2019 NASEMSO Annual Meeting

1. Using linked Trauma and Prehospital Data
to Improve Data Quality and Analysis Results
Yukiko Yoneoka, MS
Data Analyst
Bureau of EMS and Preparedness
Utah Department of Health
2019 NASEMSO Annual Meeting
Mini Presentation
May 16, 2019

2. This is Our Story of Digging
• This is about our
experience
when seeing is
not believing,
and you have to
dig deeper into
the data to get
to something
more believable.
• By digging deeper, you increase the validity of
what you want to prove and improve your
understanding of the subject matter.

3. It Started with PIPS Workgroup and Audit Filters
Mission of State Trauma Performance Improvement and Patient Safety (PIPS)
• Promote optimal care for trauma patients by matching the injured patient’s needs
to existing resources.
• Alleviate unnecessary death and disability resulting from trauma.
• Inform health care providers about trauma system capabilities.
• Encourage the efficient and effective continuum of patient care.
• Minimize the overall cost of trauma care.
Audit filters
Nine audit filters were created by the PIPS workgroup in 2016, in order to monitor key
issues in trauma care.
Audit filter #2 (Special interest)
Trauma patients with Injury Severity Score (ISS) >15 and penetrating trauma with scene
time >=10 minutes.
Goal
To examine trauma patients’ outcome (Alive or Dead) based on the prehospital scene
time.
Data
Utah Trauma Registry (UTR data).

4. Preliminary Results for AF #2 using UTR data
1. 2011-2015 Trauma patients (ISS>15)
with all transport methods
Scene time <=10 minutes had greater
survival of patients and it was statistically
significant. Chi-square P value was < .0001.
2. Same data with ground transportation only
We separated air from ground ambulance,
because:
1) It happens only about 20% of the time
2) Higher % of patients with ISS>15
3) Generally takes longer at scene
4) Almost always preceded by ground
ambulance (total scene time is unknown)
Chis-square P value was <.24. No statistical
significance.
3,467
316
3,625
363
We suspected there was something
funny going on with scene time
data elements in UTR data…
5,414
428
5,821
701

5. Decision to use linked
trauma-prehospital data
Advantage of using linked data: They are comparable and complemental
• Trauma data and prehospital data both have scene time, which we can compare.
• Prehospital data don’t, but Trauma data have ISS and patient outcome (Alive or
Dead).
About our linked data (2007-2016 UTR and prehospital data)
• UTR data do not contain patient names.
• We determined the feasibility of matching trauma patients without names by
calculating the odds of two patients with the same date of birth (dd/mm/yyyy
exactly the same) and same gender arriving at the same trauma center in any
given day by using Birthday Paradox probability theory.
(Source: https://betterexplained.com/articles/understanding-the-birthday-paradox/)
• Odds of having two trauma patients with exactly the same date of birth and
gender in a highest volume trauma center in any given day turned out to be
0.01%.
• Two data were linked by DOB, sex and/or SSN, hospital arrival date, and hospital
name.
• About 70% (59,941) of 2007-2016 UTR patients who were arrived at the hospital
via EMS (84,107) were linked to prehospital data.
• Randomly selected 500 linked pairs were examined and accuracy was determined
to be 99%.

6. Comparisons of Scene Time:
500 Randomly Selected Linked Pairs
We randomly selected 500 linked pairs of UTR and prehospital records and
compared unit arrived at scene time and unit departure from scene time.
Only about half of unit arrived at scene time was captured accurately (from
prehospital-data) in UTR data.
We decided to use original scene time from prehospital data.

7. Analysis with the Linked Data:
Data Selection Process
1. Selected 2007-2016 trauma-prehospital linked records
where patient disposition of prehospital record is “Treated,
transported by this EMS”. (N=586,49)
2. Selected records where trauma data transport mode is
ground (N=50,087). Calculated scene time for the patients
using prehospital data.
3. Excluded records with scene time <1% percentile and >99%
percentile. Then, selected records with ISS between 0 and
75 and with valid outcome values (Alive or Dead)
(N= 48,581).

8. Analysis Results with the Linked Data:
Scene Time Distribution
Scene time:
• Minimum - 1 min
• Maximum - 66 min
• Mean - 18.7 min
• Median - 17 min
• Mode - 15 min
• We realized, in reality, 10 minutes scene time actually happens
only about 17% of the time.
• Middle range is 15-20 minutes. Median is 17 minutes.

9. Analysis Results with the Linked Data:
ISS<=15 and ISS >15 Distribution by Scene Time
73% of severely
injured patients are
out of the scene
in 20 minutes.

10. Analysis Results with the Linked Data:
Distribution of ISS in 5 Smaller Groups
Minor ISS 0-8, Moderate ISS 9-15, Serious ISS 16 -24, Sever ISS 25-49, Critical ISS 50-75
47% of critically injured
patients are out of the scene
within 10 minutes,
81% in first 20 minutes.
Although scene times are not always 10 minutes or less, ground
ambulances seem to be practicing “scoop and run”.

11. Analysis Results with the Linked Data:
Distribution of Outcome for Patients with ISS>15
• The first two groups of shorter scene time have the higher
death rate than the rest, indicating the severity of injury of
the patients.
• The assumption of “the longer the scene time, the more the
death” does not seem to apply to ground ambulances in our
data.

12. What About Total Prehospital Time? Part 1
We used the same linked data and defined
total prehospital time as “Unit notified by dispatch to
unit arrived at hospital (with patient)”
Total Prehospital Time
• Minimum - 13 minutes
• Maximum - 206 minutes
• Mean - 48.3 minutes
• Median - 41 minutes
• Mode - 35 minutes

13. What About Total Prehospital Time? Part 2
Minor vs. Critical
Odds 91.3
95% CI: 61.08-136.4
P<.0001Minor vs. Sever
Odds 20.4
95% CI: 17.36-24.0
P<.0001
• Critically and severely injured
patients are more likely to die.
• But they are the ones with shorter
scene and total prehospital time.
• Our EMS people are gauging the
severity of injury and transporting
the patient to the hospital ASAP.

14. Lessons learned and Future Plans
Lessons:
• Dig deeper then you may find it.
• Our assumptions may not be always right.
• Linked data is gold.
Plans:
• Further discuss with PIPS and Trauma System Advisory
Committee (TSAC), if 10 minutes scene time is the correct
standard to hold EMS people to?
• Trauma registrar education in data entry, plus increase PCR
availability.
• Keep linking the data (Adding patient names to UTR data to
improve linkage has been approved by TSAC).
• Truly honor our EMS people’s work by keep analyzing the data
and making evidence based policy decisions.

15. Utah State Trauma Cases and Death Rates 2001-2017

16. THANK YOU for Your Time!
Special thanks to
• All Utah EMS agencies for saving countless lives everyday.
• Intermountain Injury Control and Research Center (IICRC) and trauma
registrars for the invaluable Utah Trauma Registry Data.
• State PIPS Workgroup for working to improve Utah’s trauma system.
• Trauma Analysts Network for the helpful comments and suggestions.
• Executive PIPS members: Dr. Peter Taillac, Jolene Whitney, Carl Avery,
Dr. Clay Mann, Lana Moser, Felicia Alvarez,
Patrice Secrist, and Iona Thraen
for working together to improve Utah’s
trauma system.
For questions, please contact: Yukiko Yoneoka, yyoneoka@utah.gov