What’s Next for Life Sciences? Unpacking “Everything as a Service” with Craig Davis
Craig Davis joins Atropos Health as the VP of Life Sciences, charged with growing industry access to Atropos Health’s innovative solutions for Life Science evidence generation. Get to know Craig and hear his take on the future of RWE in Life Sciences, below.
I've been fortunate to spend the last 15 years crossing over into a variety of roles, engaging hundreds of companies, and doing thousands of projects across the globe. At the end of the day, every engagement revolves around, "Will this help me make good decisions quickly?", "Will this help me influence my stakeholders?", "Can you help me answer business questions confidently?"
I can claim "yes" to all the parts above, but it's historically been a very manual, archaic process that involves a lot of consultants (business, medical, technical, and data scientists) to drive confidence and deliver these final product(s). Even with technology attempting to speed-up all groups individually, it may still take weeks to investigate a hypothetical question for a brand team or even months to respond to a major regulatory body like the FDA, EMA, etc.
To that point, I'm dedicating this article "what's next for life sciences?" to showcase what is available today and what I believe will accelerate answering tough questions confidently for all my friends in life sciences.
Can we return confident answers for stakeholders? Have we fully explored and activated our #Data #EHR #APLD assets?
Can we shorten turnaround times to hours and days for decisions vs. weeks and months and be transparent?
Can we enable better "users with experience" vs. expecting the user experience to do everything? #UwX vs. #UX (I'm coining #UwX)
To me, purposeful data activation doesn't just mean putting an LLM on a very expensive dataset. It means:
Getting the most out of data investments before they go stale - That’s why Atropos helps life science teams make the most of their data assets. It’s one thing to buy data for a question. It’s another thing to have a company run thousands of analyses on the data to show you what questions you are missing for clinical trials and brand planning.
Selecting the right dataset for each use case - We have fitness scoring for internal datasets that matches each question to the best dataset to answer it. It’s something unique that we offer and necessary - since we sit on dozens of #RWD #EHR #EMR #APLD datasets. Instead of measuring twice and cutting once, we like to save everyone time and materials until we know it's going to work.
Getting credible answers back in time for people to make decisions - Data science is powerful. If you can couple that with practicing clinicians, validation happens quickly and decisions can be made quickly.
Conversely #HEOR, #Medical, #Brands #DataScience should absolutely be able to explore every inch of your own datasets as it relates to our products and clinical trials. There's some great projects you can do quickly to inform brand strategy for next year as well as emulate existing trials (shameless plug). In a short amount of time you can drive new educational material as well as accelerate growth with outcomes and evidence that are highly beneficial to certain patient subtypes. In a perfect world I want every life science company to do this and educate every clinician.
When it comes to innovation, speed, and acceleration... #COVID19 shifted quite a few things in our industry. If I wrote "what's next" articles over the past few years…
2019 - would have been about how so many small pharma companies are doing amazing things on-site and in-person.
2020 - the pandemic shifting commercial, dominance of telemedicine, and"all-in" on digital.
2021 - might have been "digital channels are clogged" - “social and asynchronous is the future”.
2022 - felt like the "everything-as-a-service" year, while simultaneously feeling like it wasn't the change we were hoping for.
2023 feels distinct. AI shocked the world along with a culmination of "all things needing acceleration" after 3 years of “wait and see.” We’re in an exciting time. Companies like Atropos are well positioned to make the most of innovative, generative technologies to drive that speed and fulfill the promise alluded to in years prior. Here’s what we can do for life sciences that’s truly special.
Rapid analytics - Analytics is generally a slow process as it has numerous people, tools, and systems involved. Everything is project based. We’re changing the game because our technology (TQL) and upskilling clinicians allows it. If I can arm our MDs with our ACE Workbench on a massive EHR dataset. They can confidently answer a question or knock out a project in hours and days vs. weeks and months. This includes calculating cox proportional hazards model, p-values, outcomes, matching, etc.
Clinical expert services - To the point above, I can shorten the time to the answer. If a clinician performs the intake of the project, inputs code into the platform and a second clinician does the final validation, I don’t need as many resources in the equation and my customers feel confident in the answers. Especially as they interact with our clinicians on important project outputs.
Maximizing evidence generation - AI and machine learning have dominated the airwaves recently, but it’s been around for years. When clinicians built out platform (see our “green button” NLM Funding Spotlight | The Green Button), evidence generation was/is the focus. As this has evolved (along with a 10 year head-start), we can now generate thousands of pieces of evidence for a R&D, Medical, Brand team in just a matter of days. Exploring every inch of a customer’s RWD is what really excites me. We go from being reactive to proactive when it comes to evidence generation.
In summary, I recommend we do everything we can to activate our in-house data assets while we wait for the next big thing. We no longer need to write 200 lines of SQL for each question and spend days in meetings to finalize “what cohort is best?”. We should truly look at the fitness of the data for the questions asked and deploy new tools and services that can help us just get to the answers we need and explore our existing data thoroughly. Finding partners and hiring "users with experience" #UwX upskill existing teams' knowledge and shorten the path to delivery.
Hopefully this article was helpful and here's a few acronyms / buzzwords of where our team can help! #LifeSciences #DataScience #HEOR #RWE #RWD #DaaS #EvidenceNetwork #Consulting #BI #AI #LLM #PaaS #SaaS #UX #PICOT #ICD10 #CPT #APLD #RX #DX #PX #EHR #EMR #AWS #AMC #CMC #RCTs #LS #Tech #Healthcare