In an effort it says will help hospitals and health plans to better aggregate data from disparate electronic health records after mergers, Wolters Kluwer has developed new technology that uses machine learning to improve the process mapping lab results and other data to standardized LOINC codes.
WHY IT MATTERS
Mapping lab results to LOINC manually can take weeks or months, and can be error-prone. Wolters Kluwer says its Sentri7 clinical surveillance technology can leverage artificial intelligence to help customers more quickly and accurately complete the process.
“AI creates tremendous business value in healthcare helping to dramatically scale how healthcare organizations map troves of fragmented, non-standardized data,” said Jean-Claude Saghbini, chief technology officer at Wolters Kluwer Health.
“With a record number of M&A deals observed in 2018, the need for data science and mapping expertise to enable timely onboarding and provide a big picture, population level view of health system data in post-M&A organizations has only intensified.”
THE LARGER TREND
A recent study of hospital mergers published on the Health Affairs Blog found that, while health systems often join forces to enable information-sharing through EHR integration, that value proposition is dubious.
“As policy makers and regulators consider how much additional horizontal consolidation in the hospital industry to allow, claims of EHR system integration as a method to deliver benefits should be taken with healthy skepticism,” authors of the study said.
ON THE RECORD
“Healthcare has reached a tipping point when it comes to actionable data, but the language of healthcare remains largely unstandardized, limiting what can be understood across platforms and the people who need to act upon the information,” said Saghbini. “AI is increasing data-mapping capabilities exponentially, which enables the rapid identification of findings that benefit decision-making.”
Healthcare IT News is a HIMSS Media publication.