Unlocking the Human Genome: New Challenges and Opportunities for Health Plans

Human-Genome-Golden-Gene-300x225Molecular Diagnostics (MDx), the next wave of innovation in healthcare technology, is arriving. Exciting, challenging, transformational, hopeful, complex, alarming, terrifying and expensive are all descriptions that are being used to describe the dawn of this new world. In an earlier post, I discussed the need to build a new diagnostic testing vocabulary that can be shared across payer, provider and lab stakeholders in order to improve collaboration and processing in diagnosis and claims.

Following several generations of emerging technology involving digital imaging in the ‘80’s, 90’s, and ‘00’s, Molecular Diagnostics is now arriving with an entirely new view of our genetic blueprint – showing which genes are normal and which may be abnormal. A whole new (and very unfamiliar) jargon comes along for the ride: codon, exon, exome, single nucleotide polymorphism (SNP), whole genome sequencing (WGS) and Next Generation Sequencing (NGS) — just to name a few of the new terms and acronyms.

At least three overlapping ‘mini-waves’ of emerging technology can be distinguished:

  • Single gene probes (e.g. Tay-Sach’s, Huntington’s chorea, many others)
  • Multiple gene probes (e.g. micro-arrays), algorithms, and exome sequencing (e.g. MAAA codes)
  • Whole genome sequencing (WGS), a specific map of every gene

The opportunities for the communities of healthcare providers are not difficult to appreciate; more specific diagnostic and pinpoint therapeutic interventions are arriving every day. Molecular therapeutics, or, quite literally, gene repair, is now on the horizon and may allow clinicians to avoid certain genetically-mediated disorders entirely! But how do payers, clinicians, and laboratories manage all these new tests?

What is the role for Health Plans?

One key role for health plans is to drive toward Value-based Purchasing. In essence, does this new technology pay for itself? The evaluation process needs to be objective, transparent, and balanced; all stakeholder perspectives need to be considered, but ultimately health plans will be the primary drivers toward value. There is a 7-step process for accomplishing this goal.

  1. Endorse the joint coding and nomenclature initiative being pioneered by the AMA/CPT® Panel, Change Healthcare, and others to assign one code (i.e. Z-Code™ Identifier) to each unique test and associate each test (with established clinical utility) with a CPT billing code.
  2. Use a universal test catalog (i.e. Diagnostic Exchange™) for classifying and tracking ALL molecular diagnostic tests from invention to full clinical deployment.
  3. Collaborate in developing a standardized and transparent, technical assessment process for the Diagnostics industry.
  4. Establish uniform Medical Coverage and Payment Policies that build on the Value-Based purchasing principles of Analytical Validity, Clinical Validity, and Clinical Utility.
  5. Endorse automated Decision Support strategies (e.g. evidence-based guidelines that help clinicians select and utilize the correct test).
  6. Support objective Analytical Techniques for industry-wide assessment of molecular diagnostic efficacy (utilization and cost).
  7. Support pricing models that encourage/reward innovation but still promote competition and enhanced value.

There will always be emerging technology. As we begin to move from an era of single gene probes to much more complex multiple gene panels (and exome sequencing), new challenges for classifying and validating testing protocols are already arriving. There may be an interesting analogy in clinical laboratory science to Moore’s Law — the observation in the mid-1960’s that the number of transistors on integrated circuit boards doubles every 18 months. “Moore’s Corollary” may become “The number of genes assayed in ‘Next Gen’ sequencing increases 10-fold every 2 years…”

Finally, whole genome sequencing is not an ‘if’ question, but a ‘when’ question. ‘When’ referring to the practicality of performing the laboratory sequencing and writing this data strand ONCE, and then accessing and/or reading the genetic data on the genome many times.

In summary, the challenge for health plans is to find the balance between ‘nice to know’ and ‘need to know.’ This distinction means that health plan should have an understanding of ‘clinical utility’ — when a test really matters to the clinical care process – and what price is reasonable for this information. Finally, better transparency about coverage benefits and payment policy is essential to assure the appropriate level of clinical care while managing costs – for optimum results.