McDermott’s Managing the Transition to Transformation series is designed to help health systems and other health care industry leaders address the many challenges presented by the transformation in payment and care delivery models. The goal of this series is to help organizations prepare so that they are not only competitive, but can also thrive under alternative payment models (APMs) and quality-based reimbursement models (QBRs). This installment of the series examines the way alternative payment models will change incentives and behavior, giving rise to new and different compliance issues and risks.
Alternative payment models will change incentives and behavior, and in doing so, give rise to new and different compliance issues and risks. Counsel and compliance officers should adapt their compliance and risk management structures to reflect the changing risks.
Different Incentives, Different Compliance Challenges
Alternative payment models are designed to change incentives for providers—to reward quality and efficiency, rather than volume. It has always been the case that as incentives change, and behavior changes, so too do the compliance risks. The shift to alternative payment models will be no different.
Cost Reimbursement to Prospective Payments
Medicare’s transition several decades ago from cost-based reimbursement to prospective payments was accompanied by changes in focus for compliance programs. For example, before the advent of the Prospective Payment Systems in the early 1990s, when hospitals, home health agencies and other providers were reimbursed on a “reasonable cost” basis, compliance issues centered on whether unallowable costs were being incurred and submitted on cost reports. Criminal prosecutions and False Claims Act cases pursued instances in which providers improperly charged unallowable costs: costs such as for entertainment, overhead, related-party costs and a host of other categories that were allegedly violating Medicare requirements.
As cost-based reimbursement was phased out, the incentive to incur and charge the government for excessive costs evaporated. In its place, different compliance issues came to the forefront. These included whether services are reasonable and necessary under Medicare standards, the “misclassification” of services such as through Diagnostic Related Group reporting and the influence of inappropriate financial relationships (implicating the federal anti-kickback statute, the Stark Law and the beneficiary inducement prohibition). The movement to prospective payments in Medicare home health payments (Home Health Resource Group [HHRG] payment system) in 2000 saw a similar shift in the key compliance issues. Indeed, every change in payment methodologies has changed the nature of the compliance risks.
Transition from Volume to Efficiency and Quality
The current transition seeks to pay based on quality and outcomes. Under the currently prevailing model, physicians are paid more for providing more services, and hospitals are paid more for admitting more patients. For physicians, each procedure performed yields an additional Current Procedural Terminology (CPT) code for payment, and more complex services are represented by higher CPT codes. More (and more complex) services result in more payments. As a result, in the current model, government audit activity and compliance efforts are geared to preventing overutilization (e.g., provision of services that are not “reasonable and necessary,”) and “upcoding,” in which providers submit claims for a higher level of service than was documented.
Alternate payment models may reduce or eliminate the incentives that result in overutilization, while simultaneously creating new incentives. In capitated payment models for example, more services may not result in more revenues. Similarly, more intensive services (reflected in higher CPT codes) may also not result in higher revenue. In these types of payment models, the auditing and monitoring of CPT codes may take a backseat to other compliance issues.
What might the new issues be? The data and factors that increase payments and revenue under alternative payment models provide some clues.
Quality and Outcomes Data
Alternative payment arrangements that tie reimbursement to quality outcomes will require submission of extensive data to determine quality—data that will raise compliance concerns if inaccurate. Under the Merit-Based Incentive Payment System (MIPS) that was implemented pursuant to MACRA, physicians must submit a variety of quality data including clinical outcomes, as well as data on participation in clinical improvement activities. As payments increasingly rely on clinical outcomes and related measures, increased scrutiny of the underlying data will assuredly follow, along with expectations to monitor the data’s accuracy. Medicare Advantage provides a window into the future. Since health plan quality star ratings affect payments to Medicare Advantage organizations, CMS has begun to monitor and test that data, and in some instances, has imposed civil monetary penalties and other financial remedies for inaccurate data.
Another factor affecting payment in some alternative payment models is health status, used to “risk adjust” payments in capitation payment methodologies. Capitated payment arrangements give providers a fixed payment amount for a given population, placing the providers at financial risk that the payments will not cover the medical costs. In order for capitation models to function fairly and effectively—and eliminate incentives for “cherry-picking” more healthy individuals—capitated payment amounts are “risk adjusted” in an effort to reflect the expected costs of serving different populations. Thus, capitated payments for less healthy or older patients are higher, to reflect the higher expected costs for that population.
The Medicare Advantage and Medicaid managed care programs pay health insurers on a risk adjusted basis. And the Affordable Care Act has a risk adjustment mechanism to address the fact that insurers selling insurance to individuals may no longer charge higher premiums based on an individual’s health status.
As providers enter into more capitated and other alternative payment arrangements, those arrangements, like payments to health insurers, may also be “risk adjusted” for the same reason providers who serve patient populations with more health care conditions, and higher expected medical expenses, will need higher capitated payments.
Various risk adjustment models are currently in effect, using varying sources of data—and the accuracy of that data will understandably be an important compliance challenge. Many risk adjustment systems use the ICD-9 (or ICD-10) codes that providers report in their claims to compile “risk scores” for particular patients and populations. The capitated payments are then adjusted using those risk scores. As such, physician compliance efforts will need to include diagnosis coding and documentation, which have historically been less important than CPT coding and medical necessity.
Risk adjustment is already a compliance focus in Medicare Advantage. False Claims Act cases and investigations are now pending against health insurers and providers, and one provider-owned Medicare Advantage organization entered into a settlement a few years ago, (United States v. Janke). In Florida, a physician recently pled guilty to a criminal charge for allegedly falsifying diagnoses in order to increase capitated payments he received from a health plan (United States v. Thompson).
Today’s fraud and abuse laws were built for a fee-for-service world, in which volume is the king. In that world, the Anti-Kickback Statute (AKS) and Stark Laws were enacted to guard against payments for referrals of business, and to mitigate risks of overutilization resulting from self-referrals.
Under many of the new payment models, however, providers are at financial risk, and the incentives to induce referrals may be reduced or even be non-existent. Government enforcement efforts will shift focus to ensuring that the information and data impacting payment (e.g., risk adjustment and quality data) are accurate. Compliance programs will need to shift their focus as well—if they do not, the health systems they protect will be put at risk.