Providers begin using AI to improve clinical decision making

Healthcare organizations across the country are beginning to cash in on early efforts in artificial intelligence and data visualization.

First reports on initial efforts to use these advanced technologies show tantalizing potential.

  • At Massachusetts General Hospital researchers have developed an AI algorithm to rapidly diagnose and classify brain hemorrhages from unenhanced computed tomography scans, detecting acute incidents and offering prediction capabilities that eventually could help staff in hospital emergency departments evaluate patients with acute stroke symptoms.
  • Researchers led by Boston Children’s Hospital are using AI in combination with two forecasting methods to produce what they call the most accurate estimates of flu activity to date—a week ahead of traditional healthcare-based reports, at the state level across the United States. That could help providers get ahead of resource demands and better respond to current influenza trends.
  • HCA Healthcare, a Nashville, Tenn.‐based chain operating hospitals nationwide, estimates it has saved more than 5,500 lives using sepsis algorithms that monitor every patient in every hospital that’s been part of the health system for more than a year. The algorithmic system is getting impressive results in combating this life-threatening condition that is the ninth leading cause of death in U.S. hospitals.

Indeed, these clinical applications of AI and data visualization are where most healthcare organizations are concentrating early efforts in using this advanced assistive and predictive technology, according to results of a recent poll of Health Data Management readers.

More than half (53 percent) of respondents to the survey say their organizations are using these advanced analytic technologies to improve clinical decision making. That’s far and away the leading use of this information technology, but a range of other applications are being implemented, respondents say.

The study was conducted by Health Data Management and SourceMedia Research, the research arm of HDM’s parent company. A total of 160 responses, primarily from provider organizations, were received in late 2018.

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More than a third of respondents—36 percent—say their organizations are using AI and data visualization to reduce the burdens that electronic health records systems pose to clinicians. Such efforts include initiatives to aid documentation, or to use data compiled in the EHR system to aid in making predictions to support patient care or organize preventive care efforts.

Another 34 percent of respondents are using AI and data visualization to turn the EHR into a reliable risk predictor. That’s exemplified by the work at HCA and many other provider organizations that are looking to be able to divine potential risks and make predictions on patients from the information in the clinical record.

In addition, respondents to the HDM survey say their organizations are using AI and data visualization to monitor health through wearables and personal devices (26 percent); to bring intelligence to medical devices and machines (24 percent); and to bring more precise analytics for pathology images.

Of all respondents, one in five (21 percent) say their organizations are not currently using AI and data visualization.

Despite all the budding efforts in using AI and predictive technologies, most respondents to the HDM survey believe their organizations still have work ahead to become more effective in the use of this IT.

In fact, only 7 percent say their organizations are extremely effective in using AI and data visualization, and only another 19 percent say they are very effective. More than a third—36 percent—say they are only somewhat effective, while a total of 27 percent of respondents say their organizations are either not at all effective or not very effective.

Those results make sense, because most of the work being done to integrate AI and other predictive technologies is happening at large academic medical organizations or hospital chains, which can bring sufficient economies of scale and research resources to investigating uses.

Beyond that, many organizations are still in the beginning phases of studying individual algorithms in specific use cases. Beyond identifying practical AI/data visualization uses, organizations still must determine how best to deploy multiple algorithms widely, having them work with minimal human intervention in the background of patient care.

But nearly half of respondents say figuring out how to deploy a wide range of AI and visualization tools will be crucial for their organizations. A total of 48 percent of respondents say that AI and data visualization will be either very important or extremely important to their organizations, while another 26 percent believe it will be somewhat important.

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