For HIT Consultant, Agate Health AI's founder Kevin Agatstein shares how AI-enabled automation is streamlining the prescription renewal process—reducing administrative burden for clinicians while enhancing efficiency and improving patient outcomes.

Artificial intelligence (AI), particularly natural language processing (NLP), aids in summarizing patient charts. AI's portrayal in the popular press is notable, with articles both about and by AI models. Often depicted as a "black box," AI is described in monolithic terms as "the AI," "the model," or personified as Siri, Alexa, or ChatGPT. While this simplifies marketing, it obscures understanding of AI's functions, suitable solutions for specific tasks, and integration into

In healthcare, "good" patient data—accurate, timely, accessible, and relevant—enables "good" care. However, delivering such data to the point-of-care is challenging due to fragmented medical records across different systems focused more on billing and liability mitigation than care delivery. Patient data is often a mix of structured EMR tables, medical claims, and free-text notes, usually organized chronologically, requiring clinicians to sift through EMRs for insights and ca

For MedHealth Review, Agate's founder Kevin Agatstein shares his view on the AI trends to watch in healthcare with a focus on text analytics.

Patient data needs to be searchable for software to meet the needs of those who review clinical charts. Leveraging clinical natural language understanding (cNLU) to rapidly structure clinical notes and make the entire medical record searchable holds the potential to free up massive labor pools for more valued tasks. Kevin Agatstein and Clinical AI Specialist Dimitri Linde cover how AI can make medical data searchable for HIT Consultant.

Across healthcare, clinical natural language processing continues to play an ever more influential role. Kreimeyer et al.’s “Natural language processing systems for capturing and standardizing unstructured clinical information” identified over 70 different CNLP systems in the literature, spanning multiple clinical domains. Unfortunately, few of these directly address the applicability of CNLP to care management. This lack of CNLP supporting care managers will and should chang