Hospitals Build the Infrastructure that Makes AI Possible

With the right tools in hand, innovative hospitals are forging ahead with artificial intelligence.

In the next few years, the presence of artificial intelligence in healthcare is set to explode. According to Frost & Sullivan, by 2021, the healthcare AI market will jump to $6.7 billion — that’s up from just $633 million in 2014.

“Already playing a critical role in other industries, AI systems are poised to transform how we think about disease diagnosis and treatment,” said Frost & Sullivan Transformational Health Industry Analyst Harpreet Singh Buttar in the press release. “Augmenting the expertise of trained clinicians, AI systems will provide an added layer of decision support capable of helping mitigate oversights or errors in care administration.”

While still in the early stages, innovative providers such as the Cleveland Clinic, MedStar Health and others are setting the stage to reap the financial and workflow benefits of AI in healthcare organizations.

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The Cleveland Clinic Calls on IBM Watson Health

After spending three years setting up the infrastructure necessary to make AI possible, the Cleveland Clinic has combined several solutions to create the database and analytics capabilities necessary to achieve machine learning, Health Data Management reports.

By combining the machine learning infrastructure with IBM’s Watson Health cognitive platform, the organization was able to create a “problem list” for patients that called on de-identified data. These lists were then translated through a variety of methods into actionable data that could be used to identify patients who were at risk for racking up huge hospital bills, or those that might be high-cost cases in the future. Although they are not yet using this data to inform patient interactions, they are working on ways to do so.

“We also recognize that we are not always going to be starting from scratch,” Christopher Donovan, executive director of enterprise information management and analytics at the Cleveland Clinic, tells Health Data Management. “We also think about how we are going to engage with partners in the system.”

The Cleveland Clinic isn’t alone in its move toward using AI and machine learning to inform actionable data. The MedStar Institute for Innovation worked with Booz Allen Hamilton to develop a tool for emergency department staff called Dictation Lens, which “uses natural language processing to sort through unstructured electronic patient data, such as clinicians’ notes, and pull out those that are relevant to a patient’s current medical complaint,” Health Data Management reports.

Tested on a small group of physicians, with the aim to roll it out to a larger group this year, the tool takes between 10 and 20 seconds to look through a patient’s data and present relevant information to attending physicians. Although this seems relatively quick, in an emergency situation it can be too slow, says Kevin Maloy, an emergency department physician and informaticist at the MedStar Institute for Innovation, to the source. To fix this, MedStar is changing the process so that the tool begins combing through patient data when the patient registers at the ER, so the valuable data points are available for doctors when they first see the patient.

New Tools Expand Healthcare AI’s Reach

Prepping infrastructure for AI is no easy task and can seem like a massive undertaking for IT teams, making it impossible for smaller health organizations or those with tighter purse strings. This is where new tools, such as Dell EMC’s new deep learning and high-performance computing solutions designed to bring AI to enterprises, step in to fill the gaps.

“While AI techniques, such as machine learning and deep learning, are rapidly being deployed by many organizations across several industries, only a small number possess the expertise to design, deploy and manage such systems to use them effectively for rapidly gaining new insights,” a press release from Dell EMC states. The new solution aims to help healthcare organizations “harness the power of the massive amounts of their collected data, delivering faster, better and deeper business insights in real time.”

Similar technology from Dell EMC has already been used by Texas Advanced Computing Center at The University of Texas at Austin to explore how machine learning can better identify brain tumors. Moreover, in conjunction with technology from NVIDIA, Simon Fraser University in British Columbia built a supercomputer that studies “the continually changing DNA code in bacteria, with public health agencies worldwide, implementing faster and more effective infectious disease control measures,” according to the release.

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Mar 27 2018