To develop an effective legal mechanism to combat chargemaster abuses and to facilitate price transparency.

Applying legal doctrines to out-of-network (OON) billing disputes.

We reviewed rudimentary contract law and examined the law’s handling of contracts where prices have not been specified in advance. These cases are the controlling authority to guide courts, handling of surprise and OON billing problems. We then compared legal remedies that correct OON billing abuses to prevailing legislative and regulatory approaches.

Our analysis suggests that providers have no legal authority to collect chargemaster rates from surprise and OON billing abuses. A proper application of contract law can end such abuses and would facilitate superior pricing incentives to other strategies designed to end balance billing disputes.

Chargemaster rates on uninsured and OON patients impose significant financial burdens on the vulnerable, distort medical prices, and inflate healthcare costs.  Applying rudimentary contract law to these practices offers a solution that is simpler and more effective than other administrative and legislative schemes recently adopted in several states. It will prevent providers from hiding behind a convoluted hospital pricing system, encourage the development of attractive narrow-network insurance products, and shield urgently sick individuals from the dread of medical predation. Patients and payers should know that they are under no obligation to pay surprise bills containing chargemaster rates, and state attorneys general can use the law to prevent providers from pursuing chargemaster-related collection efforts against patients.

Many healthcare providers seek to collect exorbitant chargemaster rates from uninsured and insured out-of-network patients. These efforts impose significant burdens on financially vulnerable patients and hinder efforts to create affordable narrow-network insurance plans.

		Contract law does not support the collection of chargemaster rates, which have little relation to either actual costs or market prices. Instead, proper contract law supports imputing market-negotiated rates.

		Applying these well-recognized legal principles to the complex world of out-of-network billing provides a simpler and less costly approach than those recently adopted in some states to combat the distortive effects of chargemaster billing.

Over the last several years, numerous articles in the lay press have documented one of the most disturbing practices in the healthcare system: providers billing exorbitant charges to the nation’s most vulnerable patients. These patients are those who either lack insurance or who have the misfortune of requiring services rendered by an out-of-network (OON) provider, usually in emergent circumstances, and the prices charged are neither approved nor seen by patients in advance. The resulting medical bills—euphemistically described as “surprise bills”&mdash;place significant and sometimes ruinous burdens on patients’ lives.

These patients are more than just collateral damage to skyrocketing healthcare costs. Recent examinations of OON billing reveal that inflated charges are often part of a deliberate strategy by providers to apply negotiation leverage against insurers. The primary tool in this strategy is the notorious “chargemaster,” a master file built within hospital information systems that contains a comprehensive listing of prices for all billable services. Inflated chargemaster charges have been used tactically both to secure higher payments from Medicare and private payers and to threaten insurers seeking to create affordable insurance offerings through narrow networks. The viability of narrow networks, which are one of the few health insurance innovations associated with gains in affordability and quality of care, depends on successfully battling the chargemaster.

Within the past 2 years, several state policy makers have taken notice and enacted legislation designed to curtail chargemaster collection efforts. A handful of states have protected healthcare consumers by enacting “balance billing” legislation that prohibits providers from charging patients for deficiencies between chargemaster prices and an insurer’s reimbursement, while others have required insurance companies to shelter plan members from these deficiencies. Some states have gone further to set payment rates for certain OON charges either by statute or special administrative mechanism, and some states have approached the problem more gingerly by introducing efforts to bring transparency to provider pricing.

However, these state solutions—to use a medical analogy&mdash;treat the symptoms of chargemaster abuses without addressing the underlying problem. They prohibit the worst abuses, but they neither recognize that the root cause is unilateral price setting nor do they empower consumers to counteract unilateralism with a market response. We propose a simpler and better approach to stemming the growing role and distortive power of the chargemaster, one that requires neither additional legislation nor regulatory changes and yet preserves market incentives to craft more affordable insurance products. We illustrate how payers and patients can invoke rudimentary common law principles to challenge inflated chargemaster charges, replace inflated charges with amounts that instead reflect prevailing market prices, and correct some of the health sector’s worst market failures.

The Problem: The Chargemaster and OON Bills

Most Americans use insurers as intermediaries to negotiate rates for healthcare services in advance with providers, and when an insured patient receives care, the provider is paid rates that are fully detailed in a contract between the provider and the patient’s insurer. This arrangement benefits from the opportunity for providers and payers to negotiate deliberately, without emergency conditions and with the knowledge of prevailing market prices and costs of service.

When patients require or unknowingly receive care from providers who have not entered into a contract with their insurers (so-called OON care), or when patients are uninsured, providers typically charge rates in accordance with their chargemaster. The chargemaster and similar charge strategies are responsible for exorbitant prices for emergency care (eg, the $500 stitch),

 for OON physicians serving as consultants and “drive-by” doctors (eg, the $117,000 medical bill from an unknown doctor),

 and for charging more to the uninsured than to the rest of the population. "The rules are completely crazy,” concedes one provider.

The chargemaster has been described as the “central mechanism for the revenue cycle” of hospitals, but its defining feature is that it is “devoid of any calculation related to cost” and is not based on market transactions.

 It lies in stark contrast to alternative billing and accounting systems that take cost into consideration, such as accounting systems that assimilate multiple factors in determining the cost of providing medical services and generating prices that reflect those costs.

Because chargemaster prices are calculated without regard to costs, and because of the underlying complexity of hospital pricing and billing practices, hospitals have resorted to chargemaster price inflations to meet financial demands. Hospital accounting experts agree that hospital billing practices “encourage manipulation of the [chargemaster] to maximize revenue”

 and have created a “legal fiction” that now serves as the basis of billing uninsured and OON patients.

 In determining the amount that providers accept from third-party payers, “[c]hargemaster rates, in reality, serve as nothing more than the [hospital’s] starting point for negotiations.”

 A hospital spokesperson, when speaking about the hospital’s chargemaster rates, said “[t]hose are not our real rates . . . most people never pay those prices.”

 In addition to inflating prices paid by private insurance, higher hospital chargemaster rates also manipulate Medicare reimbursements. By using chargemaster prices to charge substantially more for Medical Severity—Diagnosis Related Groups, even when patients have similar lengths of stay as those in all other hospitals, hospitals can generate higher outlier payments under Medicare’s inpatient prospective payment system.

For these reasons, hospitals typically set chargemaster prices several times higher than prices in negotiated contracts. One study found that chargemaster prices are 2.5 times what most health insurers pay and more than 3 times hospitals’ actual costs.

 Another study found that the first 30 to 74 minutes of critical care delivered by a California provider could cost an OON patient as much as 2897% of what Medicare would have paid for the same services,

and a new survey shows that health plans and patients routinely receive charges from OON physicians that range from 118% to 1382% of amounts paid by Medicare.

 highlighted these abusive billing practices, such as charging $2200 for 3 stitches on a patient’s knee, $1700 for a dab of skin glue to close a cut on a child’s head, and more than $36 for a single Tylenol pill with codeine.

Although federal law offers few remedies against surprise balance billing and similar chargemaster strategies, many state policy makers have appropriately recognized that surprise OON bills cause genuine hardship to patients, impose unnecessary complexity to an already burdensome world of hospital billing, and pose a major threat to the availability of affordable narrow-network insurance plans.

 The state approaches have varied, but their assorted elements can be categorized into 4 distinct strategies—although some state efforts have pursued several elements simultaneously.

The least interventionist approach has been to bring transparency to healthcare prices and the consequences of obtaining OON care. Although insurers are generally obligated to inform their subscribers of the financial consequences of going out of network, some states additionally require insurers to provide accurate network directories, publicize both summary and specific information on the costs of receiving care out of network, and alert consumers at the point of service of network participation. These policies go in hand with other state efforts to bring transparency to healthcare costs, including the growing effort to assemble all-payer claims databases that will allow patients to compare prices for common services between in-network and OON providers.

 Some insurers have also established their own independent systems to inform both plan members and the general public of the costs associated with medical care by certain providers.

 However, while greater transparency is desperately needed in the health sector, these efforts will stem surprise bills in only the most avoidable circumstances and do little to stop the most harmful abuses, including surprise bills from emergency care or from OON physicians consulting for in-network hospitals.

Other states have passed what have been called “balance billing laws,” which prohibit OON providers from directly billing patients for certain deficiencies between their insurer’s reimbursement and their provider’s chargemaster rates.

 These laws hold neither patients nor their insurers responsible for the surprise charges and refuse to reward providers for certain chargemaster abuses. However, unlike the objective behind transparency initiatives, they do not encourage patients to be price-sensitive nor do they induce providers to compete on price. Moreover, these balance billing prohibitions tend to restrict only specific conduct—Maryland’s balance billing prohibition, for example, only applies to certain services and is triggered only if certain disclosures are not made.

 Because they are targeted prohibitions, providers will continue to find pathways to implementing the chargemaster strategy. Finally, because they dilute providers’ ability to distinguish in-network from OON prices, they remove providers’ incentives to participate in and offer competitive prices for narrow network plans. Thus, balance billing prohibitions are neither likely to categorically prevent surprise bills nor likely to incentivize market-oriented behavior toward affordable care.

An increasingly common approach by states has been a “hold harmless” policy that requires insurance companies to shelter plan members from surprise bills. For example, Colorado law prohibits insurers from passing along to members the costs of treatment from certain non-network providers. Thus, insurers are required to pay the chargemaster prices, negotiate a lower price with the provider, or fight the bill in court.

 Although this approach might incentivize insurers to anticipate and preempt surprise bills—either by negotiating agreements with more providers or actively steering subscribers in-network&mdash;it places a significant cost burden on payers, which will more likely pass along costs to consumers and through increased insurance premiums.

The most comprehensive approaches to solving surprise bills have come from New York and California. The New York law, which came into effect on April 1, 2015, bans balance billing from OON emergency services and establishes an independent dispute resolution for providers and health plans to settle on a fee for OON services performed.

 California is similar and also establishes a default charge of 125% of Medicare reimbursement for surprise physician charges for in-network care.

 This administrated negotiation approach is comparable to surprise billing laws in Illinois and Florida, and other states have used similar approaches to resolve OON bills in other contexts. In Michigan, for example, state statutes limit automobile insurers’ responsibility to pay only “reasonable” and “customary” charges for healthcare expenses required of insureds.

Similar regulatory schemes have also been established in the worker’s compensation context, requiring insurers to reimburse providers only a “fair and reasonable reimbursement amount [to] . . . ensure that similar procedures provided in similar circumstances receive similar reimbursement.”

 This administrative solution, however, imposes significant transaction costs to achieving solutions. Patients with surprise bills must be aware of the regulatory remedies and submit substantial paperwork before triggering protections. These bureaucratic hurdles not only prevent many consumers from receiving adequate protection, but they also dull providers’ exposure to price competition. Alternative dispute resolution might offer effective mechanisms for finding compromise prices to resolve particular disputes, but those prices are not felt in the marketplace.

The seriousness and pervasiveness of surprise bills encouraged the National Association of Insurance Commissioners in November 2015 to update its Model Act to institute additional consumer protections. The new Model Act, which is represented as a “compromise among all of the participating stakeholders,” reflects New York’s effort to increase transparency and sponsor provider-payer mediation.

 It requires health carriers to offer accurate network directories to warn patients before they seek OON care and provides a mediation process for providers and payers to resolve disputes over OON remittances. In addition, influential think tanks have focused on problems from surprise bills, with one recently recommending (in what amounts to a full frontal attack) a combination of federal, state, and private interventions.

 The Model Act has not yet been passed by any state, nor have federal policy makers responded to the call to action, but these recent efforts reveal the pressing need to find a solution to surprise bills. Meanwhile, the diversity of legislative activity reveals that legislators have not yet arrived at an adequate policy.

A Better Solution: Rudimentary Contract Law

Despite this legislative activity in many states, protecting consumers from surprise OON bills requires neither new legislation nor new regulatory mechanisms. To the contrary, consumers are already protected by current law—bedrock, rudimentary contract law&mdash;and require only its proper application to end harmful chargemaster practices.

Contract law offers not only a promising solution, but a better one. It has the virtue of simplicity. It does not create a new fiduciary duty or consumer protection. It neither expands the reach of a federal statute nor limits the reach of state regulatory power. It avoids the imposition of a new regulatory apparatus. And perhaps best of all, it triggers market solutions to address healthcare costs. A contract law solution empowers the very parties who currently are being exploited by OON charges.

In the accompanying eAppendix, we detail our legal analysis, which concludes that providers do not have a legitimate legal claim to collect chargemaster charges. This analysis is in line with a growing chorus of legal scholars seeking to end chargemaster abuses.

 The key motivation is that mutual assent is at the core of commercial transactions. Chargemaster prices, in contrast, are prices that neither patients nor payers accepted in advance nor are they prices to which payers would 

 assent.  Instead, the law entitles providers, as one court ruled, to “the average amount that [the provider] would have accepted as full payment from third-party payers such as private insurers and federal healthcare programs.”

 The law therefore entitles providers to collect no more than prevailing negotiated market prices for any OON services.

This leads to a stark conclusion: providers have no legal authority to collect chargemaster charges that exceed market prices for OON services, and thus neither patients nor payers are under any obligation to pay such chargemaster prices. Consistent efforts to enforce this interpretation of contract law would go far in addressing abuses. Moreover, judges, public law enforcement officials, and private attorneys can use this interpretation to combat abusive or harassing efforts that providers pursue to collect such charges. And, perhaps most important, payers that form narrow provider networks can be confident that they will not have to pay extortive chargemaster prices if their insureds require emergency OON care.

Contract law sets a clear baseline for what may be collected, and prevailing data resources can enable courts to calculate appropriate market prices with little difficulty (see eAppendix). A common law solution therefore lucidly demarks what patients and payers owe providers for OON care without costly litigation or cumbersome administrative procedures. It also encourages providers to be transparent with their prices, for higher prices are attainable only if providers obtain assent from payers in advance.

For these reasons, empowering courts to resolve surprise billing disputes—and to set the rules that govern surprise billing&mdash;is preferable to relying on new legislation. Although state policy makers are to be lauded for addressing chargemaster abuses, none of the 4 prevailing strategies&mdash;increasing transparency, prohibiting balance billing, requiring insurers to cover OON costs, and providing mediation&mdash;offer specific advantages from relying on courts applying contract law. This is because chargemaster and OON charges are pernicious not just because they allow providers to exploit a moment of vulnerability or a temporary information failure, but because they impose enormous dynamic costs as well.

An effective policy response to OON billing will not just protect patients from surprises, but is also how patients can benefit from the market. Laws that place a simple ban on balance bills from providers do not incentivize efficient providers to price competitively, join narrow networks, or encourage patients toward efficient care. Reciprocally, laws that obligate insurers to cover their patients’ bills might provide patients with temporary salvation, but because they retain the potential for extortive billing, patients are likely to pay the inflated bills indirectly through higher insurance premiums. Thus, regulatory bans, whether they impose residual costs on insurers or on providers, fail to harness market forces that encourage price competition and quality improvements.

Administrative efforts to define reasonable reimbursement rates, whether through administrative fiat or through dispute resolution mechanisms, aim to mimic what a court would do in imputing market prices. If designed properly and executed efficiently, they could reflect what reasonable parties would have agreed to had there been an opportunity for meaningful bargaining. But administrative procedures are subject to due process safeguards and introduce transaction costs and delays that court proceedings do not. More significant, administrative structures introduce the significant risk of enshrining the sentiments of entrenched stakeholders, whereas courts are much less prone to capture by special interests. For these reasons, administrative solutions would fail to address the dynamic costs of surprise bill strategies, and if used in conjunction with court solutions, they would interfere with and thereby undermine the many benefits of invoking contract law remedies.

 summarizes our comparative assessment of these alternative strategies.

Chargemaster abuses from OON and emergency care inflict serious financial harm to the most vulnerable while undercutting the functioning of healthcare markets and the creation of valuable insurance products. At the same time, they present straightforward questions of contract law and lead to a simple conclusion: providers are entitled only to collect prevailing negotiated prices for OON services, and patients and payers are under no legal obligation to pay higher chargemaster charges.

Applying this interpretation of contract law will prevent providers from hiding behind a convoluted hospital pricing system, will encourage the development of attractive narrow network insurance offerings, and will shield urgently sick people from the dread of medical predation. State legislators are to be congratulated for recognizing that chargemaster abuses require attention. But rather than seeking legislative solutions, they should pursue court remedies that correct both the immediate and the long-term dynamic harms caused by chargemaster strategies.

Creators of narrow-network plans should be emboldened by our conclusions, and we particularly urge the legal community to take our conclusion to heart.  Public law enforcement officials have an opportunity to give immediate relief to constituents who are routinely injured by chargemaster abuses. For example, a state attorney general who announces a commitment to enforcing contract law in chargemaster disputes would both protect vulnerable patients and bring some clarity to healthcare prices. Providers will know that subversive pricing strategies will be ineffective, and that they instead must forthrightly disseminate and obtain assent to their prices in a transparent market.&ensp;

School of Law (BDR, NK), Fuqua School of Business (BDR), and School of Medicine (KAS), and Duke Clinical Research Institute (KAS), Duke University, Durham, NC; Clinical Excellence Research Center, Stanford School of Medicine, Stanford University (AM), Stanford, CA.

Mr Kitzman reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (NK, AM, BDR, KAS); acquisition of data (NK, AM, BDR, KAS); analysis and interpretation of data (NK, AM, BDR, KAS); drafting of the manuscript (NK, AM, BDR, KAS); critical revision of the manuscript for important intellectual content (NK, AM, BDR, KAS).

Barak D. Richman, JD, PhD, Duke University School of Law, PO Box 90360, Durham, NC 27708. E-mail: richman@law.duke.edu.

 website. http://www.nytimes.com/interactive/2014/health/paying-till-it-hurts.html. Published December 18, 2014. Accessed March 2015.

2. Rosenthal E. As hospital prices soar, a stitch tops $500. 

 website. http://www.nytimes.com/2013/12/03/health/as-hospital-costs-soar-single-stitch-tops-500.html. Published December 3, 2013. Accessed March 2015.

3. Rosenthal E. After surgery, surprise $117,000 medical bill from doctor he didn’t know. 

website. https://www.nytimes.com/2014/09/21/us/drive-by-doctoring-surprise-medical-bills.html. Published September 20, 2014. Accessed March 2015.

4. Cooper Z, Morton FS. Out-of-network emergency-physician bills—an unwelcome surprise. 

. 2016;375(20):1915-1918. doi: 10.1056/NEJMp1608571.

5. Lagnado L. Anatomy of a hospital bill. 

 https://www.wsj.com/articles/SB109571706550822844. Published September 21, 2004. Accessed June 2016.

6. Brill S. Bitter pill: why medical bills are killing us? 

website. http://content.time.com/time/subscriber/article/0,33009,2136864-1,00.html. Published March 4, 2013. Accessed April 2015.

Articles

WEB EXCLUSIVE

The authors propose a simple legal mechanism to combat chargemaster abuses and encourage provider price competition. This solution is superior to prevailing legislative and regulatory responses to surprise out-of-network bills.

Battling the Chargemaster: A Simple Remedy to Balance Billing for Unavoidable Out-of-Network Care

The projections for the rising cost of healthcare have spurred robust dialogue, and among the many targets for cost control are specialty drugs. An important question thus becomes: Are behavioral economic factors driving physician prescribing? This article presents a review of leading behavioral economic theories and their application to the results of an Oncology Medical Home pilot that reversed incentives from drug administration to patient care. A host of these theories may explain the irrational economic actors in regard to physician prescribing, including heuristics, framing, and defaults. Ultimately, the complex interplay of behavioral economics may result in reimbursement methodology alternatives to the prevailing fee-for-service payment system having less impact on prescribing behavior than has been conjectured.

The complex interplay of behavioral economics may result in reimbursement methodology alternatives to the prevailing fee-for-service (FFS) having less impact on prescribing behavior than has been conjectured.

		Physicians, being human, are subject to irrational economic behaviors in their professional decision making and prescribing.

		It may be prudent to defer radical reimbursement reform until research into effectiveness of FFS alternative reimbursement methodologies is conducted and the results subjected to peer review.

The projections for the rising cost of healthcare have spurred robust dialogue from every sector of the healthcare economy.

 Among the many targets for cost control are specialty drugs, distinguished clinically by their route of administration, synthesis or bioengineering, mechanism of action, and cost itself.

 This terminology likely originated from payers who designated these drugs for special attention, not only because of price, but also the need for distinctive handling or particular patient monitoring.

 Although there are examples of competition emerging to tamp down prices to more acceptable levels (eg, pharmacy benefit manager negotiations for hepatitis C drugs), stakeholders (eg, policy makers, insurance carriers) and nongovernmental groups (eg, the American Society of Clinical Oncology) are seeking other market-based solutions.

The fee-for-service (FFS) payment system has been identified as one of the main drivers of rising healthcare costs; the more that is done for patients, the larger the reimbursement to the healthcare provider.

 In the FFS model, providers may directly purchase the drugs they administer to patients in their in-office infusion suites from manufacturers and/or wholesalers, then bill the payer for cost plus margin.

 Many argue that this “buy-and-bill” model encourages physicians to overprescribe, creates incentives for price inflation, and thereby drives up the costs of patient care.

 A few studies have even suggested that providers’ choices of drug treatment can be affected by reimbursement, resulting in their overutilization of more costly brands rather than less expensive brands or generic alternatives.

 Such suggestions draw the ire of providers who believe the portrayal of patient care as being driven solely by financial incentives and behavioral economics is insulting, demeans their professional integrity, and is inconsistent with real-world evidence that demonstrates highly variable regional resource utilization, as well as few differences in prescribing patterns among community, staff model, and academic physicians when controlling for these geographic variances.

 article, “The Cost Conundrum,” that “[h]ealth-care costs ultimately arise from the accumulation of individual decisions doctors make about which services and treatments to write an order for. The most expensive piece of medical equipment, as the saying goes, is a doctor’s pen.”

 Are there influences beyond clinical evidence that impact the physician’s pen? In their editorial in the 

, Khullar et al outline the basic tenets of behavioral economics established by Kahneman et al: behavioral economics views incentives as fundamental determinants of behavior; individuals often make decisions that deviate from those expected of “rational” economic actors.

By their assessment, most behavioral economics work in healthcare has focused on those factors influencing patients’ behavior, with less attention paid to applying behavioral economics to understand physicians’ performance and decision making.

 Behavioral economists have identified certain patterns, or biases, in their observations of economically “irrational” behaviors, a few of which merit attention as they are germane to healthcare provider decision making:

 individuals often make decisions based on approximate rules of thumb and not strict logic.

 the collection of anecdotes and stereotypes that make up the mental and emotional filters individuals rely on to understand and respond to events.

 a preference for continuing the current state of affairs or status quo.

 losses have about twice the psychological impact of commensurate gains.

These theories—experimentally tested and validated&mdash;not only explain financial decision making, but likely much of decision making in general. Physicians, being human, are subject to irrational behaviors in their professional decision making just as they are in any other aspect of their life. Pursuant to this logic, the practice of evidence-based medicine is likely to be significantly impacted by these realities of human thought and behavior. This helps explain the need for both heavily regulated clinical trial research and for real-world evidence to understand how such trial results are adopted into everyday practice. My research the past 5 years has focused on exactly this topic: the variances between clinical trial research and real-world evidence and the behavioral economics that underscore them.

Much of this research emerged from my work with collaborative clinical pathways programs between insurers and their specialty physician—provider networks. In partnership with a single large nonprofit healthcare insurer in the Mid-Atlantic region of the United States, the first network-wide collaborative cancer clinical pathway was launched in August 2008.

 Due to high levels of physician participation, compliance, and behavior change, the adoption of the pathway resulted in a 15% cost-of-care reduction and led to improved patient outcomes, including a 7% reduction in emergency department visits and hospitalizations.

 In Krey and Koeller’s analysis of this same program, they reported a $10 million annual savings, with a $30 million potential should it be expanded across all providers.

 To test the further impact of reimbursement reform, a second-generation pathways program—the Oncology Medical Home (OMH)&mdash;was piloted in 2011.

 This program modified the traditional physician reimbursement model used in the pathway program, shifting the source of revenue from drug reimbursement margin to enhanced professional charges for cognitive services (evaluation and management [E&M] codes).

 The OMH removed financial incentive from drug delivery while dramatically increasing it for patient care.

A select group of practices that participated in the first-generation pathways program were invited to voluntarily participate in the OMH. The intent was to compare physician behavior parameters pre- and post implementation and between the OMH practices and the first-generation pathways control group.

 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 was controlled for in analysis by propensity score matching using the following variables: disease focus, diagnosis mix, number of heavily treated versus newly treated patients, early-stage versus later-stage treatment, total chemotherapy lines (“extent of treatment”), and patient comorbidities.

 Taken together, we believe these measures yielded propensity scores indicative of case mix.

Although I take pains to explain the quality of the research, this is a separate issue from the main question of whether physicians behave to maximize their financial gain. The behavioral economic incentives designed within the model predicted that the OMH physician participants would increase the frequency of patient visits and likely decrease the percent of patients undergoing chemotherapy treatment, as well as increase the use of generic drugs in those who did. Surprisingly, physician behavior was not significantly modified by the cognitive weighted reimbursement model.

, revealed no significant change in the frequency of office visits for established patients, no change in the pattern or choice of chemotherapy prescribing, and the observed increases in generic regimen use were no different than that of the matched control.

How do we reconcile these findings and the prevailing dogma that resulted in a 2014 editorial in 

, co-signed by 20 leading academics citing FFS reimbursement as the primary driver for the spiraling cost of cancer care in this country?

 What are the behavioral economic explanations for the economically irrational pattern of care we observed? To what extent did the prior pathway experience influence behavior (default bias)? Were the observed behaviors driven by a mix of other factors, including guidelines from the National Comprehensive Cancer Network and others (default bias), brand name prescription drug detailing (attribute priming bias), cognitive dissonance (heuristics bias), or our culture of medicine (framing bias)? Without more detailed research, any conclusions can only be speculative.

Additionally, the results from this study contradict one of the more powerful behavioral economic incentives: loss aversion. The participating physicians experienced an annualized 2% reimbursement reduction on average, and despite a nearly 3-fold increase in some E&M code reimbursement, no significant change in established patient visits was observed to mitigate said loss. This was contrary to expectation and could be related to external influences on physician practice behavior (framing bias), including the historically lower contribution of E&M reimbursement to revenue (heuristics bias), standardized and established practice patterns (default bias), and maximized throughput within office flow (heuristics bias).

We believe our research was methodologically sound, adequately powered, and the results unequivocal; given the opportunity to maximize revenue by increasing select cognitive services, physicians remained unchanged in their behavior.

 Furthermore, removing economic incentives from drug prescribing did not alter the pattern of prescribing. Our research should make economic Darwinists take heed as the bedrock on which much of reimbursement reform is founded assumes a more rational economic behavior. The myriad factors that weigh on physician prescribing behavior for complex, disabling, and potentially life-threatening diseases like cancer, multiple sclerosis, Crohn’s disease, and a host of inflammatory and infectious diseases, as well as those factors impacting the patients receiving the treatment, eclipse the most sophisticated of behavioral economic modeling. In this context, the speculated impact of reimbursement reforms, like the Medicare Incentive Payment System and alternative payment models (eg, the oncology care model), may be overestimated. Observations from this OMH pilot program suggest that reimbursement methodology alternatives to the prevailing FFS model may have less impact on prescribing behavior than has been conjectured.

 Future research is needed to validate these observations and assess additional influences on prescribing behavior. It may be prudent to defer radical reimbursement reform until such research is conducted and results subjected to peer review.

 Cardinal Health Specialty Solutions, Dublin, OH.

 The author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design; acquisition of data; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; statistical analysis; administrative, technical, or logistic support; and supervision.

 Bruce Feinberg, DO, Cardinal Health Specialty Solutions, 7000 Cardinal Pl, Dublin, OH 43017. E-mail: bruce.feinberg@cardinalhealth.com. 

1. Smith TJ, Hillner BE. Bending the cost curve in cancer care. 

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2. Feinberg B, Lal LS, Swint JM. Is there a mathematical resolution to the cost-versus-value debate? 

3. Kesselheim AS, Avorn J, Sarpatwari A. The high cost of prescription drugs in the United States: origins and prospects for reform. 

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4. Sprandio JD. Oncology patient-centered medical home. 

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 website. https://opinionator.blogs.nytimes.com/2013/03/23/a-plan-to-fix-cancer-care/. Published March 23, 2013. Accessed February 2017.

7. Jacobson M, O’Malley AJ, Earle CC, Pakes J, Gaccione P, Newhouse JP. Does reimbursement influence chemotherapy treatment for cancer patients? 

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15. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. 

COMMENTARY

The complex interplay of behavioral economics may result in reimbursement methodology alternatives to the prevailing fee-for-service payment system having less impact on prescribing behavior than has been conjectured.

Real-World Evidence and the Behavioral Economics of Physician Prescribing

To determine whether a treating oncologist’s characteristics are associated with variation in use of chemotherapy for patients with advanced non—small cell lung cancer (aNSCLC) at the end of life.

Using the 2009 Surveillance, Epidemiology, and End Results—Medicare database, we studied chemotherapy receipt within 30 days of death among Medicare enrollees who were diagnosed with aNSCLC between 1999 and 2006, received chemotherapy, and died within 3 years of diagnosis. A multilevel model was constructed to assess the contribution of patient and physician characteristics and geography to receiving chemotherapy within 30 days of death.

 Among 21,894 patients meeting eligibility criteria, 43.1% received chemotherapy within 30 days of death. In unadjusted bivariate analyses, female sex, Asian or black race, older age, and a greater number of comorbid diagnoses predicted lower likelihood of receiving chemotherapy at the end of life (

 ≤.038 for all comparisons). Adjusting for patient and physician characteristics, physicians in small independent practices were substantially more likely than those employed in other practice models, particularly academic practices or nongovernment hospitals, to order chemotherapy for a patient in the last 30 days of life (

 <.001 for all comparisons); female physicians were less likely than males to prescribe such treatment (

Patients receiving care for aNSCLC in small independent oncology practices are more likely to receive chemotherapy in the last 30 days of life.

Oncologists’ characteristics explain significant variation in patients’ receipt of chemotherapy in the last 30 days of life:

		Patients should understand the variation in practices among oncologists treating the same condition.

		Awareness of such variation may influence an individual oncologist’s practice decisions and eventually lead to consensus practices at end of life; practices may already have changed since the period under study.

		Less variation is likely to yield better alignment between patient goals and treatment received, and result in higher value care at the end of life.

		Payers may wish to consider oncologist practice type in determining network participation.

Despite a half century of treatment advances, lung cancer—the most common solid tumor in the United States&mdash;remains among the cancers least responsive to treatment.

 A majority of patients are diagnosed at an advanced stage and, even with the newest therapies, barely 1 in 10 are alive a year after diagnosis.

 In the final weeks of life, many patients with advanced non—small cell lung cancer (aNSCLC) undergo aggressive treatment that can include repeated emergency department visits, prolonged hospitalizations, intensive care, and additional lines of chemotherapy that contravene guidelines.

 Attendant effects on quality of life are well documented, and the value of such expensive treatment is debated in the lay, clinical, and policy realms.

Why aggressive end-of-life treatment occurs is not clearly understood. Regional variations in the aggressiveness of cancer treatment have been well established and patient factors have been explored

; however, the degree of variation between individual physicians is not known.

 We conducted this study to determine the extent to which physician characteristics explain patients’ receipt of chemotherapy in the 30 days prior to death among patients with aNSCLC.

We used the 2009 Surveillance, Epidemiology, and End Results (SEER) cancer registry and linked Medicare claims

 to identify patients and describe treatment patterns. SEER regions include 28% of the US population. Approximately 68% of lung cancers diagnosed in SEER regions are in Americans older than 65 years.

 Using the Unique Physician Identification Number Registry, we linked the physician submitting a Medicare claim to their characteristics in the American Medical Association (AMA) Masterfile.

 details the initial sample and number of patients dropped due to each exclusion criterion. We first identified all patients in the SEER registry diagnosed with lung cancer between 1999 and 2006. We selected 1999 due to changes in physicians’ unique identification codes that year and 2006 to allow for up to 3 years of data after diagnosis. We limited the sample to those 65 years or older at diagnosis who were enrolled in traditional Medicare Part A and Part B from at least 12 months prior to a first diagnosis of lung cancer until 3 years after diagnosis or until death. Patients with other incident cancers were excluded to avoid erroneously counting chemotherapy directed toward another cancer.

Among 193,200 subjects satisfying all conditions (ie, lung cancer, age ≥65, continuous enrollment in Medicare A and B, no other cancer), we excluded 4518 subjects who died on unknown dates and 123 subjects with charges for chemotherapy after their recorded date of death. Our cohort was then limited to 155,794 patients with stage 3b or 4 aNSCLC; an additional 38,311 were excluded for diagnosis dates out of range. We excluded 89,069 who had not received chemotherapy within 3 years of initial diagnosis and compared their characteristics with those who received chemotherapy. To avoid insufficient data biasing interpretation of physicians’ practice patterns, subjects were excluded if not treated by an identifiable oncologist who provided care to 5 or more patients in the sample. In sensitivity analyses, we evaluated the impact of physician characteristics on receipt of chemotherapy in the last 30 days of life for all physicians, irrespective of the number of patients seen, and physicians with 10 or more patients. Our final analytic sample comprised 21,894 aNSCLC subjects.

Chemotherapy use was established by Medicare charges in outpatient, inpatient, or physician claims for chemotherapy-related encounters (

International Classification of Diseases, Ninth Revision, Clinical Modification

] diagnosis codes V58.1, V66.2, and V67.2), chemotherapy administration (

 procedure code 99.25; Current Procedural Terminology codes 96400-96549; Health Care Common Procedure Coding System codes J8530, J8560, J8600, J8610, J8999, Q0083-Q0085, and G0921-G0932; and Revenue Center Codes 0331, 0332, and 0335), or chemotherapy agents (J Codes other than diethylstilbestrol and leuprolide). The primary outcome was whether a patient who ever received chemotherapy received a final dose during the last 30 days of life.

Administration of chemotherapy at the end of life was attributed to the oncologist submitting a Medicare claim with the latest date of service rendered prior to a patient’s death. Sensitivity analysis was performed, attributing the patient to the oncologist with the most visits. Physicians were considered oncologists if the billing physician’s specialties in either Medicare claims or AMA-linked physician files included oncology, hematology-oncology, or hematology.

Patient were classified by race (white, black, Hispanic, Asian, other), sex, age at diagnosis (65-69, 70-74, 75-79, 80-84, ≥85 years), last known marital status, median income by zip code (by quartile, as a proxy for socioeconomic status), and year of diagnosis. Time between diagnosis and last chemotherapy was calculated and grouped (<1, 1, 2-3, 4-5, 6-7, 8-9, 10-11, 12-23, 24-36 months), as it was hypothesized that a recent diagnosis might be associated with receiving treatment at the end of life. We also included the proportion of blacks in the patient’s residential zip code and birthplace outside of the United States.

We calculated a modified Charlson Comorbidity Index score for each patient using 

—coded diagnoses from inpatient claims, carrier claims, and outpatient claims using the Wang method.

 In order to approximate patients’ health leading up to death, comorbidity scores were calculated from claims for services provided during the 12 months prior to the month of death.

Physician characteristics included in the model were sex and year that medical training was completed. Age was strongly correlated with year of training completion and thus excluded. We examined the type of practice based on the present employer variable from the AMA Masterfile and classified this variable into 6 categories: small independent (physician-owned, 1-2 physicians), group practice (physician-owned, >2 physicians), government (employed by city, county, state, or federal government), academic (employed by medical schools), hospital (employed by non—government-owned hospital), and other.

As there is good evidence supporting geographic variations in treatment practices, we sought to control for such variation based on SEER registry sites; however, because of its size and previously demonstrated practice variation,

 California was split by county into 4 zones: Los Angeles, non-LA Metro-South (San Diego, Riverside, San Bernardino, Orange, Ventura), Metro-North (San Francisco, Alameda, Santa Clara, Contra Costa, San Mateo, Marin), and Other. Rural Georgia, with just 47 eligible cases, was combined with Atlanta, yielding a single site for all of Georgia. SEER sites were otherwise categorized according to the SEER 17 registry.

Frequency distributions were calculated for patient, oncologist, and geographic variables. We used a multilevel logistic regression mixed model with dichotomous outcomes to estimate the probability of receiving chemotherapy treatment in the last 30 days of life. Patients were nested within physicians, who, in turn, were nested within geographic locations (SEER site, modified as above) as a random intercept at the highest level. The model adjusted for the patient and physician covariates, as described above. To facilitate interpretation of the magnitude of the effects, adjusted relative risks are presented along with the coefficient estimates and 

To calculate the marginal effect of the physician’s type of practice on receiving chemotherapy at the end of life, each patient’s probability of receiving treatment was recalculated as if all received treatment under a uniform type of practice, adjusting for patient variables and other physician variables. This was repeated for each type of practice.

University of California, Los Angeles, Institutional Review Board approved the study.

We identified 23,687 continuously enrolled Medicare (parts A and B) patients diagnosed with aNSCLC between 1999 and 2006. 

 shows the distribution of patient characteristics and associated probability of receiving chemotherapy within 30 days of death, among the 21,894 (92.4%) patients receiving chemotherapy within 3 years of diagnosis. Of these, 9447 (43.1%) received chemotherapy within 30 days of death.

In bivariate analyses, men were more likely than women to receive chemotherapy near the end of their lives (45.9% vs 39.5%; 

 <.001) (Table 1). Patients were less likely to receive chemotherapy at the end of life if they were Asian (33.3%) or black (40.0%) compared with whites (43.9%; 

 <.001), older (40.8% among those aged ≥85 vs 44.4% among those aged 65-69; 

 = .001), or diagnosed in 2005 (37.8%) or 2006 (37.5%) compared with 1999 (43.9%) (

 <.001). Despite these differences in treatment, none of these variables were associated with a difference in survival (data not shown).

Characteristics of the 89,069 patients excluded for nonreceipt of chemotherapy matched closely on race, sex, year of diagnosis, and SEER site categories. Younger patients and those with low comorbidity scores were more likely to have started chemotherapy than those who were older and sicker; females were slightly more likely than males to have never received chemotherapy.

Physician characteristics and the numbers of patients attributed to physicians with each characteristic are shown in 

. Oncologists were primarily male (77.4%) and in group practices (61.2%). Physicians in small independent practices were significantly more likely to administer chemotherapy during the last 30 days of life (Table 2). Adjusting for all patient and provider covariates, the predicted probability of receiving chemotherapy in the last 30 days of life was 1.4-fold greater (0.56) for patients receiving care in small independent practices relative to those seeing oncologists in academic centers (0.40; 

Patients receiving care for advanced non—small cell lung cancer in small, independent oncology practices are more likely to receive chemotherapy in the last 30 days of life.

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