Medicare Part D Claims Rejections for Nursing Home Residents, 2006 to 2010

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The American Journal of Managed Care, October 2012, Volume 18, Issue 10

This study presents data on paid and rejected claims submitted by 1 large long-term care pharmacy over the initial 5 years of Medicare Part D.


Much has been written about trends in Medicare Part D formulary design and consumers’ choice of plans, but little is known about the magnitude of claims rejections or their clinical and administrative implications. Our objective was to study the overall rate at which Part D claims are rejected, whether these rates differ across plans, drugs, and medication classes, and how these rejection rates and reasons have evolved over time.

Study Design and Methods:

We performed descriptive analyses of data on paid and rejected Part D claims submitted by 1 large national longterm care pharmacy from 2006 to 2010. In each of the 5 study years, data included approximately 450,000 Medicare beneficiaries living in long-term care settings with approximately 4 million Part D drug claims. Claims rejection rates and reasons for rejection are tabulated for each study year at the plan, drug, and class levels.


Nearly 1 in 6 drug claims was rejected during the first 5 years of the Medicare Part D program, and this rate has increased over time. Rejection rates and reasons for rejection varied substantially across drug products and Part D plans. Moreover, the reasons for denials evolved over our study period. Coverage has become less of a factor in claims rejections than it was initially and other formulary tools such as drug utilization review, quantity-related coverage limits, and prior authorization are increasingly used to deny claims.


Examining claims rejection rates can provide important supplemental information to assess plans’ generosity of coverage and to identify potential areas of concern.

(Am J Manag Care. 2012;18(10):647-654)Our findings suggest that medication-specific and planwide rejection rates could be useful information, especially for policy makers and clinicians.

  • Policy makers could monitor rejection rates across plans to identify potential access problems for Medicare beneficiaries, to inform the Part D appeals process, and to make adjustments to regulatory guidance.

  • More specific to the nursing home population, policy makers could consider using rejection rates in decisions about plan assignment for dually eligible residents or which plans are eligible to serve dually eligible individuals.

  • Similarly, providers and pharmacies could monitor rejection data in establishing formularies and prescribing practices more generally.

The US government relies on private plans to administer the Medicare Part D prescription drug benefit, including the establishment of premiums, pharmacy networks, and formulary design. Within limits, Part D plans (PDPs) have flexibility to shape enrollees’ drug use through levers such as coverage, cost-sharing, and utilization management techniques including prior authorization, step therapy, and quantity limits. The underlying expectation of this approach is that informed consumers will choose the plan that best suits their needs and that price competition across plans will optimize government payments for drugs.1

Although Part D includes special protections for nursing home residents, the program’s administrative reliance on private plans and the emphasis on consumer choice is similar across institutional and community settings. In addition to nursing home residents having relatively high levels of medication use and physical and cognitive frailty as compared with other Medicare beneficiaries, a key difference in the nursing home setting is that almost two-thirds of long-stay residents are dually eligible for Medicare and Medicaid.2 Part D’s implementation shifted medication coverage for duals from Medicaid to Medicare and randomly assigned these individuals to plans with premiums at or below regional benchmark rates set by the Centers for Medicare & Medicaid Services (CMS). Under Part D, nursing homes and the pharmacies with which they contract no longer function under a state’s Medicaid policies for dually eligible residents but instead work across multiple plans, each of which may have different formulary designs and administrative procedures.3 The transition to Part D in the nursing home sector has not always been smooth, with early concerns about coverage adequacy and administrative burdens expressed by physicians, pharmacists, and administrators working in nursing homes.4,5

Little has been written about the extent of claims rejections or their clinical and administrative implications. One previous study using data on claims in rejected status at the end of 2006 found considerable variation in the reasons for rejection across medications and in the relative rejection rates across PDPs.6 However, no published information exists about the overall rate at which Part D claims are rejected; whether these rates differ across plans, drugs, and medication classes; and how these rejection rates and reasons have evolved over time. To address these questions, we examined data on paid and rejected Part D claims submitted by 1 large national long-term care pharmacy (LTCP) over the initial 5 years of Medicare Part D.


eAppendix A

We obtained data on all paid and rejected Part D claims from Omnicare, Inc (Covington, Kentucky), the nation’s largest long-term care pharmacy, operating in 47 states and serving approximately half of US nursing home residents. A claim is defined as a demand of payment for a particular drug, for a particular individual, on a particular date. Data include Part D claims for nursing home and assisted living residents, although more than 8 in 10 claims are estimated to be for nursing home residents. Data capture all claims filed in the month of March for each of 5 study years (2006-2010) (although we purposely avoided using data from January because of potential transition challenges at the beginning of the year, the choice of March was somewhat arbitrary. March 2010 also was the latest month available when the study began). All observations include the claim date; unique identifiers for residents and facilities; the National Drug Code (NDC) of the product; the plan to which the claim was submitted; and up to 3 reasons for denial of rejected claims. For ease of presentation, we group rejections into 3 broad categories (see , available at 1) Product not covered—capturing instances where the product is not covered; 2) utilization management techniques—capturing instances where coverage applies only after plans approve necessary documentation from pharmacies and clinicians (prior authorization), where less expensive medications must first be tried and failed before more expensive medications are dispensed (step therapy), and instances where plans limit the number (or amount) of drugs covered within a certain time period (quantity limits and refill too soon); and 3) administrative rejections—including instances where claims have non-matched pharmacy or member identification numbers and missing/invalid information about the prescriber, patient, or prescription itself. Importantly, administrative rejections can stem directly from coverage restrictions (eg, missing or inadequate justification for a “dispense as written” prescription order is the second-most prominent code in this category).

We define the total number of claims as the sum of paid claims and the subset of rejected claims that remain unpaid at the end of the month. We are able to flag claims that were rejected multiple times (around 1 in 4 rejected claims) and claims that were rejected and then paid during the 1-month windows of the 5 study years. We count these flagged claims only once. We define the total number of rejections to include all rejected claims, regardless of whether the claim was paid subsequently. If a claim was rejected multiple times for the same reason(s), we count these as 1 rejection. If a claim was rejected multiple times for different reasons, we count each unique set of reasons separately. We are unable to observe the life cycle of claims outside our 1-month windows (eg, if a claim was rejected in March and paid in a subsequent month, we have no record of the later payment). To calculate the rejection rate, we divide the total number of rejections by the total number of submitted claims. We present the rejection rate by year, PDP, and product. We consider generic and brand formulations of the same molecule as distinct products for our analyses; similarly, we treat different formulations of the same molecule (eg, tablets, solutions, and extendedrelease formulations) separately. We exclude 248,026 rejections (7% of all rejections) that were rejected due to problems transmitting claims electronically, such as “host processing error” or “system unavailable.” Based on conversations with the data provider, these rejections typically are resubmitted automatically, either to be resolved or rejected for another reason. We also exclude from our analyses the small number of claims that could not be matched to a drug name (1.6% of rejected claims; 0.1% of paid claims) and that were missing a rejection reason (0.02%).


Table 1

Our data include approximately 450,000 unique individuals and 4 million total claims in each of the 5 study years (). The overall rejection rate ranged from 14% to 19% over the 2006 to 2010 time frame, increasing slightly in recent years. The percent of rejections due to products not being covered declined considerably over the study period, from 21% of all rejections in 2006 to 10% in 2010. In contrast, rejections due to utilization management techniques such as prior authorization, drug utilization review, and other coverage restrictions grew in prominence, from 33% of all rejections in 2006 to 44% in 2010. Within the utilization management category of rejections, the most prominent subcategories of rejections were “refill too soon” (43% of utilization management rejections), “Drug Utilization Review Reject Error” (30%), “plan limitations exceeded” (17%), and “prior authorization required” (8%) (eAppendix A). Administrative rejections were consistently high over the study period (approximately 43% of all rejections, on average). Specific codes comprising most administrative rejections were non-matched pharmacy numbers (31% of administrative rejections); missing/ invalid information for requests to “dispense as written” (ie, requests to fill prescriptions for brand-name drugs, even though generic substitutes might be available) (8%); nonmatched plan member numbers (7%); and missing date of service (6%).

Table 2

details product-level rejections for our most recent year of data (2010), with the top and bottom panels describing rejections for the most prescribed medications and medications with the highest rejection rates among drugs with at least 5000 claims, respectively. Among the 20 most prescribed drugs (13 of which are generics), the rejection rate was between 13% and 19%, with denials generally divided between administrative rejections and utilization management, and rarely due to lack of coverage. For the 20 most commonly rejected drugs (9 of which are generics), the rejection rates (23%-62%) and reasons for denial varied more widely. Lack of coverage factored more prominently into these rejections, especially for some alternate formulations. Administrative rejection codes accounted for more than half of denials for 10 out of 20 medications.


eAppendix B

eAppendix C

The shows rejection rates over time for 7 classes commonly used in long-term care settings—antidepressants, angiotensin receptor blockers, atypical antipsychotics, cholinesterase inhibitors, long-acting opioids, nebulized inhalants, and osteoporosis medications ( lists drugs by class). Other than the rejection rates for nebulized inhalants, which declined from 2008 to 2010, class-level rejection rates declined initially and then increased in subsequent years. Some increases were relatively large—for instance, after falling to a low of 16% in 2007, the rejection rate for long-acting opioids increased to 28% in 2010. Rejection reasons varied across classes (see for details).

Table 3

displays rejection rates and the reasons across plans with higher claims volume in 2010. If a company had multiple PDPs nationwide, the information is aggregated across these plans. The overall rejection rates varied (6%-30%), as did the distribution of rejection reasons. Among plans with higher rejection rates, administrative rejections were relatively prominent. The 3 plans with the highest proportion of rejections for administrative reasons (74%, 68%, and 65%) were among the 3 plans with highest rejection rates overall (30%, 24%, and 29%, respectively). Translated to an administrative rejection rate (overall rejection rate x proportion of rejections for administrative reasons), the rate at which claims were rejected because of administrative reasons generally was between 2% and 9% of claims (not shown). Relatively high rates of administrative rejections arose in smaller and larger plans alike. The rates of claims rejections in the other 2 categories did not convey anything consistent about plans’ overall rejection rates.


Over the initial 5 years of Medicare Part D, nearly 1 in 6 drug claims for beneficiaries living in nursing homes and other long-term care settings was rejected. Although one might have expected the rejection rate to decline over time after the initial transition to the program and as pharmacies and clinicians grew more accustomed to working across private plans, this has not occurred. After an initial decline, the rejection rate has increased slightly each year. At the same time, reasons for denials have evolved considerably. Lack of coverage has become less of a factor in claims rejections than it was initially; increasingly, other formulary tools such as drug utilization review, quantity-related coverage limits, and prior authorization are used to deny claims. Although these results are consistent with broader trends concerning PDP formularies, somewhat more surprising is the persistence of administrative claims rejections throughout the study period, a feature that likely reflects the complexity of working across multiple plans and policies in a given year, as well as changes in coverage policies over time.

Not surprisingly, rejection rates and reasons varied considerably across products. Medications most commonly used by nursing home residents generally have below average denial rates (around 1 in 7 claims), with coverage issues rarely noted. Given the relatively low cost of many of these drugs (9 out of 10 most commonly used drugs are generics), these features are unsurprising. Among drugs with the highest rejection rates, explanations for denials are complex and vary across products, which include generics, brand drugs with generic alternatives, and brand drugs without generic alternatives.

Among the most rejected generics, half of the denials for pantoprazole sodium extended release tablet (a proton pump inhibitor for conditions such as gastroesophageal reflux disease) were due to the product not being covered. The primary reason for rejections of fluticasone propionate nasal spray (for asthma and allergy symptoms) stemmed from claims having missing/invalid information about days-supply, a problem also affecting other solutions and inhalation products. Three-fourths of rejections for potassium chloride solution (for nutritional deficiencies) were due to non-coverage, a feature likely reflecting a CMS ruling that the formulation did not meet Part D coverage requirements and the subsequent shift by PDPs to reimburse only non-liquid formulations of the drug.

Denials for brand name drugs with generic equivalents likely reflect PDPs’ efforts to promote use of cheaper generic alternatives. The high rejection rate for Flomax (tamsulosin) (for symptoms of an enlarged prostate) largely stemmed from claims having missing/invalid “dispense as written” information, an issue that presumably arose following the availability of generic tamsulosin in early 2010. Among the handful of brand drugs without generic equivalents, an example worth highlighting is Procrit (epoetin alfa), an expensive blood modifier used to treat anemia due to chronic renal failure and other select conditions. More than three-fourths of Procrit’s rejections were due to prior authorization issues, something not unexpected given the drug’s high cost and potentially harmful side effects.7

A striking feature of our product-level findings is that 6 out of 20 medications with the highest rejection rates in 2010 are used to treat pain (oxycodone/acetaminophen, oxycodone hydrochloride, morphine sulfate extended release, fentanyl patch, propoxyphene napsylate/acetaminophen, and Lidoderm (lidocaine). With the exception of propoxyphene napsylate/acetaminophen (an opioid combination of questionable effectiveness and safety in older adults8,9) and Lidoderm (an expensive topical anesthetic patch used to treat pain from shingles and other sources of neuropathic pain), denials for pain medications arise primarily from administrative rejections and utilization management techniques. An illustrative example is that nearly 1 in 3 claims was denied for the fentanyl patch in 2010—a rate nearly double its 2006 level; and administrative rejections and utilization management accounted for almost all of these rejections. Although rejection rates for pain medications could reflect their considerable abuse and diversion potential, concerns must be weighed against the need for timely access to effective medications for individuals living with chronic pain.10,11

Rejection rates and reasons also vary considerably across PDPs. In the context of a facility’s residents being enrolled in multiple plans, this variation presents administrative challenges for facility and pharmacy staff and may undermine the predictability of coverage across residents with similar conditions. Although it is difficult to convey a clear, concise story in the context of the PDP rejection rates, plans’ higher administrative rejection rates generally imply higher rejection rates overall. Further context for administrative rejections relates to continuing challenges that providers and pharmacies face in updating residents’ Medicaid eligibility status, delays which can result in enrollment gaps and incorrectly collected copayments.5 Nonetheless, the cross-plan variation in the extent to which these issues lead to rejected claims implies that there may be room for improvement at plans with especially high administrative rejection rates.

It is unclear what the optimal or expected rejection rate should be in Part D generally or in the long-term care setting specifically. Besides administrative errors that must be corrected before payment, plans have legitimate reasons to deny claims; and the optimal rejection rate is not zero. Prior authorization, for instance, could add valuable safeguards when prescribing is of questionable efficacy or appropriateness (eg, in the historically problematic area of psychotropic drug use among elderly nursing home residents12-14). Similarly, step therapy edits could push clinicians to prescribe less costly medications that are clinically similar or, in the case of brand-to-generic comparisons, equivalent. Nonetheless, use of these strategies creates a tension between the push to lower costs and the importance of ensuring access to needed medications.

The clinical impact of claims rejections on long-term care residents is unclear. We do not have information about beneficiaries’ diagnoses or health outcomes and are unable to assess the potential impact of denials on individuals’ health or on the overall appropriateness of prescribing. Similarly, we do not have information about the absolute or relative administrative burden or costs associated with rejected claims. It is likely that some denials are rectified relatively easily, while others require more coordinated effort. Because of the limited time frame (1 month each year) for which we have data, we are unable to estimate the extent to which individuals switch or discontinue medications because of claims denials (or, conversely, obtain their needed medication in the end). Prescribing behavior also may adjust to PDPs’ formularies and administrative practices (eg, if clinicians reduce prescribing a particular medication because it is difficult to obtain), something we are unable to observe. Important context for these points is that regulations require that nursing homes adhere to residents’ clinical care plans regardless of financial coverage, meaning that clinicians must either work within plans’ extant formulary constraints or leave facilities and/or pharmacies to shoulder the financial cost of medications. Finally, although the same PDPs serve individuals across institutional and community-based settings, our data are from individuals living in long-term care settings and come from a single (albeit very large) LTCP. Thus, generalizing our findings to the Part D program as a whole or even to the entire long-term care sector should be done with caution.

Nonetheless, our results have several implications for policy. Our findings suggest that medication-specific and planwide rejection rates could be useful information for policy makers, beneficiaries, and clinicians. Policy makers could monitor and possibly report rejection rates across plans to identify potential access problems for Medicare beneficiaries, to inform the Part D appeals process, and even to make adjustments to regulatory guidance if necessary. Given uncertainty about the optimal or appropriate rejection rate in Part D, related oversight likely would focus on identifying outliers at the plan, product, or class levels for further investigation. For example, CMS currently requires that plans cover at least 1 formulation of all medications in selected medication classes.15 CMS could consider rejection rates in decisions about whether to incorporate special protections around formulary coverage or the use of utilization management for these or other drugs. Although beneficiaries could use rejection rates in evaluating plans (eg, to compare plans with comparable coverage on drugs of interest), previous research suggests consumers already are overwhelmed by available information and could benefit from having fewer, more targeted data points.16 More specific to the nursing home population, policy makers could consider using rejection rates in decisions about plan assignment of dually eligible residents (eg, not assigning individuals to plans where medication access looks especially problematic) or even about which plans are eligible to serve dually eligible individuals altogether. Similarly, providers and the pharmacies they work with could monitor rejection data in establishing formularies and prescribing practices more generally.

Throughout the brief history of the Medicare Part D program, comparing generosity of coverage across private PDPs has been a key point of interest for consumers, policy makers, and researchers.17 Claims rejections have been an unobserved feature of these comparisons to date. Our research shows that examining rejection rates across plans and medications can provide important supplemental information to assess plans’ generosity of coverage and to identify potential areas of concern. Going forward, information about claims rejections should be more systematically monitored and analyzed for oversight purposes, not only to identify administrative challenges that arise in the Part D marketplace but also to ensure the program is working well for Medicare beneficiaries as a whole.Author Affiliations: From Department of Health Care Policy (DGS, HAH), Harvard Medical School, Boston, MA: Department of Health Services, Policy, and Practice (LK), Alpert Medical School of Brown University, Providence, RI; Hebrew SeniorLife (SLM), Institute for Aging Research, Boston, MA; Omnicare, Inc (BJZ), Livonia, MI.

Funding Source: This work was supported by a grant from the National Institute on Aging (NIA), grant number R01 AG034085. Dr Stevenson also was supported by an NIA Career Development Award (K01 AG038481). Dr Huskamp also was supported by a Robert Wood Johnson Foundation Investigator Award in Health Policy Research. Dr Mitchell was supported by NIA grant number K24AG033640.

Author Disclosures: Dr Zarowitz reports receiving consultancies from the Health Technology Assessment Council and sanofi-aventis, as well as employment and stock ownership in Omnicare, Inc. The other authors (DGS, LK, SLM, HAH) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DGS, LK, SLM, BJZ, HAH); acquisition of data (DGS, LK, BJZ, HAH); analysis and interpretation of data (DGS. LK, SLM, HAH); drafting of the manuscript (DGS, LK, SLM); critical revision of the manuscript for important intellectual content (DGS. LK, SLM, BJZ, HAH); statistical analysis (DGS, LK, HAH); provision of study materials or patients (BJZ); obtaining funding (DGS, HAH); administrative, technical, or logistic support (DGS, LK, BJZ, HAH); and supervision (DGS, HAH).

Address correspondence to: David G. Stevenson, PhD, Associate Professor, Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave, Boston, MA 02115. E-mail: Duggan M, Healy P, Morton FS. Providing prescription drug coverage to the elderly: America’s experiment with Medicare Part D. J Econ Perspect. 2008;22(4):69-92.

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12. US Office of the Inspector General. Medicare Atypical Antipsychotic Drug Claims for Elderly Nursing Home Residents. Washington, DC: OIG; May 2011.

13. Gurwitz JH, Field TS, Judge J, et al. The incidence of adverse drug events in two large academic long-term care facilities. Am J Med. 2005;118(3):251-258.

14. Institute of Medicine (US). Committee on Nursing Home Regulation. Improving the Quality of Care in Nursing Homes. Washington, DC: National Academies Press; 1986.

15. US Centers for Medicare and Medicaid Services. MA and PDP Programs MIPPA Drug Formulary & Protected Classes Policies. Vol CMS-4138-IFC4 2009.

16. Rice T, Cummings J. Reducing the number of drug plans for seniors: a proposal and analysis of three case studies. J Health Polit Policy Law. 2010;35(6):961-997.

17. Gruber J. Choosing a Medicare Part D Plan: Are Medicare Beneficiaries Choosing Low-Cost Plans? Washington, DC: Kaiser Family Foundation; March 2009.