Big data analytics and machine learning could be a major benefit for providers – if they can develop the skills and competencies required to leverage advanced health IT.
Instead of treating health IT as one of the biggest problems facing the medical profession, providers, educators, researchers and developers should embrace the vast potential of machine learning and big data to deliver the high level of decision support necessary to tackle today’s complex diagnostic challenges.
The availability of EHRs, coupled with older, sicker patients and the falling costs of advanced diagnostics like genetic testing, has produced an explosion of data tied to each individual that no single human mind can adequately process.
“Every patient is now a ‘big data’ challenge, with vast amounts of information on past trajectories and current states,” write Ziad Obermeyer, MD, and Thomas H. Lee, MD.
“It’s ironic that just when clinicians feel that there’s no time in their daily routines for thinking, the need for deep thinking is more urgent than ever,” the authors said, but “all this information strains our collective ability to think. So it’s not surprising that we get many of these decisions wrong.”
Many stakeholders have pointed to machine learning as the solution for these big data woes, and health IT vendors are starting to invest heavily in making artificial intelligence a reality for EHR users.
Algorithms that can automatically identify discrepancies in documentation, suggest medications, help read pathology slides or diagnose cancers, and flag patients at risk of developing sepsis are currently in development at organizations all over the country, promising enhanced decision support and smoother workflows.
This article originally appeared in healthitanalytics.com. To read the full article, click here.
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