Let’s Start With Star Trek, Look At Baymax, And Finish With Spike Jonze: Part 1

I apologize in advance to my fellow movie geeks. I may have deliberately misled you into believing that this post is about Jean-Luc Picard, Oscar-winner Big Hero 6, or Scarlett Johansson. Sorry. But since you’re already here…

Machine learning, predictive analytics, and artificial intelligence (AI) are super sexy topics in commercial pharma these days. If you work in BioPharma Patient Support or Patient Marketing, agencies and vendors will pitch their AI capabilities nonstop.

Raise your hand if you think you only kind of know what they’re talking about.

Believe me: you know more than you think you do. Technology is only as good as the people using its outputs. Each of these disciplines is rooted in data. And guess what? Your brand’s Patient Support strategy should be, too.

Data: Ask for it upfront.

Predictive analytics leverages existing data sets to model future consumer or user behavior. Let’s say your team is about to launch its first therapy. There is no historical data set. Now what?

Market Research: If you don’t know, you won’t know.

Y’know that patient journey slide that is in every launch deck… how do you know that it is right?

I have been genuinely surprised by the number of times a prospective client (profile: small biotech start-up, prepping for first commercial launch) says, “We haven’t hired our Market Research team, but our Patient Advocacy person is terrific,” when I ask about where they are in their market research.

Patient Advocacy folks can offer fantastic insights, but Patient Advocacy is not the same as Market Research.

Patient Advocacy does not examine customer insights, competitive intelligence, behavioral tendencies, and product positioning; Market Research does. If you do not have this capability in-house, please reach out to me, and I’m happy to refer you to solid teams doing excellent work in this space. No referral fee or cut; just smart people using their superpowers to help manufacturers help patients.

Are there longitudinal patient data sets for your therapeutic area? Or for products that may be suitable analogs for your therapy? Purchased claims data can help your team in developing your forecast and your patient models.

Launch Planning: Measure twice. Use the Metric System.

I’m all for the financial stewardship of an organization’s resources. I appreciate how pre-launch start-ups need to keep their commercial organizations lean until, well, they have a commercial product. 

That said, if you want your launch to be successful, you need data team memberS dedicated to post-launch data capture and analysis at least 12 months before PDUFA. 


You want to track prescriber uptake, payer coverage, channel distribution, and patient behavior right out of the gate. You want to reach patients and health care providers with insights-based messaging

You need to be able to track quickly and, if necessary, change even faster

Data Strategy: Building a puzzle is MUCH easier if you can see the picture on the box first.

Imagine your brand’s  “command center” two weeks post-launch. You want control, not chaos. So plan now for what happens then. Six months before launch, you need:

  • Executive leadership sign-off on the launch key performance indicators (KPIs).
  • Clearly articulated and consistently communicated commercial data strategy. WARNING: I’ve previously shared some thoughts on data and communication. I am a bit of a broken record on this topic.
  • Data sources identified.
  • Data access.
  • Data privilege and access levels as defined by business need, role, and compliance considerations.
  • Commercial, Medical Affairs, Legal and Compliance alignment on which programs require documented HIPAA authorization, patient consent to participate in these programs, acknowledgment of the Telephone Consumer Protection Act (TCPA), or all of the above.


I want to flag that when you’re reporting on Patient Support and Access programs, you need to be very careful about these program’s objectives and how you measure their success.

These services are not promotional; the patient’s HCP has already decided to prescribe.

Describing a program’s Return on Investment (ROI) in terms of additional prescription fills or prescriptions written is problematic. In fact, a listener could walk away with the perception that the manufacturer offers these services as a form of kickback to prescribers and patients. I strongly encourage clients to embrace the phrase Return on Objective. It’s not semantics or a “wink, wink; you know what I’m really saying” code phrase. I like ROO because it reinforces Why Manufacturers Offer Patient Support Services in the first place:  to enable patients to start and stay on therapy.

Data: Don’t just ask; contract.

If someone tells you that getting data from Specialty Pharmacies is easy, they have never done it (inventory data, sure! payer data, no).

Do your data sources provide the data you want as part of their core service offering? If not, you will need to include these data requests in your contract negotiations. And these negotiations will gum up your contracting timelines.

As tempting as it is to think, “We just need to get the drug out the door, we can figure out data after the fact,” your commercial team will struggle if you have not done this pre-launch work:

  • Determine the desired data elements: Agree upon delivery cadence, format, validation, tokenization, and patient de-identification.
  • Decide how these different data sources live in your data environment.

Almost all your distribution partners and service vendors offer dashboards and portals where you can quickly glance at the #s for that service line.

Question: How will you leverage multiple data sources and different data types to create a holistic understanding of your patients? Are you going to work with a data aggregator?

For example, for an oral medication distributed via Specialty Pharmacy, you may have access to:

  • Pharmacy dispense files all patients on therapy
  • Copay support enrollment and redemptions commercially insured patients meeting the manufacturer’s program eligibility
  • Education and adherence programs patients who have enrolled in nurse educator programs or opted into marketing campaigns
  • Patient Assistance Programs enrollment files as well as non-commercial dispensing pharmacy files 
  • Insurance coverage support patients who have enrolled in the manufacturer’s sponsored support programs:
    • Hubs 
    • Electronic Prior Authorization (ePA) provider 
    • Digital patient concierge (side note: I had a tough time finding a source on mobile concierge that was NOT a service provider’s website; anyone can help me out and share a great reference on this topic?)
    • I’m excluding field reimbursement managers (FRMs) from patient data sources, with a caveat.
      • FRMs are field personnel, sometimes “blue badge” manufacturer employees, or sometimes employed by a 3rd party vendor, who educate HCP offices on how to work with local health plans to apply for, and secure coverage on behalf of a patient.
      • The data captured from these FRM-HCP interactions may (or not) have patient-level information. By the way, that patient-level information should be de-identified.
      • As I was drafting this blog post, I realized that I could, and want to, write an entire post on FRMs. Stay tuned.
      • But back to data. If your brand does have an FRM team, your patient data aggregator may not be receiving FRM data. FRM data may be going to a separate data warehouse altogether. It’s tied to the prescriber, not the patient.
      • Here’s my caveat: don’t forget to examine your FRM data when developing patient uptake models. The FRM file and the pharmacy dispense file should have one critical element in common: prescriber names.
      • If they don’t, your brand is suffering from a gap in either strategy or execution. Regardless, this is an opportunity you can address immediately. We can help.

Mapping all those different data fields to understand your brand’s patient journey can be a tedious, often manual exercise. Obtaining patient data is not as straightforward (?!?) if your therapy is administered in a healthcare setting. Things get even trickier if your therapy falls under a buy-and-bill reimbursement model. Don’t even get me started on trying to get data on patient medication use in closed environments like dialysis centers or Kaiser. If your patients are not required to participate in a mandatory Hub, you will have little real-time data. You might be able to purchase claims data, but it will be delayed by as much as 90 days from the patient treatment date. And if your patient receives their therapy in an in-patient hospital setting…

It’s complicated. But you do this not to say you collected data for data’s sake. You do it to identify actionable insights and give your colleagues the tools they need to deliver on your brand’s plan of action (POA).

Patient Engagement Marketing and Patient Support Services can be rewarding career fields. Because the patient is at the heart of this work, it’s natural to see the patient in your mind’s eye and jump to tactics. But as any strategic leader knows, you can’t decide your destination until you know where you already are. Data is your strategic GPS.

I’ve only skimmed this topic, and I’m certainly no expert. I’d like to think I’m an informed user. Hopefully, I’ve teed up next week’s post on Predictive Analytics (Baymax) and Artificial Intelligence (Spike Jonze) in Patient Engagement and Support.