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Webinar: Clinical Validation – How NLP and AI Assist CDI and Coding Professionals

Clinical validation is having a growing impact on CDI programs.  There is the financial risk of potential denials and the operational challenges of staying aligned with HIM teams where coding guidelines don’t necessarily require supporting clinical data.  These factors, along with the knowledge and experience needed by a specialist to accurately recognize diagnoses that have weak supporting evidence, are prompting leaders to look for solutions. 

Natural language processing (NLP) and artificial intelligence (AI) are technologies which are playing a larger role in applications that support CDI professionals.  In traditional use cases looking for missing  definitive diagnosis, NLP and AI are helping to interpret clinical documentation and data, thus discovering the key indicators that correlate to potentially missing CCs or MCCs.  

AI technologies are also being used to prioritize cases for review and, in advanced settings, find the specific cases with the highest likelihood of documentation gaps.  For clinical validation, a different mindset is needed, in which the lack of clinical evidence is the key criteria.  Accurately detecting the absence of key clinical evidence can require a painstaking review of the patient record.  We will discuss how the process of clinical validation can be modeled, how it differs from other use cases in CDI, and how NLP & AI can be applied to support CDI professionals to perform clinical validation.

Learning Objectives:

  1. Align to the challenges of clinical validation, by recognizing how the thought process differs from traditional CDI use cases.
  2. Learn how NLP & AI technologies are being integrated into CDI applications and where they can have benefit.
  3. Understand how the clinical validation process can be modeled and how the model can be implemented using NLP and AI

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