IVDs and data requirements in a value-based reimbursement landscape

The value of data to the in vitro diagnostics industry has never been more apparent. At every corner of the healthcare industry, data is being used to help doctors make the best treatment decisions for their patients, to help hospital executives prioritize value-based initiatives, and to find new indications for existing medications. Because in vitro diagnostics are so integral to patient care, particularly for oncology, developers must be aware of how quality and efficacy data pertaining to their device will be used.  

In general, information about the device will be used for two purposes: to satisfy regulatory requirements and to obtain reimbursement from payers (insurance companies). Sometimes, the evidence generated for one stakeholder is sufficient to satisfy the needs of the other, but that is not always the case. For example, analytical validity and clinical validity are reviewed by FDA and are included in health technology assessments performed by payers. But the FDA doesn’t evaluate the clinical utility of a device, information that payers find essential.

Clinical trials that provide clinical validity data, such as strong associations between the test result and an indication will be valuable for both regulatory and payer assessments. However, if studies stop short of answering whether or not clinical practice changed or don’t assess if there is a meaningful difference in patient outcomes based on having the test (clinical utility), insurers may be hesitant or unwilling to issue positive coverage determination policies or reimburse for the assay.

Consequently, companies need to prepare for evidentiary demands of both in advance. The study design hierarchy can be helpful when planning what type of study to perform. Observational studies, such as case series, cohort studies, and case-control studies, can be less challenging to design, less expensive to run, and may generate results more quickly than a randomized controlled trial (RCT). Yet RCTs are near the top of the study design hierarchy and the quality of evidence generated by a well-designed RCT would be considered superior to that of a case series or a case-control study.

However, it isn’t feasible or necessary for every IVD to be assessed in an RCT. Cohort studies, prospective or retrospective, can be suitable alternatives. The type of study performed depends on the requirements of the regulatory agency and how the test will be used. The resources and time allotted by the manufacturer and the manufacturer’s reimbursement strategies will further influence clinical study decisions. For a test with multiple indications for use, a variety of studies and study designs might be appropriate.

In addition to the performance characteristics of the assay, outcomes such as overall mortality or time to recurrence may be investigated. A predictive IVD, such as those designed to yield risk estimates of developing hereditary cancers, could use a combination of different observational studies for its evidence base. A companion diagnostic, developed in parallel with a drug, might have prospective observational studies or registry data in addition to any RCTs that were performed. Working with experts in study design to develop appropriate outcomes is essential, regardless of study type.

Though the importance of generating appropriate evidence for the FDA (or other regulatory bodies) hasn’t changed substantially for IVDs, it is becoming more urgent for devices developed as laboratory-developed tests (LDTs). The FDA has historically exercised enforcement discretion over LDTs, but more recently, the agency has indicated a desire to become more involved in LDT oversight. Beyond regulatory requirements, as healthcare shifts from a fee-for-service reimbursement model to a value-based one, satisfying providers’ and payers’ needs for evidence will become even more crucial. Providers will be pressed to use IVDs that are high-value, with a demonstrable impact on patient care (clinical utility) and that are cost-effective.

For example, an IVD to estimate risk of metastasis and that predict the benefit of a particular chemotherapy for breast cancer may be clinically useful, but if the cost to perform the assay is higher than the cost of competitor’s IVD or of alternate methods to obtain the same/similar data, providers may be unwilling to use the IVD and payers are not likely to reimburse for it. Particularly for genomic tests, which are largely considered experimental or unproven by insurers, generating the evidence (analytical and clinical validity, clinical utility) that will be used in health technology assessments is essential.

Whether your company’s IVD is a predictive test, one used for diagnosis or screening, or is designed to monitor a patient’s condition, it is critical to have the right data to satisfy regulatory requirements. As healthcare moves toward value-based reimbursement models and away from fee-for-service schemes, the data generated by clinical studies can further be used to support coverage determination policies and reimbursement levels set by insurers. IVD manufacturers should be aware of this and plan their clinical studies accordingly.

Harry is the author of two related books: Commercializing Novel IVD’s; A Comprehensive Manual for Success and MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.

Photo: Natali_Mis, Getty Images

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