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.
In a recent focus group of hospital executives, three issues emerged as high priorities for 2018 for hospitals and health systems: achieving interoperability, ensuring the best and most cost-effective health outcomes for patients, and securing data.
There are substantial—and growing—costs to inaccurate patient matching. Learn how a groundbreaking solution can improve match rates, thereby improving healthcare data exchange.
A new study sheds light on the importance of patient-reported data in developing longitudinal records from past events, filling gaps in patient records, and preventing hospital readmissions and other poor outcomes.
The volume of healthcare data being collected today is as vast as the hurdles present in putting it to use, according to an editorial analyzing the pros and cons of integrating provider-collected health data and patient-generated data.