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The American Journal of Managed Care October 2017
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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.
To quantify the magnitude of our heart attack results from equation 1, we calculated how many additional hospitalizations would have occurred in San Diego County if there was no association between UBP and age-adjusted hospitalization rates. Furthermore, we calculated how many fewer hospitalizations would have needed to not occur in the rest of California if the same association between UBP and San Diego County’s age-adjusted hospitalization rates was also found in the rest of California. To translate hospitalizations into dollars, we assumed a hospitalization cost a payer $22,427 in 2014 inflation-adjusted dollars.32

Our study was approved by the California Health and Human Services Agency’s and University of California, Berkeley’s institutional review boards.

RESULTS

Figure 1 shows the age-adjusted number of hospitalizations per 100,000 adult population for heart attacks and strokes in San Diego County versus the rest of California between 2007 and 2014 (see eAppendix Table 1 for the underlying data). During this period, the rate for heart attacks decreased sharply in San Diego County from 180.8 to 137.4 (24.0%), but the decrease was more moderate in the rest of California, from 185.5 to 160.8 (13.3%). For strokes, the rate decreased in San Diego County from 310.1 to 277.6 (10.5%), but the percentage decrease was larger in the rest of California, from 323.6 to 269.5 (16.7%). During the UBP period, the rate for heart attacks decreased more in San Diego County (23.5%) than in the rest of California (7.6%), but decreases for strokes were similar in San Diego County (13.8%) and the rest of California (12.3%).

Figure 2 shows the age-adjusted number of hospitalizations per 100,000 adult population for heart attacks by gender, which shows the rate for males is approximately twice the rate of females, but both genders experienced similar decreases during the UBP period.

Table 1 shows the DID logistic regression results for heart attacks (models A1-A3) and strokes (models A4-A6). The top portion of the heart attack and stroke sections show the summary DID results based on the logistic regression models: they report each odds ratio (OR) that makes up the DID parameter, which is a ratio of 2 ORs. The summary result from equation 1 for San Diego County is β2 + β3, for the rest of California is β2, and for the DID ratio of the ORs is β3. Below the summary DID results, the table reports the regression parameter estimates for each model. 

In the UBP versus pre-UBP period, the odds of adults being hospitalized for a heart attack in San Diego County decreased (OR, 0.84), while the odds stayed the same for adults in the rest of California (OR, 1.00): DID ratio of OR, 0.84 (P <.001) (model 1). The results were substantively the same for males and females (models 2 and 3, respectively). In the UBP period versus pre-UBP period, the odds of adults being hospitalized for a stroke in San Diego County decreased (OR, 0.95) slightly more than for adults in the rest of California (OR, 0.97), but the ratio of the ORs was not significant at the 0.05 level (OR, 0.98; P = .08) (model 4). The stroke results were sensitive to inclusion of the control variables, whereas the heart attack results were not. For example, if the percentage of the adult population that reports no usual place of care is removed from the stroke model 4, then the DID ratio of OR increases to above 1.00 (DID ratio of OR, 1.05; P <.001). It is important to control for this covariate, but the significant change from its exclusion makes the original result less robust.

Recall that 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 Compared with the rest of California during the pre-UBP period, the San Diego County hospitalization trend approached being higher for heart attacks and was higher for strokes (see eAppendix Table 3). Assuming the phenomenon driving the nonparallel trends continued into the UBP period and assuming UBP was associated with lower hospitalization rates, then San Diego County’s pre-UBP higher hospitalization trend would cause the DID results to be understated. This may be the case for heart attacks; however, for strokes, the pre-UBP trend differences are primarily because of San Diego County’s age-adjusted hospitalization rate in 2010 being an outlier. Its rate increased by 5.1% from 2009 to 2010, while the rest of California’s rate decreased by 0.3%. Therefore, it is difficult to know whether San Diego County’s 3.7% rate decrease in 2011 was because of UBP (ie, UBP reversed the pre-UBP trend difference) or if the 2011 decrease represented a regression to the mean. However, the DIDID results in eAppendix Table 4 do not provide evidence to support that UBP was associated with a lower hospitalization rate trend for strokes during the UBP period: San Diego County’s UBP hospitalization trend (OR, 0.954; P <.001) was similar to the rest of California’s (OR, 0.959; P <.001), resulting in a UBP DID trend of 0.995 (P = .51) (see Model A10 in eAppendix Table 4).

Because of the difficulty of interpreting the DID parameter, a ratio of ORs, Table 2 presents the magnitude of the heart attack results from Table 1. From 2011 to 2014, there were 13,849 hospitalizations for heart attacks in San Diego County. Based on the logistic regression results in model 1, the difference between San Diego County’s and the rest of California’s age-adjusted hospitalization rate for heart attacks from 2007 to 2010 was 1.5 hospitalizations per 100,000 adults on average. If this difference had continued from 2011 to 2014, then there would have been 16,584 hospitalizations in San Diego County from 2011 to 2014. Therefore, this translates into the UBP being associated with 2735 hospitalizations being avoided in San Diego County (or 16.5% of the 16,584 potential) that would have cost payers $61 million (2014 dollars) in hospitalization costs from 2011 to 2014.

From 2011 to 2014, there were 174,195 hospitalizations for heart attacks in the rest of California (Table 2). Again, if the above rate difference of 1.5 had continued from 2011 to 2014, meaning that the rest of California experienced the UBP association with fewer hospitalizations for heart attacks, then there would have been only 153,994 hospitalizations in the rest of California from 2011 to 2014, a difference of 20,201 (11.6%) fewer hospitalizations at a cost of $453 million (2014 dollars).

Sensitivity Analysis

Sacramento and Los Angeles counties began less-robust UBPs in July 2012 and April 2013, respectively. If these UBPs were associated with fewer hospitalizations, then they would have attenuated our original results because they were included in the control group. When the DID regression model in equation 1 was re-estimated excluding these counties, the heart attack result did not substantively change (model 1 re-estimate: DID ratio of OR, 0.85; P <.001), but the stroke result actually became weaker (model 4 re-estimate: DID ratio of OR, 1.00; P = .79), contributing to the original stroke result’s lack of robustness.

Furthermore, we conducted a falsification test and re-estimated our DID regression model in equation 1 for heart attacks (model 1 in Table 1) by treating the 8 counties with populations of more than 750,000 adults in 2014 as though they received the UBP treatment beginning in 2011. In summary, we did not find consistent evidence that any of these 8 counties experienced what San Diego County did. Although Alameda and Orange counties’ DID results were lower or similar to San Diego County’s, those results were partially caused by their pre-2011 lower hospitalization rate trend compared with the rest of California (see eAppendix for details).

DISCUSSION

The California RCI’s UBP in San Diego County, a countywide physician organization learning collaborative, was associated with a lower level of age-adjusted adult hospitalization rates for heart attacks compared with the rest of California. The UBP was associated with 2735 (16.5%) fewer hospitalizations for heart attacks, totaling $61 million (2014 dollars). If the rest of California had experienced the same reduction in hospitalizations for heart attacks associated with UBP, then there would have been 20,201 (11.6%) fewer hospitalizations at a cost of $453 million (2014 dollars). This result is consistent with other regional collaborative efforts to improve hypertension care7-9 and inter-organizational learning activities being associated with performance improvement.33 However, a similar robust association between UBP and hospitalization rates for strokes was not found.

Our findings extend the current literature by providing evidence that physician organizations operating in the context of a highly competitive managed care market can collaborate and exchange best practices to achieve shared community-wide goals to decrease hospitalizations for heart attacks. Although there is limited evidence about the relative impact of various learning collaborative components, recent research has identified the core components of learning collaboratives that participants value most.4 Collaborative faculty, solicitation of staff ideas, change package, Plan-Do-Study-Act cycles, facilitated learning sessions, and a collaborative extranet have been identified as collaborative components that provide participants with motivation, social support, and project management skills.4 These components leverage intrinsic motivation among physicians for quality improvement.34 The UBP collaborative incorporated these core components directly or with modification. Our findings suggest that a combination of components may be needed in collaboratives to achieve desired performance outcomes as opposed to any single component. Future research should attempt to identify the key combinations of components that have the greatest impact for a particular context. For example, the UBP’s emphasis on the use of collaborative faculty (including a cardiology expert) and facilitated learning sessions warrant further study to determine if these 2 components are among the most important for achieving successful outcomes.4

Alternative Explanations and Limitations

 
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