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5th February 2019

The AI Revolution in Healthcare: Is poor data management holding you back?

Not long ago, the words “Artificial Intelligence” sounded like something out of a science fiction movie. Today, AI has become mainstream; according to a report from technology consulting firm Infosys titled, “Leadership in the Age of AI.”

“We are no longer on the brink of change resulting from AI – we are already immersed in a world with software-driven machines learning to process unstructured information in meaningful ways; something that until relatively recently was the domain of humans alone.”

AI is present in almost every field, and healthcare is no exception. That said, there may be one large factor preventing the healthcare industry from utilizing AI to its fullest potential: poor data management. The previously mentioned Infosys report found that among 1,000 surveyed healthcare industry business IT and decision makers, 71% “strongly agreed or somewhat agreed” that their future business plans will be informed and transformed by the deployment of AI technology. However, almost 50% said their organizations are still unable to deploy the AI applications they’d like to because their data isn’t yet ready to support it, and 77% said they are investing in data management technology to tackle this problem.

The findings in the Infosys survey – and the general idea that strong data management is the backbone of AI are not surprising, given that machine learning is simply the act of machines looking for patterns in the data. So, if the data is of poor quality – due to poor data management – from the outset, failure is inevitable. The quality of your machine learning and your algorithms is dependent upon the quality of your data. And the quality of your data is, in turn, dependent upon the quality of your data management processes and systems. Like falling dominoes, when one of these pillars fall, so do the rest.

There are several ways in which poor data quality – fueled by poor data management – could be holding your hospital or health system back, preventing you from taking part in the AI revolution:

  • Poor data governance – a data management platform that is intuitive to operate for anyone in the enterprise, regardless of department or level of data analysis expertise, can help to assess the quality of data, integrate your data, and discover a good match for your data.
  • Incomplete, inaccurate or duplicate information – hospitals need data that show a complete, 360-degree view of each patient, with as little duplicate or incomplete information as possible. Duplicate or inaccurate records in this complicated landscape can lead to duplicate diagnostic testing, the wrong medications, and more.
  • Loss of revenue – poor data quality, including duplicate and unmatched patient records, is likely costing you more than you realize. Inefficiencies caused by duplicate records infect operations in numerous departments of a hospital. Understanding the scope of these problems can be an important first step towards taking actions to drastically reduce the incidence of duplicates and mitigate their cost impacts.


How does one begin to tackle these issues? With a quality Enterprise Master Patient Index (EMPI). Utilizing an EMPI can enable your organization to maintain the highest integrity, “golden view” of the patient – leading to high-quality, comprehensive care for patients and cost-savings for hospitals. The best way to ensure that your health system starts with trustworthy data that offers a complete, 360-degree view of the patient – rather than fragmented information from different siloes across the organization – is with a next- generation EMPI. Are you ready to learn more about how an EMPI could benefit your organization?

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