The Shift to Value Demands Real World Data

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

Todd Greenwood, PhD, MPH
Todd Greenwood, PhD, MPH

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.

Benjamin Dean
Benjamin Dean

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.

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The Shift to Patient-Centric Care and the Implications of Value-Based Medicine

Guest post by Brian Irwin, VP of Strategy, SHYFT Analytics.

Brian Irwin
Brian Irwin

The days of life science companies focusing on physicians and medical professionals as their main “customers” are numbered. Larger healthcare market trends, better access to real world data and increasing costs are driving the shift to a new customer – the patient.

As total spending on medicines globally reaches the $1 trillion level annually, payers are, not surprisingly, placing greater emphasis on ensuring they are receiving value for their investments. Payers are bringing closer scrutiny to all parts of the healthcare system they are funding. Patients, too, are spending more in today’s system and in turn are changing their expectations about value and outcomes. As a result, we are seeing a new focus on real world evidence as payers and patients seek proof that the medicines are contributing to improved patient outcomes, reduction in hospital admissions or re-admissions, and more efficient use of resources.

In response, life science companies are seeking new and more effective ways to leverage data and analytics across the clinical-commercial continuum and to adapt their go-to-market strategies to reflect their focus on patient outcomes. Capturing results-oriented data and making it usable is critical for this new model to work and for the patient to receive the most appropriate care.

With this shift to outcomes-based results and real world evidence, many questions arise around the data and analytics. Who defines “value” and what does success look like? Is it long-term value or short-term results? Additionally, how should this information be distributed to and evaluated by the various stakeholders? How do we achieve a level of consistency when data sources are not yet fully interoperable?

As the pharma industry starts to work through the answers to these questions and begins to redefine go-to-market strategies and commercial models, effective utilization of data and analytics will prove to be one of the greatest competitive advantages.

Defining Value in the New Healthcare Era

This focus on patient outcomes has broader implications for the industry with data sources now being aligned with the patient to enable decisions across the enterprise including drug development, market access, and commercial performance.

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