Kevin A. Schulman, MD

 Hospitals have rapidly acquired medical oncology practices in recent years. Experts disagree as to whether these trends are related to oncology-specific market factors or reflect a general trend of hospital—physician integration. The objective of this study was to compare the prevalence, geographic variation, and trends in physicians billing from hospital outpatient departments in medical oncology with other specialties.

 Retrospective analysis of Medicare claims data for 2012 and 2013.

We calculated the proportion of physicians and practitioners in the 15 highest-volume specialties who billed the majority of evaluation and management visits from hospital outpatient departments in each year, nationally and by state.

We included 338,998 and 352,321 providers in 2012 and 2013, respectively, of whom 9715 and 9969 were medical oncologists. Among the 15 specialties examined, medical oncology had the highest proportion of hospital outpatient department billing in 2012 and 2013 (35.0% and 38.3%, respectively). Medical oncology also experienced the greatest absolute change (3.3%) between the years, followed by thoracic surgery (2.4%) and cardiology (2.0%). There was marked state-level variation, with the proportion of medical oncologists based in hospital outpatient departments ranging from 0% in Nevada to 100% in Idaho. 

 Hospital—physician integration has been more pronounced in medical oncology than in other high-volume specialties and is increasing at a faster rate. Policy makers should take these findings into consideration, particularly with respect to recent proposals that may continue to fuel these trends.

Hospitals have rapidly acquired medical oncology practices in recent years. It is unknown whether this is due to oncology-specific factors or reflects a general trend. We analyzed publicly available physician-specific billing data and found: 

In 2012, 35.0% of medical oncologists, and in 2013, 38.3%, were primarily billing from hospital outpatient settings, a proportion substantially higher than in other high-volume specialties.

There was wide geographic variation in the proportion of hospital-based medical oncologists by state, ranging from 0% in Nevada to 100% in Idaho.

Hospitals have rapidly acquired medical oncology practices in recent years.

 It remains unclear whether this practice is unique to medical oncology or reflects a broader trend. Some stakeholders have argued that the trend is driven by advantageous acquisition costs for high-priced cancer drugs, particularly for hospitals participating in the Health Resources and Services Administration (HRSA)’s 340B program, which requires manufacturers to provide deep discounts for outpatient drugs.

 Other market factors favoring acquisition of physician practices are not specific to oncology, including hospital leverage to negotiate higher prices with commercial payers, higher payments for comparable services (eg, office visits) delivered in hospital outpatient departments, and infrastructure to support risk-based payment models.

 Comparison of contemporaneous prevalence of and trends in hospital—physician integration across specialties can provide policy makers with insights into whether general or oncology-specific factors are driving these trends in order to guide appropriate policy.

Understanding the factors driving hospital—physician integration is critically important as multiple policies have been proposed recently that will have direct effects on the medical oncology market. In 2015, HRSA released updated guidance for the 340B program,

 notably requiring that off-site clinics meet hospital outpatient department billing requirements to be eligible for discounts, potentially creating an additional incentive for integration. The Bipartisan Budget Act of 2015 requires CMS to implement site-neutral payments for comparable services delivered in freestanding clinics and clinics based in hospital outpatient departments, which may reduce incentives for hospital—physician integration.

 This legislation does not apply to practices that have already been acquired, and it is unclear whether newly acquired practices will be eligible for 340B discounts. CMS has considered experiments with alternative payment models for Medicare Part B drugs, including a proposal to reduce payments for high-cost drugs, which was subsequently withdrawn, that may have adversely impacted providers with high acquisition costs and incentivized integration.

 The Medicare Payment Advisory Commission has recommended that CMS reduce Medicare payments to 340B hospitals for Part B drugs, potentially reducing the incentive for these hospitals to acquire physician practices.

 Finally, CMS is testing a value-based payment model for medical oncologists that rewards high-value care and could help support less-costly practices.

Previous evaluations have relied on surveys of physician practices to determine integration with hospitals, typically considering whether physician practices report ownership by hospitals. In contrast, we determined integration of practices by examining the site of care based on hospital outpatient department billing records. To qualify for this level of billing, physician practices must demonstrate financial integration that includes sharing income and expenses with the hospital. Physician practices must also meet clinical integration requirements, such as having medical directors report to the chief medical officer or similar official at the hospital and granting responsibility to the hospital’s medical staff committee for clinical activities at the site. Physicians who are clinically and financially integrated with hospitals may bill for services at a higher rate, which is indicated on the claim. This approach allows for a complete national sample of physicians and reflects a well-defined degree of clinical and financial integration consistent across specialties.

Our data are from the CMS Medicare Physician and Other Supplier data files.

 These publicly available files contain billing information for individual physicians and other practitioners (hereafter referred to as “providers”). Providers are included if they delivered a service to at least 10 Medicare beneficiaries during a calendar year. We examined provider outpatient evaluation and management services (Healthcare Common Procedure Coding System codes 99201-99215) billed during the 2012 and 2013 calendar years.

We considered providers to be based in hospital outpatient departments if the majority (more than 50%) of visits during the year were billed from a hospital outpatient department. We calculated the proportion of hospital outpatient department providers for each specialty in each year among the 15 specialties with the highest volume of outpatient visits. We combined “hematology/oncology” and “medical oncology” into medical oncology. For medical oncology, we also calculated the proportion of hospital outpatient department providers by state in 2013.

The institutional review board of the Duke University Health System approved the study.

We included 338,998 and 352,321 providers in 2012 and 2013, respectively, of whom 9715 and 9969 were medical oncologists. Among the 15 specialties, the proportion of providers in hospital outpatient departments varied markedly, with medical oncology having the highest proportions in 2012 and 2013 (35.0% and 38.3%, respectively). Medical oncology also experienced the greatest absolute change (3.3%) between the 2 years, followed by thoracic surgery (2.4%) and cardiology (2.0%) (

). Cardiology experienced the greatest relative increase (15.2%) and ophthalmology the smallest (1.4%), with medical oncology at 9.4%.

 is a map of the proportion of medical oncologists in hospital outpatient departments by state or territory in 2013. We observed marked variation, with 14 states and territories (including Washington, DC, and the US Virgin Islands) having more than 50% of medical oncologists in hospital outpatient departments. Conversely, 7 states had less than 15%. The proportion of medical oncologists in hospital outpatient departments ranged from 0% in Nevada to 100% in Idaho. New England, Pacific Northwest, and Mountain West states tended to have higher concentrations of medical oncologists in hospital outpatient departments. However, there was marked variation among many regions, with several states having more than 50% of medical oncologists in hospital outpatient departments adjacent to states with less than 15%. We compared these data to recent estimates of accountable care organization penetration

 and noted that some states had high prevalence of both hospital outpatient department billing and accountable care organization activity (eg, Maine, Vermont, New Hampshire, Massachusetts, Wisconsin), some had low prevalence of both (eg, Nevada, Oklahoma, Alabama), and several states had marked discordance (eg, New Jersey, Kentucky, Alaska).

Among high-volume specialties, medical oncology had the highest proportion of providers billing from hospital outpatient departments in 2012 and 2013, as well as the highest absolute growth in this proportion. We found wide geographic variation in hospital—physician integration among medical oncologists, suggesting that local factors may also contribute. These findings suggest that factors specific to medical oncology are influencing hospital–physician integration and predate the study period.

Our definition of integration differs from approaches taken by others. For medical oncology, our results are comparable to a recent survey in which 32.1% of oncology practices reported vertical integration with a health system or hospital in 2013.

 Using Medicare claims data, the results of a study by Neprash et al

 examining the relationship between physician-hospital integration and commercial prices indicated that 32.2% of medical oncologists were based in hospital outpatient departments in 2012. Our results likely differ in part because we counted medical oncologists billing from multiple practices only once and required a minimum threshold of evaluation and management services.

For other physician specialties, surveys of hospital ownership have reported higher rates in 2012, including 45% for internists and 37% for family practitioners in single-specialty practice,

 compared with our findings of 13.2%, 10.0%, and 14.3% for the 3 specialties, respectively. This discrepancy may be partially explained by the possibility that these specialists are less likely to completely financially and clinically integrate with hospital owners. The study by Neprash et al

 reported rates of 23.9% and 28.4% for primary care physicians and cardiologists, respectively, in 2012, which may differ from our findings for the reasons noted previously. All approaches have strengths and limitations to evaluating hospital—physician integration. Ours represents a high degree of integration that has clear financial significance, as shifting the site of service for office visits results in direct price increases for the Medicare population, and clinical significance, as clinical care provided during these visits is brought under the purview of the hospital.

The geographic variation in integration suggests that these findings are unlikely to reflect clinical need; however, little is known about the impact on patient outcomes. Empirical evidence has demonstrated an association between integration and increased prices billed to commercial payers,

 with some evidence that integration may increase the use of care management processes.

 For the Medicare population, average payments are higher for patients treated in hospital outpatient departments for cancer compared with office settings; however, these results may relate to differences in patient populations across sites of care.

 As commercial payers and patients may bear the financial brunt of integration while public policies are potentially driving this behavior, more research is needed to examine the impact of these trends on public payers and beneficiaries.

The 340B program in particular has been identified as a potential root cause of integration in medical oncology,

 noting, for example, the increase in affiliated clinics and share of chemotherapy delivered by 340B clinics.

 Although our study results strongly support arguments that hospital—physician integration is substantially greater in medical oncology, further research is needed to delineate the specific causes.

It should be noted that our study has several limitations: 1) we did not evaluate lesser degrees of integration, 2) the study was limited to 2 years of data, and 3) we were unable to delineate the specific causes of integration.

A greater proportion of medical oncologists bill from hospital outpatient departments than providers in other high-volume specialties, with continued growth between 2012 and 2013. Policy makers should be cautious about further contributing to consolidation and monitor hospital—physician integration on an ongoing basis. Research is needed to better delineate the causes of consolidation and implications for patients, physicians, and payers.

 Duke Clinical Research Institute and Department of Medicine, Duke University School of Medicine (JDC, MAD, KAS), Durham, NC.

 This work was supported internally by the Duke Clinical Research Institute.

 The authors report 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 (JDC, MAD, KAS); acquisition of data (JDC); analysis and interpretation of data (JDC, MAD, KAS); drafting of the manuscript (JDC, MAD); critical revision of the manuscript for important intellectual content (JDC, MAD, KAS); statistical analysis (MAD); obtaining funding (JDC, KAS); and supervision (KAS). 

 Jeffrey D. Clough, MD, MBA, Duke Clinical Research Institute, PO Box 17969, Durham, NC 27715. E-mail: jeffrey.clough@duke.edu. 

1. Conti RM, Landrum MB, Jacobson M. The impact of provider consolidation on outpatient prescription drug-based cancer care spending. Health Care Cost Institute website. http://www.healthcostinstitute.org/files/HCCI-Issue-Brief-Impact-of-Provider-Consolidation.pdf. Published March 2016. Accessed April 12, 2016.

2. Hirsch BR, Balu S, Schulman KA. The impact of specialty pharmaceuticals as drivers of health care costs. 

. 2014;33(10):1714-1720. doi: 10.1377/hlthaff.2014.0558.

3. Kantarjian H, Chapman R. Value of the 340B drug discount program. 

. 2015;1(8):1029-1030. doi: 10.1001/jamaoncol.2015.2168.

Report to the Congress: overview of the 340B drug pricing program

. Medicare Payment Advisory Commission website. http://www.medpac.gov/docs/default-source/reports/may-2015-report-to-the-congress-overview-of-the-340b-drug-pricing-program.pdf?sfvrsn=0. Published May 2015. Accessed September 15, 2017.

5. Robinson JC, Miller K. Total expenditures per patient in hospital-owned and physician-owned physician organizations in California. 

. 2014;312(16):1663-1669. doi: 10.1001/jama.2014.14072.

6. Kocher R, Sahni NR. Hospitals’ race to employ physicians—the logic behind a money-losing proposition. 

. 2011;364(19):1790-1793. doi: 10.1056/NEJMp1101959.

7. 340B Drug Pricing Program omnibus guidance. 

 website. https://www.federalregister.gov/articles/2015/08/28/2015-21246/340b-drug-pricing-program-omnibus-guidance#h-15. Published August 28, 2015. Accessed April 12, 2016.

8. 114th Congress. H.R. 1314 - Bipartisan Budget Act of 2015. Congress website. https://www.congress.gov/bill/114th-congress/house-bill/1314/text. Published November 2, 2015. Accessed April 12, 2016.

9. CMS proposes to test new Medicare Part B prescription drug models to improve quality of care and deliver better value for Medicare beneficiaries [press release]. Baltimore, MD: CMS; March 8, 2016. https://www.cms.gov/Newsroom/MediaReleaseDatabase/Press-releases/2016-Press-releases-items/2016-03-08.html. Accessed September 15, 2017.

10. MedPAC backs recommendation to cut 340B drug payments. Advisory Board website. https://www.advisory.com/daily-briefing/2016/01/19/medpac-backs-recommendation-to-cut-340b-drug-payments. Published January 19, 2016. Accessed April 12, 2016.

11. Oncology Care Model. CMS website. https://innovation.cms.gov/initiatives/oncology-care/. Published February 12, 2015. Accessed April 12, 2016.

12. Medicare provider utilization and payment data: physician and other supplier. CMS website. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Physician-and-Other-Supplier.html. Updated June 15, 2017. Accessed September 2017.

13. Muhlestein D, McClellan M. Accountable care organizations in 2016: private and public-sector growth and dispersion. 

 blog website. http://healthaffairs.org/blog/2016/04/21/accountable-care-organizations-in-2016-private-and-public-sector-growth-and-dispersion/. Published April 21, 2016. Accessed September 26, 2016.

14. Neprash HT, Chernew ME, Hicks AL, Gibson T, McWilliams JM. Association of financial integration between physicians and hospitals with commercial health care prices. 

. 2015;175(12):1932-1939. doi: 10.1001/jamainternmed.2015.4610.

15. Kane CK, Emmons DW. New data on physician practice arrangements: private practice remains strong despite shifts toward hospital employment. New Mexico Medical Society website. http://www.nmms.org/sites/default/files/images/2013_9_23_ama_survey_prp-physician-practice-arrangements.pdf. Published September 2013. Accessed March 14, 2016.

16. Medicare payment differences across ambulatory settings. In: Report to the Congress: Medicare and the health care delivery system. Medicare Payment Advisory Commission website. http://www.medpac.gov/docs/default-source/reports/jun13_entirereport.pdf?sfvrsn=0. Published June 2013. Accessed September 15, 2017.

17. Robinson JC, Miller K. Total expenditures per patient in hospital-owned and physician-owned physician organizations in California. 

18. Baker LC, Bundorf MK, Kessler DP. Vertical integration: hospital ownership of physician practices is associated with higher prices and spending. 

. 2014;33(5):756-763. doi: 10.1377/hlthaff.2013.1279.

19. Bishop TF, Shortell SM, Ramsay PP, Copeland KR, Casalino LP. Trends in hospital ownership of physician practices and the effect on processes to improve quality. 

20. The Moran Company. Cost differences in cancer care across settings. Community Oncology Alliance website. http://www.communityoncology.org/UserFiles/Moran_Cost_Site_Differences_Study_P2.pdf. Published August 2013. Accessed April 12, 2016.

Articles

The authors used Medicare claims data to examine trends in hospital–physician integration in high-volume specialties, including medical oncology.

Trends in Hospital–Physician Integration in Medical Oncology

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.

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

Several states offer publicly funded—care management programs to prevent long-term care placement of high-risk Medicaid beneficiaries. Understanding participant risk factors and services that may prevent long-term care placement can facilitate efficient allocation of program resources.

To develop a practical prediction model to identify participants in a home- and community-based services program who are at highest risk for long-term nursing home placement, and to examine participant-level and program-level predictors of nursing home placement.

In a retrospective observational study, we used deidentified data for participants in the Connecticut Home Care Program for Elders who completed an annual assessment survey between 2005 and 2010.

We analyzed data on patient characteristics, use of program services, and short-term facility admissions in the previous year. We used logistic regression models with random effects to predict nursing home placement. The main outcome measures were long-term nursing home placement within 180 days or 1 year of assessment.

Among 10,975 study participants, 1249 (11.4%) had nursing home placement within 1 year of annual assessment. Risk factors included Alzheimer's disease (odds ratio [OR], 1.30; 95% CI, 1.18-1.43), money management dependency (OR, 1.33; 95% CI, 1.18-1.51), living alone (OR, 1.53; 95% CI, 1.31-1.80), and number of prior short-term skilled nursing facility stays (OR, 1.46; 95% CI, 1.31-1.62). Use of a personal care assistance service was associated with 46% lower odds of nursing home placement. The model C statistic was 0.76 in the validation cohort.

A model using information from a home- and community-based service program had strong discrimination to predict risk of long-term nursing home placement and can be used to identify high-risk participants for targeted interventions.

Approximately 11% of participants in a home- and community-based service program were placed in a nursing home within 1 year.

Risk factors for long-term nursing home placement include Alzheimer's disease, money management dependency, living alone, and the number of prior short-term skilled nursing facility stays.

Use of a personal care assistance service is associated with significantly lower odds of nursing home placement.

Lifetime risk of nursing home use is estimated at more than 40% and is projected to increase with greater life expectancy among Baby Boomer retirees.

 Medicaid is the primary payer of nursing home services in the United States at an average annual cost of $84,000 per beneficiary.

In 2010, long-term care services for older patients accounted for more than one-third of state Medicaid spending.

 At a total annual cost of over $140 billion, Medicaid costs for long-term care will likely be part of ongoing discussions about state and federal deficit reduction.

In efforts to curb these costs, many states have moved towards home- and community-based services (HCBS)—programs that aim to prevent placement of high-risk Medicaid beneficiaries into long-term nursing facilities. These programs account for 45% of Medicaid long-term care spending,

 and research has shown that such programs may be effective.

 The Patient Protection and Affordable Care Act (PPACA) provides monetary incentives to states that implement HCBS programs as alternatives to nursing homes.

Although a fair amount is known about the predictors of nursing home use,

 validated prediction models for long-term nursing home placement in high-risk, HCBS populations have not been studied.

 Understanding the risk factors for nursing home placement and identifying services that may prevent such placement can facilitate efficient allocation of resources among HCBS program participants. We used clinical and administrative databases for elderly participants in a state- and waiver-funded HCBS program to develop a practical model to predict the risk of long-term nursing home placement and to examine associated participant characteristics and program services.

We used clinical and administrative data from the Connecticut Home Care Program for Elders (CHCPE) provided by Connecticut Community Care, Inc (CCCI). CHCPE is a publicly funded care-management program that provides preventive home care services to older Connecticut residents who are at risk for permanent nursing home placement. CCCI provided deidentified data for all clients from 2005 through 2011. The data included baseline eligibility evaluations and annual reassessments conducted by CCCI primary care managers, which contain demographic characteristics, medical history, functional ability, social support, and financial assistance data elements in the “Modified Community Assessment Tool” published by the Connecticut Department of Social Services (CDSS).

 Additional data included information on program status, funded and unpaid program services, hospital visits, short-term skilled nursing facility stays, and medications.

The study population included at-risk residents who were referred to and deemed eligible for the CHCPE program. Program eligibility is based on the number of critical needs, income, and total assets. The state defines critical needs as functional dependencies in specific activities of daily living (ADLs) and instrumental activities of daily living (IADLs) and/or cognitive impairment requiring supervision.

We included CHCPE participants 65 years and older who completed an annual assessment between January 1, 2005, and December 31, 2010. If a participant had multiple assessments, we used the earliest reassessment for analysis.

Potential Predictors of Long-Term Nursing HomePlacement

Potential predictors included demographic characteristics, clinical characteristics, social support, living arrangements, financial assistance, and program-level variables. Demographic information included age, sex, race, marital status, and primary spoken language. We categorized participants’ race/ethnicity as black, Hispanic, white, or other. Clinical information included medical diagnoses (coded 0 [none], 1 [secondary], or 2 [major]); ADLs and IADLs (coded 0 [independent], 1 [requires assistance], or 2 [total dependence]); mental status quotient (0 to 10 errors); behavioral and psychological issues; vision and hearing assessments; and medications. A dichotomous variable for “meets nursing home level of care” was based on 3 or more critical care needs as defined by the CDSS.

Program-level variables included healthcare utilization, program services, and the patient’s primary care manager and team. We assessed hospital admissions, emergency department visits, and short-term skilled nursing facility stays during the year before the assessment. We grouped services into categories (

); identified the services in place at the time of and in the 12 months before the assessment; and calculated average monthly total costs, medical costs, and social service costs. The personal care assistant service pilot offered during the period of our study gave participants authority to hire a single person, including a family member, to perform services that might otherwise be provided by multiple persons. The assessment year, time since the initial assessment, and time since program activation were used to account for variation in subjects’ program participation time.

Most variables had low rates of missingness (ie, less than 2%). We imputed missing values as follows: “no” for dichotomous variables, the most frequent level or category for multichotomous variables, and median values for continuous variables.

Since missing values for the mental status quotient (3%) were likely attributable to pronounced cognitive or communication impairments, we imputed missing values to 11.

The primary outcome of interest was placement in a long-term nursing home within 180 or 365 days after the assessment. We calculated the days from assessment to nursing home placement based on a termination record in the program status file.

We present patient characteristics at the time of the annual assessment, using proportions for categorical variables and using means with standard deviations or medians with interquartile ranges for continuous variables. We calculated the frequency and Kaplan-Meier estimates of 1-year or 6-month nursing home placement.

Since the goal of this study was to develop a prediction model that would be useful in practice, we carefully preselected potential predictors based on clinical knowledge and previous literature,

and adhered to the rule of 10 events per examined variable to avoid overfitting.

 We used logistic regression models to predict 1-year and 6-month nursing home placement. We chose logistic regression to facilitate both internal- and external-model validation using well-established methods and practical implementation of the prediction model scoring mechanism in the electronic data systems of 2 HCBS programs. In all models, we incorporated random effects to account for variance in nursing home placement among primary care-manager teams. As a sensitivity analysis, we used Cox proportional hazards models with robust standard errors to account for clustering of participants within primary care-management teams. For the Kaplan-Meier and Cox survival analyses, we censored data for participants if they terminated the program and at the time of death.

We randomly selected a derivation sample consisting of 66% of the study cohort and a validation sample consisting of the remaining 34% of the study cohort. We developed the logistic regression models in the derivation sample and then applied the results from these models to the validation sample. We evaluated the calibration and discrimination of all models in both samples and refit the models for the entire study cohort.

 Using the derivation model estimates, we generated predicted probabilities of nursing home placement in the derivation and validation samples. To assess the clinical usefulness of the prediction models, we considered patients who were in the top 10%, 15%, 25%, or 50% of the predicted probabilities to be those whom the model predicted would have nursing home placement.

 At each threshold, we calculated the model’s sensitivity, false-negative rate, specificity, false-positive rate, and positive predictive value. We calculated the area under the curve (C statistic) to assess overall model discrimination. To assess model calibration, we plotted percent of predicted nursing home placement versus percent of observed nursing home placement by decile of predicted probability and calculated E

 Additional details about methods are provided in 

Among 10,975 CHCPE participants who completed an annual assessment between 2005 and 2010, the mean time since program activation was 425 days. The mean available follow-up for the study outcome was 320 days (range, 1-365). The median age was 75 to 79 years, 74% of participants were women, and 74% were white (

). More than two-thirds of participants were eligible for Medicaid. Common medical diagnoses included hypertension (59%), diabetes (33%), and Alzheimer's disease (20%). Common functional dependencies included bathing (80%), meal preparation (92%), housework (97%), and money management (75%). Most participants were able to perform eating/feeding (89%) and toileting (86%) activities without assistance. Over half of participants lived alone, and most had regular contact with their support system.

The most common services received were: housekeeping or shopping (95%); skilled nursing care or medication administration (80%); emergency response system (72%); and adult care, companionship, or supervision (62%) (

). In addition to formal services paid for through the program, most participants also received unpaid services typically performed by someone in their support system, including household/shopping, safety checks, and hands-on care. The pilot personal care assistance service was used by 160 (1.5%) participants, for an average of about 90 hours per month. The mean total monthly cost for paid program services was $2296, the mean non-Medicare medical service cost was $714, and the social service cost was $1549.

The authors developed a model to identify participants in a home- and community-based services program who are at highest risk for long-term nursing home placement.

Kevin A. Schulman, MD

Articles

Trends in Hospital–Physician Integration in Medical Oncology

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

Predicting Nursing Home Placement Among Home- and Community-Based Services Program Participants

Removing a Constraint on Hospital Utilization: A Natural Experiment in Maryland

The Mis-Measure of Physician Performance

Relationship Between Patient Satisfaction With Inpatient Care and Hospital Readmission Within 30 Days