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Countywide Physician Organization Learning Collaborative and Changes in Hospitalization Rates
Brent D. Fulton, PhD, MBA; Susan L. Ivey, MD, MHSA; Hector P. Rodriguez, PhD, MPH; and Stephen M. Shortell, PhD, MPH, MBA
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Countywide Physician Organization Learning Collaborative and Changes in Hospitalization Rates

Brent D. Fulton, PhD, MBA; Susan L. Ivey, MD, MHSA; Hector P. Rodriguez, PhD, MPH; and Stephen M. Shortell, PhD, MPH, MBA
The University of Best Practices physician organization learning collaborative in San Diego County was associated with lower hospitalization rates for heart attacks.
ABSTRACT

Objectives:
In 2011, the California Right Care Initiative implemented a countywide physician organization learning collaborative called University of Best Practices (UBP) in San Diego County for major healthcare systems and physician organizations to share best practices in managing cardiovascular and cerebrovascular risk factors. Our objective was to examine whether UBP was associated with fewer hospitalizations for heart attacks and strokes.

Study Design: A quasi-experimental design was used to compare age-adjusted adult hospitalization rates before UBP initiation (2007-2010) against rates after UBP initiation (2011-2014) in San Diego County versus the rest of California.

Methods: Difference-in-differences (DID) logistic regression models were estimated using hospitalization data from the California Office of Statewide Health Planning and Development for 2007 to 2014, including 372,205 and 642,455 hospitalizations for heart attacks and strokes, respectively.

Results: In the UBP versus pre-UBP period, the odds of adults being hospitalized for a heart attack in San Diego County decreased (odds ratio [OR], 0.84), whereas the odds stayed the same for adults in the rest of California (OR, 1.00): DID ratio of OR, 0.84 (P <.001). This relative decrease was equivalent to 2735 (or 16.5%) fewer hospitalizations, totaling $61 million (2014 dollars). No robust association was found between UBP implementation and hospitalizations for strokes.

Conclusions: A countywide physician organization learning collaborative was associated with fewer hospitalizations for heart attacks, but not for strokes. Healthcare systems and physician organizations should consider forming collaboratives to share best practices to manage patients’ cardiovascular and cerebrovascular risk factors, which may lead to fewer hospitalizations and reduced healthcare costs.

Am J Manag Care. 2017;23(10):596-603
Takeaway Points

This study evaluated the California Right Care Initiative’s University of Best Practices (UBP) in
San Diego County that began in 2011. 
  • UBP is a countywide physician organization learning collaborative that shares best practices in managing cardiovascular and cerebrovascular risk factors. 
  • UBP was associated with lower age-adjusted hospitalization rates for heart attacks in San Diego County compared with the rest of California. 
  • Healthcare systems and physician organizations should consider forming collaboratives to share best practices to manage patients’ cardiovascular and cerebrovascular risk factors, which may lead to fewer hospitalizations and reduced healthcare costs.
Cardiovascular disease is the leading cause of death in the United States, and its annual costs, combined with those of stroke, averaged $316.6 billion per year in 2011 and 2012, including $193.1 billion in direct healthcare costs and $123.5 billion in indirect costs.1-3 Achieving lower heart attack and stroke rates will not only save lives, but also generate substantial healthcare cost savings.

Quality improvement collaboratives have become a well-established approach for improving the quality of care in the United States.4,5 Several national and regional models have emerged to reduce cardiovascular and cerebrovascular diseases and their risk factors. Million Hearts, for instance, an initiative launched by HHS in 2011, aims to prevent 1 million heart attacks and strokes over 5 years by convening and educating communities, physician groups, federal agencies, and other partners.6 Regional models that aim to reduce cardiovascular and cerebrovascular risk factors include physician collaboratives focused on education, training, and data sharing. For example, South Carolina’s Hypertension Initiative, which spread to 4 states in the stroke belt, contributed to a 43% decline in coronary heart disease deaths and a 42% decline in stroke deaths between 1996 and 2006.7 Another collaborative in Ontario, Canada, promoted education in 11 primary care clinical practices, which resulted in reductions in patients’ systolic blood pressure and 10-year cardiovascular risk scores during the 3-year study period.8 Finally, a community-wide cardiovascular disease prevention collaboration in rural Franklin County, Maine, was associated with lower hospitalization and mortality rates over a 40-year period.9

University of Best Practices

The California Right Care Initiative (RCI) began in 2007, focusing on evidence-based and outcomes-improvement strategies to reduce preventable morbidity and mortality among Californians, particularly those with cardiovascular and/or cerebrovascular disease.10 One of RCI’s main strategies is to stimulate the sharing of best practices for managing risk factors (including diabetes) for these diseases among physician organizations, integrated delivery systems, and other stakeholders through regional inter-organizational learning communities known as University of Best Practices (UBP).11

The San Diego County UBP (now called “Be There San Diego”) is the longest running RCI learning collaborative. San Diego County’s efforts began with a National Institutes of Health Grand Opportunity grant in October 2009, in which the California Department of Managed Health Care and physician leaders began to plan for the learning collaborative. The UBP meetings began in February 2011, and the monthly learning collaborative is attended by medical, pharmacy, and quality improvement directors from all of the major physician organizations, integrated delivery systems, and community clinics in the county. The attendees represent organizations that serve approximately 80% of San Diego County patients, including those who are commercially insured, Medicare, Medi-Cal, safety net, US Navy, and US Department of Veterans Affairs. Each meeting is devoted to presentations and discussions of better ways to care for patients with cardiovascular and cerebrovascular disease risk factors (eg, high blood pressure, high lipid levels, high blood sugar levels, diabetes, obesity, and smoking) through patient activation methods, healthcare team coordination, and uptake of medication protocols. Each organization reports and shares its progress on these measures because controlling these intermediate outcomes prevents unnecessary heart attacks and strokes.12-15

The objective of this study is to examine whether the California RCI’s UBP in San Diego County was associated with fewer hospitalizations for acute myocardial infarctions (hereafter “heart attacks”) and cerebrovascular events (hereafter “strokes”). Learning collaboratives to improve coordination and quality of care are becoming more prevalent, as evidenced by recent initiatives including the CMS State Innovation Model Initiative, CMS’s Transforming Clinical Practice Initiative, the Medicaid program, and the Children’s Health Insurance Program.16-20 In spite of large-scale initiatives using regional learning collaboratives, no study has examined the impact of a physician organization learning collaborative in the context of a highly competitive managed care market that is attempting to collaborate on shared community-wide goals to better manage cardiovascular and cerebrovascular risk factors.

METHODS

Data

Inpatient hospitalization data are from California Office of Statewide Health Planning and Development’s (OSHPD) nonpublic Patient Discharge Data for 2007 to 2014 for adults 18 years or older. OSHPD collects these data from all hospitals in California. To identify relevant hospitalizations, we examined the principal discharge diagnosis code, which was based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). We used code 410 to capture acute myocardial infarction (heart attack)21,22 and codes 430 to 438 to capture strokes.23 Hospitalizations were converted to age-adjusted rates, based on California’s 2014 population,24,25 using the direct standardization method for those aged 18 to 64 years and those 65 years or older.26

During the 2007 to 2014 study period, there were 25,287,552 hospitalizations in California, including 437,774 for heart attacks and 667,776 for strokes in acute care hospitals. To avoid counting multiple hospitalizations for the same event, we excluded hospitalizations when a patient was discharged to the admitting or another hospital for acute inpatient care. These transfers likely occurred because the patient needed care at a higher-acuity hospital. This reduced the number of hospitalizations to 372,205 (–15.0%) for heart attacks and 642,455 (–3.8%) for strokes.

Statistical Models

To estimate whether the California RCI’s UBP in San Diego County was associated with changes in hospitalization rates for heart attacks and strokes, we estimated difference-in-differences (DID) models, which are used in quasi-experimental research designs.27,28 A DID model reduces the potential for bias by controlling for baseline differences in hospitalization rates between San Diego County and the rest of California, and by controlling for hospitalization rate reductions that occurred in the whole state during this entire period. Equation 1: 

 
ln ( p(hospi,c,t )=1  ) = β0+β1SDc+β2year11_14t+β3SDc×year11_14t+β4Xc,t
1−p(hospi,c,t )=1
 

shows the logistic regression model used to estimate the DID models, where Hosp is 1 for a hospitalization for individual (i) in county (c) in year (t) and is 0 for no hospitalization; SD is 1 for a hospitalization in San Diego County and is 0 for a hospitalization in other counties; year11_14 is 1 for years 2011 to 2014 and is 0 for years 2007 to 2010; SD × year11_14 is the DID interaction term; and Xc,t are time-varying, county-level control variables from the California Health Interview Survey,29 including the percentage of the adult population that did not have health insurance, the percentage that did not have a usual place to go when sick or needing health advice, and the percentage that had an income from 0% to 199% of the federal poverty level because these factors are associated with the ability to pay for healthcare services and access to care. The DID interaction term compares how the odds of a hospitalization changed in the UBP period (2011-2014) versus the pre-UBP period (2007-2010) for San Diego County residents versus the residents in the rest of California. Separate models were estimated for heart attack and stroke hospitalizations.

In addition to the UBP in San Diego County, Sacramento and Los Angeles counties began UBPs in July 2012 and April 2013, respectively; however, they have far less physician organization participation and lower fidelity of implementation of learning collaborative models.4 As a sensitivity analysis, we re-estimated the DID models by excluding these counties.

We also analyzed pre-UBP hospitalization trends for San Diego County and the rest of California, because a DID model assumes parallel pretreatment trends, which means that absent the UBP intervention, the hospitalization rates in San Diego County and the rest of California are expected to change at the same rate.28 As a supplemental analysis, we extended the pre-UBP analysis by examining pre-UBP versus UBP period hospitalization trends in San Diego County and the rest of California using a piecewise linear regression difference-in-differences-in-differences (DIDID) model. The eAppendix describes these methods in detail (eAppendix available at ajmc.com).

Our data only include individuals who were hospitalized. Therefore, we estimated the logistic regression models using Stata version 12 (StataCorp; College Station, Texas) blogit command that implicitly includes individuals who were not hospitalized, because one variable within the blogit command is the population, which we obtained from the California Department of Finance.24,25 The number of individuals who were not hospitalized equals the population minus the number of hospitalizations from individuals who were hospitalized, which allows for more than 1 hospitalization in a year for a separate medical event for an individual (eg, ICD-9-CM code 412 is for previous heart attacks, which we did not include). The other key variable within the blogit command is the age-adjusted number of hospitalizations, which we calculated by multiplying the aged-adjusted rate by the population for each county(s)-year. The blogit command produces the same results as if we had estimated our models as logistic regressions with individual-level data that include both hospitalized and nonhospitalized individuals.

Our blogit model estimation approach is similar to 2 studies that analyzed emergency department (ED) visits, which explicitly included individuals who were not present in the ED visit data.30,31 We did not explicitly include individuals who were not present in our hospitalization data because we age-adjusted our hospitalization rates. Moreover, San Diego County did not experience gender and age demographic changes between the 2007-to-2010 period and the 2011-to-2014 period that substantively differed from changes in the rest of California.

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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