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CHSPR 29 th Annual Health Policy Conference Vancouver BC March 10 2017 Richard Birtwhistle MD MSc FCFP Data Driven Improvement: A building block of high-performing primary care

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Page 1: Data Driven Improvement: A building block of high ...chspr.sites.olt.ubc.ca/files/2017/03/CHSPR2017_Birtwhistle.pdf · Data Driven Improvement: A building block of high-performing

CHSPR 29th Annual Health Policy ConferenceVancouver BC

March 10 2017Richard Birtwhistle MD MSc FCFP

Data Driven Improvement: A building block of high-performing primary care

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CPCSSN Partner Universities

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Declaration

I have no conflicts of interest related to this presentation.

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Objectives

1. Overview of the CPCSSN.2. Use of EMR data for research,

surveillance and practice QI.3. Example of a practice and population

web based tool using CPCSSN data.

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Use of EMRs

Do EMRs improve care?

Maybe!

Do EMRs improve clinical outcomes?

?????

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• 1.5 million Canadian patients• 1200 practices• 11 PBRNs in 7 provinces, 1 territory• Some EMR data back to 2003• Started in 2008• $12.5M funding from PHAC • Strong partnerships with College of FamilyPhysicians of Canada, Queen’s and other Universities

The Canadian Primary Care Sentinel Surveillance Network:

B.C. (BCPCReN), Alberta (SAPCReN, NAPCReN), NWT, Manitoba (MaPCReN), Ontario (DELPHI, UTOPIAN, EON, MUSIC), Quebec (RRSPUM), Nova

Scotia/New Brunswick (MaRNet), Newfoundland (APBRN)

Unique pan- Canadian primary care database

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7CPCSSN Data

• Provider profile• Patient socio-demographics • Disease/ health condition • Encounter data • Risk factor data • Examination data• Medications• Laboratory data• Referral data• Procedure data

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Use of EMR Data

1. Research

2. Surveillance

3. Practice Quality Improvement

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Research

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Research

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Hemoglobin A1c control in type 2 diabetes mellitus and hospitalization and emergency room use: A linkage study using electronic medical record and administrative data in Ontario

Richard Birtwhistle MD MSc1,4,5, Michael E. Green MD MPH1,4,5, Eliot Frymire1,5, Simone Dahrouge PhD3,5, Marlo Whitehead1,5, ShahriarKhan MSc1,5 and Richard H. Glazier MD MPH2,5

Variable A1c level

<7 7-8 >8 Missing P value

N 5526 2662 1814 2356

Age (yr) Mean 65.7 64.7 58.1 61.0 <.001

Female % 50.1 47 45.4 50.3 <.001

Any acutecomplication

% 1.9 3.1 6.0 - <.001

Any chronic complication

% 2.1 3.3 3.8 - <.001

ER visits Mean 0.63 0.67 0.95 - <.001

Inpatient episodes

Mean 0.18 0.22 0.26 - <.001

ADGs 6.39 6.15 5.98 6.35 <.001

Data Linkage

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Chronic DiseaseSurveillance

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Chronic Disease Surveillance

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Practice QualityImprovement

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Quality Improvement

(Am J Prev Med 2015;49(2):264–268)

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Quality Improvement

Journal of Innovation in Health Informatics

Vol 23, No 3 (2016)

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DPT

CPCSSN Data Presentation Tool

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DPT Dashboard

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DPT Case Finder

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Custom Searches

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GIS mapping

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The Future

Electronic Medical Records are an incredibly powerful tool for improving Primary Care, Public Health and Research

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