06 Apr PODCAST: Improving Readmissions and CMS Star Ratings with IMAT’s NLP Solution

In the following IMAT Solutions podcast interview, Dr. Matt Hoffman, former Chief Medical Informatics Officer at the Utah Health Information Network (UHIN), and Strategic Consultant to IMAT Solutions, discusses dynamic use cases around how the IMAT platform improves readmissions and Star Ratings.

The IMAT Solutions platform offers the ability for healthcare organizations to leverage quality data from both structured and unstructured sources to provide true insights into all patient and population health risks.

In addition, through the use of Natural Language Processing (NLP), IMAT can help improve overall Star Ratings. In this podcast, Dr. Hoffman provides a deep-dive perspective on how the IMAT platform led to true business outcomes through decreased hospital readmissions and increased reimbursements for health payers.

Following are key highlights from this interview:

• A high-level overview of the use case around structured and unstructured data. (:40)
• About IMAT’s NLP capabilities, and how IMAT helps to meet CMS Star Quality measures. (2:25)
• Outcomes around Star Ratings, reimbursement, and Risk Adjustment Processing Systems (RAPS) improvements. (5:05)
• About a use case around the improvements in LACE scores through the use of the IMAT platform. (9:27)

Are you a health payer interested in achieving these kinds of business and care outcomes through the use of aggregated clinical data? Check out our new explainer video about our IMAT Flexible Payer Solution. In addition, contact us to learn more about the IMAT Flexible Payer Solution for health payers.