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WHITEPAPER

Quality Rankings and Documentation Accuracy

Finding Objectivity in Subjective Ratings

The Information Age we live in has impacted all areas of life — including healthcare. Thanks to a wealth of readily available information, consumers are now more knowledgeable than ever. They are taking advantage of the information available to them and, as a result, are playing a more active role in their care, serving as key decision-makers in the care and treatment processes.

Learn more about the impact of clinical documentation accuracy on quality metrics with the instant download below!

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AUTHOR
Fran Jurcak, MSN, RN, CCDS, CCDS-O

Chief Clinical Strategist

Fran Jurcak is an accomplished senior executive with over 30 years of success in healthcare practice, education, consulting and technology. As a healthcare consultant, Fran leveraged her clinical and coding knowledge to support process improvement in the mid-revenue cycle, particularly in the clinical documentation integrity space. These process improvements allowed her clients to successfully minimize mid-cycle leakage and accurately report outcomes of care. She is currently the Chief Clinical Strategist at Iodine Software, where she has worked to bring artificial intelligence and machine learning technology to concurrent CDI workflow. Fran is active in ACDIS, serving on several advisory boards, received the 2017 ACDIS award for Professional Achievement, and is the author of the CCDS Study Guide. She is recognized as a national speaker and author for ACDIS and AHIMA.

Who is Iodine?

Iodine Software is a healthcare AI company that has pioneered a new machine learning approach to help healthcare leaders build resilient organizations.

Iodine Software leverages a proprietary form of machine learning - CognitiveML - to power software modules that focus on clinical documentation reliability to better hone in on the root cause of mid-cycle leakage. 

Our technology analyzes the full clinical record for each patient much the way a clinician would -- but at a massive scale -- and determines which records are more likely to have an opportunity for documentation improvement, enabling your team to increase their query effectiveness and achieve a much higher level of documentation integrity.

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