The costs of inaccurate provider data are significant to HIM departments but difficult to quantify, and often hospitals are unaware of their data’s poor quality. Records containing errors such as wrong phone number, missing information, outdated details, and duplicate records are just a few of the accuracy problems plaguing most systems.
While the practical applications of artificial intelligence (AI) are still being discovered, one area of AI — natural language processing (NLP) — is already helping advance the revenue cycle. Discover how the right NLP can support accuracy, efficiency and revenue integrity by powering comprehensive clinical documentation improvement and coding earlier in the process.
With numerous health systems experiencing mergers and acquisitions, interoperability and health information management have presented a significant barrier toward optimal care delivery and improved patient outcomes. The integrity of patient data can be threatened when patient records converged from different electronic health record (EHR) systems can’t be correctly matched and linked.
There are substantial—and growing—costs to inaccurate patient matching. Learn how a groundbreaking solution can improve match rates, thereby improving healthcare data exchange.