Health care as an industry is undergoing a massive shift driven by high costs, shrinking margins and competitive pressure from the tech giants. As a result, entrepreneurs have raced to solve urgent problems across the health care ecosystem all with the same goal: Improved costs and better health outcomes—industry markers of success.

A growing class of health tech companies are building software with big payoffs for patients, providers and, in some cases, payers. They include the likes of Flatiron Health, CoverMyMeds, Truven Health Analytics, Reify Health, Outsomes4Me and Deep Lens.

Fascinatingly, in resolving workflow problems, these clever startups are unearthing troves of previously hidden data that pharmaceutical companies are eating up in order to solve pain points of their own. It’s a beautiful synergy that creates massive data network effects, reinventing some of the processes in drug discovery and development.

We believe this trend also may hold lessons for startup entrepreneurs losing sleep over how to ride out the COVID-19 economic downturn.

Data gets personal

The rise of personalized medicine has created a need among pharma companies for data of unprecedented depth and granularity. However, pharma is no stranger to data. The industry has been a big consumer of data since the 1950s (witness companies like IMS Health). But the volume and variety of data that’s available today is unlocking opportunities on an unprecedented scale. This is helping them fuel their transitioning business model.

Historically, pharma companies have played a numbers game, churning out scores of drug candidates in hopes that one or two in the pipeline—a Lipitor or a Lyrica, for example—will rise to blockbuster success. Once launched, a blockbuster drug like Lipitor could target millions of potential customers simply by sprinkling the word “cholesterol” across its marketing collateral. However, as technology has matured, new drugs like gene therapy and immunotherapy have emerged. This has resulted in a world where pharma has shifted its focus to high-value orphan drugs and rare diseases. This market nearly doubled to $135 billion from 2010 to 2019 and is expected to grow by a 12 percent CAGR through 2024.

In this new, data-intensive world of personalized medicine, that kind of broad-brush, one-size-fits-many approach doesn’t fly. Instead, pharma companies need more data to identify the right biomarkers for a given disease. If a pharma company is developing a breast cancer drug that’s targeted at people with the BRCA1 gene (a mutation that increases cancer risk), the company must be able to find patients that have that gene. And once the drug is launched, it must be able to sell to those patients. The ability to get the right data makes all this possible.

The rise of personalized medicine is not the only factor pushing pharma companies to reevaluate their traditional go-big-or-go-home business model. Other factors include competitive pressures from generic drugmakers and legislative efforts to rein in drug costs. Collectively, these factors are driving pharma companies to expand their portfolios and get to market quickly with more niche drugs. Again, the ability to get the right data in the form of finding patients quickly makes all this possible.

This model brings fresh challenges, such as how to identify patients for these various drugs, how to reach them early enough to help solve their problems and how to get the drugs themselves to market faster. We think startups can connect the dots leading pharma companies to the mother lode of data that can help address these challenges.

Workflow efficiency leads to better clinical trials

Flatiron is a very recent example of a company that unlocks a treasure trove of data for pharma. It solves a workflow problem facing oncology centers in need of an electronic medical record system that could gather and structure data in an easily searchable format. Combining big data and machine learning, the Flatiron platform captures structured and unstructured oncology data from scattered sources, such as community clinics, medical centers, hospitals, laboratories and more.

In doing so, Flatiron generates a ton of secondary data that happens to offer a rich vein of opportunity for pharma companies like Roche. While this newly available data was able to help Roche improve clinical trial results, it more importantly provided hard real-world outcomes data about the efficacy of Roche’s drugs. Roche acquired the company in 2018 for $2.1 billion as a result.

Breast cancer app helps patients and creates new pipeline for trial candidates

Outcomes4Me is another great example of a company looking at tackling this problem. The company offers an easy-to-use app that helps patients navigate the deluge of information they have to wade through after getting a cancer diagnosis, starting with breast cancer. The app helps them learn about treatment options, understand symptoms, and explore clinical trials. They can ask questions, share their journey and connect with other breast cancer patients across the country.

It is the resulting byproduct data that has massive appeal to pharma companies. With it they can identify patients faster and match them with the right drug and treatment. In this new world, finding patients, enrolling them in trials, and maintaining a relationship with them is critical. Startups that can solve this problem drive massive value, and those that control the data are best poised to win.

Data in the time of COVID-19

Our current economic scenario is causing health tech startups to question how they can navigate the COVID-19 pandemic. Here is yet another example of where data may play a critical role.

Health care as an industry is expected to be resilient given that it is critical in keeping people safe and, as we’ve seen all too clearly, ensuring that our economies can continue to run and remain strong. Even though coronavirus will continue to dominate the headlines, diseases like cancer are not going anywhere anytime soon, which we expect will sustain the drive for data.

That said, health tech companies selling to the enterprise will need to make some tough decisions in the short term. In order to survive, they’ll have to put nonessential activities on hold and focus on protecting their existing book of business. They’ll have to manage cash to extend their runway, and some companies may have to pivot as they gain new insights from the data they have amassed.

On the plus side, the venture capital industry itself has gone through a massive boom in fundraising over the past few years. We expect funding to be available for the companies that are able to weather the storm. However, the bar to receive funding will be significantly higher.

The determining factor in who gets funding and who doesn’t will be the urgency of the problems they target. Companies offering “must-have” products and services rather than “nice-to-have” ones—painkillers vs. vitamins, if you will—will naturally be more attractive to investors and will ultimately survive.

At the heart of these investments will be the way startups leverage data. Investors, like my partners at Sierra Ventures, will not only look at how the companies use data to set their products and services apart, but also at whether the secondary data they unearth has value to contribute to innovation elsewhere.

It’s time to think big, and it’s all in the data.

Source: https://news.crunchbase.com/news/for-health-tech-startups-data-is-their-lifeline-now-more-than-ever/