How Can Insurers Cope With Million-Dollar Drug Prices? By Embracing Data & AI
The emergence of ultra-expensive gene therapies and other advanced treatments for often-rare diseases poses a dilemma for insurance companies: Insurers could find themselves paying out large sums for treatments for rare diseases, many of which entail just a single treatment – and then find themselves dropped by customers, losing out on future payments, as well as losing out on the potential future savings that could accrue if these treatments are indeed successful and reduce the need for extended care.
This, along with the high cost of the drugs themselves, is one of the reasons that health Insurers will have to utilize advanced techniques, like data and AI, along with expanded customer care. They will need to understand where their money is being spent, what value they are getting from it, and ways to save while also providing for patients.
The Challenges of Financing Ultra-Expensive Prescription Drugs
Currently, the world’s most costly drug is Hemgenix, which treats severe hemophilia B, with a price of $3.5 million. Hemgenix is just one of a growing number of very advanced and very expensive gene-based therapies, being used to fight cancer, prevent blindness, and beat sickle-cell disease, among other things.
With the increasing availability of these high-priced single-treatment therapies, insurers will no doubt experience increased financial pressure. Insurers need to fund payments for those therapies with proceeds from payments for less-expensive treatments, and possibly raise premiums. They also need to figure out how to best retain customers in order to realize their investments in expensive therapies. Customer “abandonment” could have a major impact on payers; for example, in 2017 3% of all prescriptions paid out by California insurers were for advanced “specialty drugs” for rare diseases and conditions, according to the California Department of Insurance – but cost insurers nearly half their total outlay for that year.
The challenges for insurers will only grow as alternative payment schemes, like value-based drug pricing, are increasingly adopted. Value-based contracts price drugs based on patient outcomes and entail agreements like pharmaceutical makers reimbursing insurance companies for drugs that don’t deliver. This removes some risk from insurers, but insurance companies still must figure out how to navigate such schemes, keeping track of what money is associated with which patients and treatments, and how to proceed when people leave for other insurers, whether by choice because they have changed jobs.
Change is Critical to Advance Health and Medical Innovation
If left unaddressed, these challenges pose a threat to medical innovation. If care organizations or corporations do drop coverage en masse because of increased or high premiums, insurance companies may simply refuse to cover these innovative therapies altogether, leaving patients without the life-saving care they need.
Or, pharma firms could decide not to develop or market therapies if they can’t get insurers to pay for them. For example, Bluebird Bio decided to withdraw from the European market altogether after failing to come to terms with payers, who refused to reimburse the company for its high cost therapy for cerebral adrenoleukodystrophy. This was actually the second therapy Bluebird pulled out of the EU, after withdrawing Zybtenglo, a treatment for severe beta thalassemia, from the German market after insurers balked at paying the $1.8 million per treatment of its gene therapy.
Either way – whether the objective is to pay for expensive therapies or remain in markets – insurers are going to have to develop solid plans to avoid this scenario.
Reducing Overall Costs With AI
Primary among the strategies is for payers to ensure that organizations reduce their overall health costs – providing discounts for lower engagement with care providers. Companies such as Pitney Bowes and Marriott significantly reduced their outlays for health insurance premiums by promoting wellness programs among employees, reducing overall payments for treatment.
Like Mariott and Pitney Bowes have done, insurers could directly provide incentives to plan members for participating and succeeding in wellness and preventative health programs – and those incentives will cost insurers a lot less than paying for care. AI can help by applying pricing models to data about plan members – including data on current health conditions, lifestyle issues, future risks, treatment, outcomes, and more – insurers can determine exactly where, by offering wellness plans, they have the greatest opportunities to save money on medical costs.
Reducing health costs overall will help ensure that premium prices don't rise when one or two employees require an innovative and expensive treatment for a rare condition. Offering fair prices in addition to the coverage that people need will also lead to higher retention rates, allowing insurers to benefit from money spent on wellness programs or the ultra-expensive innovative drugs that do transform lives.
Data will play a big role in alternative payment plans
Alternative drug contracting options, like value-based pricing, also offer some relief to insurers and patients. But implementing these schemes requires real-time patient data, which is often siloed and not available to insurance or pharmaceutical companies in order to use for value-based contracting, especially in those cases when the insurer should get reimbursed when a treatment did not work, or will pay in installments according to patient progress. Privacy laws and shared electronic health record systems need to be updated to allow this. AI needs to be embraced to be able to quickly and accurately analyze data in order to determine how well a drug has worked.
In addition, government or industry regulation is needed to establish a framework that allows insurance companies that have paid for drugs under value-based contracts to still reap their reimbursements from pharma companies even after a patient leaves for another insurer. Or, perhaps such a data-based infrastructure could include a system where insurance companies pay each other back when patients switch coverages. However it works exactly, in order to be successful, insurers must leverage a modern technology infrastructure that is designed to support the complexities in configuration and administration of these risk-sharing arrangements across all stakeholders.
Clearly, the full story here hasn't been written yet; many insurers are still developing strategies on how to cope with more ultra-expensive drugs and the coming value-based pricing storm. In any event, those plans are going to entail both cooperation with organizations that pay the premiums, and use of advanced technology to reduce payment outlays where and when possible, and track expenditures, reimbursement and patient data. Tech solutions are out there; insurers need to start implementing them more fully.
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