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.
In a nutshell, ResearchKit makes it easier for researchers to create iOS apps for their own research, focusing on three key things: consent, surveys, and active tasks. ResearchKit provides communication and instruction for the study, in addition to pre-built templates for surveys that can be used to collect Patient Reported Outcomes. Plus, ResearchKit can collect sensor data (objective patient activated outcomes) on fitness, voice, steps, and more, all working seamlessly within Apple’s HealthKit API, too, which many users have on their devices already. This allows researchers to access relevant health and fitness data (passive patient outcomes).
Five months after its launch, I’d say, in no exaggerated terms, that ResearchKit has proven to be game-changing for researchers, leapfrogging patient reported outcome studies into a “mobile first” world. However, the current framework certainly doesn’t cover the full gamut of what is needed to build a patient-centered, engaging, scaleable digital outcomes solution. If you’re planning piloting a solution around ResearchKit, here’s what you need to know:
ResearchKit offers up important benefits for medical researchers, especially when it comes to recruitment capability and the speed at which researchers can acquire insightful data to speed medical progress.
The MyHeart Counts app has been arguably the most successful example of ResearchKit use to date — it’s a great example of the recruitment capabilities provided by ResearchKit. In just 24 hours, the researchers from MyHeart Counts were able to enroll more than 10,000 patients in the study. Then they clocked an unprecedented 41,000 consented participants in less than six months (even before entering UK and Hong Kong markets). As most researchers know, recruitment can be one of the biggest challenges in building a study. But with ResearchKit, scientists are able to grow their number of participants into the thousands very quickly; it would have taken the MyHeart Counts researchers a year and 50 medical centers around the country to get to 10,000 participants.
Additionally, ResearchKit also increases the speed at which researchers are able to find the insights they’re looking for. This is mostly because people use their mobile devices constantly (most Americans clock more than two hours per day), which means that the accumulation of mass amounts of subjective (surveys), objective (sensors/active tasks) and passive (background) data happens quickly. The Asthma Health app is a great example of this, as it combines data from a phone’s GPS with information about a city’s air quality and a patient’s outcomes data, all to help patients adhere to their treatment plans and avoid asthma triggers — study participants told researchers that the app was also helping them better understand and manage their condition. The app is also assisting providers in making personalized asthma-care recommendations.
Guest post by Timothy “Dutch” Dwight, vice president of business development, Medullan, Inc.
Timothy “Dutch” Dwight
Will today’s pioneer ACOs share the same demise as the HMOs of the 80s and 90s? It’s certainly starting to look that way.
Like HMOs, ACOs (Accountable Care Organizations) were created to reign in excessive fee-for-service arrangements and provide an incentive for capitating costs. The premise was that under the umbrella of an ACO, providers and payers would share in the responsibility for quality, cost and coordinated care for a defined population of patients.
If an ACO saved money for the payer without compromising quality, providers — defined as physician practices, hospitals, group practices, physician-hospital alliances and networks -–would share in the savings. And the savings were projected to be significant. Early forecasts from the Congressional Budget Office estimated that the 32 pioneer ACOs could save more than $1.1 billion in the first five years. On the other hand, if the ACO failed to meet capitation limits while providing care, the group shared in the losses.
To offset the risk and encourage membership, the early ACOs were supposed to receive multi-year compensation. However, that financial support disappeared after the first year and most provider groups did not have the business margins to carry them through a long-term investment approach. In addition, the ACO model requires a draw on scant resources from all parties to create another layer of program oversight – further cutting in to margin.
So where does the ACO model stand today? Nineteen of the 32 pioneer ACOs have left the program over the last two years, resulting in considerable wasted taxpayer dollars. As CMS moves towards the Next Generation program, can it succeed?
What will it take to save the ACO?
I believe ACOs can be saved, but significant changes must be enacted.
The fundamental problem with the pioneer ACO is that it manages the care of an unhealthy population without having sufficient oversight of that population. This leads a risk-adverse industry to hold their cash and cling to old processes.
Two years ago, Clayton Christensen rightly pointed out that the provider community must make major process and procedural changes in order for the ACO model to work. “No dent in costs is possible until the structure of healthcare is fundamentally changed.” I couldn’t agree more.
To survive, ACOs need to align with the Patient Centered Medical Home (PCMH) model, which is continuing to thrive and grow. PCMH is designed to align more holistic care management with a consumer incentive to prevent high-spend patients from seeking services from the more costly care centers such as emergency rooms. The payer, or insurance company, rewards the consumer for making smart choices by reducing deductibles and other fees if they use lower cost service centers such as primary care physicians, nurse practitioners, and urgent care centers. PCMH models use a combination of fee-for-service, value based payments to providers and align consumer incentives to reduce the cost of care. Comparatively, the ACOs capitated, “value based” payment model, intends only to lower the cost of care without having the proper procedures, tools and feedback loops in place to account how that care is provided. In other words, a visit to a PCP or ER makes no difference in the ACO model. On their own, ACOs do not have enough process control(s) and sufficient incentives to change patient behavior.
However, in combining the ACOs and PCMH model, the healthcare industry stands a much greater likelihood of meeting its goals — to improve the quality of care while containing or lowering the costs.
What needs to happen?
It starts with patient education – consumers need to be educated about their options and when and how to best use them. The next step is employing financial incentives. In short, money talks and will be key in changing old habits. When there is financial reward for going to one’s PCP or an urgent care center instead of an ER, consumers will make smarter choices. And ACOs will have an easier time capitating costs.