From REG 2015 Winter Summit

1. Using CPCSSN Data for Primary Care Research
in Canada
Alan Kaplan MD CCFP(EM) FCFP
Chairperson, Family Physician Airways Group of Canada
Chairperson, Communities of Practice, Respiratory Medicine,
College of Family Physicians of Canada

2. Outline
• Introduction to CPCSSN
• CPCSSN Data Holdings
• A Tour of CPCSSN Data Tables
• Respiratory medicine in CPCSSN
• Limitations in the
use of CPCSSN
for research
• Who to contact

3. 329 physicians in 8 provinces
using 10 EMRs
10 PC-PBRNs
•British Columbia
- BCPCReN (Wolf )
•Alberta
- SaPCReN, Calgary (Med Access, Wolf)
- AFRPN, Edmonton (Med Access)
•Manitoba
- MaPCReN, Winnipeg (Jonoke)
•Ontario
- DELPHI, London (Healthscreen, Optimed, OSCAR
- NorTReN, Toronto (Nightingale, xwave, Practice
Solutions)
- CSPC, Kingston (P&P, OSCAR, xwave)
•Quebec
- Q-Net, Montréal (Da Vinci, Purkinje)
•Nova Scotia / New Brunswick
- MarNet, Halifax (Nightingale, Purkinje)
•Newfoundland
- APBRN, St. John’s (Wolf , Nightingale)

4. Data Holdings Q2 2012
4

5. Data Cleaning/Recoding
• Currently clean and recode the following fields
• Billing, Encounter and Problem List Diagnoses (ICD9)
• Medications (ATC)
• Lab results (LOINC)
• Referrals (SNOMED CT)
• Physical signs (Wt, Ht, BP, unit conversion, calculate BMI)
• Vaccines (ATC)
• Risk factors (smoking, alcohol, diet --Text)
5

6. 6Patient Demographics
} < 5%
} < 5%
368,000 Records

7. Billing
7
6.8 Million Records
Dates of Encounter
Original diagnosis sent for
billing
Text from Code Recoded by
CPCSSN
Original Diagnosis Code sent for
billing
Recoded by CPCSSN

8. Research Discussion
• Useful for case finding
• Useful for understanding deficiencies of using
billing information for clinical research
• There is some inconsistency in use of billing codes
across the country
• CPCSSN attempts to recode all billing diagnosis
codes to a standard version
8

9. Encounters
9
5.1 Million Records
Dates of Encounter
Data inconsistent across the
Country
CPCSSN Cleaning Not Started
Active area of Cleaning
E.g., Office Visit, Phone, E-mail etc

10. Problem List Diagnoses
10
Original Diagnosis Written by User
E.g. DMT2
Recoded by CPCSSN
E.g., Diabetes Mellitus, Type 2
} Not well populated
1.8 Million Records
Active = Problem List
Inactive = Past Medical History

11. Problem List Diagnoses
11
List of cleaned up diagnoses
Chronic airway obstruction, not elsewhere classified (496)
Bronchitis, not specified as acute or chronic (490)
Chronic bronchitis (491)
Emphysema (492)
Diabetes mellitus (250)
Depressive disorder, not elsewhere classified (311)
Suicide and self-inflicted poisoning by solid or liquid
substances (E590)
Suicidal ideation (V62.84)
Adjustment reaction (309)
Post traumatic stress disorder (309.81)
Major depressive disorder, recurrent episode (296.3)
Bipolar I disorder, most recent episode (or current) (296.7)
Mental disorders complicating pregnancy, childbirth, or the
puerperium (648.4)
Essential hypertension (401)
Osteoarthrosis and allied disorders (715)
Spondylosis and allied disorders (721)
Total knee replacement (81.54)
Total hip replacement (81.51)
Polycystic ovarian syndrome (256.4)
Abnormal glucose tolerance of mother complicating
pregnancy childbirth or the puerperium (648.8)
Secondary diabetes mellitus (249)
MORE BEING ADDED SOON
Other abnormal glucose (790.29)
Migraine (346)
Heart failure (428)
Acute myocardial infarction (410)
Old myocardial infarction (412)
Other forms of chronic ischemic heart disease (414)
Cardiac dysrhythmias (427)
Essential and other specified forms of tremor (333.1)
Esophageal varices with bleeding (456.0)
Esophageal varices without bleeding (456.1)
Angina pectoris (413)
Other acute and subacute forms of ischemic heart disease
(411)
Calculus of kidney and ureter (592)
Portal hypertension (572.3)
Asthma (493)
Dementias (290)
Alzheimer's disease (331.0)
Dementia with lewy bodies (331.82)
Parkinson's disease (332)
Epilepsy and recurrent seizures (345)
Epileptic convulsions, fits, or seizures nos (345.9)

12. Research Discussion
• Sensitivity and specificity of problem list
diagnoses not currently known, so cannot
determine incidence and prevalence of
disease from problem list alone
12

13. Vital Signs
13
Name of exam (e.g., sBP)
Cleaned up result
(e.g, lbs -> kg, inch -> cm)
5 Million Records
Cleaned up unit of measure
(e.g., unit is kg, but result was lb)

14. Research Discussion
• Currently have access to
– sBP/dBP
– Ht
– Wt
– BMI
– Waist circumference
14

15. Allergies
15
Name of allergen
Cleaned up name
155K Records

16. Research Discussion
• Not yet cleaned, but will soon clean it
• Focus of cleaning will be on medication
allergies
– All other allergies will be retained as original text
• Useful when assessing why patients are not
receiving medications for a particular disease
16

17. Risk Factors
17
Name of Risk Factor (e.g., smoking)
Cleaned up version of Risk Factors.
588K Records
Working on cleaning up Current
Exposures & Cumulative Exposures

18. Laboratory Results
18
Original Lab Result Name
(e.g., Hb A1c, HGbA1c, etc)
Recoded by CPCSSN 100% LOINC
(e.g., HBA1C)
3 Million Records

19. Research Discussion
• Currently only capturing the following
• One site does not capture labs yet 19
HDL
TRIGLYCERIDES
LDL
TOTAL CHOLESTEROL
FASTING GLUCOSE
HBA1C
URINE ALBUMIN CREATININE RATIO
MICROALBUMIN
GLUCOSE TOLERANCE

20. Encounter Diagnoses
20
Original Diagnosis Recorded in Encounter
(e.g., axniety)
83% Recoded by CPCSSN
(Anxiety ICD-9 300)
6.3 Million Records
63% Originally coded by Doctor

21. Research Discussion
• Not all EMRs capture Encounter Diagnoses in
a structured manner
• This table is not ready for prime time across all
sites, but may be useful for projects where
data from just a few sites is acceptable
21

22. Medications
22
What the doctor ordered
E.g., HCTZ 25 mg bid
91% Recoded by CPCSSN
E.g., Hydrochlorthiazide
56% Coded as DIN
Strength 56%
Dose 70%
Unit of Measure 84%
Frequency 95%
Duration 52%
Dispensed 86%
72% Coded by doctor (DIN + other)
91% Coded by CPCSSN (ATC)
4.9 Million Records
}

23. Research Discussion
• Medication name data is relatively clean
• Medications coded as ATC
– Allows easy grouping by class
• Don’t have daily dose and months supply for
many records –working on clean up
23

24. Referrals
24
Original Text of Referral
80% Recoded by CPCSSN
SNOMED-CT
600 K Records

25. Procedures
25
Original Text of Procedure
Not Currently Coded by CPCSSN
1.3 Million Records
So…...NO spirometry!!!

26. Vaccines
26
What the doctor typed
93% Recoded by CPCSSN (ATC)
960 K Records
46% Coded by Doctor (DIN)

27. Limitations for Respiratory Disease
27
Currently studying 8 conditions:
five chronic and mental health
conditions
-hypertension, osteoarthritis,
diabetes, COPD and depression)
and three neurologic conditions
-Alzheimer’s and related dementias,
epilepsy and Parkinson’s disease.

30. Asthma –Text Diagnoses
• Asthma 1996 1st time in life
• Asthma 1999 ASTHMA 3 years
• Asthma age 10
• asthma and allergies
• asthma and bronchitis
• asthma attack postoperatively (493.)
• Asthma- class IV work related
• Asthma Condition
• asthma diabetes
• asthma diagnosed in 1999 and treated with rhinocort, ventolin and beclovent. off
all meds since a short course of treatment.
• ?asthma equivalent cough

31. Asthma –Challenges
• 2. ? Asthma as teenager
• Exercise induced asthma as child.
• Asthma since childhood
• Asthma age 10
• asthma Dx age 7-hospitalized few
times/gluten free diet then-but
came off after 1 yr-better during
teen yrs
• Asthma since age 17
• Asthma-age 15
• Bronchial asthma age 18.
Smoking D/C
• asthma, ? GERD
• Asthma since 2005
• asthma prior to 1980
• Asthma since teen
• Asthma?? Hx SOB 1997
• - asthma as a child;
• 1980: childhood asthma
• Asthma in childhood
• Asthma in childood
• Asthma since childhood
• Childhood Asthma
• asthma until age 13
• Asthme (MPOC), contr?l??????

32. Smoking
• Smoking data in EMRs is particularly challenging
• Most EMRs capture smoking data as text
• There are a lot of ways to say ‘the patient does not
smoke’
– Quit, ex-, non-, smoking =0, x-
– Makes separating ex-/non- from smokers difficult using an
algorithmic approach
• Dates are poorly captured
• Cumulative exposure is poorly captured

33. Smoking Variability
Site A Site B Site C
NON-SMOKER TOBACCO NON-SMOKER NON SMOKER
T TOBACCO NEVER SMOKER
EX-SMOKER TOBACCO EX SMOKER QUIT > 1 YEAR
SMOKER: QUITTING TOBACCO NON-SMOKER QUIT < 1 YEAR
SMOKER: NO PLAN TO QUIT TOBACCO SMOKER
SMOKER: ACTIVELY QUITING NEVER SMOKED
TOBACCO USE (305.1) TOBACCO NON SMOKER
SMOKER: ACTIVELY QUITTING
SMOKING
NON SMOKER
NICOTINE ADDICTION
NONSMOKER
EX SMOKER

34. Smokers vs Ex-Smokers
• Smoker: no plan to quit
• smoker: actively quiting
• SMOKER. TO STOP
• Trying to quit smoking
• smoker- 20 pyh, quit
1997
• SMOKER-NON
• smoking - quit 1997
• Stopped smoking
• ex-smoker-30 pck/year,
quit 2002
• Second hand smoke
• smoker- 30 pyh, trying
to quit

35. Improving Smoking Data
• Smoking data is uniformly poor across the
country and across EMRs
• EMRs don’t have structured data entry
templates for smoking status
• EMRs need to be able to capture
– Status: Smoker –Current, Former, Never
– Current exposure: cig/day
– Cumulative exposure: Pack-year history

36. Other groups working on this:
36

37. They have the concept

38. But, as of yet, no liasons…

39. Metropolitan Toronto database

40. Metropolitan Toronto database

41. Research Opportunities
• Population Health and Epidemiological Studies
– Incidence/Prevalence of disease
– Impact of SES on health
– Rates of treatment for diseases
– Rates of disease control
– Burden of illness and multi-morbidity
• Clinical –database studies
– Comparative effectiveness
– Case-Control
– Exposure-Outcome
– Quality Improvement
– Associations
– Intervention-Outcome
– Guideline effectiveness
41

42. Research Using CPCSSN Data
42
Researcher
Letter of
Intent
CPCSSN
Research
Committee
Writes
Letter of
Intent
Reviews
1 page, includes: Researchers,
Organization, Research Title,
Objective, Methodology,
Data Required
Approved
1. Resubmit
2. Not Feasible
3. Outside
Mandate
No
Researcher
1. Protocol
2. Data Access
Request Form
3. Data Sharing
Agreement
Letter of Acceptance Yes
Writes
CPCSSN
Research
Committee
CPCSSN
Data
ResearcherInvoice

43. Letter of intent
43

44. Letter of intent (2)
44

45. CPCSSN Contact
Tyler Williamson, Senior Epidemiologist
Canadian Primary Care Sentinel
Surveillance Network
Centre for Studies in Primary Care
Queen’s University
Kingston ON K7L 5E9
Tel: (613) 533-9300, Ext. 73838
Fax: (613) 533-9302
e-mail: tylerw@cpcssn.org
Mine: for4kids@gmail.com
45

46. So, I have an opportunity to do a study
with COPD, at least in Toronto group
• With what I have shown you, what would you
suggest I do?
• Is there an opportunity to work with any of
your existing projects?
• And then…funding…..