Electronic Health Reporter

Guest post by Todd Greenwood, PhD, MPH, director of digital strategy, and Benjamin Dean, digital and business strategist, Medullan.

Once upon a time, all that pharmaceutical companies had to do to get their drugs on formulary was to package their clinical data and convince payers that their products performed better (or better enough) in clinical trials. Contracts were struck and the revenues flowed. For most new specialty drugs, those days are now history.

With the average retail price of a specialty drug used on a chronic basis exceeding $53,000 (according to an AARP study), nearly 200 times the average price of generics, payers are demanding that pharmaceutical companies make data-driven, value-based cases before access is granted. Even when payers are convinced , they build stipulations into value-based contracts that require manufacturers to prove that outcomes are being met with their covered lives, or else the pharmaceutical company will face additional penalties or further restrictions.

This all means that the data that manufacturers have used to drive regulatory approval are insufficient for garnering payer formulary access. Companies are being required to prove that their drugs work in the real world – not just within the carefully controlled environment of a clinical study. Across therapeutic areas from osteoporosis to oncology, payers have and are currently using real world evidence studies to define their formularies. Payers want to know how expensive specialty drugs will perform as patients adhere to (or in most cases don’t adhere to) their medications, and outside of the rarified air of a traditional clinical trial.

Equally importantly, payers want to know how drugs affect the most important (and most expensive) health outcomes. Clinical data showing that a drug performed some percentage better than either a category leader or a placebo is now insufficient for new specialty drugs. Instead, payers need to know how health outcomes improved and how effective the drugs were at keeping patients out of the hospital and away from the catastrophic costs.

While it may sound easy, providing this kind of data is far from simple. Clinical trial data is controlled, clean and contained. Surveillance data (AKA real-world data) is a different beast, because patients are complicated. We have multiple conditions, take multiple medications, and we are inconsistent, rarely complying with our doctors’ orders. Moreover, the outcomes that payers care about – hospitalization, disability and death – can be difficult to distill. The data needs to be compiled from a variety of sources: medical and prescription drug claims, electronic health records, the lab (genomic and pathology data) and directly from the patient. Compounded with this, different populations of patients have different risks, and comparing one to another is fraught with difficulty. Finally, real world data can take time to accumulate. In order to know if a drug is working “in the wild”, researchers need to follow enough patients, for long enough, to observe negative health events of interest.

Take, for example, the new class of hyperlipidemia drugs, PCSK9-inhibitors. These injection drugs have been shown to cut LDL cholesterol levels in half, compared with about a 20 percent reduction for statin-class drugs like Zetia.  But given the high price of these drugs ($14,000 per year in the US) plus their potential to be prescribed to a significant percentage of the population, payers have largely refused an access foothold. Payer organizations in the US and around the world are asking the same question: how well do these drugs work in real patient populations and to what end? Given that these drugs will be sanctioned for high-risk patients (many of whom will continue to use statin class drugs as combination therapy), payers are concerned about adherence, and ultimately if there is lower cardiac risk and fewer related cardiac events in patient populations. Many economists are asking: can’t we achieve the same ends for far less money by getting patients to adhere more faithfully to their statins?

The need is clear: pharma companies who have invested significantly to develop and launch new specialty drugs have to prove their worth with real world data. But in markets like the US, where providers are typically siloed and disconnected, it’s challenging to capture patient-level condition and drug utilization data, and effectively append it with hospitalizations, other outcomes evidence and costs in order to develop a complete picture.

But there’s hope. As the specialty drug market begins to shift to a value-based model, new ways of tracking real-world usage and connecting it to outcomes are emerging. This is where digital health is poised to play a critical role.

Digital health tools in the hands of patients can be the channel to capture adherence and other real world data. And in doing so, these tools can actually help to modify and improve adherence behavior by leveraging high-impact programs and tactics. With agile integration across cloud-based databases, digital tools are the bridge between patients, providers and payers.

But the benefit of deploying these digital health tools can extend beyond the data that they convey. For example, digital solutions can:

1. Target personalized therapies. Digital health solutions that integrate digital diagnostics can help ensure that specialty drugs are effectively directed at patients that can benefit most. For example, genetic diagnostics are being used to ensure that the Hepatitis C therapy is matched to genetic markers. New targeted therapies in oncology and other chronic conditions will be most effective when they quickly direct the optimal therapies to patients, without wasting time and money having patients cycle through less appropriate treatments.

2. Increase speed to market. Digital platforms are important pre-commercial tools. When offered to clinical and preclinical populations, the cost and time spent on trial recruitment can be reduced and the speed-to-market can be cut, while the data gathered through these tools can improve overall trial insight.

3. Differentiate in a crowd. Patient support programs are an important differentiator in new and existing drugs that do not have strong perceived differentiation. For example, as biosimilars and new oncolytics are being readied for launch, digital health platforms perform the double-duty of delivering utilization data and providing the patient-centric services that create market differentiation, build patient engagement and foster brand loyalty.

4. Drive new and existing revenue. Digital tools can help drive increased revenue when commercialized in the right way. Particularly when supporting the objectives of improving medication adherence or encouraging patients to more quickly seek treatment, digital can indirectly enhance drug revenues. As well, these digital programs can function as their own PnLs when sold as a software-as-a-service (SaaS) offering.

Investment in any digital health platform is significant. Companies need to clarify how solutions will deliver value and address meaningful business goals. Taking the time to align on a digital strategy and a go-to-market plan are critical success factors for any company. In addition, sponsors will want to understand the digital health landscape to assess which resources and tools can be leveraged for buy/build/partner decisions.