National Nurses United Issues Comment on Medicare Program: Modernizing, Clarifying Physician Self-Referral Regulations

WASHINGTON, Jan. 27 -- Michelle Grisat, national director of health policy at the National Nurses United, has issued a public comment on the Centers for Medicare and Medicaid Services's proposed rule entitled "Medicare Program: Modernizing and Clarifying the Physician Self-Referral Regulations". The comment was written on Dec. 31, 2020, and posted on Jan. 15, 2020:

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On behalf of more than 150,000 registered nurses (RNs) across the country, National Nurses United (NNU) submits these comments in response to the Center for Medicare & Medicaid Services' (CMS's) proposed rule on "Medicare Program; Modernizing and Clarifying the Physician Self-Referral Regulations," 84 Fed. Reg. 55,766 (Oct. 17, 2019) (Proposed Rule). Because the full and effective enforcement of federal law against fraud and abuse in Medicare and Medicaid is necessary to ensure that patient care is unencumbered by the financial incentives of payors, NNU urges CMS not to extend existing safe harbors or exceptions under physician self-referral regulations to so-called "value-based" payment arrangements. Insofar as CMS does decide to amend the safe harbors or exceptions in the physician self-referral rules with respect to value-based arrangements, however, NNU urges CMS to narrowly limit them.

As nurses who work at the bedside, NNU members understand that providing healthcare should be based on human need, not economic incentives. Financial incentive arrangements, however, change this fundamental precept. The underlying framework of value-based payment models, as recognized by CMS, is to shift at least some insurance risk to doctors, hospitals, and other providers.

Predictably, the health information technology required by value-based payment models--to monitor the costs and quality metrics for a target population--turns human patients into pooled data sets. Thus, under a value-based payment arrangement, providers come to view the relationship with their patients as an economic relationship to an insurance risk pool, the target population, rather than as a caring relationship based on individual patient need. In other words, value-based models require healthcare providers to think about the risk to their pocket book when they provide care with foreseeable detrimental effects on patients.

CMS recognizes that value-based payment models may have detrimental effects on patients. In the Proposed Rule, CMS discusses the "inducement" to deny medically necessary items and services, stating:

Integrated into most of the CMS-sponsored models is a requirement that any remuneration between parties to an allowable financial arrangement is not provided as an inducement to reduce or limit medically necessary items or services to any patient in the assigned patient population. We believe this is an important safeguard for patient safety and quality of care, regardless of whether Medicare is the ultimate payor for the services, and propose to include it in the full financial risk exception by requiring at proposed Sec. 411.357(aa)(1)(iii) that remuneration is not provided as an inducement to reduce or limit medically necessary items or services to any patient, whether in the target patient population or not./1

NNU shares these concerns. However, the "inducement to reduce or limit medically necessary items or services to any patient in the assigned patient population" arises not only when value-based entities accept full financial risk but when they accept any financial risk. Thus, even partial financial risk threatens "patient safety and quality of care".

Contrary to health care industry claims that value-based payment arrangements focus on improving patient health outcomes while reducing costs,/2 in practice the savings accrue to payors, hospitals, and providers with questionable evidence of health benefits to patients. Indeed, rather than improving quality of care, value-based payment models create an incentive for entities to avoid treating patient populations with poorer health statistics in order to meet quality metrics or cost reduction goals at the expense of patients. In California, for example, 12 health insurance companies, representing millions of enrollees, were fined for turning a blind eye to practices under a risk-based contract by a medical group that (1) "flagged enrollee requests for treatment of expensive conditions and diverted them to a special team whose goal was to pressure the enrollees to transfer to a different medical group," (2) "conducted illegal economic profiling which restricted access to certain specialist providers," and (3) falsified records to cover their tracks./3

Both the health insurers and the contracted medical group increased their profit margins through "improper denials and delays of enrollees' [medically necessary] care" /4 and aggressive lemon dropping.

Value-based payment models also may limit needed care in ways that exacerbate health disparities. For example, a 2019 study found that a widely used population health management algorithm--purportedly designed to predict risk and allocate additional medical resources where needed--failed to identify black patients with complex medical needs.

At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts./5

The authors of the study charitably suggest that the algorithm chose future healthcare cost as a "convenient, seemingly effective prox[y]" for healthcare need./6

However, as the study reviewed only risk-based contracts, it seems more likely that the health management algorithm is geared toward cost, at the expense of providing needed care, because of the economic incentive inherent avoid the costs associated with sicker patients in the contracts. In fact, the authors created a blended variable that combined health and cost risk prediction which reduced bias by 84% and are continuing to work with the algorithm's manufacturer to make improvements. Yet, if health risk prediction were the key concern, why use a variable that includes cost at all?

NNU also is concerned that value-based payment arrangements--for example, accountable care organizations--have led to massive consolidation in the healthcare sector through partnerships and mergers supposedly geared toward better care coordination and integration. Providers in small practices have been hit the hardest. They often must choose between joining a massive healthcare corporation or closing their doors because they lack the resources to pay for the complex administrative systems needed to collect and report the data required by value-based payment models. Although reporting these data is intended to guard against harm to patients that may caused by withholding medically necessary care and shifting care to less costly (and less regulated) care settings, they are easily gamed by providers looking to enhance their bottom lines. Yet, there is little evidence of improved outcomes for patients other than these limited quality metrics. Moreover, contrary to the purported rationale for allowing increased consolidation in the healthcare sector, reducing costs, it has resulted in higher national health expenditures.

Finally, any payment model is vulnerable to fraud and abuse. CMS states in the Proposed Rule that, under Medicare's fee-for-service model, "[t]he law's prohibitions were intended to prevent a patient from being referred for services that are not needed or steered to less convenient, lower quality, or more expensive health care providers because the patient's physician can improve his or her financial standing through those referrals."/7

In contrast, as CMS also acknowledges in the Proposed Rule, value-based payment models intended to rectify the problems with the fee-for service model create new avenues for fraud and abuse: "While value-based payment models hold promise for addressing these abuses, they may pose risks of their own, including risks of stinting on care (underutilization), cherry-picking, lemon-dropping, and manipulation or falsification of data used to verify outcomes."/8

The drive to reduce costs through value-based payment models threatens the safety and quality of care not only by creating a financial incentive to inappropriately shift care to lower cost care sites but also, as CMS notes in the Proposed Rule, by the outright withholding of medically necessary care from patients in need--with potentially deadly or debilitating consequences. NNU believes that the risks to patients are far greater under value-based payment models than under a fee-for-service model.

Thus, in closing, NNU again urges CMS not to expand safe harbors in fraud and abuse statutes to include value-based arrangements. If CMS does choose to make safe harbors available to such arrangements, NNU strongly urges CMS to promulgate rules that would place narrow limits on the availability of such safe harbors to "value-based entities" and "value-based activities". Thank you for your attention to this matter.

Sincerely,

Michelle Grisat

National Director of Health Policy

National Nurses United

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Footnotes:

1/ 84 Fed. Reg. at 55,780-81 (Oct. 17, 2019).

2/ "What is Value-Based Healthcare?" The New England Journal of Medicine Catalyst, January 1, 2017 available at https://catalyst.nejm.org/what-is-value-based-healthcare/.

3/ California Department of Managed Health Care. "DMHC Fines 12 Health Plans $1.9 Million for Improperly Denying Care to Enrollees." Dec. 18, 2019 available at http://dmhc.ca.gov/AbouttheDMHC/Newsroom/December18,2019.aspx.

4/ Ibid.

5/ Obermeyer, Z, B. Powers, C. Vogeli, S. Mullainathan. "Dissecting racial bias in an algorithm used to manage the health of populations." Science. 25 October 2019: 447-453.

6/ Ibid.

7/ 84 Fed. Reg. at 55,776 (Oct. 17, 2019).

8/ 84 Fed. Reg. at 55,696 (Oct. 17, 2019).

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The proposed rule can be viewed at: https://beta.regulations.gov/document/CMS-2018-0082-0394

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