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«Comparing the Cost of Care Provided to Medicare Beneficiaries Assigned to Primary Care Nurse Practitioners and Physicians Jennifer Perloff, ...»

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Health Services Research

© Health Research and Educational Trust

DOI: 10.1111/1475-6773.12425


Comparing the Cost of Care Provided to

Medicare Beneficiaries Assigned to

Primary Care Nurse Practitioners and


Jennifer Perloff, Catherine M. DesRoches, and Peter Buerhaus

Objective. This study is designed to assess the cost of services provided to Medicare

beneficiaries by nurse practitioners (NPs) billing under their own National Provider Identification number as compared to primary care physicians (PCMDs).

Data Source. Medicare Part A (inpatient) and Part B (office visit) claims for 2009– 2010.

Study Design. Retrospective cohort design using propensity score weighted regression.

Data Extraction Methods. Beneficiaries cared for by a random sample of NPs and primary care physicians.

Principal Findings. After adjusting for demographic characteristics, geography, comorbidities, and the propensity to see an NP, Medicare evaluation and management payments for beneficiaries assigned to an NP were $207, or 29 percent, less than PCMD assigned beneficiaries. The same pattern was observed for inpatient and total office visit paid amounts, with 11 and 18 percent less for NP assigned beneficiaries, respectively. Results are similar for the work component of relative value units as well.

Conclusions. This study provides new evidence of the lower cost of care for beneficiaries managed by NPs, as compared to those managed by PCMDs across inpatient and office-based settings. Results suggest that increasing access to NP primary care will not increase costs for the Medicare program and may be cost saving.

Key Words. Nurse practitioner, cost, primary care In their role as primary care clinicians, nurse practitioners (NPs) work both autonomously and in collaboration with primary care and specialist physicians, physician assistants, and other clinicians. The preponderance of findings from research extending over several decades, including randomized control trials, indicate that NPs achieve clinical outcomes and patient 2 HSR: Health Services Research satisfaction comparable to primary care physicians across a variety of settings and diagnoses (Mundinger et al. 2000; Venning et al. 2000; Brooten et al. 2002; Sakr et al. 2003; Lenz et al. 2004; Naylor et al. 2004; Wilson et al. 2005; Sears et al. 2007b; Ohman-Strickland et al. 2008).

In recent years, policy makers have focused increasing attention on the potential of NPs to expand the capacity of the primary care workforce. Health insurance expansions under the Affordable Care Act (ACA), combined with the aging of the baby boom generation, are expected to increase the demand for primary care services and exacerbate current and projected problems in primary care physician supply and geographic distribution (Hofer, Abraham, and Moscovice 2011). Additionally, the ACA encourages the development of new models of primary care delivery that emphasize greater collaboration and teamwork between physicians and other clinicians, including NPs (Bodenheimer and Smith 2013). Finally, reports from the Institute of Medicine (2010) and National Governor’s Association (2012) recommended the removal of state scope of practice regulations that restrict NPs from practicing to the full extent of their education and licensure.

A central tenet of the argument for expanding the numbers and roles of primary care NPs is that it takes less time to educate an NP compared to their physician counterpart, and that these clinicians provide equivalent quality on many dimensions of primary care. Although a few studies have examined the costs of NP provided care compared to primary care physicians (Roblin et al.

2004; Laurant et al. 2009), these studies focused on a specific disease or care setting and analyzed costs over a limited period of time. To our knowledge, no study has used national-level data to systematically examine the cost of primary care services provided by NPs and primary care physicians over an extended time frame.

In this study we use Medicare administrative data to assess the cost of services provided over a 12-month period to Medicare beneficiaries treated by NPs billing under their own National Provider Identification (NPI) number. We apply standard methods for assigning Medicare beneficiaries to NPs and to primary care physicians, control for patient severity and other differences that may affect the cost of care, and examine the cost of services provided by both clinicians.

Address correspondence to Jennifer Perloff, Ph.D., The Heller School, Brandeis University, MS035, 415 South St., Waltham, MA 02454; e-mail: perloff@brandeis.edu. Catherine M.

DesRoches, Ph.D., is with the Mathmatica Policy Research, Cambridge, MA. Peter Buerhaus, Ph.D., R.N., F.A.A.N., is with the College of Nursing, Montana State University, Bozeman, MT.

Comparing the Costs of NP and PCMD Care 3


We use a retrospective cohort design to compare Medicare allowed payment amounts for inpatient and ambulatory services provided by NPs and primary care physicians and use propensity score weighted regression to adjust for differences between beneficiaries.

Sample We developed our sample in two stages. First, we selected a random sample of NPs and primary care physicians with NPIs and, second, gathered all claims for the beneficiaries treated by these clinicians during 2009 and 2010. As very little is known about the panel sizes of Medicare beneficiaries for NPs, we purposefully weighted our sample to include approximately two-thirds NP associated beneficiaries and one-third primary care physicians beneficiaries to help capture an adequate number of NPs. On average, NPs see about half as many Medicare beneficiaries as primary care physicians (see Table 1).

Because each of the selected beneficiaries was treated by a number of clinicians, including specialists and others over the 12-month period, gathering all claims for each beneficiary of our selected NPs and primary care physicians resulted in additional clinicians captured in the sample. The two-step process resulted in 128,006 beneficiaries with one or more claims with specialty code “50” = NP and 9,422 unique NPs (Table 1). For primary care physicians we used Part B specialty codes “08” = family medicine and “11” = internal medicine to capture 474,243 beneficiaries and 68,069 unique physicians.

–  –  –

*Derived from the 100% sample of all Medicare Part B claims maintained by Buccaneer.

† Medicare, Part B paid claims for our sample.

4 HSR: Health Services Research Attribution We assigned beneficiaries to NPs and primary care physicians based on the distribution of the beneficiaries’ total evaluation and management (E&M) expenditures. Specifically, we used the new (M1A: 99201-99205) and established office visit (M1B: 99211-99215), home visit (M4A: 99340-99345;

99347-99350), or nursing home visit (M4B: 99304-99306; 99307-99310) from the Berenson-Eggers Type of Service Codes (BETOS) to constitute primary care E&M. These codes eliminate inpatient, emergency department, and specialist services from our definition of E&M.

Because each beneficiary in the sample had claims from an average of eight unique clinicians in 2009, we used the following attribution procedure to help ensure that a relatively strong clinical relationship existed between the beneficiary and the NP or primary care physician. First, we calculated the proportion of primary care E&M paid amounts provided to a given beneciary by each clinician submitting a claim for that beneficiary in 2009 (range 0–1). Second, the beneficiary was then assigned to the clinician who provided the highest proportion of E&M paid amounts to that beneficiary.

Third, we imposed a 30 percent minimum threshold for assignment; that is, for a given beneficiary to be assigned to a primary care clinician, that clinician had to provide the highest proportion of E&M services and this proportion had to represent at least 30 percent of the beneficiary’s total E&M claims (Mehrotra et al. 2010). In the case of ties (two or more providers with the same percent of E&M paid amounts), one primary care clinician was randomly selected.

As a sensitivity test, we reproduced our results using 20 percent, 30 percent, 40 percent, and 50 percent minimum attribution thresholds (see Appendix SA2). Above 50 percent, the sample size begins to decrease, raising concerns about bias; however, the core results remain the same regardless of the attribution threshold used.

Dependent Variables Cost was defined as the Medicare paid amount on paid claims. The influence of the primary care provider on costs likely increases as the setting of care for a given beneficiary shifts from inpatient to office based. To capture this influence, we used the total Part A, or inpatient paid amount, total Part B, or clinician paid amount, and E&M amount (as defined by the BETOS procedure code groupings on the Part B claim).

Comparing the Costs of NP and PCMD Care 5 Under Medicare rules, NPs are permitted to bill their services at 85 percent of physician fees. This, by definition makes physicians more expensive, and therefore cost cannot be used as a proxy for resource use. To adjust for this payment differential, we included the work component of the resource value unit (RVU) as an additional dependent variable. An RVU consists of three components: work, practice, and malpractice expenses. The work component uses the Current Procedural Terminology code on the claim to determine the level of intensity and skill required to perform a given service. It provides a measure of resource use that is not affected by the payment differential. We multiplied the work RVU by the mean Medicare payment for primary care services in 2010 ($40) (Health Care Cost Institute 2012), to create our new dependent variable, dollar adjusted RVUs.

Independent Variables To control for the effects of geographic variation in health care spending and regional practice differences on our measures of the costs of care (Newhouse and Graham 2013), we used the beneficiary zip code from the Medicare enrollment file to identify the CMS region in which a given beneficiary resided in 2010. Regions 8 and 9 were combined because of small sample size, leaving seven regions in the model and CMS Region 4 (AL, FL, GA, KY, MS, NC, SC, TN) as the reference group. A rural flag was also added from the Area Resource File based on beneficiary’s place of residence. We included age in years, female sex, and white race as demographic controls derived from the enrollment file. Dual Medicaid-Medicare eligibility status was defined as one or more months of state buy-in, meaning the state subsidized the beneficiary’s Medicare premium for at least 1 month during the calendar year. This variable serves as a proxy for poverty and identifies approximately 75 percent of dually eligible beneficiaries (Research Data Assistance Center 2012).

Clinical severity for each beneficiary was captured by the Elixhauser comorbid conditions (Elixhauser et al. 1998). The 30 comorbidities were identified through the diagnostic codes on all of a beneficiary’s ambulatory and institutional paid claims in 2010.

Analysis We used propensity score weighting to adjust for observable differences between the beneficiaries assigned to NPs and to primary care physicians.

Propensity score weighted regression involves the estimation of a logistic 6 HSR: Health Services Research regression model predicting the probability that a given beneficiary will see an NP (Hirano and Imbens 2001; Weintraub et al. 2012). For this model, we included all available demographics (age, sex, race, dual eligible, and original reason for entitlement), CMS region, and Elixhauser comorbidities. The predicted probability was then used as a weight in the paid amount and dollar adjusted RVU ordinary least squares regression models. In these models, each NP beneficiary was weighted 1 and each physician beneficiary was weighted p/(1-p), where p is the probability of seeing an NP. The models controlled for the independent variables identified above.


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