While healthcare has lagged behind other industries in the deployment of artificial intelligence (AI) and many other advanced technologies, the COVID-19 pandemic is proving to be the mother of invention when it comes to technological innovation. Machine learning -- a key part of AI where computer algorithms automatically improve through experience -- has been called upon to leverage healthcare data to help deal with many of the challenges COVID-19 has presented. Public health systems have turned to machine learning to complement their contact tracing and other efforts to control the disease and track outbreaks. Private healthcare operators have embraced machine learning to remain competitive when faced with a drop in demand for elective surgery or, in many countries, a reluctance or inability to visit hospitals or clinics. The pace of AI and machine learning adoption is also accelerating in hospitals.

"We spent all those years adopting EHRs, and now we're wanting to get the most out of them. Now we have the digital data, so it should be more liquid and in control of patients and put to use in the care process, even if I go to multiple sites for my care." As ONC and CMS prepare to digest the voluminous public comment on their proposed interoperability rules, especially the emphasis on exchange specs such as FHIR and open APIs, he sees the future only getting brighter for these types of advances as data flows more freely. "We're in the interoperability business, and we like having data being more available and more liquid, and systems being more open to getting data out of them," Woodlock said. "A lot of customers are starting to embark on their journey with with FHIR, and they're really bullish on this as well: having a standards-based API way to interact with medical record medical record data," he added.