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The American Journal of Managed Care September 2014
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New Thinking on Clinical Utility: Hard Lessons for Molecular Diagnostics
John W. Peabody, MD, PhD, DTM&H, FACP; Riti Shimkhada, PhD; Kuo B. Tong, MS; and Matthew B. Zubiller, MBA
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New Thinking on Clinical Utility: Hard Lessons for Molecular Diagnostics

John W. Peabody, MD, PhD, DTM&H, FACP; Riti Shimkhada, PhD; Kuo B. Tong, MS; and Matthew B. Zubiller, MBA
The authors describe 5 basic requirements for planning, implementing, and proving clinical utility for diagnostic tests, drawing on recent reimbursement decisions.
To describe 5 basic requirements for planning, implementing, and proving clinical utility for diagnostic tests, drawing on recent reimbursement decisions.

Study Design Review of recent reimbursement decisions by Palmetto GBA’s MolDx program, and summary of lessons learned.

Qualitative review of publicly available coverage and reimbursement decisions, plus our industry experience.

Lack of clinical utility data is the most commonly cited reason for why companies fail to receive favorable coverage and reimbursement decisions in this rapidly growing industry. We summarize 5 strategies to establish clinical utility and secure coverage with reimbursement: 1) understanding that outcomes are hard to capture, but that clinical behavior change is always proximate to outcomes change, 2) starting clinical utility studies early, 3) learning from successes and failures, 4) determining clinical utility with rigorous science, and 5) understanding that clinical utility studies may need to involve private payers and providers from the start.

Coverage and reimbursement are shifting from relatively low entry barriers to higher, evidence-based barriers that will require test developers to generate evidence of the net clinical benefits before widespread clinical use will occur. Concerted, early investment in rigorously designed clinical utility studies is necessary.

Am J Manag Care. 2014;20(9):750-756
Clinical utility—demonstrating the usefulness of a test for clinical practice—may be the most significant hurdle facing new diagnostic technology companies and their investors. Today, without scientific evidence, even the most promising technologies may not be covered and reimbursed by payers. Five strategies to successfully secure coverage and reimbursement from payers are:
• Understanding that outcomes are hard to capture, but that clinical behavior change is always proximate to outcomes change.

• Starting clinical utility studies early.

• Learning from successes and failures of others.

• Determining clinical utility with rigorous trial design.

• Understanding that clinical utility studies may need to involve private payers and providers from the start.
The once-lucrative diagnostics testing market is at a crossroads, facing greater pressure to show value in an atmosphere of evolving regulatory priorities. Cost and patient value are in the forefront of every payer’s mind as we move deeper into the era of healthcare reform and cost consciousness. Converging efforts—such as the recent McKesson Health Corporation and American Medical Association (AMA) partnership to create a registry of molecular diagnostic tests, and the new gap-filling procedures used by CMS to set reimbursement rates—have increased both the granularity and clarity demands of diagnostic coding, emphasizing the increasing need for precision in quantifying test value and justifying price. With these pressures come insight and a clear aspiration for companies and payers alike: Better, less risky, more principled strategies are necessary for companies to generate evidence of impact and determine clinical utility.

Clinical utility—defined as the usefulness of a test for clinical practice (distinct from clinical validity, which is how well the test can determine the presence, absence, or risk of a specific disease)—is arguably the most significant hurdle facing new technologies and their investors.1 Palmetto GBA, the CMS carrier for California, Nevada, Hawaii, and Pacific Islands (the region referred to as Jurisdiction E, previously called J1), was the first to require that companies complete a technology assessment summarizing all evidence of clinical validity and clinical utility when seeking CMS coverage and reimbursement. Under its MolDx program, created in 2011, not only did it bring to the forefront the importance of clinical utility evidence, but it also recognized the need for uniquely identifying these tests with a nomenclature known as Z-Codes. Issued by McKesson (contracted technology provider for MolDx), Z-Codes offer a transparent way to identify and track unique diagnostic tests. The relevant information about these tests is captured and shareable within McKesson’s Diagnostics Exchange (DEX), an online test registry and work flow solution for test manufacturers to submit information and evidence about their tests. Each test has an electronic dossier (ie, information set) that is curated and controlled by the submitting lab, which can also be shared at the discretion of that lab. Payers, such as Palmetto GBA, are poised to use the DEX to obtain all the information they need to make coverage and reimbursement decisions.

In 2014, McKesson entered into a licensing relationship with the AMA to group and index McKesson Z-Codes with corresponding molecular pathology codes in the AMA’s Current Procedural Terminology (CPT) code set to make it possible to identify which test was performed and to help simplify the reimbursement process. The Z-Codes, in conjunction with their specified CPT codes, now provide greater transparency, enabling payers to track outcomes on specific tests and to eventually analyze the clinical utility of these tests. This information can be centralized and accessed in the DEX.

CMS has also recently (January 2013) decided to implement a gap-filling methodology to determine the clinical lab fee schedule for molecular tests on Medicare claims.In the gap-filling process, laboratories submit cost information, such as the costs of resources required to perform a given test. CMS contractors, such as Palmetto GBA, examine and compare costs from different manufacturers and determine a new pricing structure. The new clarity around utilization and costs enhances assessment of clinical utility, allowing better definition and quantification of test utility and value.

The inability to demonstrate clinical utility is now the most cited reason for diagnostic tests failing to obtain coverage. We examined Palmetto GBA’s MolDx decisions (publicly available) for the period between January 2013 and July 2013 and found that 12 of 34 applications for CMS coverage (about 40%) were denied by MolDx due to lack of clinical utility data.

Clearly, failure to secure clinical utility is one of the greatest business risks that promising diagnostic technology companies face. Failure here forces some companies to shut down when they receive a negative coverage decision, including, for instance, Predictive Biosciences Inc, which received a negative coverage decision based upon lack of clinical utility data for their CertNDx bladder cancer diagnostics. Despite these dire consequences, diagnostic test companies still pour their valuable initial resources into validation studies to show clinical and analytical validity, to gain FDA approval for their test. FDA approval, however, does not include clinical utility review, which is a prerequisite for securing Medicare coverage.

This growing awareness of clinical utility has brought to the forefront the need to understand successful coverage and reimbursement (C&R) strategies. Drawing on the recent reimbursement decisions (January 2013-July 2013) from the MolDx program, and on our experience working with payers and test manufacturers, we examine how companies have managed (and failed) to secure C&R in a timely, cost-conscious manner, and we summarize 5 basic requirements—which we call lessons—for planning, implementing, and proving clinical utility to secure C&R.

Lesson 1. Understand that outcomes are hard to capture, but that clinical behavioral change is almost always proximate to outcomes change. Establishing clinical benefit draws immediate (and important) attention to improving patient outcomes. It is often impractical, however, to follow patients over an adequate period of time to see real changes in health status outcomes—time that typically overwhelms most investment strategies and the capital of small companies. Outcomes are even more of a challenge because so many distinct events must happen between pulling a validated test off the shelf and arriving at better patient outcomes. (There are some exceptions where test results might drive patient behavior change directly, but this is uncommon.)

Two major obstacles may prevent a valid test from demonstrating its clinical utility. One occurs at the patient level: some patients do not get better even when clinical care is done correctly. The other is at the provider level: Providers practice differently, and occasionally poorly. Both confound attempts to understand whether a diagnostic test was useful.3 Because both patients and providers exhibit variation that can impact outcomes, potentially derailing an otherwise clinically valid test, the best defense is to power a study with a large number of patients and follow them over a long period of time.

An even greater risk, however, is a study that is improperly designed and uses the test in a way that may not be useful for a general population (but perhaps would be helpful in subset of patients). Avoiding this pitfall involves identifying the precise population for which the diagnostic test is useful, which, though crucial, may unfortunately be difficult in the early stages of development. One example of a design fiasco is failing to distinguish between a screening test to diagnose those at risk, which is not covered by CMS, and a diagnostic test for at-risk patients or a diagnostic test for disease activity.

Once a population is identified, clinical utility studies need to be designed in such a way that information gained from the test leads to a different treatment, to fewer (other) tests, or even a decision not to treat (eg, distinguishing benign from malignant disease). A welldesigned clinical utility study, therefore, is crafted to demonstrate that the test adds information that changes the clinical treatment course and ultimately the outcomes (diagrammed in the Figure).

In this era of ever-increasing healthcare costs, even though value and cost efficiency are of primary concern, it is worthwhile to point out that cost-effectiveness is not an explicit requirement for FDA approval, which is often obtained prior to applying for Medicare coverage and reimbursement (Code of Federal Regulations, Title 21, parts 314 [pharmaceuticals] and 860 [medical devices]). Nonetheless, commercial (private) payers can be particularly compelled to provide coverage if a test can be shown to reduce costs along with improving clinical outcomes,4 and, accordingly, we anticipate cost-effectiveness data will become an increasingly important element in payer decisions. Thus, diagnostic test entrepreneurs and investors alike may well consider cost-effectiveness to be part of a comprehensive evaluation of utility.

Lesson 2. Start early. While utility requires an explicit look at clinical value, it is clear that many companies wait too long to begin determinations of clinical utility and real-world effectiveness. Test developers, who are expert in molecular and cellular technology and not medical economics, often do not know how to demonstrate utility and do not recognize that without utility there is no payment, thwarting the ultimate realization of bringing a significant new technology into the marketplace; again, examples abound of unfavorable coverage decisions that resulted from companies not determining clinical utility in a timely manner. Examples from Palmetto GBA’s MolDx are summarized in the Table and illustrate this point.

A striking finding is that some companies fail to determine utility and effectiveness by falling into a sequencing paradox: to wit, how can we know if our test is effective if we haven’t first proved it is valid? Yes, validation studies are clearly crucial, reporting on the strength of association between the diagnostic test and a specific disease state— but the belief that it is necessary to determine a test’s clinical validity (efficacy) before determining its clinical utility is not correct. Instead, clinical utility data should and can be gathered early to carefully determine the clinical parameters around which clinical utility will be established.

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