Managing Specialty Drug Use & Cost

Mark Lelinski (CEO), PinscriptiveBoasting of a 25-years long career, Roni has held key roles at various companies such as Genesis Intermedia, PTC Therapeutics, Columbia University Medical Center and Blythedale Children's Hospital.

A well-known figure in the global Life Sciences sector, Mark specializes in delivering transformative growth strategies by leveraging experience that spans all major sectors of the Healthcare ecosystem value chain.

Stop the Madness! There's Got to Be a Better Way, Right?
Specialty drugs account for just two percent of all medicines prescribed, yet they are on pace to comprise 50 percent of the drug spend in the next few years-ballooning to $400 billion in the U.S. alone by 2020. Traditional approaches to drug utilization and cost management are simply not working. And biopharmaceutical pipelines are filled with new, high-priced, specialty drugs that continue to pressure healthcare bud-gets around the world. This trend is unsustainable to the healthcare system.

There is currently estimated to be up to $20 billion in annual, solvable specialty drug prescription inefficiencies in the U.S. alone. The hypothesis-by identifying which drugs are most effective for which patients at the best price point we can eliminate wasted cost from the healthcare system; and by applying precision analytics using Real World Evidence (RWE) and a Decision Support System Platform (DSS) we can usher in an era of predictive precision analytics that will drive point of care precision medicine-enabling the exact drug, for the right patient, all at a cost that is affordable for all who must pay for specialty drug innovation.

The U.S. market has seen very little success with the current approach to this problem; utilization management is lacking precise and predictive insights at the decision point, reimbursement or formulary management lacks competitive generic alternatives, and data management lacks integration with real world claims, clinical EMR, pharmacy and other relevant data. A strategic approach to solve this problem starts with aligning the payer, provider and patient incentives as all will bare risk as we move forward, and enable optimized decisions based on efficacy, safety, adherence, utilization and cost for a patient who belongs to a specific therapeutic area disease sub population.

A plan of action for addressing these challenges will need to be multi-prong and start with collaborating across the health ecosystem-establishing a collaborative alignment between payer, provider, biopharma, and specialty pharmacy, all working in service of the patient. Secondly, we must enable evidence backed, value-based, prescription decision making, where we go beyond macro population level clinical trial results and look at value differences between competing medications within specific micro level disease subpopulations to drive the transformation of healthcare from `volume to value'. Finally, we need to support the development of sophisticated predictive, precision analytics platforms that combine real world claims data, clinical EMR data and laboratory data, at a patient level, if we hope to fuel the uptake and
practice of precision medicine. The incorporation of machine learning then goes one step further in generating predictive analytics that ensures patients are getting the most optimal, value-based, treatment for their specific case.

The value proposition when considering such an approach will be reflected with Payers; were they can be positioned to lower specialty drug spend and ultimately disrupt today's `one size fits all' drug pricing model. With such an approach, At-Risk Provider Systems will also have a much greater chance to succeed in taking risk when operating as an ACO managing populations or individual patients within a bundled payment. Physicians will win by making more informed evidence backed and value based Rx decisions that allow them to achieve special incentives, both individually and for their network. Finally, Patients will take the right drug and manage their out-of-pocket cost efficiently and responsibly.

Roni H. Amiel (CTO), Pinscriptive
Driving value-based, real-world evidence at the core will shift IT investment focus from infrastructure to actionable analytics and insights across big data sources. The solution to optimize value-based specialty Rx decision making requires we com-bine datasets (e.g., claims, clinical, labs, and genomics) at a patient level-not an easy task today-but as interoperability issues are addressed and formerly `warring factions' (e.g., payers and providers) find common ground by sharing risk and data, the ability to manage costs while still delivering positive health outcomes becomes achievable in the very near term. By identifying the most predictive evidence `slices' that will drive better value-based specialty drug decisions for a specific patient-we can deliver real-time decision support at the point of care around the key metrics a clinician con-siders in making an Rx decision--namely comparative efficacy, safety, adherence, utilization, cost and value-a `must have' for clinicians hoping to succeed in tomorrow's precision medicine world.

A technology platform that can enable this strategy will integrate real world data from EMRs, longitudinal claims data, bio marker, and patient UM & engagement data in both structured and unstructured format, via a set of proprietary algorithms to deliver unique specialty drug use and cost insights. In the end, we expect to make better and faster Rx decisions, that cost the healthcare ecosystem far less than the status quo, all while "stopping the madness" of the heavy human and monetary capital investment in antiquated utilization management and formulary management approaches, that to date have yielded little discernable impact on today's 15-20 percent annual increases in specialty drug trend (the growth of use and cost).

Let's review an example to illustrate the opportunities: for HCV dis-ease there are currently three main branded competitors in the Hepatitis C space. These new drugs not only manage symptoms, but they actually cure the disease >95 percent of the time. With that said, a 12-week regimen can cost $84,000. Many of the patients are newly diagnosed be-cause they are being influenced by persuasive biopharma advertisements to get tested for Hepatitis C if they have ever in their life gotten a blood transfusion or used needles for anything.

Many of those who have the disease carry no symptoms, but once tested are demanding these high priced drugs to remove the disease from their bodies. On the other end of the spectrum, if you have severe Hepatitis C, you could require a liver transplant that costs upwards of $300,000 and annual maintenance costs of perhaps $40,000/year for life after the trans-plant. Sounds like a nobrainer, to give everyone the new cure-all drugs, right? Not exactly. Most folks don't ever have symptoms and most never will progress to needing a transplant. Hepatitis can be a slow progressing disease that can sometimes take more than 20 years to impact a patient's health outlook. Thus, there's an evidence backed, the value-based decision that needs to be made for each patient, where a decision support system as described above is well positioned to fill this role and current gap.

A decision support technology holds great promise to improve the quality of healthcare and reduce potential and real errors in medication management while at the same time providing cost-effective care. This article is offering a narrow window into the possibilities and opportunities awaiting the next generation of specialty drug management. The time for discussing and dabbling is over. A complex, fragmented, rapidly changing healthcare environment demands a strategic and comprehensive approach to shift once and for all from `volume to value'.