HIM professionals have worked diligently to protect data privacy and security during a time of digital transformation in healthcare. The unfortunate reality? Hackers have worked even harder to access that data. It’s a game of ‘cat and mouse,’ and regrettably, the mice (i.e., hackers) appear to have a competitive edge.
Creating a sustainable revenue cycle occurs with the interaction of a number of important functions, people, and processes, all of which must work in tandem.
Natural language processing (NLP) is delivering on the promise of artificial intelligence in healthcare by providing significant value across clinical and administrative functions. This white paper examines the capabilities of clinically aware NLP and why it’s essential to revenue cycle transformation; five things to look for in an NLP solution; and vital areas where it’s currently showing significant impact.
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 Records containing errors such as wrong phone number, missing information, outdated details, and duplicate records are just a few of the accuracy problems plaguing most systems.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.