One of the biggest problems facing the pharmaceutical industry right now is overspending on clinical trials.
According to a study published in April 2015 by the Tufts Center for the Study of Drug Development, pharmaceutical development companies spend between $4 and $6 billion each year in unnecessary clinical trial expenses.
The problem of inefficient clinical trials is widespread, too: the report found that 1 in 4 trials (24.7%) are inefficient and wildly expensive, and much of this inefficiency is due to collecting extraneous data (for example: 23% of Phase III clinical trials collect extra, unneeded data, according to the report).
Luckily, there is promising opportunity to fix this problem. Clinical trials can be made more efficient by using digital tools that already exist on the market today. These tools can allow pharmaceutical companies to collect real world data from consumers at a lowered cost and have the potential to bring indirect savings to the entire health system – during clinical trial recruitment, while confirming drug efficacy, while monitoring adverse effects, and while ensuring patient adherence to trial protocols.
But if this is solution is so obvious, why isn’t everyone using digital tools?
Before providing some examples of how nimble digital health strategies can go a long way in refining and optimizing current clinical trial processes (that’s part 2 of this series!), I should address the elephant in the room: adopting new technologies within a set traditional process is not easy. It requires executive buy in, company restructuring, and priority adjustment, which can be complicated given the traditional environment of clinical development. It also requires rethinking processes that may have been in place for years.
When I work with pharmaceutical companies to build data into their systems more effectively, there are a few concerns and misconceptions that I hear often. So before jumping into how to strategically incorporate digital tools to reduce costs and increase efficiency, let’s demystify those misconceptions:
Misconception #1: Digital health solutions are young, and therefore, unreliable.
Often, the executives I speak with think that digital health solutions are new, that the solutions aren’t validated enough just yet, and that these data solutions can’t possibly provide reliable data insights into how to better diagnose, monitor, and manage patients.
In fact, the truth is just the opposite. Right now, clinical grade solutions are trending upwards more than they have before, and these devices can capture data across several therapeutic areas reliably, too.In a study by Frost & Sullivan, it was reported that the global clinical grade wearable market reached $2B at the end of 2015 with expectations that the market will reach $8.3B by the end of 2020.
To me, it seems like the reason for this misconception comes from market overwhelm. There are solutions on the market at this moment that make dramatic claims but are not validated – so it’s easy for unreliable solutions to cover the reliable ones. Truthfully, only a few solutions address the totality of patients’ needs, but these do exist (part of my job is to help people find those vetted and effective solutions). Plus, successful digital health solutions are not advertised to the general public as a point of strategic advantage, so what you’re seeing isn’t always all there is.
The bottom line is that while some digital health solutions aren’t vetted yet, many of the solutions are more mature than most people think. There are plenty of solutions that can gather the data you need.
Misconception #2: Aggregating all the health data points from multiple sources is a cumbersome process.
I think this myth comes from an industry-wide unfamiliarity with “best in breed technology.” Most payers, providers, life-sciences companies, and health systems do not have a wide view into the breadth of the available options in the market, nor do they have the time to assess every single solution. And because most of us don’t see the process of data collection, we assume that it’s cumbersome, that the data must be tough to work with, and that it will be near impossible to read.
Fortunately, when it comes to the successful digital health solutions I mentioned above, this isn’t the case. In fact, there are many companies out there at this very moment that have low lift, high yield solutions available for use by pharmaceutical companies. Some of these companies can stand up a “from data capture to insight” program in less than 6 weeks, which is a hugely smart investment. The data is readable, user experience is strong, and the whole process is remarkably quick, often because these companies are already gathering the data you need. For example, Litmus Health is helping pharmaceutical companies to increase efficiency of phase I and II clinical trials by aggregating patient data through consumer devices. Litmus Health’s platform captures, integrates, and correlates the data with standard metrics and then reports the results.
Rather than investing millions into developing a new product for increased clinical trial agility, it will save money and time for pharmaceutical companies to look at existing suitable options in the market, which are a small fraction of the price.
Misconception #3: Learnings from data are limited and their impact on health outcomes and business is low.
Sure, it’s great to have strong, effective data. But the biggest doubt I often hear around digital solutions is how to make that data actionable. Can these “new” systems really lead to improved patient health and savings across the healthcare system? Thankfully, the answer is yes.
Many existing digital solutions offer value-based and shared savings pricing models for this very reason: to make a statement about success rates as they keep patients stable and out of the hospital. Some digital health solutions even use predictive analytics to predict exacerbations and prevent avoidable hospitalizations. For example, Predixion has been doing a great job in preventing hospital readmissions for COPD patients, with an accuracy rate north of 78%.
Of course, the ability to learn from the data you receive is highly dependent on the selection of the relevant data that’s included in the study. Asking data analysts and clinical teams to help design the study will inform the choice of devices and ultimately the entire data ecosystem, as well as the actionability of that data. So really, this misconception is about processes, not information. With a correctly-designed study and the incorporation of agile digital health solutions, the data gathered will be actionable and will improve our healthcare system as a whole.
The bottom line is that using digital solutions to improve clinical trials is an opportunity area not to be ignored.
Digital health solutions can and should be applied now, as they will allow clinical trials to become cheaper and more effective from research development through commercialization. As an example, PatientsLikeMe helps patients find clinical trials that are right for them and helps pharmaceutical companies find patients who are right for their trial, realizing several fold savings in patient recruiting. Furthermore , according to a report published by the FDA on September 20, 2016, utilizing big data and real-world evidence to assist in regulatory decision-making is a top priority of FDA’s medical device center. Additionally, the FDA approved 36 health apps and digital health devices in 2016, and will continue to in 2017.
Overall, it’s important to remember that while there is a lot of ground yet to be covered by digital solutions, there are also several models and ecosystems that are well established and quite useable already. Even the FDA is stepping up to add better and less bureaucratic processes that will improve digital health regulation and real world data capture. The data collected by these solutions can be used to reduce costs and improve processes drastically within clinical trials, too.
With all this in mind, it’s time to jump in and put together a knowledgeable team that can design the kind of ecosystem you need – one that will lead to improved clinical trials processes, increased profit margins, and reduced costs. This will benefit pharmaceutical business, but will also allow for faster and safer health care access for those in need, too.
Read Part 2 of the series, “What tools should we use?”, which talks through the best current options for digital tools that can integrate into the pharmaceutical industry seamlessly, as well as the value gaps in digital health tool efficacy right now.