Trauma-prehospital data matching for nasemso project Utah's method

1. TRAUMA-PREHOSPITAL DATA LINKAGE
FOR NASEMSO PROJECT
UTAH’S METHOD
Dec 3, 2018
Trauma Analyst Network
YukikoYoneoka, Data Analyst
Bureau of EMS and Preparedness
Utah Department of Health
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2. FIRST, DISCLAIMER
• This is a method Utah chose to use, due to having
not-perfect data, limited personnel, and restricted
time.
• We do not claim this to be the best method (or
the worst).
• This is just an example of what you can do when
the situation is not ideal.
• Call it “guerrilla” linkage tactics.
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3. LIMITATIONS WE HAD IN MATCHING
TRAUMA DATA TO PREHOSPITAL DATA
• UtahTrauma Registry currently do not collect patient names. 
• Trauma and Prehospital data has to be matched by:
1) Patient to patient
and also
2) Case to case (need to link a particular incident occurred on a
particular date for the particular patient betweenTrauma and
Prehospital data. A patient could have multiple incidents of different
causes per certain period of time.)
• Ad hoc data linking options are limited.
1) Many data linkage software require patient names.
2) Our own Utah Department of Health Master Patient Index linkage
process is also “set up” to use patient names. (It only matches patient
to patient but not case to case)
• Limited staff. (i.e. Just me…)
• This had to be done in limited time. 3

4. A rock and a hard place….
Source: cheezburger.com
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5. IS IT EVEN POSSIBLE TO LINK THE TWO
DATA WITHOUT PATIENT NAMES?
Available variables to link betweenTrauma and Prehospital data:
 Incident date/time (Often recorded differently between the
two data)
 Patients’ date of birth (DOB) *
 Patients’ sex *
 Patients’ social security number (SSN – there but sparse)
 Hospital admission date/time *
 Hospital name *
* Most reliably recorded
Main concern: False positive match
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6. THE BIG BASIC QUESTION
Considering what we have as matching variables (and
the ones reliably recorded most of the time),
What are the odds of two Trauma patients with the
same sex and the same day, month, and year of DOB
(DD/MM/YYYY) arriving at the same hospital on the
same date?
Note: It depends on the patient volume at the hospital.The
more the patients, the greater the odds.
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7. THE BIRTHDAY PARADOX
“In probability theory, the birthday paradox concerns
the probability that, in a set of n randomly chosen people,
some pair of them will have the same birthday…. .the
probability reaches 100% when the number of people
reaches 367 (since there are only 366 possible birthdays,
including February 29). However, 99.9% probability is
reached with just 70 people, and 50% probability with 23
people.These conclusions are based on the assumption that
each day of the year (excluding February 29) is equally
probable for a birthday.”
Source: https://en.wikipedia.org/wiki/Birthday_problem
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8. THE BIRTHDAY PARADOX (CONT.)
The resulting calculation ends
up exponential:
With 23 people we have 253 pairs to compare:
The chance of 2 people having different
birthdays is:
Source: https://betterexplained.com/articles/understanding-the-birthday-paradox/
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9. TESTING THE THEORY TO GET A ROUGH IDEA:
BIRTHDAY PARADOX APPLIED TO ONE OF THE
HIGHER-VOLUME LEVEL I TRAUMA CENTERS
IN UTAH
Birthday paradox only consider month and day of DOB matching.
What happens when we consider the probability of YYYY of DOB and sex also
matching?
• There are 2 sexes
• There are 365 days in a year (most of the time…)
• Trauma data year range that can be matched to Prehospital data is 10 years
(2007-2016)
• Patients’ age-in-years during the 10 year period ranged from 0 to 102. (say,
patients were born in 102 different years)
• Items (possible combinations of all variables) to consider: 2 (sexes) x
365(MM/DD) x 102 (YYYY) = 74,460
• This level I hospital had 16,597Trauma patients between 2007 and 2016
(min. /day 1, max. /day 19, aver./day 4.6, SD 2.36) – Say, about 5 patients a day.
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10. PLUGGED THE NUMBERS INTO THIS
CALCULATOR
Source: https://betterexplained.com/articles/understanding-the-birthday-paradox/
Original birthday paradox example
with 23 people
Modified example forTrauma patients
at a Level I Trauma center
Compared to that, the chances of finding two patients with
the same MM/DD/YYYY and gender at the same hospital in
any given day actually seemed very slim, even at the higher-
volumeTrauma center.
If you have 23 people in a room in any given time,
there is a 50% chance that two of them have
the same MM/DD of DOB.
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11. YOU MIGHT SAY, “YOU DON’T GET JUST 5
PEOPLE EVERY DAY. AGES WILL NOT BE
DISTRIBUTED EVENLY EITHER”
• You are right….This level ITrauma center’s patient volume varied 1 to 19 per
day and age distribution of Trauma patients actually looked like below in the
past 10 years.
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12. LET’S PLUG IN THE MAXIMUM NUMBER OF
PATIENTS THE HOSPITAL HAD PER DAY AND
LIMIT THE AGE RANGE TO 60
2 (sexes) x 365 (month/day) x 60 (years) = 43800, Max. number of patients 19
Odds are still less than 1%.....
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13. BUT WAIT…IN OUR CASE, WE ARE NOT
ACTUALLY TALKING ABOUT TWO PATIENTS
HAVING THE SAME - “ANY” - BIRTH DATE
• We are talking about a particular birthdate and gender that we
already know theTrauma patient has. E.g. Say, this male Trauma
patient has DOB 11/16/1954.
• What are the odds of one other male trauma patient withTHE
birthday among the N patients the hospital had on the day.
• In this example 0.01%.... 13

14. SO IT BEGAN……
PREPARING THE TWO DATA FOR MATCHING
• Excluded Trauma patients who arrived the hospital via privately
operated vehicle (POV)
• Truncated time and standardized date format (e.g.
MM/DD/YYYY for DOB, incident date, and hospital arrival date)
• Standardized sex (e.g. female, Female, f to F; male, Male, m to M,
unknown or blank to U)
• Cleaned and standardized hospital names by taking out symbols
(!@#$%%&*”’-) and making them all upper case (e.g. PCMC,
primary children MC, pcm, Primary Children’s to PRIMARY
CHILDRENS MEDICAL CENTER)
• Cleaned and standardizes SSN by taking out symbols and making
it 9-digit numbers then excluded numbers like 000000000,
111111111, 222222222….999999999. 14

15. RECORD MATCHING ALGORITHM
ALL EXACT MATCHING, DONE
BY SQL QUERY
• DOB + sex + SSN + hospital arrival date + hospital name
• DOB + sex + hospital arrival date + hospital name
• DOB + SSN + hospital arrival date + hospital name
Trauma data Prehospital data
DOB DOB
sex sex
SSN SSN
hospital arrival date hospital arrival date
hospital name destination name (hospital name)
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16. RESULTS
• We had 115,372Trauma patients in Utah Trauma
Registry from 2007 to 2016.
• Of those 31,355 (27%) used POV.
• That left us 84, 017Trauma patients to find a
match from Prehospital data.
• Of those, 59,941(71%) actually found a match.
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17. VALIDATION!
COMPARED 500 RANDOMLY SELECTED PAIRS
• Out of the 500 randomly selected pairs, 5 false matches were found (probably due to typo
in matching variables)
• Below is a segment of the validation spreadsheet (Blue is Trauma data, Green is
Prehospital). Narratives and other attributes (e.g. complaints, primary impression, causes
of incidents) were compared to see if the context of incidents matched. If they did, they
were highlighted in red.
• Concluded the linkage was 99% accurate for this sample. 17

18. TAKEAWAYS
• You can linkTrauma data to Prehospital data
without having patient names.
• Although only 71% of Trauma data was linked with
pre-hospital data, the accuracy of match was good
(99% in the randomly selected 500 pairs).
• We decided to do this Trauma-Prehospital data
linking routinely in our system, while requesting
patient names to be added to Trauma data.
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19. THANK YOU FOR YOUR TIME!
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