ASCO 50th Anniversary
Perspective
Oncology Reimbursement in the Era of Personalized Medicine and Big Data By Jeffery C. Ward, MD Personalized medicine—the precept that the treatment of disease is most effective when the definition of both the disease and the treatment is individualized at the patient level—is coming of age, driven by the informatics revolution popularly referred to as big data. Oncology is at the vanguard of this effort. To that end, new paradigms are being developed in basic science, drug development, clinical research, medical education, patient engagement, and care at the bedside. Personalized medicine and big data hold the promise to transform oncology practice more, and more rapidly, than anything that has happened in the first 50 years of the history of our specialty. However, little has been written about reimbursement in the era of big data— driven personalized medicine. The way the United States pays for the vast majority of its health care is a hindrance to the provision of personalized oncology. In an era of unsustainable costs, policymakers, payers, providers, and patients are exploring alternative ways to pay for cancer care. If they wish to also advance oncology care, they should constructively seek the development and adoption of new reimbursement methods that will incentivize personalized care and both incentivize and use the powers of digitalization, aggregation, and computerized analysis of data— big data.
Big Data and Personal Medicine It has been estimated that by 2020 there will be 5,200 gigabytes of data for every human being on the planet.1 Medical information is doubling every 5 years. But 90% of digital medical data was developed in just the past 2 years, and 80% of it is raw, unstructured data. Big data must be more than a data dump or data catalogue. Management of the information will require computerized intelligence typified by IBM’s Watson or the perhaps not so fictional HAL of 2001: A Space Odyssey, capable of processing and learning from the information it collects.2 The American Society of Clinical Oncology’s (ASCO’s) CancerLINQ is an example of such a rapid learning system. Its first prototype has used available open-source and proprietary software to harvest data from the electronic medical records of 100,000 patients with breast cancer in 22 practices to assess 10 Quality Oncology Practice Initiative data points and to assess the use of evidence-based regimens in therapy. Taken to completion, it will browse and search patient charts, generate hypotheses, measure quality, provide clinicians with evidence-based information and trends, and identify clinical trial eligibility— all in real time.3 Memorial Sloan-Kettering and IBM’s Watson are teaming up to develop a treatment decision tool using big data. Using 600,000 pieces of medical evidence, 2 million pages of text from 42 medical journals and clinical trials in the area of oncolCopyright © 2014 by American Society of Clinical Oncology
ogy research, and 1.5 million patient records, Watson provides on-the-spot treatment recommendations to health care providers. In one report, IBM is quoted as stating that more than 90% of the nurses who have worked with Watson follow its recommendations.4 One of the highlights of the 2013 San Antonio Breast Cancer Symposium was the update of the I-SPY 2 trial. The important takeaway was not necessarily two new tyrosine kinase inhibitors for breast cancer but proof of principle that biologic markers and big data can be used to pair eligible patients with promising drugs and to then use the collective big data experience of early patients to channel later patients to drugs most likely to personally benefit them.5 As diseases are defined on a molecular level, and patients are characterized by their own individual molecular identity in addition to demographics and comorbidities; therapeutics will explode as the patients eligible for any specific therapy will diminish. Big Data will not replace the well- controlled clinical trial, but it will refine and augment it. Common characteristics that bind a few responders to a particular therapeutic from a sea of otherwise indifferent information, and then effortlessly seek out patients with similar characteristics will allow for rapid and efficient validation trials. Only when that happens can personalized medicine deliver on its promise. The possibilities in using big data to personalize health care may be limited only by our ability to see beyond the horizon. In 2011, Hood et al6 envisioned P4 (predictive, personalized, preventive, participatory) cancer medicine in which data mined from hundreds of millions of persons would allow that “each patient will be surrounded by a ‘virtual cloud’ of billions of data points that will uniquely define their past medical history and current health status.”6(p185) From these data, an actionable algorithm will be generated that “within the context of the dynamics of biologic networks and molecular machines”6(p185) will both treat current clinical needs and provide for future wellness for each patient. There are things that big data cannot do, things that are inherent in personalized medicine. It cannot replace the caring health care professionals who interpret the data, simplify it, and inform the patient in the context of the patient’s education level, culture, social support system, and personal goals. Personalized medicine will not simplify care. The complexity and multidisciplinary nature of oncology will increase, and the role of the oncologist as team leader and coordinator needs be inherent in the process. Neither can big data ever be 100% accurate. Clinicians will need to be cognizant of the concept of garbage in and garbage out, and maintaining the integrity of the data will be their responsibility.
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Barriers to Personalized Oncology Privacy issues in general and the Health Insurance Portability and Accountability Act specifically are often cited as barriers to collecting big data and providing personalized oncology. Privacy is a serious concern that requires serious technology and policy response, but privacy concerns in big data are not unique to medicine. Vigilance will be key, and although there will be both mistakes and criminal acts, experts are confident that computer programs that strip patient identifiers from medical records before incorporating it into large databases will largely overcome this problem.1,7 Another often-cited barrier is the cost of personalized therapy. In the United States, niche oncology drugs are increasingly common and increasingly expensive, commonly surpassing $10,000 per month and more. And yet for every writer decrying the cost of therapy, another claims that fiscal concerns—more than any other concern—is driving this revolution that will produce therapies that bring greater efficacy and less toxicity to the individual patient and limit indiscriminate use of toxic treatments of little benefit, thereby bringing greater value and overall lower health care costs.6,8 The real threat to bringing big data and personalized cancer care to the communities where the vast majority of oncology patients are treated is an antiquated reimbursement system that only pays for care when it involves physician touches and infusion of drugs. It is not just fee-for-service (FFS) medicine that is the problem but a particular ilk of FFS that only pays for some services, failing to reimburse at all for other essential services provided in oncology offices and clinics. It fails to reward decision making that brings greater quality, efficacy, or value to the care equation and in fact incentivizes inefficiency and overuse of the most expensive services to maximize reimbursed services. Big data could transform the entire health care sector of our national economy, but the industry must undergo fundamental changes before stakeholders can capture its full value, and that begins with how we pay for it.8
New Reimbursement Schemes for Big Data and Personalized Medicine The indictment of FFS medicine is in the definition of the term “service.” Each physician visit or procedure results in a payment based on the extent and complexity of the service. In oncology, there are additional fees for infusions and parenteral drugs dependent on the time of the infusion and the average sales price (ASP) of the drug. Although a fee must be supported by diagnoses and a supportive documentation of need, there is no reward for the quality, efficacy, or efficiency of the service provided except in the anticipation that good care begets more business. Sadly, for the motivated buyer—patient or payer— there is no mechanism to define and measure good care that would enable them to assess and discriminate the value of the care received. In addition, reimbursement that only pays for physician services not only fails to pay for the services of other members of the cancer care team but inhibits innovation that could use each team member’s talents, particularly physicians, most effectively. 84
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Reimbursement designed to complement personalized medicine will, in turn, incentivize processes designed to improve value and measure and reward efficacy, quality, and efficiency of care. Proposed alternative payment methods designed to do this have variably included quality improvement programs and incentives, treatment guidelines and pathways to decrease variability and ensure a modicum of efficiency, and bundled or episodic payments that encourage decreased use and incentivize innovation that will bring efficiency to the care process.9,10 ASCO’s Clinical Practice Committee (CPC) is developing one example of what this kind of oncology reimbursement could look like. It is still a work in progress, but it will serve to contrast FFS to a bundled or episodic payment. In the CPC model, core payments are based on monthly episodes of care defined by the patient’s continuum of disease, new patient payments, treatment month payments, transitionof-care payments, and surveillance or nontreatment month payments. Any specific category of treatment may be expanded on the basis of acuity and/or complexity of the patient’s disease and the care given. Valuation of each core payment can be developed in a budget-neutral way to current reimbursement and increases and then tied to a medical inflation index. The new patient payment is designed to pay for the activities necessary to prepare to initiate therapy for a patient with a new diagnosis of cancer: evaluation, education, care plan development, and informed consent. FFS reimbursement requires physician presence at and documentation of each step of the process. Bundled payments pave the way for nurse educators and group information sessions. Social workers can do social histories. Past medical history and family history may be acquired and documented by computerization. Navigator programs, previously a cost center, now provide efficiencies that improve a practice’s bottom line. Treatment month payments are paid at four levels on the basis of the complexity of the patient’s disease and the regimen. One can envision that level one would include the patients with asymptomatic breast cancer on hormonal therapy and that level four might include acute leukemia induction. Hospice care would be considered a treatment month as long as the oncologist serves as the patient’s hospice physician. There is a risk, as levels of care are developed, that we might devolve into the kind of reimbursement by documentation of our current evaluation and management system, something that must be avoided by keeping it simple. Inherent in any system of bundled payments, some patients will require more resources and others less; reimbursement will have to consider the mode and mean of care costs. Initially, risk corridors, should be considered, although that would require parallel billing systems, a costly endeavor that a practice would wish to move past as quickly as it can be safely accomplished. Transition payments incorporate the extra work in redesigning and implementing the plan of care resulting from a change or reinitiation of therapy. In the impending era of personalized medicine, the work involved in developing a plan of care may be significantly greater than today when one can argue that the pathway for adjuvant breast cancer requires only one regimen.
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Copyright © 2014 by American Society of Clinical Oncology
Fortunately, the electronic medical record and big data should make it much simpler to define the real work requirements and value of distinct bundles than the American Medical Association’s much-vilified Relative Value Scale Update Committee. Surveillance and survivorship should become sciences in the era of personalized medicine and big data as our ability to define prognosis through genetic profiling improves. The CPC plan has two levels of bundles for the patients not receiving treatment: a higher level of payment for patients who have recently been treated, and a lower level for curative or adjuvant therapies after some period of time. However, one can envision an era in which the patient’s risk of relapse would define both the follow-up they would receive and the level of payment. These payments would be for a finite period of time, incentivizing oncologists to prepare patients and primary care providers for a transition back to primary care. Each of the core bundled payments would be increased or decreased by adoption of value-based pathways and quality-improvement activities. Process measurements would ultimately give way to outcomes. Big data is necessary for completing this in a manner that can be efficient and universal. The CPC’s vision is that Quality Oncology Practice Initiative fueled by CancerLINQ and augmented by value and outcome measures would provide quality measures that would be used to make positive or negative adjustments to core levels. Inducements would be phased in over time, progressing from rewards for participation to certification and ultimately to become based on performance. There are several commercial pathways for oncology and more ventures yet to come. The first pathways were little more than guidelines ranked by efficacy and toxicity and were designed to limit variation. When cost of therapy is added to the equation they become value-based pathways and carry the potential to result in savings. What constitutes a value-based pathway has yet to be clearly established, but it is evident that community oncologists will find that value-based pathways, in contrast to guidelines, will both standardize and limit care. Thus they should be eager to be involved not only in the development and updating of pathways but in defining criteria by which they will be judged or certified for use. This certification of pathways may be a role that will fit ASCO’s talents quite well. One fear is that each insurer will pick a pathway program, leaving oncologists to navigate multiple pathways each day and putting efficiency and patient safety in jeopardy. It would be preferable to allow an oncology practice to use one certified pathway program for all of their patients, although it may put the onus on oncologists to then make it or buy it. Ultimately, practices would find core bundle payments adjusted according to pathway compliance. In an era of budget neutrality, incentive payments and penalties can be constructed in a way that oncologists should find problematic. Some of the payment reform proposals that have been proposed would have a winners-and-losers approach around a mean for both quality improvement and pathway use.11 Concerns abound that this approach (which could promise to make the rich richer and the poor poorer) would, in the circumstance of universal high-quality care and pathway use, Copyright © 2014 by American Society of Clinical Oncology
still punish half of all oncologists. In addition, given physicians’ ability to benefit as much by their neighbors’ quality shortcomings as by improving their own, it promises to turn best practices into proprietary information. We can hope that cooler heads will prevail and the competition will be to reach a defined threshold of care as opposed to beating somebody else. Much has been made of the patient-centered medical home (PCMH). A concept initiated for and by primary care, the PCMH has much to intrigue the oncology field. It relies heavily on information technology data collection and analysis of patient parameters along with labor-intensive patient management that focuses on identifying patients at risk of extra services and intervening early to avoid costly events, particularly hospital stays and emergency room visits.12 In other words, it is personalized medicine and big data on an individual clinic scale. Early adaptors of patient-centered medical oncology homes have demonstrated that the PCMH approach can reap big dividends in medical oncology. They have just as clearly demonstrated that the PCMH and FFS are at odds. The investment to build the infrastructure required to develop a successful PCMH is not only unrewarded in the FFS world, but given that successful implementation of PCMH principles often decrease evaluation and management services, reimbursement will decrease— proof of the proverb that no good deed goes unpunished.12 Shared savings may be paired with FFS but at best provide conflicting incentives, and eventually the savings to be shared will dissipate as preventable use is largely prevented, regardless of the comparator. Paired instead with a bundled payment system, a PCMH will fit seamlessly within the incentives. Most discussion of oncology reimbursement reform for the past 20 years has focused on pharmaceuticals. It is with forethought that it has not been the focus of this discussion. However, to criticize FFS medicine and ignore buy-and-bill chemotherapy would be hypocritical. The cost of cancer drugs is not only the highest ticket item for many patients with cancer, but when a margin proportionate to the cost of the drug is the provider’s reward for giving chemotherapy, it becomes quintessential FFS. The incentives to not only give more chemotherapy but to preferentially use high-margin drugs, usually more expensive drugs and parenteral therapies, can only partially be overcome by strict value-based pathway adherence or enforcement. The era of personalized medicine will require that ASP-based pricing be replaced and that a margin on drugs be taken out of the reimbursement equation. However, that is not an easy task. In the past 30 years, drug reimbursement and the marginal drug revenue to practices fueled the development of a broad-based outpatient chemotherapy infrastructure and delivery model that takes drugs from manufacturer to patient seamlessly. This model continues to provide care to most patients with cancer in the United States and ill- conceived changes could threaten both the next-day delivery of chemotherapy to clinics and/or the financial viability of both private or hospital-based practices. In addition, it cannot be ignored that ASP-based pricing provides for competition among multisource pharmaceuticals and imposes moderation of price increases on single-source drugs. It is, on the surface, a
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simple proposal to take the current margin on drugs, fold it into bundled payments, and pay physicians or distributors invoice costs, but this would neither preserve downward pressure on prices nor guarantee a robust drug distribution system. It will require concerted collaboration of the pharmaceutical industry, distributors, and oncologists who are willing to take off the blinders of short-term profits to take drug reimbursement into the era of personalized medicine and big data.
The Challenge The promise of big data– driven personalized medicine is exciting, headline-grabbing stuff, and well it should be. It promises to bring medicine, now lagging behind other industries, into an informational age that can globalize access to research and personalized therapies with both medical and economic efficacy. FFS reimbursement and the role that it plays in the current US health care crisis, on the other hand, is medicine’s dark secret. FFS has been largely closeted and occasionally vigorously defended by physicians and the organizations that represent it; it is time to open the current model to scrutiny, recognize how incongruent it is to the future on cancer care, and replace it. In late 2013, Congress began a push to tackle the sustainable growth rate debacle. Three bills have come out of committees, and the work is going forward to coalesce them into a single bill.13-15 In comparison to prior health care reform endeavors, each of the bills calls on providers to play an active role in
designing new reimbursement schemes, including specialtyspecific payment plans. The danger in failing to answer this call, or more particularly in choosing to stonewall change, is that other interested parties—insurers, pharmaceutical companies, think tank health care economists, and hospital and primary care– driven accountable care organizations—will decide how oncology is to be valued and reimbursed. Only oncologists from all settings (private practice, community hospital– based, and academic), have the necessary collective knowledge to build a reimbursement system that will allow for the promise of big data– driven personalized cancer care. As our specialty turns 50, the time has come; it is essential that we give the same attention to reforming the cancer care payment system that we do to developing new therapeutics. Author’s Disclosures of Potential Conflicts of Interest Author(s) indicated no potential conflicts of interest. Corresponding author: Jeffery C. Ward, MD, Swedish Cancer Institute Edmonds, 21632 Highway 99, Edmonds, WA 98062; e-mail: jeffery. [email protected]. Jeffery C. Ward, MD is a medical oncologist at Swedish Cancer Institute Edmonds, and immediate past chair of the American Society of Clinical Oncology Clinical Practice Committee.
DOI: 10.1200/JOP.2014.001308.
References 1. Rometty V: The year of the smarter enterprise. The Economist, November 18, 2013 (p117). www.economist.com/news/21589108-new-model-firm-its-waysays-virginia-rometty-chief-executive-ibm-year 2. Dilsizian SE, Seigel EL: Artificial intelligence in medicine and cardiac imaging: Harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep 16:441, 2014 3. Sledge G, Miller RS, Hauser R: CancerLINQ and the future of cancer care. Am Soc Clin Oncol Educ Book 430-434, 2013 4. InfoWorld Tech Watch: IBM’s Watson becomes cancer treatment advisor. www. infoworld.com/t/big-data/ibms-watson-becomes-cancer-treatment-advisor-212644 5. Winslow R: Two experimental breast-cancer drugs pass major milestone in study. Wall Street Journal, December 13, 2013. http://online.wsj.com/news/ articles/SB10001424052702303293604579255831485652454?KEYWORDS⫽ i-spy 6. Hood L, Friend SH: Predictive, personalized, preventative, participatory (P4) cancer medicine. Nat Rev Clin Oncol 8:184-187, 2011 7. Bipartisan Policy Center: A policy forum on the use of big data in health care. http://bipartisanpolicy.org/sites/default/files/Use%20of%20Big%20Data%20in% 20Health%20Care.pdf 8. Kayyali B, Van Kuiken S, Knott D: The big-data revolution in US healthcare:
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Accelerating value and innovation. www.mckinsey.com/insights/health_ systems_and_services/the_big-data_revolution_in_us_health_care 9. Wilensky GR: Developing a viable alternative to Medicare’s physician payment strategy. Health Aff (Millwood) 33:153-160, 2014 10. Patel KK, Morin AJ, Nadel JL, et al: Meaningful physician payment reform in oncology. J Oncol Pract 9:49s-53s, 2013 11. Helwick C: Recent surveys highlight ongoing challenges for oncology practices. Value Based Cancer Care 4:31, 2013. www.valuebasedcancer.com/article/ recent-surveys-highlight-ongoing-challenges-oncology-practices 12. Sprandio JD: Oncology patient-centered medical home and accountable cancer care. Commun Oncol 7:565-72, 2010 13. US House of Representatives: 113th Congress. H.R. 2810, Medicare Patient Access and Quality Improvement Act of 2013. Washington, Government Printing Office 2013 14. US House of Representatives: 113th Congress. Amendment in the Nature of a Substitute to H.R. 2810, SGR Repeal and Medicare Beneficiary Access Act of 2013. Washington, Government Printing Office 2013 15. US House of Representatives: 113th Congress. S. 1871, SGR Repeal and Medicare Beneficiary Access Improvement Act of 2013. Washington, DC, US Government Printing Office, 2013
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Perspective
Oncology Reimbursement in the Era of Personalized Medicine and Big Data By Jeffery C. Ward, MD Personalized medicine—the precept that the treatment of disease is most effective when the definition of both the disease and the treatment is individualized at the patient level—is coming of age, driven by the informatics revolution popularly referred to as big data. Oncology is at the vanguard of this effort. To that end, new paradigms are being developed in basic science, drug development, clinical research, medical education, patient engagement, and care at the bedside. Personalized medicine and big data hold the promise to transform oncology practice more, and more rapidly, than anything that has happened in the first 50 years of the history of our specialty. However, little has been written about reimbursement in the era of big data— driven personalized medicine. The way the United States pays for the vast majority of its health care is a hindrance to the provision of personalized oncology. In an era of unsustainable costs, policymakers, payers, providers, and patients are exploring alternative ways to pay for cancer care. If they wish to also advance oncology care, they should constructively seek the development and adoption of new reimbursement methods that will incentivize personalized care and both incentivize and use the powers of digitalization, aggregation, and computerized analysis of data— big data.
Big Data and Personal Medicine It has been estimated that by 2020 there will be 5,200 gigabytes of data for every human being on the planet.1 Medical information is doubling every 5 years. But 90% of digital medical data was developed in just the past 2 years, and 80% of it is raw, unstructured data. Big data must be more than a data dump or data catalogue. Management of the information will require computerized intelligence typified by IBM’s Watson or the perhaps not so fictional HAL of 2001: A Space Odyssey, capable of processing and learning from the information it collects.2 The American Society of Clinical Oncology’s (ASCO’s) CancerLINQ is an example of such a rapid learning system. Its first prototype has used available open-source and proprietary software to harvest data from the electronic medical records of 100,000 patients with breast cancer in 22 practices to assess 10 Quality Oncology Practice Initiative data points and to assess the use of evidence-based regimens in therapy. Taken to completion, it will browse and search patient charts, generate hypotheses, measure quality, provide clinicians with evidence-based information and trends, and identify clinical trial eligibility— all in real time.3 Memorial Sloan-Kettering and IBM’s Watson are teaming up to develop a treatment decision tool using big data. Using 600,000 pieces of medical evidence, 2 million pages of text from 42 medical journals and clinical trials in the area of oncolCopyright © 2014 by American Society of Clinical Oncology
ogy research, and 1.5 million patient records, Watson provides on-the-spot treatment recommendations to health care providers. In one report, IBM is quoted as stating that more than 90% of the nurses who have worked with Watson follow its recommendations.4 One of the highlights of the 2013 San Antonio Breast Cancer Symposium was the update of the I-SPY 2 trial. The important takeaway was not necessarily two new tyrosine kinase inhibitors for breast cancer but proof of principle that biologic markers and big data can be used to pair eligible patients with promising drugs and to then use the collective big data experience of early patients to channel later patients to drugs most likely to personally benefit them.5 As diseases are defined on a molecular level, and patients are characterized by their own individual molecular identity in addition to demographics and comorbidities; therapeutics will explode as the patients eligible for any specific therapy will diminish. Big Data will not replace the well- controlled clinical trial, but it will refine and augment it. Common characteristics that bind a few responders to a particular therapeutic from a sea of otherwise indifferent information, and then effortlessly seek out patients with similar characteristics will allow for rapid and efficient validation trials. Only when that happens can personalized medicine deliver on its promise. The possibilities in using big data to personalize health care may be limited only by our ability to see beyond the horizon. In 2011, Hood et al6 envisioned P4 (predictive, personalized, preventive, participatory) cancer medicine in which data mined from hundreds of millions of persons would allow that “each patient will be surrounded by a ‘virtual cloud’ of billions of data points that will uniquely define their past medical history and current health status.”6(p185) From these data, an actionable algorithm will be generated that “within the context of the dynamics of biologic networks and molecular machines”6(p185) will both treat current clinical needs and provide for future wellness for each patient. There are things that big data cannot do, things that are inherent in personalized medicine. It cannot replace the caring health care professionals who interpret the data, simplify it, and inform the patient in the context of the patient’s education level, culture, social support system, and personal goals. Personalized medicine will not simplify care. The complexity and multidisciplinary nature of oncology will increase, and the role of the oncologist as team leader and coordinator needs be inherent in the process. Neither can big data ever be 100% accurate. Clinicians will need to be cognizant of the concept of garbage in and garbage out, and maintaining the integrity of the data will be their responsibility.
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Barriers to Personalized Oncology Privacy issues in general and the Health Insurance Portability and Accountability Act specifically are often cited as barriers to collecting big data and providing personalized oncology. Privacy is a serious concern that requires serious technology and policy response, but privacy concerns in big data are not unique to medicine. Vigilance will be key, and although there will be both mistakes and criminal acts, experts are confident that computer programs that strip patient identifiers from medical records before incorporating it into large databases will largely overcome this problem.1,7 Another often-cited barrier is the cost of personalized therapy. In the United States, niche oncology drugs are increasingly common and increasingly expensive, commonly surpassing $10,000 per month and more. And yet for every writer decrying the cost of therapy, another claims that fiscal concerns—more than any other concern—is driving this revolution that will produce therapies that bring greater efficacy and less toxicity to the individual patient and limit indiscriminate use of toxic treatments of little benefit, thereby bringing greater value and overall lower health care costs.6,8 The real threat to bringing big data and personalized cancer care to the communities where the vast majority of oncology patients are treated is an antiquated reimbursement system that only pays for care when it involves physician touches and infusion of drugs. It is not just fee-for-service (FFS) medicine that is the problem but a particular ilk of FFS that only pays for some services, failing to reimburse at all for other essential services provided in oncology offices and clinics. It fails to reward decision making that brings greater quality, efficacy, or value to the care equation and in fact incentivizes inefficiency and overuse of the most expensive services to maximize reimbursed services. Big data could transform the entire health care sector of our national economy, but the industry must undergo fundamental changes before stakeholders can capture its full value, and that begins with how we pay for it.8
New Reimbursement Schemes for Big Data and Personalized Medicine The indictment of FFS medicine is in the definition of the term “service.” Each physician visit or procedure results in a payment based on the extent and complexity of the service. In oncology, there are additional fees for infusions and parenteral drugs dependent on the time of the infusion and the average sales price (ASP) of the drug. Although a fee must be supported by diagnoses and a supportive documentation of need, there is no reward for the quality, efficacy, or efficiency of the service provided except in the anticipation that good care begets more business. Sadly, for the motivated buyer—patient or payer— there is no mechanism to define and measure good care that would enable them to assess and discriminate the value of the care received. In addition, reimbursement that only pays for physician services not only fails to pay for the services of other members of the cancer care team but inhibits innovation that could use each team member’s talents, particularly physicians, most effectively. 84
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Reimbursement designed to complement personalized medicine will, in turn, incentivize processes designed to improve value and measure and reward efficacy, quality, and efficiency of care. Proposed alternative payment methods designed to do this have variably included quality improvement programs and incentives, treatment guidelines and pathways to decrease variability and ensure a modicum of efficiency, and bundled or episodic payments that encourage decreased use and incentivize innovation that will bring efficiency to the care process.9,10 ASCO’s Clinical Practice Committee (CPC) is developing one example of what this kind of oncology reimbursement could look like. It is still a work in progress, but it will serve to contrast FFS to a bundled or episodic payment. In the CPC model, core payments are based on monthly episodes of care defined by the patient’s continuum of disease, new patient payments, treatment month payments, transitionof-care payments, and surveillance or nontreatment month payments. Any specific category of treatment may be expanded on the basis of acuity and/or complexity of the patient’s disease and the care given. Valuation of each core payment can be developed in a budget-neutral way to current reimbursement and increases and then tied to a medical inflation index. The new patient payment is designed to pay for the activities necessary to prepare to initiate therapy for a patient with a new diagnosis of cancer: evaluation, education, care plan development, and informed consent. FFS reimbursement requires physician presence at and documentation of each step of the process. Bundled payments pave the way for nurse educators and group information sessions. Social workers can do social histories. Past medical history and family history may be acquired and documented by computerization. Navigator programs, previously a cost center, now provide efficiencies that improve a practice’s bottom line. Treatment month payments are paid at four levels on the basis of the complexity of the patient’s disease and the regimen. One can envision that level one would include the patients with asymptomatic breast cancer on hormonal therapy and that level four might include acute leukemia induction. Hospice care would be considered a treatment month as long as the oncologist serves as the patient’s hospice physician. There is a risk, as levels of care are developed, that we might devolve into the kind of reimbursement by documentation of our current evaluation and management system, something that must be avoided by keeping it simple. Inherent in any system of bundled payments, some patients will require more resources and others less; reimbursement will have to consider the mode and mean of care costs. Initially, risk corridors, should be considered, although that would require parallel billing systems, a costly endeavor that a practice would wish to move past as quickly as it can be safely accomplished. Transition payments incorporate the extra work in redesigning and implementing the plan of care resulting from a change or reinitiation of therapy. In the impending era of personalized medicine, the work involved in developing a plan of care may be significantly greater than today when one can argue that the pathway for adjuvant breast cancer requires only one regimen.
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Fortunately, the electronic medical record and big data should make it much simpler to define the real work requirements and value of distinct bundles than the American Medical Association’s much-vilified Relative Value Scale Update Committee. Surveillance and survivorship should become sciences in the era of personalized medicine and big data as our ability to define prognosis through genetic profiling improves. The CPC plan has two levels of bundles for the patients not receiving treatment: a higher level of payment for patients who have recently been treated, and a lower level for curative or adjuvant therapies after some period of time. However, one can envision an era in which the patient’s risk of relapse would define both the follow-up they would receive and the level of payment. These payments would be for a finite period of time, incentivizing oncologists to prepare patients and primary care providers for a transition back to primary care. Each of the core bundled payments would be increased or decreased by adoption of value-based pathways and quality-improvement activities. Process measurements would ultimately give way to outcomes. Big data is necessary for completing this in a manner that can be efficient and universal. The CPC’s vision is that Quality Oncology Practice Initiative fueled by CancerLINQ and augmented by value and outcome measures would provide quality measures that would be used to make positive or negative adjustments to core levels. Inducements would be phased in over time, progressing from rewards for participation to certification and ultimately to become based on performance. There are several commercial pathways for oncology and more ventures yet to come. The first pathways were little more than guidelines ranked by efficacy and toxicity and were designed to limit variation. When cost of therapy is added to the equation they become value-based pathways and carry the potential to result in savings. What constitutes a value-based pathway has yet to be clearly established, but it is evident that community oncologists will find that value-based pathways, in contrast to guidelines, will both standardize and limit care. Thus they should be eager to be involved not only in the development and updating of pathways but in defining criteria by which they will be judged or certified for use. This certification of pathways may be a role that will fit ASCO’s talents quite well. One fear is that each insurer will pick a pathway program, leaving oncologists to navigate multiple pathways each day and putting efficiency and patient safety in jeopardy. It would be preferable to allow an oncology practice to use one certified pathway program for all of their patients, although it may put the onus on oncologists to then make it or buy it. Ultimately, practices would find core bundle payments adjusted according to pathway compliance. In an era of budget neutrality, incentive payments and penalties can be constructed in a way that oncologists should find problematic. Some of the payment reform proposals that have been proposed would have a winners-and-losers approach around a mean for both quality improvement and pathway use.11 Concerns abound that this approach (which could promise to make the rich richer and the poor poorer) would, in the circumstance of universal high-quality care and pathway use, Copyright © 2014 by American Society of Clinical Oncology
still punish half of all oncologists. In addition, given physicians’ ability to benefit as much by their neighbors’ quality shortcomings as by improving their own, it promises to turn best practices into proprietary information. We can hope that cooler heads will prevail and the competition will be to reach a defined threshold of care as opposed to beating somebody else. Much has been made of the patient-centered medical home (PCMH). A concept initiated for and by primary care, the PCMH has much to intrigue the oncology field. It relies heavily on information technology data collection and analysis of patient parameters along with labor-intensive patient management that focuses on identifying patients at risk of extra services and intervening early to avoid costly events, particularly hospital stays and emergency room visits.12 In other words, it is personalized medicine and big data on an individual clinic scale. Early adaptors of patient-centered medical oncology homes have demonstrated that the PCMH approach can reap big dividends in medical oncology. They have just as clearly demonstrated that the PCMH and FFS are at odds. The investment to build the infrastructure required to develop a successful PCMH is not only unrewarded in the FFS world, but given that successful implementation of PCMH principles often decrease evaluation and management services, reimbursement will decrease— proof of the proverb that no good deed goes unpunished.12 Shared savings may be paired with FFS but at best provide conflicting incentives, and eventually the savings to be shared will dissipate as preventable use is largely prevented, regardless of the comparator. Paired instead with a bundled payment system, a PCMH will fit seamlessly within the incentives. Most discussion of oncology reimbursement reform for the past 20 years has focused on pharmaceuticals. It is with forethought that it has not been the focus of this discussion. However, to criticize FFS medicine and ignore buy-and-bill chemotherapy would be hypocritical. The cost of cancer drugs is not only the highest ticket item for many patients with cancer, but when a margin proportionate to the cost of the drug is the provider’s reward for giving chemotherapy, it becomes quintessential FFS. The incentives to not only give more chemotherapy but to preferentially use high-margin drugs, usually more expensive drugs and parenteral therapies, can only partially be overcome by strict value-based pathway adherence or enforcement. The era of personalized medicine will require that ASP-based pricing be replaced and that a margin on drugs be taken out of the reimbursement equation. However, that is not an easy task. In the past 30 years, drug reimbursement and the marginal drug revenue to practices fueled the development of a broad-based outpatient chemotherapy infrastructure and delivery model that takes drugs from manufacturer to patient seamlessly. This model continues to provide care to most patients with cancer in the United States and ill- conceived changes could threaten both the next-day delivery of chemotherapy to clinics and/or the financial viability of both private or hospital-based practices. In addition, it cannot be ignored that ASP-based pricing provides for competition among multisource pharmaceuticals and imposes moderation of price increases on single-source drugs. It is, on the surface, a
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simple proposal to take the current margin on drugs, fold it into bundled payments, and pay physicians or distributors invoice costs, but this would neither preserve downward pressure on prices nor guarantee a robust drug distribution system. It will require concerted collaboration of the pharmaceutical industry, distributors, and oncologists who are willing to take off the blinders of short-term profits to take drug reimbursement into the era of personalized medicine and big data.
The Challenge The promise of big data– driven personalized medicine is exciting, headline-grabbing stuff, and well it should be. It promises to bring medicine, now lagging behind other industries, into an informational age that can globalize access to research and personalized therapies with both medical and economic efficacy. FFS reimbursement and the role that it plays in the current US health care crisis, on the other hand, is medicine’s dark secret. FFS has been largely closeted and occasionally vigorously defended by physicians and the organizations that represent it; it is time to open the current model to scrutiny, recognize how incongruent it is to the future on cancer care, and replace it. In late 2013, Congress began a push to tackle the sustainable growth rate debacle. Three bills have come out of committees, and the work is going forward to coalesce them into a single bill.13-15 In comparison to prior health care reform endeavors, each of the bills calls on providers to play an active role in
designing new reimbursement schemes, including specialtyspecific payment plans. The danger in failing to answer this call, or more particularly in choosing to stonewall change, is that other interested parties—insurers, pharmaceutical companies, think tank health care economists, and hospital and primary care– driven accountable care organizations—will decide how oncology is to be valued and reimbursed. Only oncologists from all settings (private practice, community hospital– based, and academic), have the necessary collective knowledge to build a reimbursement system that will allow for the promise of big data– driven personalized cancer care. As our specialty turns 50, the time has come; it is essential that we give the same attention to reforming the cancer care payment system that we do to developing new therapeutics. Author’s Disclosures of Potential Conflicts of Interest Author(s) indicated no potential conflicts of interest. Corresponding author: Jeffery C. Ward, MD, Swedish Cancer Institute Edmonds, 21632 Highway 99, Edmonds, WA 98062; e-mail: jeffery. [email protected]. Jeffery C. Ward, MD is a medical oncologist at Swedish Cancer Institute Edmonds, and immediate past chair of the American Society of Clinical Oncology Clinical Practice Committee.
DOI: 10.1200/JOP.2014.001308.
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Accelerating value and innovation. www.mckinsey.com/insights/health_ systems_and_services/the_big-data_revolution_in_us_health_care 9. Wilensky GR: Developing a viable alternative to Medicare’s physician payment strategy. Health Aff (Millwood) 33:153-160, 2014 10. Patel KK, Morin AJ, Nadel JL, et al: Meaningful physician payment reform in oncology. J Oncol Pract 9:49s-53s, 2013 11. Helwick C: Recent surveys highlight ongoing challenges for oncology practices. Value Based Cancer Care 4:31, 2013. www.valuebasedcancer.com/article/ recent-surveys-highlight-ongoing-challenges-oncology-practices 12. Sprandio JD: Oncology patient-centered medical home and accountable cancer care. Commun Oncol 7:565-72, 2010 13. US House of Representatives: 113th Congress. H.R. 2810, Medicare Patient Access and Quality Improvement Act of 2013. Washington, Government Printing Office 2013 14. US House of Representatives: 113th Congress. Amendment in the Nature of a Substitute to H.R. 2810, SGR Repeal and Medicare Beneficiary Access Act of 2013. Washington, Government Printing Office 2013 15. US House of Representatives: 113th Congress. S. 1871, SGR Repeal and Medicare Beneficiary Access Improvement Act of 2013. Washington, DC, US Government Printing Office, 2013
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