Health Practitioner Bonuses and Their Impact on the Availability and Utilization of Primary Care Services. 2. Impact of PCIP Policy on Number of Claims for E&M Services


The DID estimates from the model using the volume of E&M claims as the outcome variable are presented in Exhibit 15. The estimated coefficient of that interaction term in the first column of Exhibit 15 (i.e., column (1)) indicates that in response to the Medicare PCIP policy PCPs with PCIP eligible specialties reduced their number of PCIP eligible E&M claims by about 52 on average.43 This represents a reduction in the volume of claims per provider of a little less than 9 percent. Furthermore, we estimate that on average providers with PCIP eligible specialties had about 187 more E&M claims submitted under Medicare during the sample period than the providers with PCIP non-eligible specialties. Also, for the providers with both PCIP eligible and non-eligible specialties the average number of E&M claims submitted went down by 70 claims after 2009.

In addition, we find that as the number of primary care providers per 10,000 population increases, the number of claims per provider decreases. Finally, the volume of claims per provider increases with the provider’s age and with the increase in the size of the Medicare population. The effect of unemployment on the number of E&M claims per provider is also positive and statistically significant.

When the sample is restricted to providers who were near the PCIP eligibility threshold in 2009 (column (3)), the estimated reduction in the number of claims per provider due to the PCIP policy is smaller, only about 33 claims.44 The reduction in the number of claims per provider in response to the PCIP policy may appear counterintuitive. However, the total number and value of claims may still increase as a result of the policy, as long the number of providers with a PCIP eligible specialty increases at a higher rate than the decline in the number of claims submitted per provider. Thus, a reduction in the number of claims per provider should not be interpreted as evidence of decreased access to primary care services if the number of providers is increasing at the same time.

Exhibit 15: Impact of Medicare PCIP Policy on PCIP Eligible E&M Claims (2005-2011)

Dependent Variable: PCIP Eligible E&M Claims per Provider

Analysis Sample

(1) E&M Claims (2) E&M Claims (Providers in All Years) (3) E&M Claims (Near Eligib. '09)

PCIP Elig. Specialty Indicator

186.9* 116.6* 117.6*
(12.93) (18.38) (12.46)

Elig. Specialty Ind x Post 2009

-51.73* -36.96* -32.59*
(1.814) (2.412) (5.762)

Post 2009

-74.42* -77.27* -37.28*
(2.692) (3.423) (3.155)


8.450* 6.980* 2.241*
(0.0450) (0.0637) (0.0547)


165.7* 198.9* 90.39*
(0.965) (1.243) (1.162)


-39.78* -51.01* -49.00*
(1.712) (2.153) (2.190)

Median Income ($10k)

-7.296* -5.815* 0.0475
(0.886) (1.137) (1.057)

Percent in poverty

-4.678* -4.105* 1.523*
(0.222) (0.283) (0.272)

Population (10k)

-0.107* -0.134* -0.0710*
(0.00441) (0.00576) (0.00531)

Percent Population over 65

17.84* 20.72* 18.44*
(0.256) (0.319) (0.322)

Unemployment Rate

13.07* 17.66* 6.838*
(0.425) (0.560) (0.522)

Primary Care Phy./pop10k

-0.156* -0.172* -0.258*
(0.00716) (0.0102) (0.0146)

PC Non-phy./pop10k

-0.452* -0.627* -0.208*
(0.0154) (0.0234) (0.0228)


-346.10* -303.30* -114.60*
(16.69) (22.63) (17.34)
Specialty Fixed Effects Yes Yes Yes
Year Fixed Effects Yes Yes Yes
State Fixed Effects Yes Yes Yes
N 2,014,835 1,397,760 746,845
Adj. R-sq. 0.222 0.241 0.380

Note: Robust standard errors are in parentheses; + significance at 10 percent; * significance at 5 percent.

43 The post-post policy period in our analysis comprises of two years: 2010 and 2011. We have also estimated the impact of the PCIP policy on the volume of claims and other outcomes discussed later for each post-policy year separately and the effects in these two years are not statistically different for a given outcome of interest.

44 We focus on the primary care providers who are near the PCIP eligibility threshold because these providers are at the margin to improve the volume of their PCIP eligible services by incurring a relatively low cost and gain the PCIP incentive payment (i.e., marginal benefit is much higher relative to the marginal cost). Therefore, we would expect the impact of PCIP policy (if there is any) would be more substantial among the providers near the eligibility threshold. However, if we only focus on those who met the 60% criteria then we would lose the behavioral changes among the providers who are below the threshold may have tried to reach the 60% threshold in response to PCIP. This may bias the PCIP impact. By focusing only on those providers who met the 60% eligibility criteria, we would face the typical problem of selection bias. Hence, focusing on those providers who are near the eligibility threshold circumvents this problem but captures the essence of the hypothesis that we are referring to. In addition, we have estimated several models with different ranges (50%-65%, 50%-70%, 55%-65% and 40%-80%) of the proportion of allowed charges on PCIP eligible primary care services and tested the sensitivity of our results. At least for the first three ranges mentioned, we find that results are not sensitive to those three ranges. However, once we broaden the analysis to 40-80%, the estimated impact is in between the effects we observed for all providers and the providers in 50-65% threshold (as expected). Also the mean value of this proportion in the sample is about 53%.

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