2018 Completed Research Project

Emergency Medical Services (EMS) and the California EMS Information System (CEMSIS) Working Paper

Principal Investigator
David R. Ragland
University of California, Berkeley
(David Ragland passed away in May 2023; read more about him here)

Co-Investigator
Chris Cherry
University of Tennessee, Knoxville
View Bio

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Full Report

Project Slide Deck

Research Brief

Summary

The data shows that there are substantial differences in response, scene, and transport times between collisions that occur in urban and rural zip codes. However, there are several limitations influencing interpretation of the results.

Data on EMS response, scene, and transport times are missing for much of the state (see Fig A1- 3 in Appendix). Even for zip codes that have records of EMS events, the dataset is likely incomplete (i.e., not all EMS responses to motor vehicle collisions in a zip code are recorded). Therefore, the data recorded in CEMSIS may not be representative of EMS events throughout California.

Additionally, zip code level location data is insufficient for adequate study of the effects of the built environment and road network on prehospital time. The analyses that can be conducted using zip code level data are limited by missing data. Fields such as patient condition are not populated, thus analyses for such factors cannot be completed. For example, without data on patient condition, the effect of response time on health outcomes cannot be determined.

CEMSIS, like NEMSIS, is a convenience sample, in which data are submitted voluntarily by local EMS agencies. According to the NEMSIS User Manual, the dataset suffers from selection bias, which occurs when a perceptible difference between two groups is caused by different criteria being included. The dataset also suffers from information bias which is a difference between two groups that is due to differences in the data available for comparison (NEMSIS, n.d.). The problems that the national database experiences are likely also present in CEMSIS data.

Project Details

Project Type:	Research
Project Status:	Completed
Start Date:	3-1-2018
End Date:	1-31-2020
Contract Year:	Year 2
Total Funding from CSCRS:	$70,000
Collaborating Organizations:	University of Tennessee, Knoxville