The $2.8M Medicine: How Data Can Help Insurers Pay for Life-Changing Therapies Without Breaking The Bank

💡 This post was automatically imported from HIT Consultant. You can find the original article here.
Author: HIT Consultant Media (Girisha Fernando, CEO and Founder of Lyfegen)

Girisha Fernando, CEO and Founder of Lyfegen

Advanced medical research has brought us to the point where many serious conditions can potentially be treated and even cured – with people who have essentially faced a lifetime of being incapacitated finally able to conduct normal lives. Many of these treatments are drug-based gene therapies–- but they come with an extremely high price tag. The latest example–and most expensive medication to date– is bluebird bio’s recently-approved $2.8 million gene therapy to treat a rare blood disorder. 

The growing number of promising but expensive treatments present a challenge – to governments, caregivers, insurance companies, and ordinary citizens. Yes, the purpose of medical research is to save lives and improve the quality of life. But at what price? When a drug costs some $2.8 million per treatment, that outlay will be – perhaps needs to be – questioned. Payers, including insurance companies and government agencies, cannot help but look at the bottom line. And neither can citizens; when the outlay rises, prices for insurance, hospital stays, tests, and even tax rates rise as well. These, of course, are dilemmas that healthcare policymakers have been struggling with for many years. And for decades, no definitive answer or policy has been found.

But a solution is emerging: New data analysis capabilities, using advanced AI and machine learning, enable all those involved in treating patients – hospitals, caregivers, insurance companies, and governments – to gain insight about outcomes from specific drugs. Data can show factors like how effective a drug is in reducing total treatment costs over a lifetime or how many people may be able to avoid surgery or other procedures by using a certain drug. This advanced real-world data analysis can help determine appropriate prices for drugs, based not just on the cost of research and development, and the considerations of pharma companies, but on real-world patient outcomes. Equipped with that knowledge, payers, pharma companies, and caregivers can better allocate their resources.

While many industries have come to rely on data, drug-pricing has lagged in such efforts. Outcomes are usually not the primary consideration when it comes to pricing. And even in those small but growing number of cases where outcomes do factor into the price, such decisions are not exactly data-driven. For example, bluebird has already (admirably) said that it would be willing to reimburse insurance companies up to 80% of the cost of its new gene therapy, called Zynteglo, if patients are not able to stop relying on blood transfusions. Transfusions that treat the condition targeted by Zynteglo can cost more than $6 million over a lifetime.

But such promised reimbursements, while a step in the right direction, should be rooted in more exact data and patient outcomes. Now, with new, advanced data collection and analysis capabilities, insurers and caregivers can make rational and informed decisions on whether–and how much– to pay for treatments For payers – government or insurance companies –basing prices on patient outcomes can make their money go further, and also ensure that effective treatments are available to everyone who needs them.

At its most basic level, data can help determine if the use of a certain drug affects the amount of money spent on patient care in the long-run. For example, a child suffering from spinal muscular atrophy, a progressive neurological disease, typically requires extensive, expensive care during his lifetime. Treatment with Zolgensma, a gene therapy treatment which has been shown to be effective in treating SMA, costs $2 million –  a very high price tag. However, if lifetime care for that child exceeds $2 million – a very likely possibility- funding that treatment would make sense for payers, who stand to save money in the long-term. 

The drug Mavenclad, used to treat relapsing forms of multiple sclerosis (MS), is also prominent on the lists of “most expensive drugs” sold in the US, but is another example of an investment that can provide savings over the patient’s lifetime. Patients usually take two courses of the medication, twelve months apart – at a price of some $64,000 per treatment. Long-term studies show that Mavenclad is effective at preventing MS relapses, but it is not covered by many insurance plans, including Medicare. Leaving this out of coverage does not make sense financially, and should change; as other treatments can be significantly more expensive over time, as can the ongoing costs for assistance and care that many MS patients require.

One key to determining the value of these life-changing drugs is not just reliance on clinical data, but on long-term real-world data beyond clinical trials; with real-world data, payers will be able to see the real impact a treatment has in a far more transparent way than they would based just on data from clinical trials – which are often small and select the patients in the best physical shape, or most likely to respond well. In cases where this real-life data, along with clinical data, shows that a drug’s effects do not come close in financial terms to justifying the price tag, insurers and other payers can use this information to request a lower price. In any case, insurance companies always work out agreements with drug companies on how much they will pay for different products; data about patient outcomes should become a bigger part of such negotiations. But things are not always so simple; drugs may not  always directly carry their weight  in reducing overall financial costs. But here, too, data can help. Data can show how the savings from therapies that clearly cut long-term care costs for patients with one type of condition can help pay for other drugs that may not offer as dramatic of results in financial terms on their own, but are still very much worth a high price for ethical or other reasons. 

Luxturna is a good example of a drug that may not have a dramatic reduction in care costs for insurers, but is truly life-changing. Luxturna can cure inherited retinal disease – a specific form of blindness – and costs $850,000 for one-time treatment. As blind people are often able to live independently – and the condition Luxturna cures affects only a small number of patients – it may not offer payers significant financial savings. But by utilizing data collected on real-world outcomes from all treatments they are paying for across the board, and seeing where other drugs help reduce care costs, insurance companies can still cover drugs like Luxturna. Looking at this big picture allows insurers to balance spending to make sure all needs are met, and that all drugs are effectively meeting a need. 

The issue of how payers will handle expensive gene-based and other drug therapies is set to become far more prominent in the coming years. In addition to treating rare diseases, gene therapies are also being developed for more common conditions, including – Parkinson’s Disease, Sickle Cell Disease, and Type 1 Diabetes – which affect millions, and require expensive long-term care, both at home and the hospital. This means that payers will need to embrace data to figure out how to pay, how much to pay, and to understand the value such treatments are delivering to patients, on an individual and overall level. This will allow humanity to truly benefit from cutting-edge science, research and treatments.

About Girisha Fernando

Girisha Fernando is the CEO and Founder of Lyfegen, a software analytics company enabling the shift to value-based healthcare for health insurances & pharma. He is a value-based healthcare thought leader, driving change in the industry to increase health equity and improve access to medical innovations for patients.

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