How AI is Being Used in Healthcare Today – and Hope for the Future

By Optum, a HIMSS19 participant

Every major technological breakthrough has taken time to reach its full potential. You might be reading this on your smartphone while flying across the country, but consider that the first computers took up entire rooms.

Artificial intelligence is going through the same growing pains. AI is an umbrella term that includes using natural language processing, machine learning and deep learning, among other methods, to perform “smart” tasks we often associate with the human mind, such as learning and reasoning. Although healthcare has been slower to adopt these technologies, there is growing evidence that organizations are beginning to invest heavily.

In a recent survey of 500 U.S. healthcare leaders, 94 percent said their organizations continue to invest in and make progress in implementing AI. The inaugural OptumIQ Annual Survey on AI in Health Care also found that executives plan to make an average investment of $32.4 million per organization over the next five years.

Let’s take a look at how different AI technologies can transform healthcare:

Natural-language processing (NLP) helps computers understand and interpret human speech and writing. Electronic health records provide a rich repository of health data, but most of their information is unstructured so it doesn’t fit neatly into a database. NLP can:

  • Interpret EHR information to automate and verify billing coding, so medical coders can work more quickly and accurately.
  • Identify records that lack clear and complete documentation, so clinicians can fill in identified gaps.
  • Flag indicators of undiagnosed conditions, which might be recorded in notes incidental to the primary reason for the visit, to facilitate connections between providers and visits.

Machine learning uses advanced statistical techniques to identify patterns in data and then make predictions. This method:

  • Goes further than NLP in being able to identify early indicators of diseases, taking multiple markers into account.
  • Can identify the right prescription at the right time to reach a desired outcome, by consulting pharmacy data, lab results and other health data.
  • Helps call center agents make actionable, data-driven decisions at an individual patient or member level. Interventions are prioritized according to clinical value and an individual’s propensity to act on a given intervention.

Deep learning is a more advanced branch of machine learning that focuses less on task-specific algorithms to learn using representations of data. This domain of AI technologies operates similarly to how the brain’s neural synapses strengthen with repeated action becoming more efficient in adjusting for errors to modify its approach to logic when faced with new data.

Current applications of AI focus mostly on automating repetitive tasks and many are designed for specific use cases in healthcare. When applied to real problems facing healthcare, AI has the potential to reshape how skilled professionals do their jobs and leverage technology to perform at the top of their license.

Looking toward future applications, leaders from across the industry are optimistic that AI technologies are the most reliable path toward equitable, accessible and affordable healthcare.

Learn more about applying AI technologies in healthcare at Continue the conversation at Optum’s booth #5979 at HIMSS19.

Sponsored content. The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.


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