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Academy Health-Annual Research Meeting Presentation
1. Massachusetts Patient-Centered
Medical Home Initiative (MA PCMHI):
Impact on Clinical Quality at 30 Months
Judith Steinberg, M.D., M.P.H.
Sai Cherala, M.D., M.P.H.
Ann Lawthers, S.M., Sc.D.
Christine Johnson, Ph.D.
Commonwealth Medicine
UMass Medical School
2. Introduction
The Patient‐Centered Medical Home (PCMH) offers an
innovative model of care: comprehensive primary care, quality
improvement, care management, and enhanced access in a
patient centered environment
PCMH evaluations have shown variable impact
Aims:
To assess data trends of clinical quality measures from
participating practices in the Massachusetts Patient-Centered
Medical Home Initiative
To evaluate practice and staff level factors that may impact
clinical quality performance
3. Background: Massachusetts Patient
Centered Medical Home Initiative
Multi-payer, statewide initiative
Sponsored by Massachusetts Health & Human Services;
legislatively mandated
46 participating practices
3-year demonstration: March, 2011 − March, 2014
Includes payment reform and technical assistance
4. MA PCMHI Evaluation Questions
Question 1:
To what extent and how do practices
become medical homes?
• Extent
• Patient-family centeredness
• Care management
• Care coordination
• Access
• Teamwork
• Information technology
• Leadership
• Barriers and Facilitators
Question 2:
To what extent do
patients become
partners in their health
care?
•Perceived self-
management efficacy
•Patient-family
centeredness by chronic
and non-chronic
Question 3:
What is the initiative’s
impact on utilization, cost,
clinical quality, patient and
provider outcomes?
•Emergency Department
use
•Hospitalizations
•Cost
•Clinical quality measures
•Staff satisfaction
•Patient satisfaction
5. Methods
Design: Quality improvement study using practices’
self-reported monthly data on 22 clinical quality
measures from June 2011 through February 2014
Methods
Linear Mixed Model
Analysis
Data were divided into three-month periods:
Time 1 (June – August, 2011) ... to
Time 11 (December, 2013 – February, 2014)
Analysis of Change over Time: Baseline (Time 1 or Time
2 or Time 6) vs. Time 11
6. Clinical Quality Measures
Adult Diabetes
HbgA1c Control (<8%)
HbgA1c Control (>9%)
BP < 140/90 mmHg
LDL Control < 100mg/dL
Screened for Depression
Self-Management Goal
Adult Prevention
Adult Weight Screening and
Follow-up
Tobacco Use Assessment
Tobacco Cessation Intervention
Other Adult Target
Blood Pressure Control
Hypertension with Documented
Self-Management Goal
Depression with Documented
PHQ-9 Score
Depression with Documented Self-
Management Goal
Childhood Prevention
Immunization Status Multiple
vaccines
Weight Assessment and Counseling
for Children and Adolescents
Pediatric Asthma
Use of Appropriate Medications for
Asthma
Persistent Asthma Patients with
Action Plan
Other Pediatric Target
Follow-up Care for Children
Prescribed ADHD Medication
Management Plan for Children
Prescribed ADHD Medication
Care Coordination/ Care Management
Follow-up after Hospital Discharge
Highest Risk Patients with Care Plan
7. Results: Study Participants
Practice Characteristics Percentage
Geography
Rural (<10,000 town population) 9%
Suburban (Between 10,000 and 50,000) 20%
Urban (>= 50,000) 71%
Practice Size (Based on Number of Full Time Practitioners)
Small (< 6 FTE practitioners) 31%
Medium (Between 6 and 11 FTE practitioners) 29%
Large (> 11 FTE practitioners) 40%
Type of Practice
Community Health Center 56%
Residency or Academic Practice 11%
Group Practice 29%
Solo Practice 4%
Payer Mix (Practices with Financial Incentives N=31)
Commercial 12%
Health Safety Net 15%
Medicaid 72%
Medicare 1%
8. Clinical Quality Measures that Showed Significant
Improvement in Change over Time
25.2 23.8
37.1
82.4
46.5
16.7 17.3
11.5
18.6
46.4
22.3
36.1
48.7
32.0
47.6
90.5
51.3
25.3
21.4
19.3
62.7 63.1
61.2
64.7
0
10
20
30
40
50
60
70
80
90
100
Screened for
Depression
Self-
Management
Goal
Adult Weight
Screening &
Follow-Up
Tobacco Use
Assessment
Tobacco
Cessation
Intervention
Hypertension
Self-
Management
Goal
Depression
PHQ-9 Score
Depression
Self-
Management
Goal
Patients With
Action Plan
Immunization
Status
Multiple
Vaccines 1
Immunization
Status
Multiple
Vaccines 2
Care Plans for
Highest Risk
Patients
Percent
Baseline Time 11
11 of 22 measures showed statistically significant improvement
Adult Diabetes Adult Prevention Other Adult Measures Pediatric
Asthma
Childhood
Prevention
Care
Management
9. Values met the study’s definition of statistical significance p<.05.
Care Coordination/Care Management Measures:
Change over Time
63.3
36.1
66.5 64.7
0
10
20
30
40
50
60
70
Follow-Up After Hospital Discharge Care Plans for Highest Risk Patients
AverageRate
Measures
Baseline Time 11
10. Drilling Deeper on Change:
Methods
Correlation analysis
Variables included: Performance on clinical measures
in the last three months, change in clinical
performance over the demonstration, practice
characteristics and staff perceptions/attitudes
towards the change
Data sources: Clinical data submission, Medical Home
Implementation Quotient (MHIQ), staff survey
11. Results of Correlation Analysis:
Care Plan for Highest Risk Patients
Change over Three Years Performance in Last Three
Months
Leadership at Baseline
(staff survey) (r=0.42, p=0.01)
Leadership at Baseline
(staff survey) (r=0.45, p=0.009)
Strong team
(staff survey) (r=0.41, p=0.01)
Quality improvement culture
at Baseline
(staff survey) (r=0.39, p=0.02)
Quality improvement culture
at Baseline
(staff survey) (r=0.36, p=0.04)
Strong team
(staff survey) (r=0.32, p=0.05)
12. Processes and Practices Characteristics
Associated with Clinical Outcomes
HbA1c < 8% BP <140/90mm of Hg
Comfort with HIT
(staff survey) (r=0.61, p <0.0001)
Improved care planning for
high risk patients
(MHIQ) (r=0.50, p=0.005)
QI culture
(staff survey) (r=0.51, p <0012)
Strong leadership at baseline
(staff survey) (r=0.44, p=0.009)
Strong teamwork
(staff survey) (0.50, p=0.002)
Strong teamwork
(staff survey) (r=0.41, p=0.01)
Leadership
(staff survey) (r=0.48, p=0.002)
QI culture
(staff survey) (r=0.38, p=0.02)
13. Quality Improvement Study
No Comparison Group
Small Sample Size
Correlation Analysis
Limitations
14. At the close of the MA PCMHI initiative (3 years),
11 of 22 clinical measures showed statistically significant
improvement
Measures that showed significant improvement:
Process measures
New or newly documented processes
A solid practice QI culture, leadership and strong team
functioning were positively correlated with performance
and improvement in high risk care planning
Factors that correlated with performance on clinical
outcome measures were: QI culture, strong leadership and
teamwork, comfort with HIT
Summary
15. Conclusion and Implications for
Policy and Practice
Quality of care in the management of chronic diseases,
prevention and screening, and high risk care
management was significantly improved in this PCMH
demonstration that had a preponderance of safety net
practices
Implementation of foundational elements of the PCMH
− QI, leadership engagement, teamwork and HIT − may
foster improvement in clinical quality
Understanding factors that are correlated with clinical
performance can focus transformation efforts
16. Acknowledgments
We would like to acknowledge the Massachusetts Executive Office of
Health and Human Services (EOHHS), the MA PCMHI Leadership and
Medical Home Facilitator Teams, as well as MA PCMHI participating
practices without whom this work would not be possible.
Contact Information:
Judith Steinberg, M.D., M.P.H.
Deputy Chief Medical Officer
Commonwealth Medicine
UMass Medical School
Judith.Steinberg@umassmed.edu
Sai Cherala, M.D., M.P.H.
Senior Clinical Analyst
Commonwealth Medicine
UMass Medical School
Sai.Cherala@umassmed.edu
Editor's Notes
Good Morning. It is my great pleasure to share with you this morning how our statewide, PCMH demonstration impacted clinical quality.
By way of introduction, the patient centered medical home model offers a solution to the current state of health care delivery, which can be described as care that is provider- centered fragmented and inefficient care. In contrast, the PCMH offers an innovative model that has a whole person orientation, is comprehensive, coordinated, has a focus on care management and quality improvement.
However, PCMH evaluations have shown variable impact – in part this has been related to differing PCMH definitions, evaluation designs and short follow-up time.
So our aim was to assess the impact on clinical quality of a PCMH demonstration by analyzing data trends of clinical quality measures from practices that participated in the MA PCMH Initiative.
We also sought to understand the factors that might impact performance on these clinical quality measures.
Now for back ground
The MA PCMHI is a 3-year, statewide, multi payer medical home demonstration project .
It includes 46 practices, of these 10 were pediatric practices.
Technical Assistance
Three‐year Learning Collaborative
Periodic Learning Sessions
Monthly conference calls or webinars
Online courses
Monthly submission and review of practice‐level performance data
Support for obtaining NCQA PCMH recognition
Practice Facilitation
Financial Incentives
31/46 practices receive incentive payments
Incentives:
Start-up funding, 2 prospective payment streams, shared savings
Practices were asked to provide data on a monthly basis from their EMRs on 22 clinical quality measures. This was done for several purposes: for the practice’s quality improvement activities, as measures for the learning collaborative and as one type of data source for the overarching evaluation of the initiative. This slide summarizes the approach that we took in evaluating the initiative. The evaluation was designed to answer three questions: State the three questons.
A Mixed methods approach was taken to answer these questions, utilizing a quasi-experimental, pre-post design with a comparison group. There was no comparison group for the clinical quality measures.
The data sources for each question were:
Practice self assessment medical home transformation surveys, qualitative interviews and site visits
PCMH patient experience surveys
Claims data, clinical quality measures and staff surveys
Question 1 – MHIQ transformation survey, interviews with Medical Home facilitators, site visit/interviews
Question 2 – Patient experience survey – CAHPS
Question 3 – cost/utilization data, clinical quality measures, staff survey, patient experience survey
For this presentation we will be focusing on the impact on clinical quality measures but we also used a mixed methods approach, utilizing the transformation and staff satisfaction survey to understand factors that correlate with performance on the clinical quality measures.
So, to assess the impact on clinical quality, the design was essentially a QI study.
Data were divided into three month periods and an aggregate average for a given three month period was calculated. an analysis of change over time was performed using a linear mixed models method.
Here are the clinical quality measures grouped by domain. As you can see, they include measures of chronic disease management, prevention and screening and care coordination/care management.
Data in the form of numerators and denominators were reported through a data portal.
Note that most of these measures are process measures. There are 4 intermediate outcome measures: in the adult diabetes domain: Hgb A1C, bp and ldl control, and in other adult target conditions – bp control
Coming to results of the analysis. Here is the description of the participating pediatric practices.
As you can see 71% of practices were urban, the practice size was nearly evenly divided between small, medium and large. There was a public payer predominance to this multipayer initiative and this is reflected in the predominance of safety net practices – with 56% being CHCs and 72% of revenue coming from Medicaid.
Over the course of the 3 year demonstration, 11/22 Clinical quality measures showed statistically significant improvement from baseline.
In this graph the X axis is the measure and Y axis is percent and it is an Aggregate averages
These measures represented all of the domains except the domain, “other pediatric target conditions”, which focuses on ADHD management. The other adult measures were rollout out later in the initiative – at the x month and thus the period between baseline and the final, 11th time period was only x months. Statistically significant improvement was noted for measures in this domain but with low levels of performance even at the close of the initiative.
There were three other measures (which ones?) which showed a trend toward improvement.
Note that all of the measures that showed stat significant improvement were process measures.
Can we say anything about the other measures – 2 showed stat sig decline and 9 showed a trend toward improvement or no change – how many showed a trend and how many no change?
3 measures no change
3 measures decline
3 measures improvement
We wanted to highlight the care coordination and care management measures, since these are new services for primary care practices and of high value.
And you can see that rates are increasing across time for both measures but significant change occurred in the measure: developing care plan for highest risk patients.
Baseline is Time 2- September, October , November 2011
We decided to analyze our data further by focusing on two areas – First, Care management for highest risk patients, since this is a new service for primary care practices and a high value element of the PCMH. And second, clinical outcome measures, such as diabetes and blood pressure control. In both cases we wanted to understand factors associated with performance on these measures.. Thus, We performed a correlation analysis using the clinical quality data and data from the Evaluation, specifically practice-self assessment on transformation progress and practice staff surveys.
The variables used were: performance on these clinical measures in the last three months of the demonstration, change in clinical performance on these three measures over the demonstration, practice characteristics, related to PCMH components as assessed through the MHIQ and staff perceptions/attitudes, assessed through a staff survey.
The Transformation practice- self assessment – (MHIQ) and the staff survey were conducted at three time points – 6 , 18, 30 months (check this for MHIQ)
The practice self assessment was completed by a maximum of 3 people in the practice (separately) and the staff survey was completed by x number per practice with a total N of
Time points 1 and 2 were used for MHIQ and all three time points were used for Staff survey
Response rates for staff survey: 33% in T3, 37% in T2 response rates and 3500 is the sample size
· 55 practices (intervention and comparison) participated in the last administration (Time 3) of the Staff Members Survey. Completions per practice in Time 3 ranged from about 3-130. The number of invitations per practice ranged from 4 to 396.
Used Continuous variables:
change over time
performance at end of demonstration
Here is the correlation analysis related to care planning for highest risk patients.
Only moderated correlations were found, defined by r values ranging from 0.3 – 0.7 and moderate correlations were found only with staff survey components.
For both improvement in high risk care planning over three years and final performance on care planning, leadership and QI culture on the baseline staff survey, and strong team functioning on the timepoint 3 survey were moderately correlated.
Our Initiative did not show significant improvement in clinical outcome measures, such as diabetes and blood pressure control, so we looked at factors that might correlate to performance on these measures
The correlation analysis was performed using the final 3 month performance for these measures. Again, only moderate correlations were found (r= 0.3 – 0.7)
The strongest correlation for HbA1c < 8% was with staff being comfortable with using (staff survey) followed by QI culture (staff survey) (all time 3)
The strongest correlation with BP controlled for patients with diabetes was with Improved care planning for high risk patients (MHIQ time point 2 )and Strong leadership at baseline (staff survey)
Limitations of correlation analysis:
Wanted to do logistic regression but it was highly underpowered. Moderate correlation and can't control for other factors which you can do in logistic regression. Doesn't imply causality.
So, in summary, at the close of the MA PCMHI which was a 3 year initiative, 11/22 clinical measures showed statistically significant improvement
And these measures were process measures and in some cases, measures of new or newly documented processes. Thus they may have started out with low level performance and had much room for improvement.
On our correlation analysis, we found that ….
These findings have implications for policy and practice;
Here is a PCMH demonstration that vey clearly demonstrated statistically significant improvement in clinical quality and it was a demonstration that had a preponderance of safety net practices –implying a more complex patient population.
Our findings also suggested the importance of implementing key foundational elements of the PCMH as an approach to improving clinical quality.
Lastly, it is helpful to understand the factors that are correlated with performance on clinical quality measures to inform the focus of our transformation support.
Thank you for giving us this opportunity
Will be happy to take questions …