- Artificial intelligence and data analytics companies generated lots of interest last year, raising $1.1 billion to lead funding in all other digital health categories, according to Mercom Capital Group. Of that, $419 million went to targeted AI.
- Overall funding for digital health totaled nearly $7.2 billion on 778 deals, up 42% from $5.1 billion and 622 deals in 2016. Worldwide corporate funding for health IT firms was $8.2 billion in 2017 versus $5.6 billion the prior year.
- Despite investor interest and a strong stock market in 2017, no digital health companies went public last year.
Data analytics companies also led in mergers and acquisitions, with 21 deals in 2017, according to the report. Practice management solutions companies had the next largest number of transactions with 19, followed by mHealth apps with 17 and telemedicine with 16.
But M&A activity overall was down, compared with recent 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,” Mercom CEO Raj Prabhu said in a statement.
In addition to data analytics, top funded categories included mHealth apps ($759 million), patient engagement solutions ($708 million), telemedicine ($624 million), appointment booking ($516 million) and clinical decision support ($514 million).
The report confirms what industry watchers have already been noticing: Artificial intelligence in healthcare is hot. Investors are starting to look beyond wearables and biosensors that simply note social determinants of health to tools that will use the data in specific use cases.
With the ubiquity of networked smart devices and increasing consumer comfort with virtual services, providers are seeing the potential to apply AI in patient care and case management. One of the areas being looked at is patient re-admissions.
But for all the excitement about AI, there are hurdles to overcome before its promise can be realized in healthcare, according to an ONC report released earlier this week. Among the challenges is the acceptance of AI applications in clinical practice, difficulty leveraging disparate personal networked devices and AI tools, access to quality training data on AI uses in healthcare and incomplete data streams.
The quality and reliability of the data feeding AI solutions is particularly crucial to AI’s ultimate value in healthcare, and that could be a problem given lack of clear data standards. Progress on interoperability has been slow, but CMS Administrator Seema Verma said in town hall webcast this week with American Hospital Association CEO and President Rick Pollack that the agency is interested in seeing movement on the issue.