Make Sure Your Healthcare Data is Ready for Generative AI

Payers and providers alike have always faced difficult challenges when it comes to the quality, accuracy, and timeliness of healthcare data. With the accelerated development of powerful new generative artificial intelligence (GenAI) tools, the time has come to finally address some of these challenges head-on.

A recent poll of IQVIA webinar participants found that the biggest obstacle to setting up and maintaining a master data management (MDM) system is the lack of a clear and consistent strategy for data quality and governance (selected by 64% of respondents). With that in mind, here are three important elements of an effective MDM strategy that every organization should consider as they undertake the AI journey.

  1. Minimize data silos

    For many companies, the first impulse is to jump right into the insights and analytics with an expectation of instant results. Before any of that can happen, however, the data itself must be managed correctly. We all want to make business decisions faster and more efficiently, but doing so requires the right foundation.

    The first thing to know about GenAI tools (like ChatGPT for generating text, or DALL-E for images) is that they must be fed the right data before they can deliver the desired outputs. “Garbage in, garbage out” has been the refrain of data management for as long as there has been data to manage, but with these powerful tools designed to generate entirely new outputs, ensuring the correct inputs is absolutely critical.

    The key to making sure you have the optimal data for any given GenAI application is to overcome existing data silos. Common silos include IT-managed provider datasets (EDW, operational data, analytical data stores), business-maintained provider data (care management, utilization management, provider credentialing), core legacy systems (DB2, etc.), and external provider data sources (NPPES or third-party purchased provider data).

    In addition to uniting data spread across silos, a good data management solution should also be able to ensure data quality and accuracy through careful validation. At IQVIA, we call this type of data stewardship “data bridging,” for its ability to bridge traditional silos while creating a single source of truth capable of contributing to meaningful insights and trustworthy analysis.

  2. Generate a 360° view of providers

    The first goal in an MDM process should be to enable payers to quickly match and merge data to produce a “golden” record of their provider data. This golden record can be used to develop a 360° view of providers, showing the full range of health system relationships, associations, data points (address, phone, fax, contacts, etc.), and roles. Access of this depth and breadth of information can unlock new opportunities by streamlining operations, accelerating insights, and enabling a deeper understanding of your customers.

    When physician data is accurate, consistent, and complete, user satisfaction increases. A complete 360° view of providers also aids in maintaining compliance with constantly evolving regulations, and it enables a high degree of accountability with CMS and other regulatory agencies.

  3. Don’t break the bank

    Despite concerns that a MDM solution is too costly (46% of the webinar survey respondents said they “cannot afford the expenses of setting up and maintaining an MDM system”), it is possible to generate a golden source of truth in your healthcare data — and to do it in a cost-effective and timely manner. Where a traditional MDM implementation can take several months, IQVIA can connect disparate data sets in a much shorter timeframe, enabling users to get clean data and accurate insights much more quickly and at lower cost.

    Read more about how IQVIA Healthcare Master Data Management can help payers prepare for a world of generative AI.


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