Clinic Characteristics that May Impact Diabetes Care Outcomes and Costs: Conceptual Approach

Clinic Characteristics that May Impact
Diabetes Care Outcomes and Costs:
Conceptual Approach
Patrick J. O’Connor MD MPH
Robin R. Whitebird Ph.D.
Jon C. Christianson Ph.D.1
Paul E. Johnson Ph.D.1
Stephen E. Asche MA
Leif I. Solberg MD
A. Lauren Crain Ph.D.
William A. Rush Ph.D.
Gail Amundson MD
Andy Van de Ven Ph.D.
HealthPartners Research Foundation
& The University of Minnesota
of Minnesota
We conducted an observational
study to identify patient, physician,
& primary care clinic characteristics
that predict glycated hemoglobin
(A1c) levels and costs of care for
diabetes patients over a 3-year
period (AHRQ RO1 HS11919)
The study included 2,117 adults with an
established diagnosis of diabetes, 349
primary care physicians who provided
care to these patients, 84 clinics, and 19
Medical Groups. Data were collected for
a 36-month period that ended in 2002;
there were 5,199 patient-years of data
available for this analysis. Hierarchical
analysis was used to accommodate the
nested study design and data.
• Significant variation in costs and quality of
care would be located at the patient,
physician, clinic, and medical group levels.
• After control for patient and physician
characteristics, specific clinic and/or
medical group characteristics significantly
related to quality and costs.
Conceptual Approach
We conceptualized four principal
domains of clinic characteristics:
–Work Environment
–Clinic Systems to Improve Care
–Quality Improvement Strategies
–Financial Stability and Systems
Work Environment
Affiliation & Participation
Recognition & Compensation
Control over Work Processes
Satisfaction with Leadership
Satisfaction with Medical Group & Clinic
Does Quality Need Improvement?
Is Improvement a Priority Here?
Clinic Systems to Improve Care
Expanded Roles for Nurses/Teams
Electronic Medical Records
Monitoring of Clinical Status
Prioritization based on Risk, RTC
Active Interventions:
– Visit Planning
– Active Outreach
– Patient Activation
Quality Improvement Strategies
Leadership Stability
Improvement Priorities
Financial Incentives for Quality
Resources Allocated to Improve Quality
External Accountability for Improvement
Investments in Information Infrastructure
Feedback to Physicians (Quality, Costs)
Financial Stability & Systems
Ownership of Medical Group
Longevity of Medical Group
Recent Expansion or Contraction
Perceived Financial Pressures
Profitable in Last 3 Years?
Stability of Leadership and Physicians
Capitated or FFS?
Insurance Mix, Uninsured
Physician Compensation Arrangements
Results: A1c Values
• A1c levels improved overall across
these medical groups and clinics:
• 1999 mean A1c = 7.52
• 2000 mean A1c = 7.32
• 2001 mean A1c = 7.26
Intercept-Only MLwiN Model
• 80+% of variance at patient
• 5-10% at clinic level
• 5-7% at medical group level
Many Patient Factors Were
Associated with Better A1c:
• Older Age (p < 0.001)
• Higher Educational Level (p=0.008)
• Duration of Diabetes < 10 years (p < 0.001)
• Greater Comorbidity (p < 0.001)
• Readiness to Change (interaction effect)*
• Drug Intensification (p < 0.01)*
Patient gender (p=0.19), physician gender (p=0.16), primary
care physician specialty (p=0.18), and years of practice
experience (p=0.21) were not associated with A1c level.
After adjustment for patient and
physician characteristics, clinic
factors associated (p<0.05) with
best A1c improvement:
• Stable Medical Leadership
• Use of a Diabetes Patient Registry
• Diabetes Educator On-site at Clinic
• Financial Incentives to Improve Care
• Financial Pressures on Clinic
Clinic factors associated with future
costs after adjustment for patient
and physician factors:
•Use of a Diabetes Patient Registry
•Physician Team Meetings to Discuss Care
•Feedback to Docs on Costs of Care
•Monitoring Physician Drug Use Patterns
Strengths of Study
• Large number of clinics, physicians, and
patients with diabetes.
• Use of hierarchical analytic models to
accommodate nested data.
• Uniform data collection procedures and
standards at all clinics.
• Single data source for all cost data.
Potential Limitations
• Observational study
• Insufficient variation in measures at the
level of clinics or medical groups
• Low power at the level of the medical
groups (N=19)
• Something in the Minnesota air would
wash out the impact of things we wanted
to know about (unmeasured confounder)
Conclusions 1
• Most variation in quality of care was at
the patient level, with smaller but
significant variation at the physician
• Little significant variation was noted at
the clinic level (N=84) or at the medical
group level (N=19)
Conclusions 2
After control for patient and provider
characteristics, we identified specific
characteristics of clinics that predict
better-than-average glycemic control
improvement over a 3-year period, and
several factors related to increased or
decreased costs.
Future Directions
• Translate clinical measures into QALYs
and/or a composite risk index
• Conduct analysis to assess factors
associated with efficiency at the level of
the patient, physician, clinic, and medical
• Focus More Attention on the PhysicianPatient Dyad to Improve Care