This article was published as part of a special joint issue and also appears in the 

We practice in a tumultuous era. Healthcare costs continue to increase, and it is ever more apparent that resources are limited. Yet the understanding of the fundamentals of our biology is rapidly increasing, and that knowledge feeds the ability to positively change lives with new therapeutics, providing hope to thousands with cancer. Hence there is great tension in cancer care between new, wonderful, expensive therapeutics and a world characterized by more need and limited budgets and resources. Those  who set and administer healthcare policy and those on the front lines delivering care must develop and maintain a dialogue informed by data. It is in that spirit that this  supplemental issue, copublished by journals with different audiences, is presented. A few words of introduction to both journals are in order.

) is an independent, peer-reviewed forum for the dissemination of research relating to clinical, economic, and policy aspects of financing and delivering efficient and high-quality healthcare. Circulating to approximately 47,000 individuals with important decision-making responsibilities that affect  the access to and use of healthcare resources, 

 includes in its readership medical directors, pharmacy and therapeutics committee members, pharmacy directors,  physicians, corporate benefits managers, and other healthcare professionals across 6 market segments: health maintenance organizations/preferred provider organizations/integrated healthcare organizations; hospitals; long-term care; pharmacy benefit managers; veterans affairs/government; and employers.

), the newer of the American Society of Clinical Oncology’s (ASCO) 2 research journals, provides oncology professionals with information and tools to enhance practice efficiency and promote high-quality patient care. The 

 aspires to become the authoritative resource on clinical and administrative management for practicing oncologists by including peer-reviewed original research, feature articles, and section columns on issues pertinent to daily practice operations. JOP is available to ASCO’s worldwide membership of more than 30,000, which comprises clinical oncologists from all oncology disciplines and subspecialties; physicians and healthcare professionals participating in approved oncology training programs; oncology nurses; and other healthcare practitioners with a predominant interest in oncology.

The editors appreciate the enthusiasm of authors who submitted their work in response to the call for papers and the efforts of the publication staff at 

 for bringing this work to fruition. They are the unsung heroes. We invite you to send comments to 

Authors’ Disclosures of Potential Conflicts of Interest: 

The authors indicated no potential conflicts of interest.

Articles

INTRODUCTION

The editors introduce this special issue, which contributes to an ongoing dialogue in cancer care that explores the tension between new therapeutics and the ever-increasing need to limit resources.

Developing Dialogue in Healthcare

To characterize patterns of medical care by disease phase in patients with newly diagnosed metastatic colorectal cancer (mCRC).

Patients with mCRC newly diagnosed between 2004 and 2008 were selected from a large US national commercially insured claims database and were observed from initial mCRC diagnosis to death, disenrollment, or end of study period (July 31, 2009), whichever occurred first. The observation period was divided into 3 distinct phases of disease: diagnostic, treatment, and death. Within each phase, patterns of medical care were examined by the mutually exclusive service categories of inpatient, emergency department (ED), outpatient office and facility, outpatient pharmacy, chemotherapy, and biologic therapy, as measured by estimation of aggregate and category costs per patient per month.

A total of 6675 patients with newly diagnosed mCRC were analyzed. Mean age was 64.1 years; 55.5% were males. Mean costs per patient per month for diagnostic, treatment, and death phases were $16,895, $8891, and $27,554, respectively. Inpatient care was the primary driver of medical care for both the diagnostic (41.7% of costs) and death (71.4% of costs) phases. The largest category of medical care for the treatment phase was outpatient care (45.0% of costs). Chemotherapy and biologic therapy accounted for 15.6% and 17.6% of costs in the treatment phase, respectively.

Substantial differences in patterns of medical care were found between mCRC disease phases. Inpatient care was the key driver of medical care in the diagnostic and death phases compared with outpatient care in the treatment phase.

(Am J Manag Care. 2011;17(5 Spec No.):SP20-SP25)

	This study provides an assessment of patterns and costs of medical care by phase of mCRC disease, employing a study period (2004 to 2009) that captures the impact of biologic agents bevacizumab, cetuximab, and panitumumab.

		Substantial differences in patterns of medical care were found among mCRC disease phases. Mean costs per patient per month for diagnostic, treatment, and death phases were $16,895, $8891, and $27,554, respectively.

		Inpatient care was the key driver of medical care in the diagnostic and death phases compared with outpatient care in the treatment phase.

An estimated 51,370 Americans will die from colorectal cancer (CRC) in 2010,

 making CRC the second leading cause of death resulting from cancer in the United States.

 Approximately 20% of patients with CRC already have metastases at time of diagnosis, and up to 50% of all patients will develop metastases over the course of the disease.

Advances in systemic therapies have improved overall survival (OS) for patients with metastatic CRC (mCRC). Survival increased from 5 months with best supportive care

to 10.5 months when fluorouracil (FU) was the sole therapeutic option,

 to 12 to 14 months with FU plus leucovorin (LV),

 to 14 to 15 months with bolus FU/LV plus irinotecan,

 and up to 20 months with FU/LV plus oxaliplatin (FOLFOX) and FU/LV plus irinotecan (FOLFIRI).

 Currently, FOLFOX and FOLFIRI are the most commonly used chemotherapy regimens for first- and second-line treatment of mCRC, producing similar OS results regardless of which regimen is used first.

 Another improvement in survival occurred with the introduction of monoclonal antibodies in combination with chemotherapy. Although the benefit accrued seems to vary with the monoclonal antibody, chemotherapy backbone, and patients included in the clinical trial, improvement in median OS close to 2 years has been observed with cetuximab in combination with FOLFIRI and panitumumab in combination with FOLFOX in first-line patients with wild-type KRAS mCRC.

Given the current evidence, treatment for mCRC will continue to combine multiple approaches and therapies, including surgery, chemotherapy, and biologic therapy. It is likely that patterns of treatment and resulting medical care use and costs will differ depending on the phase of mCRC disease. Using data from 1998 to 2004, Paramore et al

examined mCRC-related monthly costs by disease phase in patients with newly diagnosed mCRC and found lower costs in the treatment phase relative to diagnostic and death phases. However, the time period for that study was before the availability of biologic agents in mCRC treatment. Lang et al

 examined lifetime and  treatment-phase costs in patients with CRC using SEER-Medicare data from 1996 to 2005 and found that patients with CRC incurred excess costs of $33,500 in the initial  phase, $4500 per year in the continuing phase, and $14,500 in the terminal phase. Their study population was patients with CRC, not mCRC, and they examined total cost instead of cost drivers (eg, inpatient costs, outpatient costs, pharmacy costs, and so on). To our knowledge, there are no current studies examining patterns of care by phase of disease in the treatment of mCRC after the introduction of biologics, which are now part of standard care in the treatment of mCRC. Our study was intended to fill this gap by providing updated cost estimates and comparing differences in patterns of care during different phases of mCRC. With a standard of care that now often includes biologic agents in combination with chemotherapy,

 data on patterns of treatment and medical care in the various phases of disease are lacking. The objective of the current study was to characterize the patterns and costs of medical care and identify cost drivers by phase of disease in patients with mCRC diagnosed from 2004 to 2008 and observed up to July 2009.

Data were derived from the Thomson Reuters MarketScan Commercial Claims and Encounter Database (commercial database) and the Medicare Supplemental and Coordination of Benefits Database (Medicare database) from January 1, 2004, to July 31, 2009. The databases are constructed from claims and enrollment data provided by medium-sized to large employer- sponsored health plans from across the United States and are Health Insurance Portability and Accountability Act compliant. Combined, these 2 databases include patient-level medical and pharmacy claim histories, and in 2008, they contained the health care experience of 37 million covered  ives. Enrollees are covered under a variety of plan types, including both capitated (eg, health management organizations) and noncapitated (eg, fee for service) product lines. The databases capture the full continuum of care for reimbursable services delivered across all settings, including physician office visits, emergency department (ED) visits, hospital stays, and outpatient pharmacy claims. Detailed cost data are available, and within the Medicare database, both the Medicarecovered portion of payment (represented as the coordination of benefits amount) and employer-paid portion are included. Age and sex distributions of patients in MarketScan are similar to those in the Medical Expenditure Panel Survey and Medicare Beneficiary Survey.

CRC was determined by the presence of at least 1 inpatient or at least 2 outpatient claims containing 

International Classification of Diseases, Ninth Revision, Clinical Modification

) diagnosis codes 153.0 to 153.4, 153.6 to 153.9, 154.0, 154.1, and 154.8 from January 1, 2004, to December 31, 2008. Metastasis was defined as the occurrence of at least 1 inpatient or at least 2 outpatient claims containing ICD- 9-CM diagnosis codes 196.0, 196.1, 196.3, 196.5, 197.0 to 197.4, 197.6 to 197.8, 198.x, or 199.0 at most 30 days before or any time after first CRC diagnosis from 2004 to 2008. The date of the first claim for metastatic disease was assigned as the index date. Patients were required to have 6 months of continuous enrollment before the index date. Patients with diagnoses of 153.5 (appendix), 154.2 (anal canal, anal sphincter), or  154.3 (anus, unspecified) before first CRC diagnosis and patients with other primary cancer diagnoses or metastases during the 6-month preperiod were excluded.  Patients included in the study were required to be 18 years or older on the index date, and there was no restriction on maximum age. The site of the index diagnosis was considered the initial site of metastasis.11,13 In cases when more than 1 metastatic diagnosis occurred on the same date, the earliest diagnosis was chosen over subsequent diagnoses.

Length of follow-up for each patient was variable, starting from the index diagnosis of metastatic disease and ending at death, the end of continuous enrollment, or the end of the study period (July 31, 2009), whichever came first. Because claims data are limited in terms of reporting mortality, we applied a previously published algorithm for identifying deaths.

 Indicators for death included the following: hospital discharge status of death; hospital or ED event within 30 days of the last date of enrollment; and use of cardiac-stimulating medications such as epinephrine or lidocaine, diagnosis codes for cardiac arrest or failure, or procedure codes for resuscitation or defibrillation within 30 days of the last date of enrollment. Patients who were still enrolled at the end of the study period and who did not have any of the preceding indicators were considered to have survived.

Each patient’s observation (follow-up) period after the date of mCRC diagnosis was divided into 3 phases of disease according to a previously published algorithm: diagnostic phase, treatment phase, and death phase.

 For patients who survived, the diagnostic phase included the index date through the first 3 months of observation. For survivors with more than 3 months of observation, the remaining months were assigned to the treatment phase. For patients identified as having died, the death phase was determined first and consisted of the last 3 months of observed claims. For those with more than 3 months but less than or equal to 6 months of observed claims, the remaining claims were assigned to the diagnostic phase. For patients who died after more than 6 months of observation, after assignment of the diagnostic and death phases, the remaining claims were assigned to the treatment phase.

Patient demographics were measured at index date and included age, sex, geographic region, urban versus rural location, insurance plan type, and commercial versus Medicare status. Race and ethnicity were not reported for the study population because of the lack of such information in the claims data. Mean length of follow-up was calculated. The Deyo-Charlson comorbidity index (CCI) was used to assess overall patient levels of comorbid conditions and was measured during the 6-month preperiod.

 The Deyo CCI takes on values between 0 and 12, with higher scores representing higher risk of impending mortality based on presence of various chronic diseases. The first CRC diagnosis was reviewed, and percentage of patients with colon (vs rectal) cancer as the initial diagnosis was determined. Percentage of patients with metastases at specific sites was also reported.

Within each phase, patterns of medical care were examined by mutually exclusive service categories of inpatient, ER, outpatient office and facility, outpatient pharmacy, chemotherapy, and biologic therapy, as measured by estimation of aggregate and category costs. Surgeries that occurred during inpatient admissions were counted as part of inpatient costs, and surgeries that occurred in the outpatient setting were counted as part of outpatient costs. Costs were all-cause total direct healthcare costs and were based on paid amounts of adjudicated claims, including insurer and health plan payments, copayments, and deductibles. Direct cost components included inpatient costs, outpatient costs, ED costs, costs of chemotherapy, costs of biologics, and other pharmacy costs. No indirect costs, such as costs associated with absence from work or lost productivity, were included in the cost calculation. Costs were determined across all patients with mCRC for the entire variable-length follow-up and  standardized as per patient per month costs. Costs were also reported per patient per month for each disease phase, using only those patients with mCRC who contributed months to a specific phase and using only the months for that phase. Costs for claims processed under a fee-for-service arrangement were the allowed charges (ie, actual amounts paid by primary and secondary insurers plus patient cost share amounts [copayments and deductibles]). Costs for claims processed under a  capitated arrangement were estimated using the average cost of noncapitated claims, by procedure, geographic region, and year. All costs were adjusted to 2009 US dollars using the medical service component of the Consumer Price Index.

Means and standard deviations (SDs) were calculated for continuous variables. Counts and percentages were calculated for categorical variables. Nonparametric   bootstrapping was used to create 95% confidence intervals [CIs] for mean cost in each disease phase using 1000 iterations. Generalized linear models with log link and gamma distribution were estimated on monthly costs for each phase, controlling for age, sex, region, urban residence, health plan type, capitation indicator, Medicare, CCI, site of cancer (colon vs rectal), site of metastasis, and length (days) of each phase. Length of each phase was included in the model to account for censoring because ofdifferent length of follow-up.

Demographics and Clinical Characteristics

A total of 6675 patients with newly diagnosed mCRC met all study criteria, with a mean age of 64.1 years (SD, 13.1 years; 

) and 55 to 75 in the quartile distribution. Males comprised 55.5% of the study sample. The largest percentage of patients resided in the South (40.6%), followed by the North Central (29.3%) and West (20.2%) regions, in predominantly urban areas (81.5%). Approximately 43% of patients were covered under Medicare. Mean length of follow-up was 492 days. Nearly 75% of patients had a CCI score of 2. Colon (vs rectal) cancer was the initial diagnosis in 69.7% of patients. The most common identified sites of metastases were the liver (46.8%), lung (15.1%), retroperitoneum and peritoneum (11.6%), other respiratory/digestive system (5.1%), and lymph nodes (2.7%). The follow-up period ended because of death for 27.2% of patients, end of continuous enrollment for 63.3%, and end of the study period (July 31, 2009) for the remaining 9.5%. The proportion of patients receiving biologics increased from 30.0% for patients diagnosed in 2004 to 39.9% for patients diagnosed in 2008. The corresponding proportion of patients receiving chemotherapy was 59.0% and 58.1% in the same time period, respectively.

Among patients with mCRC, 91%, 80%, and 27% had diagnostic, treatment, and death phases, respectively. The treatment phase had the longest duration, with a mean of 16.4 months, and durations for the diagnostic and death phases were similar at means of 2.8 months and 2.4 months, respectively (

Patterns of Medical Care by Disease Phase

Compared with mean cost per patient per month in the diagnostic phase ($16,895; 95% CI, $16,390 to $17,400), costs were significantly higher in the death phase ($27,554; 95% CI, $23,636 to $31,474) but significantly lower in the treatment phase ($8891; 95% CI, $8618 to $9165). The high costs in the diagnostic phase included initial treatment costs. Multivariate regression adjusted monthly costs were $16,938 (SD, $6642) in the diagnostic phase, $8976 (SD, $4497) in the treatment phase, and $26,689 (SD, $19,561) in the death phase. For patients with capitated insurance, mean costs per patient per month were $16,042 in the diagnostic phase, $8606 in the treatment phase, and $22,140 in the death phase; for patients with noncapitated services, the corresponding costs were $17,066, $8947, and $28,752, respectively.   Multivariate regressions suggested that capitation did not have a significant impact on costs in the diagnostic and treatment phases, but it significantly decreased costs in the death phase, compared with patients with noncapitated plans.

Patterns of medical care as measured by cost for categories of inpatient, ED, outpatient office and facility, outpatient pharmacy, chemotherapy, and biologic therapyvaried   depending on phase of disease (

). Although inpatient care was the highest component of medical care in the diagnostic (41.7% of costs) and death (71.4% of costs) phases, it accounted for only 17.9% of costs in the treatment phase. Outpatient care was the leading driver for medical care in the treatment phase (45.0% of costs). The proportion of costs resulting from biologics and chemotherapy also differed by phase of disease. Biologics contributed 7.7%, 17.6%, and 4.1% of costs, whereas chemotherapy accounted for 11.5%, 15.6%, and 2.9% of costs in the diagnostic, treatment, and death phases, respectively (Figure).

This study provides an assessment of medical care patterns during phases of mCRC disease, employing a study period that captures the impact of biologic agents bevacizumab, cetuximab, and panitumumab. Mean time in the treatment phase was the longest at 16.4 months, followed by 2.8 months in the diagnostic phase and 2.4 months in the death phase. Inpatient care drove medical care in both the diagnostic and death phases, accounting for 41.7% and 71.4% of costs, respectively, whereas outpatient care was the primary driver in the treatment phase (45.0% of costs).

Median OS for patients with mCRC has increased with use of newer therapies, from 12 to 14 months with FU/LV to approximately 20 months with the addition of oxaliplatin or irinotecan.

 Use of biologic agents with chemotherapy has increased OS even further, with recent studies demonstrating a median OS of almost 2 years.

 Other factors such as better surgical approach and better follow-up may also help increase OS. An increase in duration of survival is an important consideration as we examine care patterns in each disease phase. At 16.4 months, the treatment phase in the current study was the longest mCRC phase, yet this phase had the lowest per patient per month costs. A study by Paramore et al

 that measured mCRC disease phases before the availability of biologic agents found similar mean length compared with the current study in the diagnostic and death phases but a much shorter mean length in the treatment phase (10.1 months).

Costs in the current study were higher compared with those in that by Paramore et al,11 which found per patient per month costs of $12,205, $4722, and $12,328 for the diagnostic, treatment, and death phases, respectively. A number of factors likely contributed to the cost differences between the 2 studies. The study by Paramore et al used the amount reimbursed by payers for costs, whereas the current study also included patient cost share amounts. Costs in the study by Paramore et al were reported in 2005 US dollars. If adjusted to 2009 dollars, those costs would be $14,215, $5500, and $14,358, respectively. It is worth noting that in addition to use of newer data (2004 to 2009), which allowed capture of the impact of new treatment options (eg, biologic agents) on medical care utilization, our study also focused on examining differences in patterns of medical care between phases of mCRC disease. The Paramore et al and current studies used the same algorithm to define phases of mCRC. Patterns of mCRC care using different definitions of phases may be an interesting topic in future studies. A recent report by Lang et al

 estimated lifetime and treatment-phase costs in patients with CRC. Because of the difference in study populations between the Lang et al study and our study (CRC vs mCRC), it is difficult to compare cost estimates from these 2 studies.

In addition to the commonly recognized constraints of administrative claims data,

 several limitations specific to this study should be noted when interpreting the results. First, given the lack of a specific ICD-9-CM code that identifies mCRC, we followed the approach used by Paramore et al,

 initially identifying patients with CRC and then selectn ing a subset with indicators of metastatic disease consistent with CRC. This is also consistent with stages C and D in the Dukes staging system for colorectal cancer.

 To the extent that cancer at other sites did not result from metastases from CRC, patients may have been incorrectly identified as patients with mCRC. However, patients with diagnoses of other primary cancers were excluded from the analysis; thus, we would expect the misclassification, if any, to be minimal. Second, because death indicators were not consistently available from the data used for the analysis, we used a proxy measure for death that was used in previous publications.

 Tothe  extent that this measure incorrectly identified a survivor as having died, that patient’s follow-up period would have been inaccurately categorized by phase. However, follow-up for patients who died was first assigned to the death phase. Because death phase costs were typically the highest, including nondeath phase costs would have biased results toward lower costs; however, this phenomenon was not observed in the current study. Third, costs for patients with capitated services (25.5% of patients) were imputed based on mean cost of noncapitated claims and thus may not be reflective of the actual costs incurred. Fourth, indirect costs were not included in the analysis, and thus costs presented in the current study underestimate the economic impact of mCRC. Finally, the study population was composed of commercially insured and Medicare-insured patients, and patterns of medical care during phases of disease may not be representative of all patients with mCRC, especially the uninsured or those covered by Medicaid.

Substantial differences existed between the mCRC disease phases with regard to patterns of medical care, both in magnitude and key drivers of use. Inpatient care was the leading driver for medical care in the diagnostic and death phases, whereas medical care in the treatment phase was primarily driven by outpatient care.

Presented in part at the 46th Annual Meeting of the American Society of Clinical Oncology, June 4-8, 2010, Chicago, IL. Supported by Amgen.

uthors’ Disclosures of Potential Conflicts of Interest: 

Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a “U” are those for which no compensation was received; those relationships marked with a “C” were compensated. For a detailed description of the disclosure categories, or for more information about ASCO’s conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

 Zhongyun Zhao, Amgen (C); Beth Barber, Amgen (C); Sue Gao, Amgen (C).

Zhongyun Zhao, Amgen; Beth Barber, Amgen; Sue Gao, Amgen.

Xue Song, Amgen; Christopher Gregory, Amgen; David Schutt,  Amgen.

 Xue Song, Zhongyun Zhao, Beth Barber, Christopher Gregory, David Schutt, Sue Gao.

 Xue Song, Zhongyun Zhao, Beth Barber, Sue Gao.

Xue Song, Zhongyun Zhao, Beth Barber, Christopher Gregory, David Schutt, Sue Gao.

 Xue Song, PhD, Senior Research Leader, Thomson Reuters, 4301 Connecticut Ave, Washington, DC 20008; e-mail: 

 National Cancer Institute: Cancer Treatment (PDQ). 

http://www.cancer.gov/cancertopics/pdq/treatment/colon/healthprofessional/allpages/print

 Giantonio BJ, Catalano PJ, Meropol NJ, et al: Bevacizumab in combination with oxaliplatin, fluorouracil, and leucovorin (FOLFOX4) for previously treated metastatic colorectal cancer: results from the Eastern Cooperative Oncology Group Study E3200. 

 Kindler HL, Shulman KL: Metastatic colorectal cancer. 

CLINICAL

This study characterized patterns and costs of medical care by disease phase in patients with newly diagnosed mCRC using a large US national commercially insured claims database.

Characterizing Medical Care by Disease Phase in Metastatic Colorectal Cancer

The American healthcare system, including the cancer care system, is under pressure to improve patient outcomes and lower the cost of care. Government payers have articulated an interest in partnering with the private sector to create learning communities to measure quality and improve the value of healthcare. In 2006, the American Society of Clinical Oncology (ASCO) unveiled the Quality Oncology Practice Initiative (QOPI), which has become a key component of the measurement system to promote quality cancer care. QOPI is a physician-led, voluntary, practice-based, quality-improvement program, using performance measurement and benchmarking among oncology practices across the United States. Since its inception, ASCO’s QOPI has grown steadily to include 973 practices as of November 2010. One key area that QOPI has addressed is end-of-life care. During the most recent data collection cycle in the fall of 2010, those practices completing multiple data collection cycles had better performance on care of pain compared with sites participating for the first time (62.61% vs 46.89%). Similarly, repeat QOPI participants demonstrated meaningfully better performance than their peers in the rate of documenting discussions of hospice and palliative care (62.42% vs 54.65%) and higher rates of hospice enrollment. QOPI demonstrates how a strong performance measurement program can lead to improved quality and value of care for patients.

(Am J Manag Care. 2011;17(5 Spec No.):SP32-SP36)

	Since 2006, the American Society of Clinical Oncology Quality Oncology Practice Initiative (QOPI) has grown steadily to include 973 practices. QOPI is a physicianled, practice-based, quality-improvement program that uses performance measurement and benchmarking among oncology practices across the United States. Measurement focuses on chemotherapy management and end-of-life care for patients with lymphoma, breast, colorectal, and lung cancers. In the most recent data collection cycle in the fall of 2010, practices demonstrated high rates of performance in key measures of quality including:

		palliative care and hospice enrollment, and

		rates of chemotherapy use in the last 2 weeks of life.

The American healthcare system has long been under fire for its rapidly escalating costs and to improve patient outcomes and quality of care. Even today, a decade after the publication of the Institute of Medicine’s landmark reports “Crossing the Quality Chasm”

 both of which identified major gaps in the quality and safety of care in the United States, patient care remains highly variable. Underperformance in our healthcare system still costs our nation tens of thousands of avoidable deaths and billions of dollars in excess medical costs each year.

Given the pressure to resolve this, our system is approaching a crossroads that will demand a rigorous evaluation of clinical practice and outcomes measurement. This call for high-quality care is being heard in all sectors of our healthcare system. Newly appointed Centers for Medicare and Medicaid Services (CMS) administrator Don Berwick has stated that CMS is interested in partnering with the private sector to create learning communities to improve the value of healthcare for Medicare and Medicaid recipients.

 This statement from a key stakeholder reinforces the focus on quality and cost-containment that is driving change at the highest levels of our healthcare system. Berwick has also detailed his thinking in the triple aim for redesigning the healthcare system to achieve 3 objectives: to improve the health of the population, to enhance the quality of care, and to control the per capita cost of care.

The private payer sector has also spoken up, calling for a focus on quality and value in healthcare and on the future role of outcomes measurement in value- and quality-based payment. In their 2009 report, America’s Health Insurance Plans,

 the national association representing nearly 1300 member health insurance companies, describes 28 health insurance plan programs aimed at assessing and rewarding high-quality care. These programs that involve physicians, hospitals, and payers are part  of a larger movement toward pay for performance.

Clinical and professional associations are in a unique position to meet this call for quality measurement and reporting, given their focus on clinical and operational excellence within their specialties and their close connection to their member physicians, hospitals, and other providers. Indeed, professional association quality- improvement programs have grown to prominence by engaging broad provider participation, measuring real-world clinical practice, and driving clinical standards of care. The centerpiece of the American Heart Association’s Get With the Guidelines program is its Web-based clinical stroke registry; more than 1500 hospitals have contributed more than 1.5 million stroke episodes to the registry since its inception in 2003. Quality measures adapted from the program have been adopted by the National Quality Forum and the Joint Commission, and 7 of the 15 quality measures for hospitals in the CMS Meaningful Use Electronic Health Records incentive program are stroke measures that originate from this program.

 Both the American Heart Association stroke registry and the Society for Thoracic Surgery’s registry on cardiac surgery are under consideration for further integration into the emerging public- private system for measuring quality of care.

As professional quality-improvement programs mature, they can also take on additional important functions for physician practices. The NeuroPoint Alliance—which leverages a common registry infrastructure shared between the American Association of Neurological Surgeons and the American Board of Neurological Surgery&mdash;represents an integrated clinical registry for quality improvement and clinical research and for tracking case logs that are helpful for initial board certification and for Maintenance of Certification.

 The American College of Rheumatology sponsors the Rheumatology Clinical Registry for quality improvement, continuing medical education, and participation in the Physician Quality Reporting System.

Fundamentally, all of these programs use their registries to record measures of real-world clinical practice, including patient outcomes. These observational study methods are increasingly recognized as an important source of evidence for comparative effectiveness research,

 national clinical guidelines, and evolving payment strategies.

Improving Quality of Cancer Care: The Quality Oncology Practice Initiative Program

In the wake of the Institute of Medicine reports, discussions intensified around the need to create systems that assess the quality of cancer care and ultimately led to the establishment of the American Society of Clinical Oncology’s (ASCO’s) Quality Oncology Practice Initiative (QOPI) program. In 2006, after 4 years of development and testing, this voluntary program was opened to all of ASCO’s medical oncologist members. The ASCO QOPI program has demonstrated effectiveness in improving care and is a key component of the measurement system for quality of cancer care.

QOPI is an oncologist-led, practice-based, quality-improvement program with a goal of promoting excellence in cancer care through a practice’s voluntary self-examination and process improvement system. The QOPI process currently includes a twice-yearly data reporting and analysis cycle and offers participating practices extensive,  site-specific, and benchmarking reports that describe the continuum of care. Sites report concordance with up to 91 measures across several modules in 3 broad areas. The Core Measure module is completed by all participants. Domain-specific modules include questions related to chemotherapy symptom/toxicity management and end-of-life (EOL) care. Disease-specific modules focus on patients with lymphoma, breast, colorectal, and lung cancers. The evidence-based, peer-developed measures provide timely feedback regarding the areas of care that are important to quality and value, which allows practices to evaluate and improve their care processes over time.

Practices registered in the QOPI program currently choose whether to participate in each reporting period; continued reporting is ideal but voluntary. Participation in QOPI has grown steadily since its inception. In the first 2 reporting periods, a total of 41 sites, representing 92 physicians, were registered.

Early results from the QOPI program showed that practices that participated in the program improved performance in key clinical process measures.

 As the program has matured, new metrics have been added, and practices continue to reach ever higher levels of performance.

QOPI's Role in Demonstrating Improved EOL Cancer Care

One key area that the QOPI program has addressed and measured since 2006 is EOL care for patients with cancer. When cure is no longer possible, the goals of treatment transition to focus on the physical, psychological, social, and spiritual needs of both the patient and the family.

 One of the most important components of care at the EOL is early and clear communication between the doctor, patient, and family.

 When unrealistic expectations are held by patients, they may seek aggressive care that can actually decrease their quality of life before death.

 This pressure—combined with oncologists’ desires to provide hope and families’ unfamiliarity with palliative care services&mdash;can lead to overuse of chemotherapy at the EOL. Prior studies have shown that such treatment often proceeds without real improvements in survival.

Moreover, the costs of care near the EOL are substantial. In 2008, Medicare paid $55 billion in physician and hospital bills during the last 2 months of patients’ lives; it has been reported in the popular press that 20% to 30% of these medical expenses may have had no meaningful impact.

 Current patterns of cancer care suggest that, despite the improving availability of palliative care services and hospice, patients are increasingly receiving chemotherapy within the last 2 weeks of life, which requires more frequent visits to the hospital and emergency department in the last month of life. These patients are also often referred to hospice care in the

last days of life as a means of managing death rather than as a tool for palliation of symptoms.

 Recent research has shown that a focus on palliative care and hospice decreases unnecessary expenditures and fosters an environment of better preparedness for the EOL, decreased suffering, and improved advanced care planning.

 As an example, hospice has been shown to provide the largest cost savings for any terminal illness for patients with lung cancer on Medicare.

 studied patients with advanced cancer and found those who reported having EOL conversations with physicians were more likely to receive outpatient hospice care, were referred for hospice earlier, and had significantly lower healthcare costs in the final week of life. Higher costs were actually associated with lower quality of death.

Until recently, a shortage of easily accessible quality data about patients with advanced cancer has posed an obstacle to improving palliative and EOL care.

 It is best to use quality indicators and measures that are specific to EOL, such as use patterns of chemotherapy and the specifics of receiving hospice care. QOPI indicators were derived from clinical guidelines and published standards adapted from the National Initiative on Cancer Care Quality and others to include consensus-based and clinically relevant data.

Offering an opportunity for practices to submit EOL data on 16 different measures, QOPI is demonstrating the value of monitoring and improving quality of EOL cancer care delivery. Oncology practices typically use this EOL module in conjunction with all of the tumor types in QOPI, thus identifying ways to systematically improve communication, documentation, patient education, and work flow that will improve care at the EOL.

 presents an analysis of QOPI EOL data by comparing sites that have participated multiple times in the EOL module to those that reported EOL data for the first time  in fall 2010. Sites that contributed data more than once showed higher performance in their assessment of pain and dyspnea (shortness of breath) before death. QOPI measure 38 is a composite measure with components from 3 pain-related measures including assessment of pain, documentation of pain intensity, and the formulation of a specific plan for treating pain during the last 2 visits before death. In oncology practices that participated in multiple cycles of QOPI data collection, performance was better (62.61%) compared with sites reporting the EOL measures for the first time (46.89%). Similarly, repeat practices performed better on composite measure 41 on the appropriate assessment and care for dyspnea; sites participating in multiple QOPI data-collection cycles had higher performance at 67.99% compared with 60.82% for new  participants. Repeat QOPI participants also demonstrated meaningfully better performance than their peers in rates of documenting discussions of hospice and palliative care with patients in the last 2 months of life. Performance was 62.42% for sites participating in multiple data collection cycles compared with 54.65% for first-time sites (measure 47). This translated to higher rates of actual hospice enrollment and palliative care referrals of 56.11% and 50.01% in the respective groups (measure 43).

Recent QOPI data also demonstrate that participating in the program helps practices analyze the timing of treatment, in particular the use of chemotherapy within the last 2 weeks of life. The percentage of patients receiving chemotherapy during the last 2 weeks of life was similar at 12.49% and 11.74% (measure 48) for the 2 groups. This topic is particularly challenging to assess in traditional clinical trials and is expected to vary among types of cancer and stages of disease. It appears that QOPI practices are maintaining relatively low levels on this measure, which is considered favorable performance.

Overall, QOPI participants are appropriately transitioning their focus of care from aggressive cancer treatment to supportive care near the EOL. These early results of the QOPI program are promising, and we expect to report on the other aspects of care measured in the program. It is possible that the results are favorable partially because the program currently includes voluntary, early-adopter oncology practices. In the future, we will assess performance of new cohorts of practices and on those remaining in the program over longer time periods. In the longer term, we seek to determine whether the methods used in QOPI will translate into higher value care with reduced costs and better outcomes for patients with cancer.

We thank the many oncologists and office staff across the United States who participate in the American Society of Clinical Oncology (ASCO) Quality Oncology Practice Initiative (QOPI) program as well as the leadership of ASCO and the scientific affairs staff at Outcome Sciences for statistical analysis in the preparation of this article. ASCO created and manages QOPI, funded the clinical registry used in the QOPI program from 2006 to the present time, and managed and funded the preparation of this article.

Authors’ Disclosures of Potential Conflicts of Interest. 

Francis X. Campion, Outcome Sciences (C); Leanne R. Larson, Outcome Sciences (C); Michael N. Neuss, American Society of Clinical Oncology (C).

 Francis X. Campion, Leanne R. Larson, Pamela J. Kadlubek.

Provision of study materials or patients:

 Leanne R. Larson, Pamela J. Kadlubek, Michael N. Neuss.

Francis X. Campion, Leanne R. Larson, Craig C. Earle, Michael N. Neuss.

 Francis X. Campion, Leanne R. Larson, Michael N. Neuss.

 Francis X. Campion, Leanne R. Larson, Pamela J. Kadlubek, Craig C. Earle, Michael N. Neuss.

Francis X. Campion, MD, FACP, Director of Provider Programs, Outcome Sciences, 201 Broadway, 5th Floor, Cambridge, MA, 02139; E-mail: 

 Committee on Quality of Health Care in America, Institute of Medicine: Crossing the Quality Chasm: A New Health System for the 21st Century. National Academies Press, Washington, DC, 2001.

 Hewitt M, Simone JV (eds): Ensuring Quality Cancer Care. National Academies Press, Washington, DC, 1999.

 National Committee for Quality Assurance: State of health care quality. 

http://www.ncqa.org/tabid/836/Default.aspx

 Berwick DM: Medicare head Donald M. Berwick, MD, takes on mission of health system reform: Interview by Mike Mitka. 

 Berwick DM, Nolan TW, Whittington J: The triple aim: care, health, and cost. Health Aff (Millwood) 27:759-769, 2008.

 America’s Health Insurance Plans: Innovations in recognizing and rewarding quality. 

http://www.ahip.org/content/default.aspx?docid =26393

 Reeves MJ, Parker C, Fonarow GC, et al: Development of stroke performance measures: definitions, methods, and current measures. 

 Medicare and Medicaid Programs Electronic Health Record Incentive Program: Final Rule. The Federal Register 42 CFR Parts 412, 413, 422, et al, 2010.

 American Association of Neurological Surgeons: What services does NeuroPoint Alliance offer? 

http://www.aans.org/en/NeuroPoint/FAQAnswers6.aspx

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http://www.rheumatology.org/practice/clinical/rcr.asp

 Dreyer NA, Tunis SR, Berer M, et al: Why observational studies should be among the tools used in comparative effectiveness research. 

 Neuss MN, Desch CE, McNiff KK, et al: A process for measuring the quality of cancer care: the Quality Oncology Practice Initiative. 

 Blayney DW, Stella PJ, Ruane T, et al: Partnering with payers for success: Quality Oncology Practice Initiative, Blue Cross Blue Shield of Michigan, and the Michigan Oncology Quality Consortium. 

 Griffin JP, Nelson JE, Koch KA, et al: End-of-life care in patients with lung cancer. 

 Wright AA, Zhang B, Ray A, et al: Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. 

The American Society of Clinical Oncology Quality Oncology Practice Initiative has grown to include 973 practices as of 2010. Practices demonstrated rates of end-of-life care and other measures of quality.

Advancing Performance Measurement in Oncology

To calculate the abandonment rate of oral oncolytic medications and identify factors that may affect likelihood of abandonment.

Cross-sectional cohort study using administrative claims data.

We analyzed a nationally representative pharmacy claims database and identified 10,508 patients with Medicare and commercial insurance for whom oral oncolytic therapy was initiated between 2007 and 2009. We calculated the abandonment rate for the initial claim, in which abandonment was defined as reversal of an  adjudicated pharmacy claim without a subsequent paid claim for any oncolytic (oral or intravenous) within the ensuing 90 days. We assessed likelihood of abandonment using bivariate and multivariate logistic regression analyses including patient demographics, plan type, drug type, cost sharing, and concurrent prescription activity.

 The abandonment rate of newly initiated oral oncolytics was 10.0%. Unadjusted bivariate analyses found that high cost sharing, increased prescription activity, lower income, and Medicare coverage were associated with a higher abandonment rate (P <.05). In the logistic regression model, claims with cost sharing greater than $500 were 4 times more likely to be abandoned than claims with cost sharing of $100 or less (odds ratio [OR], 4.46; P <.001). Patients with 5 or more prescription claims processed within in the previous month had 50% higher likelihood of abandonment than patients with no other prescription activity (OR, 1.50; P <.001).

 Abandonment of newly prescribed oral oncolytic therapy is not uncommon, and the likelihood increases for patients enrolled in plans with pharmacy benefit designs that require high cost sharing. Increased concurrent prescription activity was also associated with a higher abandonment rate. These factors should be taken into account when considering likely adherence to cancer therapy.

(Am J Manag Care. 2011;17(5 Spec No.):SP38-SP44)

	Oral oncolytic therapy is an increasingly important aspect of cancer care, and adherence to treatment is critical to deriving benefit.

		Our study found that 10% of patients abandoned their anticancer medicine, and another quarter had some delay in initiating another oncolytic.

		Pharmacy plan design (cost-sharing amount) and complexity of patients’ drug therapy (prescription activity) are significant drivers of abandonment of oral oncolytic agents.

		As the structure of Medicare Part D and commercial plans is modified and health reform initiatives evolve, policy makers and stakeholders should be aware of the impact of benefit structure on adherence and access to vital oncology therapy.

Patients diagnosed with cancer require timely access to appropriate treatments to achieve optimal outcomes. Until recently, drug therapy for patients with cancer consisted of intravenous (IV) infused treatment. Oncolytic medications that can be administered orally are a relatively new addition to cancer treatment and provide patients with the  benefits of ease of use and convenience.

 Utilization of oral oncolytics is expected to increase.

 Recent reports have suggested they account for approximately 25% of the current oncology pipeline.

Because of costs associated with these newer oral oncolytic agents, pharmacy benefit plans may implement cost-containment mechanisms such as increased patient cost sharing through placement in higher copayment tiers. The degree to which increased cost sharing influences access to and utilization of oral oncolytics may be an important factor affecting patient adherence to prescribed cancer therapy.

Traditional adherence studies of oncology medication track patient utilization longitudinally by assessing refill rate, self-reported compliance with prescribed therapies, continuous dose observations, pill counting, and administrative claims analysis.

Studies of nonadherence have found that barriers to appropriate care include cost-sharing requirements, agent toxicity, patient- and disease-related factors, social issues, and financial status.

The purpose of our study was to assess abandonment of newly initiated oral oncolytics. Specifically, we were interested in examining patients who had been prescribed oral oncolytic therapy, submitted prescriptions for their first oral oncology medications to pharmacies, and then reversed claims after adjudication (ie, initial approval of claim). If patients did not follow up with a subsequent oncolytic agent, we noted their reversed claim as an abandoned prescription.

Our objective was to calculate reversal and abandonment rates of newly initiated oral oncolytic medications using an approach similar to those found in the literature.

 Building on previous research, we investigated the degree to which abandonment is affected by patient and plan characteristics, specifically cost-sharing requirements, patient income, concurrent prescription activity, and insurance type.

We acquired administrative claims data from the Wolters Kluwer Dynamic Claims Lifecycle Database, a source of nationally representative pharmacy utilization data, for 20,607 patients for whom at least 1 pharmacy claim was adjudicated for 1 of the oral oncolytic agents included in our study between January 1, 2007, and June 30,2009. The data set included pharmacy claims and demographic data for all patients as well as medical claims for a subpopulation of patients. We studied patients using capecitabine, imatinib, sorafenib, lenalidomide, sunitinib, erlotinib, temozolomide, and lapatinib. These agents represented widely available oral oncolytic agents at the time of our analyses. We then identified a subset of adjudicated oral oncolytic claims between May 1, 2007, and March 31, 2009 (defined as the index period) to determine if a patient had newly initiated therapy with a study drug. For each claim, we looked back 120 days in the patient’s claim history from first adjudication of an oral oncolytic claim during the index period to exclude patients with previous oral or IV oncolytic treatment. To confirm that the data set included prescription data for each patient both before and  after the newly initiated oral oncolytic, we restricted the sample to those patients who had at least 1 claim for any type of medication at least 120 or more days before and at least 90 days or more after the first oral oncolytic claim, a methodology employed to assess patient eligibility during the observation period.

To calculate the portion of newly initiated oral oncolytics that were ultimately abandoned, we defined adjudication status of the oral oncolytic as paid, reversed with follow-up, or abandoned. Patients with claims defined as reversed with follow-up had a successfully paid IV or oral oncolytic claim within 90 days after submission date of the reversed newly initiated oncolytic. Patients with claims defined as abandoned had reversed the newly initiated oncolytic but did not have a paid oncolytic claim within 90 days of submission date of the reversed claim.

We required the sample to include only patients insured by a non-Medicare commercial or Medicare plan, including prescription drug plans and Medicare Advantage Prescription Drug plans. We also restricted the sample to claims with complete patient- and claim-level data for all variables utilized in the regression model.

To conduct bivariate and multivariate analyses, we identified patient-level characteristics such as age (<40, 41 to 65, 66 to 80, or >81 years), sex, and geographic region (Northeast, South, Midwest, or West). A variable for annual patient income was included in the data set and categorized as less than $40,000, between $40,000 and $75,000, or more than $75,000. Cost-sharing amounts were collected for each paid and reversed claim and were grouped into the following categories: $0 to $100, $101 to $150, $151 to $200, $201 to $250, $251 to $350, $351 to $500, or more than $500. We also created a variable to measure concurrent prescription activity for each patient based on the number of claims processed within 30 days before the submission date of the newly initiated oral oncolytic. The prescription activity variable was defined as follows: no claims, 1 claim, 2 to 3 claims, 4 to 5 claims, or more than 5 claims. Variables were created to control for the specific oral oncolytic agent that the patient was prescribed.

2 analyses were conducted to compare abandonment rates across the independent variables previously described. A logistic regression model was constructed using abandonment status (0 = no, 1 = yes) within 90 days after the initially adjudicated oral oncolytic claim as the dependent variable. Covariates included all independent variables described to identify significant predictor variables (P <.05). Odds ratios and 95% confidence intervals [CIs] were calculated for all predictors in the model using selected references for comparison.

Of the initial 20,607 patients in the data set, we identified 10,508 patients who met the final inclusion criteria for our study. Sixty-seven percent of the claims were adjudicated and paid for by the patient and/or pharmacy benefit plan, whereas 33% of claims were reversed. Of the total number of reversed claims, 23% of patients followed up with another oncolytic agent within 90 days. The remaining 10% of patients had no follow-up and thus abandoned the oral oncolytic agent. A sensitivity test to extend the follow-up window from 90 to 120 days did not significantly affect the portion of claims defined as abandoned.

 provides demographics and plan characteristics for patients included in our study. Approximately half of the patients in our sample were younger than 65 years. Patients were slightly more likely to be female, more likely to have an income between $40,000 and $75,000, and most commonly from the South. Most patients were insured by a commercial plan.

Bivariate analyses comparing the combination of paid claims and reversed claims with follow-up versus abandoned claims indicated significant differences across age groups and rate of abandonment. Thirteen percent of patients older than 80 years abandoned their first oral oncolytic compared with 10% of patients 40 years or younger (P <.05). Insurance status was also a significant variable, with abandonment rates of 16% for Medicare claims versus 9% for commercial insurance claims (P <.05).

Lower annual household income was associated with higher abandonment rates. Patients with incomes of less than $40,000 per year had an abandonment rate of 11%, decreasing to 10% for incomes between $40,000 and $75,000 and 9% for incomes above $75,000 (P <.05).

Twenty-three percent of patients submitted more than 5 claims for nononcolytic medications within the previous month of initiating the oral oncolytic, whereas 29% of patients had no concurrent prescription activity. Prescription activity was significantly associated with increased abandonment rates. Patients with more than 5 claims in the previous month had an abandonment rate of 12% compared with 9% for patients with no claims in the previous month (P <.05).

Seventy-three percent of newly initiated oncolytics had a cost-sharing amount of $100 or less, although 16% required an out-of-pocket cost of more than $500. For reference, median cost-sharing amount in the sample for all patients was $30. The abandonment rate increased with cost-sharing amount. Claims with cost sharing above $500 had the highest abandonment rate (25%) compared with an abandonment rate of 6% for claims with cost sharing of $100 or less (P <.05).

Among the 8 oral oncolytics in our study, capecitabine accounted for more than one-third of the sample. Imatinib, lenalidomide, erlotinib, and temozolomide were also commonly used medications. The unadjusted abandonment rate for each oral oncolytic agent varied greatly.

After controlling for multiple factors in the logistic regression model, we found that patients experiencing higher costsharing amounts were significantly more likely to abandon the oral oncolytic agent, compared with patients with the lowest cost-sharing amount (

). Claims with cost sharing over $500 had more than 4 times the likelihood of abandonment versus claims with cost sharing of $100 or less (P <.05). Patients with between 2 and 5 prescription claims and patients with more than 5 claims in the previous month had a 26% and 50% higher likelihood to abandon the oral oncolytic agent, respectively, versus those patients with no concurrent prescription activity (P <.05). A sensitivity analysis in which we calculated the prescription activity variable with only paid claims (not including reversals) produced similar regression results.

In the multivariate logistic regression, age and annual income (which showed significant bivariate relationships with abandonment) were no longer significant predictors once we controlled for other factors. In addition, patients with incomes less than $40,000 were 20% more likely to abandon versus patients with incomes greater than $75,000 (P = .058).

We completed a subanalysis to explore the distribution of cost-sharing amounts by insurance type (

). Eighty percent of commercially insured patients paid $100 or less out of pocket for the first oral oncolytic claim, compared with 35% of Medicare patients. Only 11% of commercially insured patients paid more than $500 versus 46% of Medicare patients. A 

2 test confirmed that Medicare patients pay significantly more out of pocket than commercially insured patients (P <.001).

To estimate the impact of insurance plan type on abandonment rates while controlling for other factors, we constructed an additional logistic regression model as part of this subanalysis. This model included the same covariates as our logistic regression model, with the addition of an insurancetype variable (commercial vs Medicare). We also restricted the sample to claims submitted in calendar year 2008 and added a variable to designate calendar quarter of submission to control for the impact of the Medicare coverage gap. The results of this analysis confirmed relationships found in the main regression analysis. Cost-sharing amount continued to be a significant predictor of the likelihood of abandoning the newly initiated oral oncolytic. Insurance type was not a significant predictor of abandonment, perhaps because of the strong relationship between cost sharing and insurance type. We also found that abandonment rates were significantly higher between April and December than in the first quarter of the year (P <.05).

This research provides new insight into factors that affect the abandonment rate of oral oncolytic medications at pharmacies. We found that 1 in 10 patients abandoned their first prescription for an oral oncolytic agent, a rate similar to those reported for specialty medications to treat rheumatoid arthritis and multiple sclerosis and higher than those for other oral chronic medication classes (eg, antihypertensives, antipsychotics, and antidepressants).

 We found that almost a quarter of patients reversed their newly initiated oral oncolytic and subsequently followed up with another oncolytic prescription, potentially causing an unnecessary delay in treatment. Approximately two-thirds of patients successfully paid for their first oral oncolytic claim on initiation of the medication.

Out-of-pocket costs played a significant role with regard to the likelihood that a patient would abandon the first fill of the oral oncolytic agent. One in 4 patients filling prescriptions with cost-sharing amounts over $500 abandoned the prescription and did not follow up with another oncology medication within 90 days. Cost sharing was also highly correlated with insurance type. Patients enrolled in Medicare plans, including prescription drug plans and Medicare Advantage Prescription Drug plans, had higher cost sharing than those insured by commercial insurance plans.

Although the relationship between cost sharing and abandonment rate has been previously investigated and presented elsewhere, we identified other factors that also significantly increased likelihood of abandonment.

 Patients with higher concurrent prescription activity were more likely to abandon their first oral oncolytic claim at the pharmacy. Lower annual household income was also associated with higher abandonment rates, although this relationship was not statistically significant after controlling for other factors.

Differences in abandonment rates across oncolytic agents may be related to specific cancer diagnoses for reasons unrelated to factors in our analysis. Further research that integrates additional clinical data elements derived from administrative or medical records may more appropriately assess differences across oral oncolytic agents.

Ten percent of patients abandon newly initiated oral oncolytics at the pharmacy. Patients facing higher cost sharing or increased concurrent prescription activity have a higher abandonment rate.

Special Issue: Payer/Provider Relationships in Oncology

Developing Dialogue in Healthcare

Characterizing Medical Care by Disease Phase in Metastatic Colorectal Cancer

Advancing Performance Measurement in Oncology

Patient and Plan Characteristics Affecting Abandonment of Oral Oncolytic Prescriptions

Pathways, Outcomes, and Costs in Colon Cancer: Retrospective Evaluations in 2 Distinct Databases

Health Technology Assessment and Private Payers's Coverage of Personalized Medicine

Do Economic Evaluations of Targeted Therapy Provide Support for Decision Makers?

Trying Something New: Episode Payments for Cancer Therapy

Are We Winning the War on Cancer?