Generative AI Underlines Continuing Value of Healthcare Data Archiving Solutions

Every contract is a promise between the vendor and the client. At its core, a vendor promises a certain service standard in exchange for a prompt payment over the contract period. Payer contracts can be wide-ranging in terms of scope and payments for various visit types, procedures, disease states, patient acuity, and more. Before contract renewal time, payers have crunched data from the claims to determine what they want to pay for the next contract period for various visits and procedures. But how is the payer doing in terms of the current contract? Hospitals are using current and archived data coupled with AI technologies to answer these questions, creating payer scorecards. Exploring claims performance against contract schedules can uncover instances where the payer has fallen short, either in terms of responsiveness to deadlines or paying less than the contracted rate for certain procedures and visit types. These scorecards are useful at contract negotiation time to hold payers to account for current actions.

Hospitals should be using current claims data to explore why claims get denied to determine both what went wrong and how to do better in the future. However, payers aren’t infallible. While many hospitals examine current claims to uncover denial management trends, it also makes sense to explore historic claims. Consider this example. A health system pulls together current data and the last 10 years of historic data from the archive to explore denial management opportunities. Rather than preserve hospital archive data for potential outside audits that could cost the health system money, it is actively seeking additional revenue by investigating past claims practices.

Physicians want to make every second count at the point of care, although authorization burdens and increasing workloads often cut into that valuable time. EHRs are great for accessing current patient information, but system upgrades or migrations only bring over a bare minimum of data, leaving years of patient information in legacy systems or in an archive. The process of EHR data archiving involves validating and normalizing data before placing it in an active archive, where physicians can easily search for current and historical data. While AI can enhance some processes, we focus on ensuring archived data is readily accessible and integrated into normal workflows. One health system runs more than 25,000 daily FHIR transactions through an interoperability platform that integrates archived data into current workflows. This enables physicians to access both current and historical data seamlessly, allowing them to make more informed decisions at the point of care. Historic patient data maintains its utility for understanding the often-subtle beginnings of diseases such as multiple sclerosis, Alzheimer’s, and other forms of dementia. Nascent AI technologies are examining historic data from patients with these diseases to better understand the origins of these maladies. By building a picture of early warning signs for debilitating diseases, clinicians can begin treatments earlier, prolonging lives or the quality of life for currently incurable conditions. But that research relies on examining longitudinal patient data contained in current systems and within an EHR archive.

Physicians want to make every second count at the point of care, although authorization burdens and increasing workloads often cut into that valuable time. EHRs are great for accessing current patient information, but system upgrades or migrations only bring over a bare minimum of data, leaving years of patient information in legacy systems or in an archive. The process of EHR data archiving involves validating and normalizing data before placing it in an active archive, where physicians can easily search for current and historical data. While AI can enhance some processes, we focus on ensuring archived data is readily accessible and integrated into normal workflows. One health system runs more than 25,000 daily FHIR transactions through an interoperability platform that integrates archived data into current workflows. This enables physicians to access both current and historical data seamlessly, allowing them to make more informed decisions at the point of care. Historic patient data maintains its utility for understanding the often-subtle beginnings of diseases such as multiple sclerosis, Alzheimer’s, and other forms of dementia. Nascent AI technologies are examining historic data from patients with these diseases to better understand the origins of these maladies. By building a picture of early warning signs for debilitating diseases, clinicians can begin treatments earlier, prolonging lives or the quality of life for currently incurable conditions. But that research relies on examining longitudinal patient data contained in current systems and within an EHR archive.