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The American Journal of Managed Care February 2017
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Sustained Participation in a Pay-for-Value Program: Impact on High-Need Patients
Dori A. Cross, BSPH; Genna R. Cohen, PhD; Christy Harris Lemak, PhD; and Julia Adler-Milstein, PhD
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Sustained Participation in a Pay-for-Value Program: Impact on High-Need Patients

Dori A. Cross, BSPH; Genna R. Cohen, PhD; Christy Harris Lemak, PhD; and Julia Adler-Milstein, PhD
Among Michigan primary care practices, sustained participation in a pay-for-value program appears to contribute to improved utilization outcomes for high-need patients.
The distribution of our outcome measures fell into 1 of 2 categories. Most of our outcomes were 0-inflated; for these outcomes, our models simultaneously estimated both a binary outcome (odds of a patient incurring any cost or use), as well as continuous outcome (eg, estimated cost or number of encounters, conditional on a patient incurring at least some use in that category of service). The remaining outcomes were quality measures (for which all patients received a score) or use categories that are much more frequently used (ie, nearly 100% of patients in the sample would be expected to [and did] incur use greater than 0): total medical–surgical costs, outpatient costs, and PCP visits. For these measures, we only considered the continuous outcome. For continuous outcomes, costs were modeled using a log-normal distribution, use indicators were modeled using a Poisson distribution, and quality indicators with a normal distribution. In robustness tests, we ran models with alternate distribution assumptions (gamma for cost measures, negative binomial for use).


Patient and Practice Population

Our analytic sample included 17,443 unique patients, each with 2 or more medical conditions (Table 1). Average age (51.8 years) and gender (47.9% male) in our sample did not differ significantly from the remaining population of patients with 0 or 1 condition. However, our sample population had (by definition) significantly higher incidence of disease and greater healthcare utilization across all metrics. The most common medical conditions were type 2 diabetes (52.9% of the focal population) and chronic obstructive pulmonary disease (27.2%), followed by liver disease (22.3%), asthma (20.2%), and cancer (17.6%).

The patients included in our sample were seen in 1582 unique practice locations (Table 2), 1401 of which had 4 continuous years of continuous PGIP participation. Our control group (n = 181) was made up of 114 practices with no PGIP participation, and 67 with partial participation (average duration of 6 months over the 4 years we examined). More than half of the practices were solo physician offices (56.1%). Practices had an average attributed panel size of 825 BCBSM patients, with high-need patients comprising, on average, 4.1% of that panel. The average PCP age across practices was 51 years.


Outcomes: Cost

In 2010, total medical–surgical cost did not differ for patients in PGIP and control practices (P = .123) (Table 3). Over time (2010-2013), patients in PGIP practices had similar trajectories of medical–surgical cost (+0.6% for PGIP relative to control; P = .668) (Table 3).

Although patients in PGIP practices, relative to control, incurred lower average inpatient, outpatient, and ED costs in 2010, only the difference in outpatient costs was statistically significantly: PGIP patients incurred 10.6% lower outpatient costs compared with control patients (P = .002). However, over the 4-year study period, patients in PGIP and control practices did not differ in their odds of incurring any inpatient, outpatient, or ED costs, nor in the amount of spending conditional on having any spending.

Patients in PGIP practices experienced lower odds of incurring any drug costs (odds ratio [OR], 0.80; P = .003 [Table 3]) than patients in control practices in 2010, whereas average total drug costs for patients with drug spending greater than $0 did not differ between PGIP and control. Over time, PGIP patients further reduced their odds of any drug spending (OR, 0.82; P <.001 [Table 3]), but, conditional on incurring any drug costs, total drug costs increased at a steeper rate for PGIP patients relative to control (+3.9%; P <.001).

Outcomes: Utilization

In 2010, we observed no difference between PGIP and control patients on the odds of incurring any utilization, or amount of use (conditional on having any), for inpatient admissions or ED visits. Over time, PGIP and control patients had similar odds of any hospitalization (OR, 0.93; P = .108), but, among patients who incurred at least 1 hospitalization, PGIP patients experienced a steeper increase in number of hospitalizations relative to control patients (+5.7%; P = .047 [Table 3]). For ED visits, PGIP patients had lower odds of incurring any ED visit over time compared with control patients (OR, 0.88; P = .0002 [Table 3]), but did not differ in the number of ED visits (+3.2%, P = .132).

In 2010, PGIP patients had lower 30-day readmissions (–38.2%; P = .002 [Table 3]) and 90-day readmissions (–25.7%; P = .018 [Table 3]) compared with control patients. Over the 4-year study period, PGIP patients continued to significantly outperform control patients, both in terms of odds of incurring any readmission over time (OR, 0.65 for 30-day and 0.63 for 90-day; P <.001 for both [Table 3]), as well as the number of readmissions, conditional on having any (30-day: –19.9%, P =.008; 90-day:–27.5%, P <.001 [Table 3]) (Figure).

Finally, in 2010, PGIP patients had fewer PCP visits (–4.8%; P <.001) and more specialty visits (+12.7%; P <.001). Over time, for both PCP and specialty visits, patients in PGIP and control practices did not differ in either their odds of incurring any visits, or the number of visits, conditional on having any (Table 3).


Outcomes: Quality

In 2010, there was no difference in overall quality or medication management quality between the 2 patient groups. Over time, PGIP patients realized significantly greater improvement relative to control patients for both overall quality (+1.6%; P ≤.009), as well as medication management quality (+3.0%; P <.001).

Robustness Tests

In models with alternate distributional assumptions (gamma distribution for cost measures, negative binomial distribution for utilization measures) and robust standard errors, our primary results largely persisted. At baseline, these models provided consistent or stronger evidence that PGIP practices outperform non-PGIP practices (eAppendix Table 2). Trend results were also consistent with our original results (eAppendix Table 3); however, in trend models with robust standard errors and our original distributional assumptions, our results related to drug costs, ED visits, and overall quality were no longer statistically significant at traditional thresholds. Given the fact that some coefficients changed as well, we suspect that these differences reflect instability in this particular specification of the model, and they were incorporated into our analysis with caution.



Our longitudinal analysis of more than 1500 primary care practices in Michigan over a 4-year period suggests that sustained participation in a pay-for-value program results in modest but meaningful improvements in care for high-need patients. Performance for practices participating in the PGIP pay-for-value program improved relative to nonparticipants in 3 domains. First, PGIP practices consistently and significantly outperformed control practices on 30- and 90- day readmissions. In 2013, compared with 2010, sustained PGIP participation resulted in a reduction of 25 readmissions per 1000 patients. Second, we found suggestive evidence that PGIP practices were able to reduce odds of incurring any ED utilization over time to a greater extent than control practices. Finally, we also found suggestive evidence that patients in PGIP practices saw significantly greater improvement over time in the quality of overall quality, as well as medication management quality (which could explain the increase in drug costs over time). However, total medical–surgical cost was not reduced, likely because avoided use was for relatively rare events and was partially compensated for by increased drug spending. In addition, overall quality did not improve over time. Taken together, our results suggest that sustained participation may be an important factor in improving specific dimensions of care for high-need patients under a pay-for-value program.

In order to see the benefits of participation in a pay-for-value program for high-need patients, practices appear to need to engage with the program in a sustained way. The changes in primary care practices that are required to improve care for high-need patients— including significant changes in organizational culture, an emphasis on teamwork, and staff-level buy-in to new care processes— likely require pursuit over multiple years.19,35 Practices also need time to understand program expectations and develop and reinforce new behaviors and processes that support redesigned care. The rapid growth in the PCMH component of PGIP over the study period is likely a key contributor to observed changes in our outcome measures; however, we believe the additional PGIP programs beyond PCMH play a critical role in providing additional resources and incentives to support and sustain practice changes that lead to higher-quality care.

We observed heterogeneous effects of sustained PGIP participation across our outcomes that are mostly consistent with these expectations. Specifically, sustained participation was associated with reductions in readmissions, better control over any ED use, and improved quality. Changes in these measures likely result from changes that take time to implement but lie within the control of primary care practices. For example, high-need patients are likely to have a high volume of healthcare encounters with many different providers, both specialists and hospital-based clinicians. Providers need time to develop and implement new systems and workflows for managing patient transitions and the volume of information flowing in and out of their practice, such as regular medication reconciliation checks and active follow-up after hospital discharge. In contrast, we found no program effect on inpatient utilization or total medical–surgical cost, which may reflect the fact that these 2 measures are less sensitive to changes that can be made by primary care practices. Significantly improving these outcomes, even among high-need patients who offer the greatest opportunity for gains, likely requires broader changes to the health system and to patient behavior—both of which are complex and require a long time frame to address.


Our study has several limitations to be considered when interpreting the results. First, because providers are not randomly assigned to PGIP (ie, providers self-select to participate), there may be unobserved differences between practices that sustained participation and those that did not, which might influence our patient outcome measures. For example, practices that already had support and resources from an umbrella provider organization prior to the start of our study period may have been more likely to sustain participation and also have better performance. We therefore focused on an associational analysis; however, we were able to use panel data with patient-level random effects to control for time-invariant patient characteristics. We compared performance in the baseline year, as well as trends over time between PGIP and control practices, to distinguish between regression to the mean and true improvements due to sustained program participation. Finally, the availability of control practices in the sample helped isolate sustained PGIP participation effects from secular time and maturation effects.

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