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Trend of quality measures following implementation of electronic health record systems amongst practices in underserved urban areas
Jason Wang, Ph.D.Sr. Director of Program Evaluation and AnalysisPrimary Care Information ProjectNYC Department of Health & Mental Hygiene
Primary Care Information Project
APHA 140th Annual Meeting San Francisco, CAOctober 30, 2012
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Objective: Assess the overall trend for some key quality measures for practices in underserved urban areas after implementation of EHR.
Background:
In transforming primary care, studies showing sustained improvements in the delivery of clinical preventive services are limited. Fewer demonstrate sustained improvements among independent practices that are not affiliated with hospital or integrated health systems. This study examines the continued improvement in clinical quality measures for a group of practices using electronic health records and receiving technical support from a local public health agency.
Methods: Clinical quality measure performance data were analyzed from a cohort of primary care practices that implemented an electronic health record (EHR) at least three months before baseline (October 2009). Trends were observed for four key quality measures: antithrombotic therapy, blood pressure control, smoking cessation intervention, and A1c testing based on monthly summary data transmitted by the practices over a two-years period.
INTRODUCTION
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Advancing NYC’s Health Priorities
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• Large burden, killing thousands of NYers and causing hundreds of thousands of preventable illnesses or disabilities each year
• Proven amenable to intervention • Best addressed through coordinated action by City agencies, public-private
partnerships, health care providers, businesses, individuals
Important and winnable battles that affect every New Yorker
1) Have a Regular Doctor or Other Health Care Provider2) Be Tobacco-Free3) Keep Your Heart Healthy4) Know Your HIV Status5) Get Help for Depression6) Live Free of Dependence on Alcohol and Drugs7) Get Checked for Cancer8) Get the Immunizations You Need9) Make Your Home Safe and Healthy10) Have a Healthy Baby
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Primary Care Information Project (PCIP)
PCIP started as a mayoral initiative in 2005
Mission
•Improve the quality of care in medically underserved areas through health information technology (HIT)
Success
•Over 6,200 providers receiving EHR and Meaningful Use assistance
• 915 small practices, 23 large practices
• 50 community health centers
• 54 hospitals & outpatient clinics
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Measure Eligible patients (denominator) Patient Goal (numerator)
A1c Testing Patients 18-75 years with diabetes Hemoglobin A1c test recorded in the past 6
months
Antithrombotic
Therapy
Patients 18+ years with ischemic
vascular disease or 40+ with diabetes
Taking Antithrombotic/other Antithrombotic
therapy
Blood Pressure
Control
Patients 18-75 years with hypertension
and no diagnosis of ischemic vascular
disease or diabetes
Systolic <140 mmHg and Diastolic <90 mmHg
Smoking Cessation
Intervention
Patients 18+ years with a "current
smoker" smoking status
Smoking cessation intervention (Rx or
Counseling) received in the past 12 months
Table 1. Description of quality measures
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Table 2. Baseline Practice Characteristics Mean Practice Values for Small Practices (SP) and Community Health Centers (CHC)
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Characteristic All Practices SP CHC
(n=151) (n=140) (n=11)
Sites 1.4 1.2 3.1
Providers 4.5 2.6 29.3*
Encounters per month 925 749 3169
Unique Patients per month 742 612 2392
% of Medicaid/ Self-insured >=20%
(Practice self-reported) (T1) 33% 29.3% 81.8%**
Months using EHR
up to Oct 2009 (T1) 13.7 13.8 13.0
*p=0.03 **p=0.0004
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Graph 1. Overall Progress in PCIP – 2 year trend
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Time Antithrombotic therapy
Blood pressure control
Hemoglobin A1c Testing
Smoking Cessation Intervention
Oct 2009 (T1) 58.4 55.3 46.4 29.3Oct 2010 (T2) 66.7 58.5 50.6 34.5Oct 2011 (T3) 74.8 64.1 57.7 46.2Difference between T1 and T2 8.3* 3.1 4.2 5.2Difference between T2 and T3 8.1* 5.5* 7.1* 11.7*Difference between T1 and T3 16.4** 8.8* 11.3* 16.9**
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Table 3. Improved Quality Measure Performance over Time Stratified by Various Practice Characteristics
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Antithrombotic Therapy BP Control Hemoglobin A1c Testing Smoking Cessation
Practice
Characteristics
n T1 T3 p value n T1 T3 p value n T1 T3 p value n T1 T3 p value
Organization SP 93 58.9 73.1 <.0001 102 56.6 64.3 0.002 91 48 56.1 NS 81 30.1 45.1 0.0006
Type CHC 11 54.2 72 0.02 11 43.2 62 0.006 11 33 72.5 0.004 11 23.6 56.9 0.003
p value NS NS 0.01 NS NS 0.003 NS NS
Adoption time Early 61 61.8 75.9 <.0001 65 57.3 62.4 NS 61 52.3 58.2 NS 52 34.9 50.4 0.001
Later 43 53.6 73.3 <.0001 48 52.6 66.2 0.0002 41 37.6 57 0.005 40 22 41.1 0.004
p value NS NS NS NS 0.02 NS 0.01 NS
% Medicaid <20% 67 56.8 75.7 <.0001 74 57 63.9 0.017 66 44.4 55.4 0.049 61 26.7 39.3 0.008
and selfpay >=20% 37 61.3 73.5 0.005 39 52 64.3 0.002 36 50.1 60.7 NS 31 34.5 56.9 0.002
p value NS NS NS NS NS NS NS 0.003
No. of providers Single 42 59.4 76.4 0.0005 48 55.8 66.8 0.0008 41 50.9 56.8 NS 34 32.6 48 0.04
Multiple 62 57.7 73.5 <.0001 65 54.9 61.7 0.03 61 43.3 58.3 0.007 58 27.4 44.9 0.0001
p value NS NS NS NS NS NS NS NS
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Table 4. Results of Generalized Estimating Equation (GEE) Model
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Quality Measures Antithrombotic Tx BP ControlHemoglobin A1c
Testing
Smoking Cessation
Intervention
Practice
Characteristics OR (CI) p value OR (CI) p value OR (CI) p value OR (CI) p value
Months Since EHR 1.03
(1.02, 1.04)
<.0001 1.006
(1.0008, 1.01)
0.0243 1.03
(1.01, 1.04)
<.0001 1.04
(1.02, 1.05)
<.0001
CHC 0.84
( 0.43, 1.63)
0.6027 0.56
(0.38, 0.81)
0.0022 1.13
(0.32, 3.98)
0.8444 0.58
(0.28, 1.20)
0.1414
Months Since
EHR*CHC
1.003
(0.98, 1.02)
0.7628 1.01
(1.002, 1.02)
0.016 1.02
(0.99, 1.06)
0.1447 1.006
(0.96, 1.05)
0.7975
Medicaid/Selfpay >
20%
1.21
(0.89, 1.66)
0.231 1.08
(0.89, 1.31)
0.4647 1.21
(0.83, 1.78)
0.3212 1.78
(1.13, 2.82)
0.0135
Practice has more
than one provider
1.24
(0.83, 1.85)
0.2865 0.93
(0.76, 1.13)
0.4592 1.12
(0.74, 1.70)
0.5798 0.70
(0.45. 1.08)
0.1043
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Two year trends of 151 independent practices show significant gains on four quality measures. Our findings suggest that independent small practices and community health centers, with the assistance from a community EHR extension program such as PCIP, can achieve clinical quality gains similar to those observed in larger, well-resourced integrated delivery systems. Our findings are particularly relevant to independent practices serving resource-challenged urban areas. Of the practice characteristics we analyzed, none accounted for consistent differences in the increases observed with the exception of duration using an EHR.
In this study we observed increases of several percentage points per year, suggesting that long-term improvement can also occur. This continued progress supports the idea that urban independent practices can drive long-term improvements in population health, a finding that is especially promising since inner-city independent practices like those served by PCIP tend to see a larger than average number of patients who are both uninsured and suffering from more severe health issues.
Continued support is needed to help independent primary care practices get the most from health IT as an investment to improve health care and focus on patient-centered, outcomes driven care and coordination. .
DISCUSSION
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Several practices that adopted an EHR in the timeframe eligible for inclusion in the analyses were not able to transmit data and their performance on the indicators in this study are unknown, though practices with missing data have similar characteristics as those represented in the study. Providers working with PCIP represent a group of EHR users who have received a variety of assistance from PCIP staff, including training and guidance on quality improvement strategies, technical support on EHR software (upgrades, patches, and configuration), and connection for health information exchange. Comparable data are not available to ascertain whether providers who do not have access to the same types of assistance would experience similar improvement trends.
Improvement due to better documentation alone in the EHR was not tested in this study. For lab tests where an electronic lab interface was not available and the practice does not routinely enter results into the patient’s record, practice rates on these tests will be under reported.
Factors such as incentives, availability of CDSS alert, number of QI visits, PCMH recogization, etc. will be put into the analysis to detect significant driving forces for the quality measures improvement.
LIMITATION and NEXT STEPS
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OTHER PCIP STUDIES (1). Health Information Systems in Small Practices: Improving the Delivery of Clinical Preventive Services. AJPM. November 2011.
30.4
31.0
77.9
24.0
32.3
83.9
53.2
55.0
78.3
62.4
31.3
31.3
76.6
20.5
27.8
77.4
45.6
45.9
65.5
46.3
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0
A1c Control
Smoking Cessation Intervention
Cholesterol Control
Influenza Vaccination*
Breast Cancer Screening*
Smoking Status recorded*
Aspirin Therapy*
Blood Pressure Control*
Body Mass Index recorded*
A1c Screening*
% eligble patients recieving CPS
Pre EHR Upgrade
Post EHR Upgrade
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OTHER PCIP STUDIES (2). Validity of EHR Derived Quality Measurement for Monitoring Population Health & Clinical Quality. JAMIA. Feb 2012.
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OTHER PCIP STUDIES (3). Decline and Rebound: Population Trends in Performance on Clinical Quality Measures in Small Practices Adopting Electronic Health Records. Academy Health Annual Meeting, Orlando, FL. June 2012.
0
20
40
60
80
100
% e
ligib
le p
ati
en
ts r
ec
eiv
ing
se
rvic
e
Antithrombotic therapy 26.3 28.0 49.6 57.1
Body mass index recorded 84.4 75.6 64.5 77.9
Blood pressure control 46.3 48.4 49.4 59.8
Cholesterol control 81.4 81.0 83.2 82.4
Cholesterol testing 80.1 80.1 45.8 63.3
Hemoglobin A1c testing 52.5 45.1 41.0 34.3
Hemoglobin A1c control 65.2 66.6 43.8 61.3
Smoking cessation intervention 26.1 23.6 24.8 28.4
Smoking status recorded 69.0 70.3 76.3 84.5
2 years before EHR 1 year before EHR EHR, no CDSS EHR and CDSS
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OTHER PCIP STUDIES (4). Two year quality trends for independent practices adopting an EHR and achieving PCMH recognition. AHRQ Annual Meeting, Washington DC, September 2012.
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Acknowledgments
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Authors:Jason J. Wang PhD, Kimberly M. Sebek MPH, Colleen M. McCullough BA, Sam C. Amirfar MD, Amanda S. Parsons MD, MBA, Jesse Singer DO, MPH, Sarah C. Shih MPH
Contact: Jason Wang, Ph.D., Sr. Director of Program Evaluation and AnalysisPrimary Care Information Project, NYC Department of Health and Mental HygieneLong Island City (Queens), NY 11101. Tel: (347) 396-4859, Email: [email protected]
Acknowledgments:
The authors would like to acknowledge Dr. Thomas Farley, Commissioner of the New York City Department of Health for the valuable input on this study. The authors also wish to thank the PCIP staff for their tremendous dedication and participating practices’ commitment to improving health in New York City.
Funding: A portion of this study was supported by the Agency for Healthcare Research and Quality (grant #s R18HS17059 and 17294). The funder played no role in the study design, in the collection, analysis and interpretation of data, in the writing of the report or in the decision to submit the paper for publication