This document summarizes the mid-point findings of a study assessing diabetes care trends in medical practices participating in Massachusetts' Patient Centered Medical Home Initiative. It found that the percentage of diabetic patients screened for depression significantly improved over the first 21 months, rising from 20.5% to 45.9%. While other measures like blood pressure and cholesterol control showed no significant changes, the authors note that process measures may improve earlier than outcomes. They conclude that practices will need more time under the new model to demonstrate broader improvements in care.

1. Diabetes Care Trends in the Massachusetts Patient Centered Medical Home Initiative at Mid-Point
Sai Cherala1, MD, MPH, Judith Steinberg1, MD, MPH, Stephen Baker 2, MScPH & Christine Johnson1, PhD
1Office of HealthCare Innovation and Quality, Center for Health Policy and Research, Commonwealth Medicine, 2Quantitative Health Sciences, Biostatistics and
Health Services, University of Massachusetts Medical School, 2013
Introduction
• Massachusetts Patient Centered Medical Home Initiative
(MA PCMHI) is a multi-payer, statewide initiative,
sponsored by MA Health & Human Services
• 45 Participating practices
• 3-Year demonstration; Start: March 29, 2011
• Includes payment reform
VISION: All MA primary care practices will be PCMHs
by 2015
Aim
• Assess data trends of adult diabetes clinical measures
from participating adult practices for first 21 months of
the initiative
Design
• Quality improvement study using practices’ self-reported
monthly data on adult diabetes clinical quality measures
from June 2011 to February 2013 for 38 adult practices
• Diabetes measures include: Blood pressure, LDL
cholesterol and hemoglobin A1C control and depression
screening
Intervention
• Technical Assistance: Three‐year Learning Collaborative,
including: learning sessions, regular conference calls,
webinars, online courses, support for obtaining NCQA
PCMH recognition and practice facilitation
• Financial Incentives: 27/38 adult practices receive
incentive payments
Methods
• General Linear Mixed Model Analysis of Variance
(ANOVA)
Analysis
• Data were divided into three-month periods: Time 1
(2011-June, July and August)….. to Time 7(2012-
December, 2013- January and February)
• Analysis of Change over Time: Time 1 vs. Time 7
Practice Characteristics Percentage
Geography (N=38)
Rural 8%
Urban 92%
Practice Size (Based on Number of Full Time
Practitioners N=38)
Small (1-6 FTE ) 34%
Medium (7-13 FTE) 32%
Large (> 13 FTE) 34%
Type of Practice (N=38)
Solo Practice 5%
Group Practice 24%
Residency or Academic Practice 10%
Community Health Center 61%
Average Payer Mix (Practices with Financial Incentives
N=27)
Commercial 7%
Health Safety Net 16%
Medicaid 76%
Medicare 1%
Average Patient Demographics-Age Range (Practices
with Financial Incentives N=27)
0-17 years 30%
18-64 years 68%
65+ years 2%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
1 2 3 4 5 6 7
Percentage
Time Period
BP <
140/90
mmHg
HbA1c <
8%
LDL <
100mg/dL
Screened
for
Depression
HbA1c >
9%
Results
• Percentage of diabetic patients
screened for depression measure showed
statistically significant improvement (20.5% to
45.9%, p<0.0001)
• Other measures showed no change or
improvement without statistical significance
Conclusion
• At midpoint of this 3-year PCMHI
demonstration, practices showed statistically
significantly improvement in screening adult
diabetes patients for depression
Discussion
• During the first 21 months of transformation effort, there
has been a significant improvement in the one diabetes
process measure
• Process measures may be expected to show improvement
earlier than outcome measures
• Given the complexity of the PCMH model and the practice
transformation that is required, practices likely need more
time to show improvements
• Factors which may impact improvement rates: Payer mix,
practice size, financial incentives/resources, practice
leadership engagement and adaptive reserve
Next Steps
• Analyze the impact of these factors on practice performance
on clinical quality measures
• Identify high and low performing practices
• Enhance technical assistance by sharing best practices of
high performing practices and identifying barriers to change
experienced by low performing practices
Table 1: Practice Characteristics Figure 1. Adult Diabetes Measures Over Time