Smart data: Interview with Tazmin Merali
July 24, 2019
SMART DATA: INTERVIEW WITH TAZMIN MERALI
As co-founder and partner at Drug Intelligence, Tazmin Merali has helped numerous innovative pharma companies break through to commercial success. A pharmacist by trade and graduate of Harvard Business School, Tazmin has worked in every corner of the industry—as a pharmaceutical sales representative, advisor to private payers, health informatics specialist, at CHUM in Montreal and UHN in Toronto, among other positions—and brings a unique and powerful skill set to Drug Intelligence. Her current role has Tazmin leading the charge on the collection and analysis of Canadian health outcomes data. In this discussion with 20Sense, she shares her expert opinions about the role of data for specialty pharmaceuticals and gives us an inside look at Drug Intelligence.
Q: What led you to start your own data company, and what does Drug Intelligence do?
When I was working within the hospital system, we had reports telling us how much drug was purchased and who was using it, but we didn’t know if the drugs were being used for the right patients at the right time. We also didn’t fully understand how clinicians were making treatment decisions. So, we started to collect holistic clinical data on drug use, which allowed us quantify the impact of our initiatives over time. Drug Intelligence was a natural outgrowth of this process: we started the company to capture, analyze and interpret real-world treatment data that can be used to inform clinicians, industry and payers.
Q: What kind of data do you collect and how do you use it?
We collect real-world clinical data, data on treatments used and their outcomes, data on health resource use, and patient-reported data such as the impact of treatment on their everyday activities. We continuously track the treatment of specific diseases – which for some we’ve been doing over a 25-year period – and use scientifically validated methods across all sites and update our data on a regular basis. We don’t just collect the data, we connect it. By linking clinical data to patient-reported data, we gain insights into why patients start or discontinue treatment, or what clinicians who keep them on treatment are doing differently. We may also use our data to demonstrate a gap in current treatment and highlight a need for innovation.
Q: How do you obtain your clinical data?
We get it from physicians who treat patients in the real world. In oncology, for example, a panel of about 100 oncologists across the country, representing two-thirds of tier-one and tier-two cancer centres in Canada, provides us with anonymized patient data across a variety of cancers. We also work with physicians in other specialties to obtain curated data about the conditions they treat.
Q: How does data benefit manufacturers, payers, prescribers, and patients?
Here’s a simple metric: What proportion of patients eligible for a treatment are receiving treatment? The answer gives manufacturers a world of insight into market size, gaps, needs, and opportunities. Payers need data on patients and anticipated uptake over time, so they can allocate their budgets to the right treatments. Prescribers can use the data to adjust treatment decisions. Patients themselves want access to treatments that will make a real difference in their lives, and the data can help them decide if a treatment makes sense for them.
Q: Are pharmaceutical manufacturers using data to its full potential? How could the industry improve its use of data?
Some forward-thinking companies are using local data the way it should be: to inform decisions, monitor performance and now also to support HTA and payer submissions. For this to happen, traditional silos need to break down. The information needs to flow between the people within the organization who capture or buy the data and those who deal with commercialization and access. With drug submissions, you get one kick at the can and need to put your best foot forward. Good data is critical to good submissions.
Ideally, data should lead to an action—think of it as a lever. Let’s say the benefit of a particular specialty treatment depends on testing positive for a biomarker. If the data reveals a low rate of testing, the manufacturer can work with clinicians and laboratories to ensure the testing gets done. That’s the lever.
Q: How have you seen the data needs of the industry change over the years?
We now have treatments for more advanced stages of disease, especially in oncology. Let’s say you’ve launched a third-line colorectal cancer treatment, suitable for those with relapsed disease. You’ll need data on time to relapse, proportion of patients who relapse, and biomarkers that correlate with relapse, among other parameters. Today, we also have opportunities to capture data from multiple distribution channels—from hospitals to infusion clinics, pharmacies to patient support programs.
Q: How has Drug Intelligence evolved to serve the changing industry?
When we started out, we did chart audits for individual companies that needed real-world data. With the proliferation of specialty drugs, we began collecting our own comprehensive data on a range of diseases and making it available to all our clients.
Q: Could health outcomes data be used to support outcomes-based agreements (OBAs)?
Yes, for some treatments. With so many transformative but costly treatments being developed, we really have no choice. The challenge is to decide on what constitutes a good outcome. People also need to trust that the data is reliable and objective. To make OBAs a reality, we need to think creatively and keep our focus on the patient.
Q: Do you have any advice to give manufacturers about harnessing the power of data?
Data can help match the right treatments to the right patients at the right time, resulting in better outcomes. What’s more, the commercial success of a specialty brand depends heavily on data. Become familiar with, and leverage, multiple data sources. Frame the right data to meet payer needs. Base your decisions on robust, reliable and credible data and monitor the impact of your strategies over time. At the same time, be comfortable with less than perfect data, and harness the power of predictive analytics to plot a longer-term strategy.
Q: What is a “typical” workday for you at Drug Intelligence, if there is such a thing?
A typical day involves reviewing analyses of databases to address specific business questions, digging further to uncover relevant insights, and talking to clinicians and clients across the country.Having said that, every disease is different, and I am constantly learning. I have never been bored! That’s what I love about this work.
Pushing the levers
Tazmin is passionate about data-driven decisions—what she calls levers. Here, from Tazmin’s files, are two examples of how data helped pharma companies push the right levers and get results.
A manufacturer with a novel treatment for metastatic prostate cancer gathered data to put the costs of treatment in perspective (for example, cost savings from avoiding or delaying hospitalizations) and used the data in their market access submissions. As a result, the company was able to secure access on the earliest timeline after the treatment entered the market.
In a case involving a drug that raised payers’ concerns about budget impact due to its proposed broad use, the manufacturer used outcomes data and predictive analytics to build an argument to fund the medication for patients at highest risk. Over time, as more people started using the medication, outcomes data confirmed and quantified its benefits to patients, supporting further dialogue about broader access and pricing between the manufacturer and payers.