Deficiency of access to health information said to restrict potential of machine learning technology

As machine learning technology sustains to advance at a rapid pace, providers are excited by the potential of this kind of artificial intelligence to predict which sufferers are most at risk for clinical events that need early intervention.

Although, these medical breakthroughs are being hampered by the deficiency of health data necessary to learn the complex patterns needed to positively affect patient care.

That is the consensus of healthcare stakeholders who gathered at Wednesday’s Machine Learning in Healthcare: Industry Applications conference in the Boston to discuss the technology’s promise and challenges.

Research released earlier this month by MarketsandMarkets projected that the healthcare artificial intelligence market is hoped to grow from $667.1 million in the year of 2016 to more than $7.9 billion by the year of 2022, a compound yearly growth rate of 53% over the forecast period. Machine learning technology is accelerating at a rate beyond Moore’s Law, with algorithms and models doubling in capability every 6 months.

Among the potential applications: medical imaging, drug discovery, diagnostics, precision medicine, as well as patient information and risk analysis. In fact, a study presented this week at the American Thoracic Society International Conference in Washington demonstrated that a machine-learning algorithm has the capability to recognize hospitalized patients at risk for severe sepsis and septic shock using information from electronic health records (EHRs).

In accordance to Russ Wilcox, partner of venture capital firm Pillar, machine learning technology is presently benefitting from a “trifecta” of technology trends—big data (a flood of digital information that doubles every 3 years), better hardware (optimized processors and storage) and smarter algorithms.

“90% of the world’s digital information is less than 2 years old, and (that trend) is accelerating even faster,” Wilcox told the Machine Learning conference.

Yet, in healthcare, he lamented the fact that much of the information is trapped in silos, which is stifling machine learning’s promising applications in medicine.

“So several of the other industries are way ahead of us in terms of considering about how to bring automation and digital tools to personalize our access” to data, stated John Brownstein, chief innovation officer at Boston Children’s Hospital, who summed up the issue in healthcare as being a deficiency of data accessibility and quality.

On the flip side, Brownstein pointed out that the large consumer technology companies have access to good quality information that enables automation and machine learning, resulting in a high level of personalization.

In the public sector, AI isn’t on the agenda of the Centers for Medicare and Medicare Services (CMS), even though the agency has increasingly been issuing updated healthcare data to improve transparency in the Medicare program and to provide more timely data for providers, researchers and beneficiaries, claims Niall Brennan, former chief data officer at CMS.

“While we did a ton of data work and re-centered and reengineered CMS as a more data-driven organization, I am afraid AI is so far off its radar screen that if you said AI to somebody at CMS they might consider you were talking about Allen Iverson,” said Brennan.

CMS is the greatest single U.S. health payer, generating enormous amounts of data. Although, as Brennan pointed out, it is primarily claims data, with the agency “missing nearly all of the clinical and genomic data.” Even so, he believes AI and machine learning technology could give CMS the capabilities to utilize data in new and creative ways and to generate actionable insights, he said.

Over the next 5 years, Brennan asserts that one of the key issues related to whether or not artificial intelligence and machine learning technology gain traction with huge public payers is “translating it into something tangible that will resonate with payers and lead them to consider them about realigning financial incentives” to make better the patient outcomes and reduce healthcare costs.

On the provider side, he made the case that the single most important driver for spurring the adoption of AI and machine learning technology is the transition from fee-for-service to value-based care “because it develops incredible incentives for providers to innovate and attempt to provide better care at lower costs.” At the similar time, Brennan cautioned against the hazards of “bolting innovative solutions on to a fee-for-service chassis.”