As healthcare has become digitized over the last fiveish decades, a top challenge for the industry has been figuring out how to make patient data interoperable—i.e., making it possible to share patient information easily between different doctors and computer systems to improve care and reduce unnecessary spending.
The industry has made “huge progress” in the last decade, Steven Lane, chief medical officer at interoperability startup Health Gorilla, told Healthcare Brew. But, he added, there are still “tremendous inefficiencies” when sharing information across the different facets of healthcare, making interoperability a field ripe for startups.
“There are many workflows that are still manual, still using fax,” Lane said. “Anybody who can beat out even a little bit of inefficiency […] has the opportunity to add value and take some of that profit by lowering the costs for the other participants.”
Funding for US-based health data infrastructure and interoperability startups totaled $2.2 billion in 2021, nearly triple the $736 million raised in 2020, according to data from digital health strategy group and venture fund Rock Health. Since then, funding has somewhat cooled, but startups were still able to raise $883 million in 2022 (a nearly 20% increase over 2020) and $265 million in the first quarter of 2023.
Why now?
Though interoperability is a challenge the healthcare industry has been working for decades to solve, now may be a particularly good time for startups to get into the field, experts told Healthcare Brew.
Health systems have access to more data than ever thanks to the rise in consumer health wearables. And recent legislation like the Cures Act—which passed in 2016 and required “all electronically accessible health information” to be exchanged “without special effort on the part of the user” by the end of 2022—is making it easier for companies to access and share this data.
“A lot of the things that have historically gotten in the way of interoperability—specifically the desire of data holders to hold on to their data and silo that and derive value out of keeping it out of other people’s hands—I think, with the information blocking rules, what we’re seeing is increasing understanding that the data is going to be freed up,” Lane said.
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Plus, digital health companies are feeling pressure from health systems to make their point solutions—or programs built to solve a singular issue—interoperable, according to Rock Health.
Rock Health reported that there are more than 100 digital health startups working in the interoperability space, and there’s a lot of variability in what those startups are trying to accomplish.
Some are focused on patient records, like Particle Health, Moxe Health, and Credo Health, which have raised a combined $78.2 million and work to pull a patient’s data together from different electronic health records and put that data together into one comprehensive record, according to Rock Health.
Other startups, like Reveleer, which has raised $145.8 million, aggregates patient records for hospital quality reporting processes.
Still others, like Health Gorilla and Clearsense, which have raised a combined $178.2 million, are creating health intelligence platforms that pull data from various sources and put it all in one place for health systems to access.
The next piece of the interoperability puzzle
The first major challenge in solving interoperability in healthcare was getting access to data and getting it to move between systems. The next piece of the puzzle is “putting that data to use,” Lane said.
“Just because data is available doesn’t mean it’s usable or interoperable,” Adriana Krasnianky, head of research at Rock Health’s advisory arm, said. “There is definitely a place for some of these startups to come in and help make that data more ready for use.”
Artificial intelligence (AI) is going to play a “central role” in this next phase of interoperability, she added.
“[AI] can take a lot of burden off IT systems to structure data in certain ways in order to gain insights,” she said. “The ability for a lot of these models to glean insight from unstructured data is really helpful, right? Because it opens up possibilities without requiring a ton of infrastructure labor.”