Scott Milligan, PhD

 We analyzed the demographics and disease characteristics of patients prescribed treatment for chronic hepatitis C virus (HCV) infection from 2013 through 2017, a time frame that encompasses the expansion of available direct-acting antiviral inhibitors.

 Using a proprietary disease-management program, data for 19,944 patients receiving HCV treatment were collected from providers and specialty pharmacies. Six-month time periods accounting for introductions of novel treatments were established as follows: December 2013 to May 2014 (n = 1438), simeprevir and sofosbuvir; October 2014 to March 2015 (n = 2242), ledipasvir/sofosbuvir and ombitasvir/paritaprevir/ritonavir plus dasabuvir; October 2015 to March 2016 (n = 5514), daclatasvir; July 2016 to December 2016 (n = 5562), elbasvir/grazoprevir and sofosbuvir/velpatasvir; and July 2017 to December 2017 (n = 5188), sofosbuvir/velpatasvir/voxilaprevir and glecaprevir/pibrentasvir. Changes over time were evaluated for statistical significance.

 In the 2013-2014 time period, 44% of patients receiving prescriptions for HCV treatment were treatment-experienced and 45% had cirrhosis. By 2017, only 14% were treatment-experienced (

 <.001). The percentage of patients with HCV genotype 1 increased from 69% to 87% from 2013-2014 to 2014-2015 (

 <.001) but subsequently decreased to 74% in 2017 (

 <.001). The percentage of patients receiving HCV prescriptions in an academic setting declined from 61% in 2013-2014 to 13% in 2017 (

 In the United States, since the introduction of interferon-free HCV regimens, the patient population prescribed treatment has changed, becoming predominantly treatment-naïve, without cirrhosis, and treated in nonacademic centers.

Data from a network of providers and specialty pharmacies in the United States suggest that since the introduction of interferon-free treatments for chronic hepatitis C virus (HCV), the population receiving prescriptions has changed.

There has been a shift toward lower percentages of patients being treatment-experienced, having cirrhosis, or receiving care in academic centers. Although these changes were expected, published data were lacking.

As sites of care shift to nonacademic centers, there will be increased need to educate public health providers on screening, diagnosis, and treatment for HCV.

Chronic hepatitis C virus (HCV) infection affects approximately 2.7 million individuals in the United States

 and is associated with substantial morbidity and mortality. Liver-related complications of chronic HCV infection include cirrhosis, liver failure, and hepatocellular carcinoma.

 Treatment that results in a sustained virologic response reduces the risk of liver-related complications and can lead to regression of hepatic fibrosis.

 Prior to 2013, all treatment options for chronic HCV required peginterferon and ribavirin for 24 or 48 weeks and had limited efficacy with significant tolerability issues. Since 2013, the HCV treatment landscape has dramatically changed with the expansion of available direct-acting antiviral (DAA) inhibitors, providing multiple options for relatively short (8-12 weeks), simple (once-daily oral tablets), and better-tolerated regimens with high efficacy rates.

 From 2013 to 2017, 13 DAA inhibitors for HCV received approval from the FDA for use in 8 all-oral combination regimens.

 The first all-oral regimens contained simeprevir and sofosbuvir with ribavirin for HCV genotype 1 for up to 24 weeks, but the more recently approved regimens are pangenotypic and 8 or 12 weeks in duration.

As treatment options expanded and improved, there was an expectation that the patient population initiating treatment for chronic HCV would shift from an older, treatment-experienced population with more fibrosis to a younger, treatment-naïve population with milder liver disease. However, there has been a lack of investigation into the specific demographics and disease characteristics of DAA-prescribed patients relative to the availability of different DAA regimens. Using data from a network of providers and specialty pharmacies in the United States, we evaluated changes in patient demographics and practice settings from 2013 to 2017.

Clinical, demographic, prescription, and dispensing data for adult (≥18 years) patients with HCV were collected from specialty pharmacies and physicians in the United States through Trio Health’s proprietary Innervation Platform, a portal for managing patient information. Where mandated by state law, patients signed an electronic informed consent allowing data collection. Each clinician and specialty pharmacy providing data signed agreements attesting to compliance with all rules and regulations regarding the disclosure of patient information to a shared database. Embedded in the data collection process are quality control assessments that identify potential errors within fields (eg, data type mismatches, unit conversion errors) and between fields (eg, sequential order of events, improbable combinations of vital or laboratory measures). Data identified as questionable are routed back to the source for confirmation or correction. This data collection process and study plan were approved by Western Institutional Review Board as an institutional review board exemption under 45 CFR 46.101(b)(4).

The analysis population included patients from the 50 US states plus Washington, DC, who were prescribed care for HCV from 1 of 2827 providers and were serviced by 1 of 16 specialty pharmacies.

Six-month evaluation periods that spanned the introductions of treatment options were established as follows: December 2013 to May 2014 (n = 1438), inclusive of simeprevir and sofosbuvir availability; October 2014 to March 2015 (n = 2242), inclusive of ledipasvir/sofosbuvir and ombitasvir/paritaprevir/ritonavir plus dasabuvir availability; October 2015 to March 2016 (n = 5514), inclusive of daclatasvir availability; July 2016 to December 2016 (n = 5562), inclusive of elbasvir/grazoprevir and sofosbuvir/velpatasvir availability; and July 2017 to December 2017 (n = 5188), inclusive of sofosbuvir/velpatasvir/voxilaprevir and glecaprevir/pibrentasvir availability (

). All statistical analyses were conducted using SPSS 25 (IBM Corp; Armonk, New York) or JMP 14.2 (SAS Institute Inc; Cary, North Carolina). Differences between periods were assessed in SPSS 25 using χ

 (categorical variables) or Kruskal-Wallis (continuous variables) tests with Bonferroni correction for repeated measures. Column (period) proportions for categorical variables were subsequently analyzed using Z tests with Bonferroni correction. Trends were evaluated using either the Cochran-Armitage (linear trends for proportions; JMP 14.2) or Jonckheere-Terpstra (ordered trends for continuous or ordinal variables; SPSS 25) test.

A total of 19,944 patients who were prescribed anti-HCV therapies were included in the analysis. From 2013 through 2017, the mean age of patients receiving prescriptions varied between periods but did not change appreciably (range, 56-58 years) (

). In 2013-2014, 44% of patients in this cohort were treatment-experienced and 45% had cirrhosis. By 2017, 14% of patients were treatment-experienced (

 <.001). The percentages of patients with cirrhosis were comparable between the 2016 and 2017 time periods (20% vs 21%). At all time periods analyzed, the majority of patients had HCV genotype 1 (range, 69%-87%). Significant linear trends were identified for HCV genotype proportions and were evident starting from the second evaluation period (2014-2015). The percentage of patients with HCV genotype 1 increased from 69% to 87% from 2013-2014 to 2014-2015 (

 <.001). The percentages of patients with genotypes 2 and 3 decreased from 19% and 10% to 6% and 5%, respectively, from 2013-2014 to 2014-2015, and they subsequently increased to 12% and 11% in 2017.

In the Trio Network, the percentage of providers from academic practices dropped from 32% in 2013-2014 to 12% in 2017 (

 <.001), and the percentage of patients receiving HCV prescriptions in an academic setting declined from 61% to 13% (

 <.001). These changes translate into a decrease from 12 patients per academic provider (in a 6-month period) to 4 patients per academic provider (

 <.001). For the corresponding periods, the average count of patients per community provider remained largely stable at 3.6 to 3.8 (

 = .412), although the percentage of providers from community settings increased from 39% to 87% (

 <.001). The change in site of care was greatest between the 2014-2015 and 2015-2016 evaluation periods; the proportion of patients treated in an academic setting decreased from 48% to 17% (

Among patients prescribed HCV treatments, the percentage of patients with Medicaid coverage was 24% during both the 2013-2014 and 2017 time periods, although during the intervening periods there was some variation. Overall, there was a decline in the percentage of patients who were commercially insured (51% in 2013-2014 to 30% in 2017; 

 <.001), and the largest decline occurred between the 2014-2015 (56%) and 2015-2016 (33%) time periods (

 <.001). The percentages of patients reporting Medicare as their primary insurer increased from 24% in 2013-2014 to 42% in 2017 (

 <.001), with the largest shift occurring between the 2014-2015 (26%) and 2015-2016 (48%) time periods (

In this study of patients receiving prescriptions for HCV treatment from 2013 to 2017, there was a substantial decline in treatment-experienced patients with cirrhosis and a corresponding increase in treatment-naïve patients predominantly without cirrhosis. The percentages of patients with cirrhosis who received prescriptions for HCV treatment leveled out in 2016 and 2017 at approximately 20%. Our data are consistent with data from the German Hepatitis C Cohort (N = 2367), which indicated decreases in percentages of treatment-experienced patients (83% vs 24%) and patients with cirrhosis (42% vs 17%) from February 2014 to September 2017.

 Similarly, among commercially insured patients in the US administrative claims IQVIA PharMetrics Plus Database who received treatment for HCV, from 2014 to 2017 there was a decline in the percentage of patients having advanced liver disease (29% to 24%).

 In our data, genotype distribution did change significantly from 2013-2014 to 2017, although in each period the majority of patients had HCV genotype 1 (range, 69%-87%). The percentage of patients with HCV genotype 1 peaked in the 2014-2015 period, coincident with the launch of ledipasvir/sofosbuvir in October 2014. The peak may reflect “warehousing” of patients waiting for an all-oral, ribavirin-free regimen with high efficacy against HCV genotype 1.

In addition to changing demographics, this study found a significant shift toward care in community sites versus academic centers, with the largest change occurring between the 2014-2015 and 2015-2016 periods. In 2014-2015, 48% of the patients were treated in academic centers compared with 17% in the 2015-2016 period. At the same time, the largest observed shifts occurred in the percentages of patients with cirrhosis (37% in 2014-2015 to 26% in 2015-2016) and treatment-experienced patients (38% in 2014-2015 to 21% in 2015-2016). These shifts suggest that the movement to community care may have been driven in part by the reduced number of patients with cirrhosis or prior treatment failure, which are characteristics historically associated with harder-to-treat HCV.

Although the percentage of Medicaid recipients has stayed relatively stable, there has been an increase in Medicare-covered patients and a corresponding decline in those with commercial insurance. After the first all-oral DAA regimens became available, restrictive payer policies limited treatment access for patients with Medicaid or commercial insurance.

 The denial of care for Medicaid-covered patients was nearly instantaneous in the Trio Network, occurring mid-2014, 6 months after the first DAA approval.

 Denial rates for commercially covered patients increased rapidly between the 2014-2015 and 2015-2016 time periods.

 The observed trends reported here suggest that high denial rates subsequently led to the avoidance of prescriptions for patients with Medicaid or commercial coverage.

Our analysis has several limitations. First, the data were captured from a limited number of sources and may not reflect trends in the United States as a whole. In addition, the specialty pharmacies and providers from which data were captured changed over time, and therefore we cannot rule out the possibility that the changes we saw were a result of differential sampling over time. Populations that were not represented or were underrepresented in the study included those receiving care in drug addiction treatment centers or correctional institutions. Race, which is patient-reported and patient-consented in the Trio Network, was specified for 38% of the data set. Based on patient reporting, the composition of the study sample was 70% white, 19% black, 2% Asian, less than 1% Native Hawaiian or other Pacific Islander, and 8% multiple or other races. This is directionally aligned with reported values,

 although the actual composition of the entire study population may be different. Finally, for a minority of patients in our data set, treatment history or cirrhosis status was not reported, and where reported, the information was based on physician input as opposed to demonstrated results from noninvasive test results or liver biopsy.

In summary, the results of our analyses indicate that the characteristics of populations prescribed HCV treatments have changed as a result of the availability of highly effective, well-tolerated DAAs. In our data set, in 2017, treatment-naïve patients without cirrhosis with HCV genotype 1 receiving care in a community setting represented the major prescribed demographic in the United States.

Writing assistance was provided by Jennifer King, PhD, of August Editorial.

 University of Hawaii (NT), Honolulu, HI; Saint Louis University School of Medicine (BB), St. Louis, MO; Beth Israel Deaconess Medical Center (MC, NA), Boston, MA; Northwestern University Feinberg School of Medicine (SLF), Chicago, IL; Trio Health Analytics (SM, NW), La Jolla, CA; Center for Liver Diseases, Department of Medicine, Inova Fairfax Hospital (ZY), Falls Church, VA.

 Funding for this study was provided by Gilead Sciences, Inc.

 Dr Tsai consults for and has received grants and honoraria from Gilead, AbbVie, Merck, and Bristol-Myers Squibb; he has received lecture fees from Gilead and AbbVie. Dr Curry consults for Trio Health Analytics and Gilead. Dr Bacon is the chair of the American Association for the Study of Liver Diseases Foundation Board and has received honoraria/lecture fees for speaking for AbbVie and Gilead. Dr Flamm consults for and has received lecture fees from Gilead, AbbVie, and Merck; he has received grants from Gilead and AbbVie. Dr Milligan and Ms Wick are employed by Trio Health Analytics and have received research support from Gilead, Merck, and AbbVie. Dr Younossi has received grants from Gilead, Intercept, Bristol-Myers Squibb, GlaxoSmithKline, and Novo Nordisk. Dr Afdhal is a paid consultant/advisory board member for Gilead, Echosens, Ligand, Shionogi, and Trio Health; owns stock in SpringBank and Allurion and has stock options in SpringBank; receives royalty income from UpToDate; and is on the board of directors for the nonprofit Liver Institute for Education and Research.

 Concept and design (NT, BB, SLF, SM, NW, ZY, NA); acquisition of data (NT, BB, MC, SM, NW); analysis and interpretation of data (SLF, SM, NW, ZY, NA); drafting of the manuscript (NT, SM, NW, NA); critical revision of the manuscript for important intellectual content (NT, BB, MC, SLF, SM, NW, ZY, NA); statistical analysis (SM, NW); provision of patients or study materials (NT, MC); obtaining funding (NA); and supervision (NA).

 Naoky Tsai, MD, University of Hawaii, 642 Ulukahiki St #103, Kailua, HI 96734. Email: naoky@hawaii.edu.

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Articles

From 2013 to 2017, the population of US patients prescribed treatment for chronic hepatitis C virus (HCV) changed, becoming predominantly treatment-naïve and having received care in nonacademic centers.

Changing Demographics Among Populations Prescribed HCV Treatment, 2013-2017

 In partnership with a large nonprofit healthcare insurer for the Mid-Atlantic region of the United States, we launched the first cancer clinical pathway in the United States in August 2008. Due to its early success with regard to savings and physician participation and compliance, a second-generation pathways program—the Oncology Medical Home&mdash;was piloted in 2011. This program offered a physician reimbursement model that shifted the source of revenue from drug reimbursement margin to professional charges for cognitive services (evaluation and management codes). We report our observations of the impact of that reimbursement model on physician prescribing behavior.

A select group of practices that participated in the first-generation pathways program were invited to voluntarily participate in the Oncology Medical Home and its cognitive weighted reimbursement design. A matched control group was chosen from the first-generation pathways participants. Comparisons of physician behavior parameters were made pre- and postimplementation and between the Oncology Medical Home practices and the first-generation pathways control group.

Physician behavior was not significantly modified by cognitive weighted reimbursement. No significant change in frequency of office visits for established patients was observed. No change in chemotherapy prescribing was observed. Observed increases in generic regimen use were no different than matched control.

Observations from this oncology medical home pilot program suggest that reimbursement methodology alternatives to the prevailing fee-for-service may have less impact on prescribing behavior than has been conjectured. Future research is ongoing to validate these observations and assess additional influences on prescribing behavior.

Increased use of chemotherapy and more expensive drugs has been correlated with the “buy-and-bill” reimbursement model; therefore, we shifted the primary source of provider revenue from drug reimbursement to professional charges in our payer-sponsored Oncology Medical Home pathways program.

		Analysis showed that this novel reimbursement model did not alter physician prescribing behavior with regard to the type or frequency of chemotherapy administrations, or established and new patient visits.

		These observations suggest that medical oncology treatment selection and cancer care practice patterns may not be influenced by fee-for-service reimbursement.

		Research is ongoing to validate these observations and assess additional influences.

United States direct medical costs associated with cancer are projected to increase exponentially, from $104 billion in 2006 to over $173 billion in 2020.

 Variability in medical practice plays a large role in increasing costs without achieving better patient outcomes.

 Clinical care pathways have been cited as solutions to help bend the rising costs of cancer care by reducing unnecessary and costly treatment variation while improving quality of care.

In August 2008, a large nonprofit healthcare insurer for the Mid-Atlantic region of the United States partnered with Cardinal Health to launch the first inclusive provider network cancer care pathway in the United States. This program marked a milestone in the evolution of cancer treatment by demonstrating that an oncology pathway program can be deployed across an entire plan network comprised of disparate providers (single/group, community/academic, independent/affiliated) benefitting all parties by improving consistency and quality of care while reducing costs without reducing provider reimbursement.

We previously reported that high participation and compliance levels in our pathways program led to changes in physician patterns of care that resulted in significant decreases in overall oncology expenditures.8 Similar physician behavior changes were observed in a Blue Cross Blue Shield Michigan pathway program, where aligned stakeholder incentives drove high levels of provider participation and compliance.

 Despite these and other pathway program successes, critics suggest that the observed savings benefits are small and unsustainable.

The fee-for-service payment system has been identified as one of the main drivers of cancer care cost—the more physicians do for patients, the more reimbursement they receive.

In this model, oncologists directly purchase chemotherapy from manufacturers and/or wholesalers (typically below, at, or slightly above average sales price [ASP]) and are reimbursed by the payer at prices usually exceeding ASP by 6% (Medicare presequestration) to 30% or more (commercial payers). Studies have shown that providers’ choice of chemotherapy can be affected by reimbursement, resulting in their prescribing chemotherapy more often and utilizing more costly brand name chemotherapy over less expensive brand or generic alternatives.

Many argue that this “buy-and-bill” model encourages physicians to overprescribe chemotherapy, creates incentives for price inflation, and distorts clinical decision making, thereby driving up the costs of cancer care.

With these factors in mind, we piloted a second-generation pathways program, the Oncology Medical Home, in January 2011. The goal of this program was to decrease cancer costs beyond those observed with the first-generation pathways model, and as such, it would address the concern that chemotherapy prescribing is influenced by a “pay-for-volume” rather than a “pay-for-value” reimbursement methodology. In the Oncology Medical Home, physician reimbursement would shift the drug reimbursement margin to professional charges for cognitive services through a dramatically enhanced professional service evaluation and management (E&M) fee schedule. Other cost-saving measures for the Oncology Medical Home included physician commitment to an intensive continuous quality improvement initiative and an end-of-life program, which have been previously presented and will be addressed in a future manuscript.

This new cognitive weighted reimbursement model provokes several questions. If payment is based on professional charges rather than drug reimbursement, will physicians then evaluate patients more frequently? If chemotherapy reimbursement margins are at or near cost, will the use of chemotherapy decrease? Will there then be a shift away from brand name drugs to less costly brand drugs and/or generic drugs? To answer these questions, we evaluated the impact of the change in reimbursement on business practices and prescribing behavior for physicians participating in the Oncology Medical Home.

The first-generation oncology clinical pathway program was initiated in August 2008 as a collaborative effort between the payer and its contracted providers. Participation in the program was voluntary; however, providers were given financial incentives to participate in the form of an increased brand and generic drug (Jcode) fee schedule as compensation for the additional work flow required to maintain program compliance. A physician steering committee led participating physicians, who jointly and independently developed the content, structure, and implementation of the pathways.

The Oncology Medical Home was initiated in January 2011, with first patients accrued April 1, 2011 (with the delay needed for fee schedule implementation). The mechanism of drug reimbursement was changed for Oncology Medical Home—participating physicians from drug reimbursement margin to cognitive weighted reimbursement (

). A portion of the overall reimbursement to participating physicians was transferred from intravenous drug fees to E&M code reimbursement, while keeping revenue at the same level, when weighted by utilization. Twelve months of claims for intravenous chemotherapy, supportive care, and E&M codes were analyzed. The change in reimbursement between the preprogram rate of 24.5% margin over ASP and the Oncology Medical Home rate of 8% margin over ASP was calculated for each drug for which there were claims. Fourteen generic drugs were kept at higher reimbursement as an incentive for use where the level of reimbursement was the same for both Oncology Medical Home and control practices. In total, 70 drugs were reduced in price. This reduction was transferred as an increase to a select list of 17 E&M and chemotherapy administration codes. This resulted in an aggregate 62% increase in these fees, with an emphasis on new patient consult codes, which were increased by 166%. When weighted by utilization, the increase in revenue to the Oncology Medical Home physicians was equal to the value of the decrease in drug revenue.

The Oncology Medical Home program was offered to a subset of first-generation pathways participants, who had recently organized to form a membership association, Therapeutics and Research in Oncology (TRIO). The TRIO practices operate independently, retaining their individual tax identification numbers, and consist of providers from small and large practices, urban and rural geography, representing the payer’s 2 largest metro areas. The initial membership in TRIO included 42 physicians in 16 practices, all of whom participated in the first-generation pathway program. Thirty-three physicians in 13 practices subsequently chose to participate in the Oncology Medical Home. Reasons for nonparticipation included sale of practice to hospital, retirement, and distrust of program.

Selection of the Study and Control Groups for Comparative Analyses 

Of the 50 practices that participated in the first-generation pathways program, 32 were chosen for initial data evaluation based on the following criteria: (1) consistent data volume throughout the baseline and evaluation periods, defined as April 2010 to March 2012; (2) volume of >100 patients during the baseline period; and (3) location in Virginia, Maryland, or Washington, DC. Ten of these 32 practices were TRIO practices that participated in the Oncology Medical Home. Data from all 32 practices in the baseline year were used to create propensity scores via logistic regression.

K-nearest neighbor analysis of scores and 1-1 matching with replacement resulted in the pairing of 8 Oncology Medical Home practices to 7 first-generation pathways control practices.

 The logistic regression for Oncology Medical Home participation included the covariates of cancer and chemotherapy-treated patient volumes, cancer type, patient age group, extent of treatment, and Charlson comorbidity scores.

Comparisons of physician behavior were made between the Oncology Medical Home participating practices and the first-generation pathways control group for the year prior to (year —1) and the year following (year +1) Oncology Medical Home implementation. Behavioral comparisons included number of patients per practice, number of visits per patient, number of patients receiving chemotherapy, chemotherapy administrations per patient, and use of all generic chemotherapy regimens. Claims data from the insurance network database were collected for year –1 (representing April 1, 2010, to March 31, 2011) and for year +1 (representing April 1, 2011, to March 31, 2012).

To measure extent of treatment, therapy lines were assigned based upon grouping of chemotherapy drugs. Drugs given within 30 days of each other were grouped as a drug combination. Changes that occurred beyond 30 days triggered drug combination reassignment and incremented the line of therapy. Exceptions were made for sequential therapies, which were accounted for by distinct grouping rules. Cormorbidity scores were calculated according to Charlson using Deyo’s mapping of 

International Classification of Diseases, Ninth Revision, Clinical Modification

 A single modification to the mapping was implemented in flagging claims likely to represent solid tumors with metastases. In addition to using claims with ICD-9 codes 196.x through 199.1, patients with solid tumors who received 2 or more lines of chemotherapy were marked as with metastatic solid tumor.

All statistical analyses were conducted using SPSS 19 statistical software (IBM, Armonk, New York) with the exception of k-nearest neighbor analyses, which were performed using SPSS Modeler 14.2. Due to the large sample sizes, χ² analyses were not used for categorical comparisons. Instead, individual measures were calculated at the practice level and group means were compared by independent 

 tests. All chemotherapy evaluations were based on intravenous chemotherapy claim code 96413. New and established patient visit claim codes 99201-99205, 99241- 99245, and 99211-99215 were used in these analyses.

A total of 33 physicians from 13 TRIO practices within the insurance network who participated in the first-generation pathways project chose to join the Oncology Medical Home program. Propensity score analysis identified 8 Oncology Medical Home practices and 7 firstgeneration pathways practices that were well matched for use in these analyses (

). The matching variables were chosen as a reflection of practice disease focus, treatment preferences, and overall patient volume. After matching, baseline demographics and characteristics were similar in the 2 groups (

). The first-generation pathways control group treated 4847 patients at baseline versus 7213 for the Oncology Medical Home. The ages of patients were similarly distributed between the 2 groups. Approximately 13.5% of patients were under age 50 years, 35% were aged 50 to 64 years, approximately 25% were aged 65 to 74 years, and 25% were aged 75 years or older. Both study groups treated primarily solid tumors (85%). Other tumor types were hematologic (11%) and gynecologic (5%). Seventy percent of patients recieved first-line chemotherapy, 22% percent recieved second-line chemotherapy, and approximatley 9% recieved third-line or higher chemotherapy.

There was minimal difference in behavior change between the Oncology Medical Home and first-generation pathways providers and between study years among providers (

). The number of established patient visits in the Oncology Medical Home group remained stable from year —1 to year +1, with a mean of 3.7 and 3.8 patient visits, respectively. The same was true for the firstgeneration pathways control group, with a mean of 4.6 and 5 patient visits, respectively. New patient visits increased 2% from year –1 to year +1 for Oncology Medical Home providers compared with a decrease of 2% for the first-generation pathways control group. The percentage of chemotherapy administrations per patient remained stable among study years for both groups, with approximately 8 chemotherapy administrations per patient in each group for each year. The percentage of patients who received chemotherapy remained stable for each group among study years at approximately 15%.

There was no difference in the percentage of patients who received all-generic chemotherapy regimens between the study groups, though there was a trend for increased use of generics in year +1 for both groups; Oncology Medical Home providers increased the use of generic-only regimens by 43% in year +1 compared with first-generation pathways providers, who increased use of generics by 42%. The migration of broadly used agents gemcitabine, docetaxel, irinotecan, and oxaliplatin (transiently) from brand to generic status during the study period was largely responsible for the increase in generic-only regimen prescribing among both groups. The generic regimens lacking any of these 4 drugs accounted for 25% and 23% of control and Oncology Medical Home patients, respectively, in the program year. In the preprogram year, all-generic regimens accounted for roughly 25% of control and 28% of Oncology Medical Home patients.

We found that, surprisingly, moving financial incentives from drug administration toward cognitive services did not alter physician behavior with regard to type or frequency of chemotherapy administrations. We say surprisingly, as expectations based on prevailing wisdom suggest physicians behave in their economic best interest as long as patient outcomes are not jeopardized. This has been demonstrated in previously published reports showing that decreased reimbursement schedules correlate with increased rates of chemotherapy administration and use of more costly chemotherapeutic agents.

 In fact, a recent New York Times editorial cosigned by 20 leading academics cited fee-for-service reimbursement as the primary driver for the spiraling cost of cancer care in this country.

 Our results are quite inconsistent with this idea. Whether this pattern of care was pathway-influenced or is the result of National Comprehensive Cancer Network and other guidelines, brand name prescription drug detailing, cognitive dissonance, our culture of medicine, or other factors, is speculative without more information.

Additionally, the results from this study indicate that, despite a nearly 3-fold increase in E&M code reimbursement, no significant change in established or new patient visits was observed. This was contrary to expectation and could be related to external influences on physician practice behavior, including the historically lower contribution of E&M reimbursement to revenue, standardized and established practice patterns, and maximized throughput within office flow. If so, then the speculated impact of reimbursement reform may be overestimated.

The reimbursement level for generics did not differ between the control and the Oncology Medical Home participants. We acknowledge the potential impact due to the patent expiry of 4 drugs in the program year, which may have minimized pressure to increase reimbursement from E&M claims. However, this is a separate issue from the main question of whether physicians behave to maximize financial gain. The data we provided suggest that given the opportunity to maximize revenue by increasing select cognitive services, physicians remained unchanged in their behavior.

The nature of this observational study may raise questions over bias, and ultimately, conclusions. The methodology of conducting research in such circumstances is difficult; by definition, selection bias exists when programs are voluntary and financially incentivized. However, any selection bias incurred impacted both control and experimental cohorts, which were then matched by propensity scoring. To account for differences in disease focus, diagnosis mix was considered. To account for differences in heavily treated versus newly treated patients, and in earlystage versus later-stage treatment, the distribution of chemotherapy lines (“extent of treatment”) was considered. To account for overall treated patient burden, comorbidity index was considered. Taken together, we believe these measures yielded propensity scores indicative of case mix. However, we acknowledge that claims data, and nearly all secondary data sources, are not sufficient to account for more diverse disease stratifications. For example, race, socioeconomic status, biomarker status, disease stage, or disease histology are not sufficiently represented in claims data. Despite the absence these factors, we believe these cohorts are appropriately matched and the comparisons are valid.

We initiated the Oncology Medical Home program with the intent of further removing financial impact by offering a “white bag” drug delivery system where patient specific medications and supplies are delivered directly to practices from a dedicated specialty pharmacy. Alternatively, practices could continue their legacy buy-and-bill arrangement at a reduced fee schedule, 8% margin over ASP predetermined to be adjusted annually, where they would assume risk for price increases in brand drugs not mitigated by price equilibration for those drugs recently converted to generic. The 8% margin was selected for 2 reasons. First, this was the lowest bid by specialty pharmacy suppliers, as they are unable to purchase drugs at the same discounted prices as oncologists. Second and more importantly, modeling suggested that the 8% margin over ASP, drug utilization from the baseline control period, and the most recent Medicare fee schedule would create no net profit. All practices in the Oncology Medical Home chose to retain their current buy-and-bill practice for drugs. Acceptance of white bagging would have made cohort comparisons less complicated, but the continued buy-and-bill preference by participants, despite profit neutrality and assumed risk, represents an interesting behavioral observation. We recognize that the Oncology Medical Home program was limited to a single payer, which may not have accounted for sufficient per practice volumes to impact behavior. However, that payer represented approximately one-third of the payers of first-generation pathway providers and more than 50% of their profit, making the program financially relevant to participating practices. Although Medicare may contribute the majority of patients to an oncologist’s practice, its contribution to profit is much less significant, making commercial payers increasingly relevant to a practice’s financial integrity.

Finally, we recognize that Oncology Medical Home providers, being mature pathways participants of nearly 3 years, may have had established patterns of care that limited variance, thereby reducing the influence of reimbursement. If this is true, then pathways programs such as the first-generation program described herein may be a more palatable provider solution to an unsustainable cost curve than radical reimbursement reform.

Surprisingly, these findings are not revelatory, as related research into patterns of cancer care has resulted in similar observations. Morden et al found that hospice referral rates, hospitalizations, intensive care unit admissions, and chemotherapy use in the last weeks of life were remarkably uniform regardless of institution; profit versus nonprofit, academic versus community hospital, or small versus large facility.

 These direct observations imply that salaried physicians practice similarly to those reimbursed by fee-for-service methodology, despite vastly different economic incentives.

Observations from Morden and this study suggest that, contrary to prevailing dogma, medical oncology treatment selection and cancer care practice patterns may not be influenced by fee-for-service reimbursement as is often ascribed. Such observations warrant careful consideration as reimbursement methodology is modeled as part of healthcare reform. Research is ongoing to validate these observations and assess additional influences. Other value measures of our Oncology Medical Home that were believed to be both cost-saving and qualityenhancing, included physician commitment to an intensive continuous quality improvement initiative and their participation in an end-of-life care coordination program. The observations related to these components of this Oncology Medical Home pilot were separately reported at the American Society of Clinical Oncology Annual Meeting in 2013 and will be published in the near future.

 Cardinal Health Specialty Solutions, Dublin, OH (BF, SM, TO, JS); CareFirst BlueCross Blue Shield, Baltimore, MD (WW, DW); Greater Washington Oncology Associates, Rockville, MD (RT).

The Pathways Programs, data from which are used in this study, are a business line of Cardinal Health Specialty Solutions (CHSS). Several of the study authors are employed by and own stock in CHSS (BF, SM, TO, JS). The other authors (WW, DW, RT) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Shifting physician revenue from drug sales to cognitive services for mature pathways providers did not affect practice behavior in this payer-sponsored Oncology Medical Home.

Physician Behavior Impact When Revenue Shifted From Drugs to Services

Cancer death rates in the United States have declined 20% from their peak in 1991 (215.1 per 100,000 population) to 2009 (173.1 per 100,000 population).

 Patients are living longer with cancer, and the improvement in survival rates reflects progress in diagnosing certain cancers at an earlier stage and improvements in treatment.

 The tremendous scientific progress that has led to new therapeutic agents and tools for diagnosis and follow-up has resulted in: (1) marked increase in cancer survivorship; (2) longer periods of active treatment; and (3) marked increased variability in treatment options, all at great economic cost.

 In the United States, direct medical costs associated with cancer are projected to increase exponentially from $104 billion in 2006 to over $173 billion in 2020.

 Oncology cancer treatment guidelines, or clinical pathways, have been suggested as a way to curb the rising cost of cancer care by preventing unnecessary and costly treatment variations while improving quality of care.

Cardinal Health, based in Dublin Ohio, partners with payers to establish evidence-based oncology treatment pathways with the intent of eliminating unnecessary medical interventions and promoting the most cost-effective treatments to realize enhanced care and significant savings. In August 2008, Cardinal Health Specialty Solutions partnered with a large nonprofit healthcare insurer for the Mid-Atlantic region of the United States to launch the first oncology clinical pathways program by a major regional insurer across its entire network of disparate providers, which included hospital- and community based practices as well as practices affiliated with academic institutions. The program included pathways for breast, lung, and colon cancer. Participation was voluntary; however, providers were incentivized to join, with increased fee schedule payments for compliance. Using claims—based analysis, results of this program were reported in a poster presentation at the annual meeting of the American Society of Clinical Oncology in 2010.

 Decreases in drug and nondrug expenses for patients treated on pathways net of program management fees and provider participation incentives resulted in more than $8 million in savings from this program.

Athough cost savings from pathways in the integrated practice setting have been reported, more studies are needed to validate the real-world cost savings of pathways programs among disparate provider networks. The US Oncology Network reported significantly lower costs for patients with non-small cell lung cancer and colon cancer treated on pathway programs versus patients treated off pathways, with similar overall survival rates.

 Similarly, Via Oncology pathways reported preliminary cost data from a pathways pilot project with Highmark Blue Cross Blue Shield, which showed that the total cost of care for patients under pathways practices grew at a slower rate than the same costs for patients in nonpathways programs. 

 These results are limited to the integrated practice setting, where uniform technology platforms and centralized management can drive compliance. Akin to single-institution clinical research, we believe that assumptions of similar cost savings are not applicable to disparate providers across a regional payer network or defined geography without validation. Such validation has been thwarted by the absence of unified technology platforms to drive the pathway, consensus among pathway participants, and financial sponsorship of such an initiative.

The purpose of this study was to have a third-party actuary/analytical team validate the observed savings and financial impact of this pathways program.

This was an observational study with a retrospectively identified control group. Milliman Inc, an international provider of actuarial and related products and services, performed the analyses. Claims data from the payer database from January 2007 to December 2010 was used to identify the pathways cohort. Patients with a primary or secondary diagnosis of cancer (International Statistical Classification of Diseases-9 diagnosis codes 140-239) who initiated chemotherapy between April 1, 2007, and April 5, 2010, at the pathways-participating practices were included. Patients older than 63 years, patients treated by practices that exited pathways or joined pathways after 2008, or patients with no allowed costs (due to billing oddities) were excluded. The control group was identified using Truven Health Analytics MarketScan databases. MarketScan is a large commercial claims database that has included more than 170 million unique patients since 1995. All patients with a primary or secondary diagnosis of breast, colon, or lung cancer who initiated chemotherapy between April 1, 2007, and April 5, 2010, located in the Mid-Atlantic (Delaware, North Carolina, irginia, and West Virginia) and New England (Massachusetts, Maine, New Hampshire, and Vermont) regions of the United States were included. Patients who were older than 63 years, received inpatient chemotherapy, had no allowed costs, or were from Alexandria, Virginia (to avoid overlap with our pathways-participating oncologists), were excluded. Pennsylvania was excluded due to claims costs that were more than 20% higher than the included geographies and an active competitor to our pathways.

Propensity score weighting by cancer type (breast, colon, lung), age, and date of first chemotherapy treatment was used to further balance the 2 groups. We utilized a boosted decision tree model to calculate the probability of being in the treatment group based on known features.

The predicted probabilities were then used to develop weights for the control group that distort the distribution of their features to match those of the pathways study group, estimating the average treatment effect on the study population. The Kolmogorov-Smirnov test was used to compare the distribution of the features.

The primary outcome was the sum of a patient’s allowed cancer costs up to 270 days after initiation of chemotherapy. The primary linear model used the natural logarithm of cancer costs as the outcome and included at least a unique intercept for each data source, parallel cost curves, and a treatment indicator for patients who began treatment under pathways. The linear models used balancing weights derived from the propensity scores, unless otherwise stated in the sensitivity analysis. The complete list of features in the linear models included: cancer type, calendar time (splined), age (splined), data source indicator (our pathways, MarketScan Atlantic, MarketScan New England), treatment indicator (pathways or all other), nd MarketScan seasonality artifact (splined).

Sensitivity testing was performed using a variety of alternative model specifications to add credibility to the results of the primary linear model, including the following: less cost trend wiggle, reducing the degrees of freedom for the cost curve spline, which results in the cost trend component of the model absorbing less cost variation; more cost trend wiggle, expanding the degrees of freedom for the cost curve spline, which results in the cost trend component of the model absorbing more cost variation; Drop 5 (Cook’s distance), a model measuring the effect of deleting the 5 most influential observations; Drop 5 (DFBeta), a model removing the 5 most influential observations based on their direct influence on the treatment coefficient; gamma generalized linear model, a model changing response to its original scale, and then fitting a generalized linear model assuming a conditional gamma distribution and a log-link; and no MarketScan seasonality artifact, a model removing the feature that captures the fact that claims data from the last quarter of a calendar year may be incomplete in MarketScan.

A secondary outcome was the probability of an inpatient hospital admission during the 270 days after chemotherapy initiation. A logistic regression model was used that included the same feature set used in the primary linear model. Sensitivity testing was also performed using several alternative models.

Boosted decision tree models also were fit to the primary and secondary outcomes to explore all nonlinearities and interactions. In addition to the primary and secondary outcomes, analyses were performed to determine whether there was a reduction in the number of drug combinations used for patients treated under pathways.Only patients undergoing first lines of therapy were used in these analyses because the cohorts were limited to patients initiating treatment, and, therefore, fewer patients received late lines of therapy during the study period. Variability was calculated by cancer type; colon cancer was excluded due to the minimal number of drug combinations used in practice.

Propensity score weights were not utilized. Three different variability metrics were considered: (1) percentage of lines of therapy utilizing the 5 most common drug combinations; (2) percentage of lines of therapy utilizing the 10 most common drug combinations; and (3) effective number of drug combinations. The effective number of drug combinations measures the concentration in the most prevalent drug combinations. If all drug combination possibilities were equally utilized, the effective number would be the actual number. In reality, the effective number will always be ess than the number of possible drug combinations because it discounts those applied rarely. All of the variability metrics were computed for numerous centered rolling windows over the study period, which allowed visualization of smoothly changing results.

	A total of 2424 patients from the payer database met eligibility criteria for the pathways cohort, and 1490 patients from the MarketScan database met eligibility criteria for the control group.Propensity score weighting further reduced the control group to an effective sample size of 1400.16 Table 1 shows the selection of the final cohorts. Propensity score analysis is shown in Table 2, which gives simple summary statistics of the known features before and after balancing. For the categorical variable (cancer type), the P value is approximated by a χ2 test and is only reported for a single value (breast cancer). The boosted decision tree model was optimized to minimize the largest Kolmogorov-Smirnov statistic. This value was minimized from 0.06 in the unbalanced table to 0.01 in the  balanced table. Most of the balancing effort was focused on the age and date of starting chemotherapy treatment. The boosted decision tree model additionally balanced the interaction of each of the features, such as the age curves by cancer type. The actual distortion was modest, however; the groups were already fairly well aligned for the known features.

	Using the primary linear model, the treatment coefficient for the primary outcome was —0.16 with a z value of –3.0, which translates into a savings estimate of  15% under this Clinical Pathways program. 

shows sensitivity testing results comparing the primary linear model with alternative linear modeling scenarios, which show similar savings for pathways under alternative reasonable assumptions. The treatment coefficient for the secondary outcome of inpatient admission reduction was —0.29 with a z value of –2.5, which corresponds to a 7% reduction in the probability of a hospital admission under pathways (from 50% to 43%). The coefficients of the sensitivity models are shown in Table 3 and were relatively consistent with the –0.29 coefficient from the reference model. The boosted decision tree models confirmed results of a more moderate magnitude.

	We found that both of the groups experienced reductions in the variability of drug combinations used as first-line treatment for patients, but the pathways group experienced greater reduction

. In breast cancer, variability started at the same level, but the pathways group declined further. In lung cancer, the control group had consistently better variability, but the pathways group reduced the difference over time.

	We previously reported more than $8 million in savings for patients treated under the Cardinal Health Specialty Solutions Clinical Pathways program.

In this study, we show that the same pathways program resulted in up to 15% savings on cancer-related claims costs. These savings estimates are net of participating provider incentives, which included significantly enhanced reimbursement on generic and branded drugs. An important part of our pathways product is encouraging oncologists to achieve consensus toward consistent utilization of chemotherapy treatment, when appropriate. As a part of our analysis, we investigated whether there was a reduction in the variety of drug combinations used for patients treated under pathways. We found that while both groups experienced reductions in variability, the pathways group experienced a greater reduction than the control group. Reducing treatment variability and encouraging proper supportive care fosters an environment in which cancer costs are lowered and lessens the likelihood of acute care interventions. The benefits of greater familiarity with regimens, the reduction in complex chemotherapy in late-line treatment, and the avoidance of chemotherapy when evidence does not support it are demonstrated in the reduced probability of an inpatient admission.

	The limitations of this study include potential sample bias in the form of a motivated physician participant base, and the use of MarketScan as the surrogate for a control group. The generalization of this 1 payer experience to all plans and geographies may not be suitable, though the disparate provider networks in the Cardinal Health Specialty Solutions Clinical Pathways program may more closely represent the variability observed in the real world than single-practice or single-institution programs. Savings experience may be different ferent if another plan has already implemented some of the pathways program, or to the extent they experience different unit costs or unit cost trends. Unit cost trends were not normalized between the payer and MarketScan populations as a part of this study. Notably, the savings estimates do not reflect those attributable to patients who historically would have received chemotherapy and did not receive chemotherapy under pathways. In addition, this analysis limited the costs incurred 270 days following the initiation of the first line of therapy, and may not represent the totality of savings possible over the full course of a patient’s treatment.

	A pathways program instituted as a collaboration between a payer, the majority of community providers, and other network providers, with proper incentives, can modify physician behavior.

 Our results show that cancer cost savings and reductions in cancer related inpatient admissions can be achieved with provider participation in payer—supported oncology pathwaysprograms. This analysis affirms that savings on aggregated breast, colon, and lung cancer spending as high as 15% can be achieved in the first year of our pathways program concurrent with as much as a 7% reduction in hospital admissions.

From Cardinal Health Specialty Solutions (BAF, SM, JC, JS), Dublin, OH; CareFirst BlueCross Blue Shield (WW, DW), Baltimore, MD; Milliman Inc, Indianapolis, IN (NS, SP).

 The authors (BAF, SM, JC, WW, DW, NS, SP, JS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of thisarticle.

 Concept and design (BAF, WW, NS, JS); acquisition of data (BAF, JC, JS); analysis and interpretation of data (BAF, SM, JC, DW, NS, SP, JS); drafting of the manuscript (BAF,SM, WW, NS, SP); critical revision of the manuscript for important intellectual content (BAF, SM, NS, JS); statistical analysis (BAF, JC, NS, SP); administrative, technical, or logistic support (JS); and supervision (BAF).

 Bruce A. Feinberg, DO, 7000 Cardinal Place, Dublin, OH 43017. E-mail: bruce.feinberg@cardinalhealth.com1. Siegel R, Naishadham D, Ahmedin J. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11-30.

2. American Cancer Society. Cancer Facts and Figures 2013. Atlanta, GA: American Cancer Society; 2013. http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-036845.pdf. Accessed May 9, 2013.

3. Fisher ES, Bynum JP, Skinner JS. Slowing the growth of health care costs—lessons from regional variation. N Engl J Med. 2009;360(9):849-852.

4. Wennberg JE, Brownlee S, Fisher ES, Skinner JS, Weinstein JN. An agenda for change: improving quality and curbing health care spending: opportunities for the Congress and the Obama administration. The Dartmouth Institute for Health Policy & Clinical Practice. www.dartmouthatlas.org/downloads/reports/agenda_for_change.pdf. Published December 2008. Accessed May 9, 2013.

5.Meropol NJ, Schulman KA. Cost of cancer care: issues and implications. J Clin Oncol. 2007; 25(2):180-186.

6. Fisher E, Goodman D, Skinner J, Bronner K. Health care spending, quality, and outcomes: more isn’t always better. The Dartmouth Institute for Health Policy & Clinical Practice. http://www.dartmouthatlas.org/downloads/reports/Spending_Brief_022709.pdf. Published February 27, 2009. Accessed May 9, 2013.

7. Smith TJ, Hillner BE. Bending the cost curve in cancer care. N Engl J Med. 2011;364(21):2060-2065.

8. Gesme DH, Wiseman M. Strategic use of clinical pathways. J Oncol Pract. 2011;7(1):54-56.

9. Rotter T, Kinsman L, James E, et al. Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database Syst Rev. 2010;17(3):CD 006632. doi: 10.1002/14651858.CD006632. pub2.

10. Pearson SD, Goulart-Fisher D, Lee TH. Critical pathways as a strategy for improving care: problems and potential. Ann Intern Med. 1995; 123:941-948.

11. Scott JA, Wong W, Olson T, et al. Year one evaluation of regional pay for quality (P4Q) oncol-ogy program. Presented at: the American Society of Clinical Oncology; June 4-8, 2010; Chicago, IL. Abstract 6013.

12. Kreys E, Koeller J. Documenting the benefits and cost savings of a large multi-state cancer pathway program from a payers perspective. J Oncol Pract. In press.

13. Neubauer MA, Hoverman JR, Kolodziej M, et al. Cost effectiveness of evidence-based treatment guidelines for the treatment of non—smallcell lung cancer in the community setting. J Oncol Pract. 2010;6(1):12-18.

14. Hoverman JR, Cartwright T, Patt D, et al. Pathways, outcomes, and costs in colon cancer: retrospective evaluations in two distinct databases. J Oncol Pract. 2011;7(3S):52s-59s.

15. Farina K. Forging a pathway to quality cancer care. Am J Manag Care. 2012;18:SP116-SP118.

16. McCaffrey D, Ridgeway G, Morral A. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods. 2004;9(4):403-425.

17. Feinberg BA, Lang J, Grzegorczyk J, et al. Implementation of cancer clinical care pathways: a successful model of collaboration between payers and providers. J Clin Pract. 2012;18(3S):e38se43s. 

Third-Party Validation of Observed Savings From an Oncology Pathways Program

Despite rising medical costs within the US healthcare system, quality and outcomes are not improving. Without significant policy reform, the cost-quality imbalance will reach unsustainable proportions in the foreseeable future. The rising cost of healthcare in part results from an expanding aging population with an increasing number of life-threatening diseases. This is further compounded by a growing arsenal of high-cost therapies. In no medical specialty is this more apparent than in the area of oncology. Numerous attempts to reduce costs have been attempted, often with limited benefit and brief duration. Because physicians directly or indirectly control or influence the majority of medical care costs, physician behavioral changes must occur to bend the healthcare cost curve in a sustainable fashion. Experts within academia, health policy, and business agree that a significant paradigm change in stakeholder collaboration will be necessary to accomplish behavioral change. Such a collaboration has been pioneered by Blue Cross Blue Shield of Michigan and Physician Resource Management, a highly specialized oncology healthcare consulting firm with developmental and ongoing technical, analytic, and consultative support from Cardinal Health Specialty Solutions, a division of Cardinal Health. We describe a successful statewide collaboration between payers and providers to create a cancer clinical care pathways program. We show that aligned stakeholder incentives can drive high levels of provider participation and compliance in the pathways that lead to physician behavioral changes. In addition, claims-based data can be collected, analyzed, and used to create and maintain such a program.

The overall incidence of cancer in the United States is projected to increase by 45% in the next 2 decades from 1.6 million in 2010 to 2.3 million in 2030. Direct medical costs associated with cancer are also projected to increase exponentially from $104 billion in 2006 to more than $173 billion in 2020 as a result of increases in both the cost and quantity of cancer therapies.

Newer cancer treatments are not only likely to be more expensive than the existing standard of care, but they will expand the pool of available treatment options.

Despite these rising medical costs and treatment options, quality and outcomes are not improving.

Without significant policy reform, the cost-quality imbalance will reach unsustainable proportions in the foreseeable future.

Many factors play into the rising costs of healthcare, including an aging population, an expanding arsenal of therapeutics for chronic disease states, increasing regulatory demands on stakeholders, inefficiencies in delivery, and archaic information technologies. All of these factors result in significant variations in practice patterns among medical oncologists.

Costs can vary dramatically because physicians often treat patients with the same condition differently in choice of drugs, referrals for surgery and radiation, referrals for palliative and end-of-life care, and types of supportive care. Strategies such as prior authorization and decreasing fee schedules have been implemented in an effort to lower healthcare costs.

However, because neither of these strategies addresses practice variances or the rapidly rising cost of cancer therapeutics, their impact on healthcare costs has been of limited benefit and brief duration.

Clinical pathways are a method to reduce unnecessary and costly treatment variation; however, physician participation is crucial for their success.

The perceived challenge of cookbook-style medicine to physician autonomy from an external authority, such as insurance companies or academic advisory boards, can be one barrier to pathway adoption among others such as time constraints and comfort with previous practice patterns.

Without accountability or incentives, physicians might comply partially with the terms of a pathway program or not participate at all. Therefore, a collaborative effort between pathway-developing parties and oncology groups is needed.

Physicians directly or indirectly control or influence the majority of cancer care costs.

Therefore, physician behavior change can be used as a surrogate marker of cost savings, which can be difficult to demonstrate as a result of evolving patterns of care, new drug technologies, patent expirations, and data capture problems from revenue code and charge bundling to name a few.

This article details a collaborative statewide cancer clinical pathway program in which provider network medical oncology physicians played an integral role in developing oncology clinical pathways. The success of the program was determined by evaluation of physician compliance with the pathways and their behavioral changes in the first year of the program resulting from the inherent difficulties in determining cost savings in this setting.

In mid-2009, 3 groups&#151;Blue Cross Blue Shield of Michigan (BCBSM; Detroit, Michigan), a large single-state not-forprofit BCBS plan; Physician Resource Management (PRM; Novi, Michigan), a state physician organization; and Cardinal Health Specialty Solutions (CHSS; Dublin, Ohio), an oncology benefit management company (previously P4 Healthcare)—partnered to develop a clinical pathway program whereby physicians would jointly develop the content, structure, and implementation of the program pathways. Involving physicians in the clinical pathways development process would provide an incentive for physicians to participate in the pathways themselves. Care pathways for breast, colon, and lung cancer were developed in the first year of the program and expanded by 5 additional malignancies in the second year. The program would benefit all parties involved in patient care, including patient, provider, and payer, by aiming to improve the consistency and quality of patient care while also reducing costs. The clinical cancer care pathways program was operated and funded through the Oncology Physicians Resources (OPR; West Bloomfield, Michigan) group, an existing statewide physician-owned general purchasing and management organization and subsidiary of the Michigan Society of Hematology and Oncology (MSHO; Rockingham Royal Oak, Michigan). PRM is an administrative arm of OPR and would manage the BCBSM pathway program.

Before the development of this partnership, BCBSM instituted a payfor- performance program in 2005 for Michigan physicians, the Physician Group Incentive Program (PGIP). This program added a physician organization fee component to each professional service payment. The physician organization component was held in an incentive pool and fully distributed twice each year to physicians who participated in PGIP. It was determined that the BCBSM cancer clinical care pathways program would be placed under the aegis of PGIP and that funding would be paid directly to OPR/PRM according to PGIP bylaws. Figure 1 presents a schematic of steps involved in the pathways development and the implementation of BCBSM oncology treatment pathways.

Scott Milligan, PhD

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