Managing Specialty Drug Use and Cost

By Roni H. Amiel, Co-Founder & CTO, Pinscriptive

Roni H. Amiel, Co-Founder & CTO, Pinscriptive

Specialty drugs account for just 2 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 fiin it with new, high-priced, specialty drugs that continue to pressure health care budgets 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 right drug, for the exact right patient, all at 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.
 

"Driving value-based, real-world evidence at the core will shift IT investments’ focus from infrastructure to actionable analytics and insights across big data sources"
 

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, bio-pharma, 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; where 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.
 

Driving value-based, real-world evidence at the core will shift IT invest­ments’ focus from infrastructure to ac­tionable analytics and insights across big data sources. The solution to opti­mize value-based specialty Rx decision making requires we combine datasets (e.g., claims, clinical, labs, genomics, etc.) 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 considers 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 disease there are currently 3 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 because they are being influenced by persuasive bio-pharma advertisements to get tested for Hepatitis C if they have ever in their life gotten a blood transfu­sion or used needles for anything (think baby boomers experimenting with drug use back in the ‘60s and ‘70s!).
 

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 $300K and annual maintenance costs of perhaps $40K/year for life after the transplant. Sounds like a no-brainer, 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 health care 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.”