Private equity and venture capital raised nearly $7.2 billion globally in digital health in 2017, led by $1.1 billion in funding for artificial intelligence (AI) and data analytics, according to Mercom Capital Group.
Across all categories, digital health saw a 42 percent increase from 2016’s record year of $5.1 billion raised in 622 deals. When accounting for debt and public market financing, total corporate funding for health IT companies climbed from $5.6 billion in 2016 to $8.2 billion last year.
Of the $1.1 billion for AI and data analytics, $419 million went to targeted AI. The other top funded catgeories were:
- mHealth applications: $759 million
- Patient engagement solutions: $708 million
- Telemedicine: $624 million
- Appointment booking: $516 million
- Clinical decision support: $514 million.
Dating back to 2010, mHealth apps have been the top funded category, with a total of $3.5 billion raised.
“However, a buoyant stock market did not translate into companies going public. For the first time in years we did not see any companies issue an IPO in all of 2017,” Mercom CEO Raj Prabhu said in a statement. “M&A activity, on the other hand, has been declining slightly over the last few years. Investors do not want to miss out on the sheer size and potential of this growing market, but the exit path for many companies remains elusive.”
Data analytics companies also topped the list on mergers and acquisitions (M&A) activity, being involved in 21 transactions in 2017, followed by 19 transactions involving practice management solutions firms and 17 involving mHealth.
Excitement over AI’s applications in healthcare reached new levels in 2017, from being touted at major conferences to beginning to see real usage in back office and supply chain roles and success in diagnosing rare diseases.
While physicians think AI has the potential to live up to its hype, there are still plenty of hurdles to overcome before its delivering on that promise. A Dec. 2017 report from the Office of the National Coordinator for Health IT said one of the chief challenges will be reliable data standards across different vendors and IT platforms, along with the need for “rigorous approval procedures” for AI algorithms to gain acceptance in clinical practice.