous technology would increase productivity and add value by decreasing unit workload and increasing system capacity. The “As Is” model showed a return on knowledge (ROK) across all medical-based subprocesses but an inverse relationship between ROK and subprocess time, meaning that ROK drops when responders perform non-medical tasks and worsens the longer a subprocess takes. Moreover, driving is a poor use of the employee’s overall knowledge as ROK for driver subprocesses was as low as 38 percent during long transport times. The “To Be” model showed superior ROK across all variations of driver and most medical subprocesses, and all driver subprocesses showed exponential increases in ROK. This thesis finds that increased transport times and call volumes increase ROK in the “To Be” model, indicating a quantifiable value-add from autonomous technology.
Type
Thesis
Description
Series/Report No
Department
National Security Affairs (NSA)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.