Currently Viewing:
The American Journal of Managed Care October 2012
Change to FIT Increased CRC Screening Rates: Evaluation of a US Screening Outreach Program
Elizabeth G. Liles, MD, MSCR; Nancy Perrin, PhD; Ana Gabriela Rosales, MS; Adrianne C. Feldstein, MD, MS; David H. Smith, RPh, MHA, PhD; David M. Mosen, PhD, MPH; and Jennifer L. Schneider, MPH
Toward Tailored Disease Management for Type 2 Diabetes
Arianne M. J. Elissen, MSc; Inge G. P. Duimel-Peeters, PhD; Cor Spreeuwenberg, PhD; Marieke Spreeuwenberg, PhD; and Hubertus J. M. Vrijhoef, PhD
Implementation of EHR-Based Strategies to Improve Outpatient CAD Care
Stephen D. Persell, MD, MPH; Janardan Khandekar, MD; Thomas Gavagan, MD; Nancy C. Dolan, MD; Sue Levi, RN, MBA; Darren Kaiser, MS; Elisha M. Friesema, BA, CCRP; Ji Young Lee, MS; and David W. Baker, MD, MPH
Pediatric Integrated Delivery System's Experience With Pandemic Influenza A (H1N1)
Evan S. Fieldston, MD, MBA, MSHP; Richard J. Scarfone, MD; Lisa M. Biggs, MD; Joseph J. Zorc, MD, MSCE; and Susan E. Coffin, MD, MPH
Currently Reading
Medicare Part D Claims Rejections for Nursing Home Residents, 2006 to 2010
David G. Stevenson, PhD; Laura M. Keohane, MS; Susan L. Mitchell, MD, MPH; Barbara J. Zarowitz, PharmD, FCCP, BCPS, CGP, FASCP; and Haiden A. Huskamp, PhD
EMR-Based Medication Adherence Metric Markedly Enhances Identification of Nonadherent Patients
Shepherd Roee Singer, MD, MPH; Moshe Hoshen, PhD; Efrat Shadmi, PhD; Morton Leibowitz, MD; Natalie Flaks-Manov, MPH; Haim Bitterman, MD; and Ran D. Balicer, MD, PhD
Financial Incentives and Physician Commitment to Guideline-Recommended Hypertension Management
Sylvia J. Hysong, PhD; Kate Simpson, MPH; Kenneth Pietz, PhD; Richard SoRelle, BS; Kristen Broussard Smitham, MBA, MA; and Laura A. Petersen, MD, MPH
Identifying Frail Older People Using Predictive Modeling
Shelley A. Sternberg, MD; Netta Bentur, PhD; Chad Abrams, MA; Tal Spalter, MA; Tomas Karpati, MD; John Lemberger, MA; and Anthony D. Heymann, MB BS
Application of New Method for Evaluating Performance of Fracture Risk Tool

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

David G. Stevenson, PhD; Laura M. Keohane, MS; Susan L. Mitchell, MD, MPH; Barbara J. Zarowitz, PharmD, FCCP, BCPS, CGP, FASCP; and Haiden A. Huskamp, PhD
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.
Objectives: 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.

Results: 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.

Conclusions: 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.


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 eAppendix A, 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%).


Our data include approximately 450,000 unique individuals and 4 million total claims in each of the 5 study years (Table 1). 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.

The Figure 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 (eAppendix B 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 eAppendix C 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.


Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up