the Art of the Possible Presentation

Report
The Art of the Possible
Using CPCSSN Data for Primary Care Research
Family Medicine Forum
Nov 16, 2012
Karim Keshavjee - EMR Consultant & Research Data Architect
Ken Martin - Information and Technology Manager
Outline
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Introduction to CPCSSN
CPCSSN Data Holdings
A Tour of CPCSSN Data Tables
Current Research Projects at CPCSSN
The Art of the Possible
How to use CPCSSN data for your research
Goodies for Today
10 PC-PBRNs
•British Columbia
- BCPCReN (Wolf )
•Alberta
329 physicians in 8 provinces
using 10 EMRs
- 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)
CPCSSN population
CPCSSN Population
Data Extracted on all patients in the practice, including children
Studying patients with the following chronic diseases
• Chronic Obstructive Lung Disease
• Depression
• Diabetes
• Hypertension
• Osteoarthritis
Chronic Neurological Disease
• Dementia
• Epilepsy
• Parkinson's Disease
Data Holdings Q2 2012
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Database Schema - ERD
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Data Cleaning/Recoding
• We clean and recode the following fields
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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)
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Patient Demographics
368,000 Records
} < 5%
}
< 5%
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Provider Information
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Billing
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
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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 recodes all billing diagnosis codes to a
standard version
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Encounters
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
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Research Discussion
• Can we segment patients by pattern of visits?
• Does pattern of visits predict other things?
– Control of disease
– Frequency of prescriptions
– Multiple comorbidities
• Does visit type affect quality of care?
• Reason for Encounter is poorly captured in
most EMRs
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Problem List Diagnoses
1.8 Million Records
Original Diagnosis Written by User
E.g. DMT2
Recoded by CPCSSN
E.g., Diabetes Mellitus, Type 2
}
Not well populated
Active = Problem List
Inactive = Past Medical History
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Problem List Diagnoses
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)
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Research Discussion
• Sensitivity and specificity of problem list
diagnoses not currently known, so cannot
determine incidence and prevalence of
disease from problem list alone
• Need to develop case finding criteria for
diseases (includes diagnosis, meds, labs, etc)
• Need to identify sensitivity and specificity of
having a diagnosis in the problem list
• Currently in the process of validating 8 case
finding criteria across the country
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Vital Signs
5 Million Records
Name of exam (e.g., sBP)
Cleaned up result
(e.g, lbs -> kg, inch -> cm)
Cleaned up unit of measure
(e.g., unit is kg, but result was lb)
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Research Discussion
• Currently have access to
– sBP/dBP
– Ht
– Wt
– BMI
– Waist circum
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Allergies
155K Records
Name of allergen
Cleaned up name
Data will be coded as ATC
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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
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Risk Factors
588K Records
Name of Risk Factor (e.g., smoking)
Cleaned up version of Risk Factors.
Working on cleaning up Current
Exposures & Cumulative Exposures
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Research Discussion
• Risk factors are actively being cleaned
• Getting the status of the risk factor (i.e.,
smoker/non-smoker) is difficult, but easier
than
• Current levels of exposure (e.g., # of cig/day)
• Cumulative exposure (e.g., pack years)
• Alcohol use is also being cleaned up
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Laboratory Results
3 Million Records
Original Lab Result Name
(e.g., Hb A1c, HGbA1c, etc)
Recoded by CPCSSN 100% LOINC
(e.g., HBA1C)
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Research Discussion
• Currently only capturing the following
HDL
TRIGLYCERIDES
LDL
TOTAL CHOLESTEROL
FASTING GLUCOSE
HBA1C
URINE ALBUMIN CREATININE RATIO
MICROALBUMIN
GLUCOSE TOLERANCE
• One site does not capture labs yet
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Encounter Diagnoses
6.3 Million Records
Original Diagnosis Recorded in Encounter
(e.g., axniety)
83% Recoded by CPCSSN
(Anxiety ICD-9 300)
63% Originally coded by Doctor
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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
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Medications
4.9 Million Records
56% Coded as DIN
What the doctor ordered
E.g., HCTZ 25 mg bid
91% Recoded by CPCSSN
E.g., Hydrochlorthiazide
72% Coded by doctor (DIN + other)
91% Coded by CPCSSN (ATC)
}
Strength 56%
Dose 70%
Unit of Measure 84%
Frequency 95%
Duration 52%
Dispensed 86%
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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
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Referrals
600 K Records
Original Text of Referral
80% Recoded by CPCSSN
SNOMED-CT
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Procedures
1.3 Million Records
Original Text of Procedure
Not Currently Coded by CPCSSN
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Vaccines
960 K Records
What the doctor typed
93% Recoded by CPCSSN (ATC)
46% Coded by Doctor (DIN)
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Disease Cases
Case Definitions are developed by CPCSSN and are in the process of
being validated through chart reviews
173,000 Records
How a Case is identified is recorded
in this table
Allows full traceability for each case
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Current Research Projects at CPCSSN
N=46
Association Study
Attitudes
Audit and feedback
Case control study
Case Finding
Clinical Quality Improvement
Continuity of Care
Data Quality
De-identification
Denominator
Descriptive Study
EMR Adoption
Feasibility
Intervention Assessment
Medication
Practice Profile
Prevalence
Prevalence, Case finding
Resource Use
SES Study
Treatment pattern
Validation
9%
2%
2%
7%
9%
2%
2%
20%
2%
2%
2%
2%
2%
2%
2%
4%
7%
2%
7%
4%
4%
4%
Research Opportunities
• Population Health and Epidemiological Studies
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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
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Comparative effectiveness
Case-Control
Exposure-Outcome
Quality Improvement
Associations
Intervention-Outcome
Guideline effectiveness
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Research Opportunities
• Clinical –prospective, interventional studies
– Conduct pragmatic RCTs –data is already collected
– Conduct in-clinic interventions
– Not ready for these yet
• Health Services
– EMR adoption
– Resource Utilization (consults, labs, procedures)
– Policy Intervention (cross-province comparisons)
– Patient behaviors –frequency of visits
– Medical errors and patient safety
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Research Opportunities
• Health informatics
– Natural language processing
– Machine learning
– De-identification algorithms
– Predictive Analytics
• eHealth and mHealth
– Develop and test apps using CPCSSN data
– Patient education apps with their own data
– Apps for healthcare providers to educate patients
about their disease with nice visualizations
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Research Using CPCSSN Data
Researcher
CPCSSN
Research
Committee
Writes
Letter of
Intent
Reviews
Letter of
Intent
Researcher
CPCSSN
Research
Committee
No
Approved
Yes
Letter of Acceptance
Writes
1 page, includes: Researchers,
Organization, Research Title,
Objective, Methodology,
Data Required
1. Protocol
2. Data Access
Request Form
3. Data Sharing
Agreement
Invoice
1. Resubmit
2. Not Feasible
3. Outside
Mandate
CPCSSN
Data
Researcher
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Goodies For Today
• Copy of the presentation: The Art of the Possible: Using CPCSSN
Data for Primary Care Research
• Sample of CPCSSN data for 200 patients
– Anonymized and scrambled to protect patient privacy
– (MS Access file format)
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CPCSSN database entity relationship diagram (ERD)
CPCSSN database data dictionary
CPCSSN central repository data holdings summary
CPCSSN Data Access Request Form Central Repository
Process for Requesting Access to CPCSSN Data
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Next Steps
• Sign a License Agreement today to get your copy of
the CPCSSN Data Product
• Evaluate the data CPCSSN has
• Plan your next grant application around CPCSSN data
• Add CPCSSN Data as a budget item into your next
grant application
– You can contact us to get a quote
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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: [email protected]
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Thanks to all Funders, Stakeholders,
Partners, AND sentinel Physicians
Funding for this publication was provided by the Public Health Agency of Canada The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada.
Cette publication a été réalisée grâce au financement de l'Agence de la santé publique du Canada. Les opinions exprimées ici ne reflètent pas nécessairement celles de l'Agence de la santé publique du Canada.

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