Cognitive Emulation: Insights from Iodine Data Scientists

July 28, 2020 - By Ashley Binford

By Lance Eason, Chief Data Scientist & Jon Matthews, Data Science Manager 

Q: Iodine has pioneered a new machine learning approach called Cognitive Emulation. How would you summarize this approach?

Lance Eason (LE): There are two sides of the picture to consider when thinking about clinical documentation integrity (CDI): 1) what is really happening to the patient, and 2) what has been documented and written about the patient. CDI is all about making those two pictures align, specifically making sure the documentation actually reflects what is clinically going on with the patient. Iodine’s Cognitive Emulation approach helps identify where there are discrepancies between these two sides by looking at the entire clinical picture, not just what has been documented or what aligns with certain rules or thresholds.

Q: Iodine has iterated on its intellectual property over time as new technology becomes available. How have different types of artificial intelligence been utilized, and what has been the experience with each?

Jon Matthews (JM) and LE: Iodine started off with a rules-based approach just using NLP, which calculated the probability conditions were present based on a simple set of rules. We started identifying challenges and limitations – issues with false positives and false negatives, for example – so, we decided we needed a more advanced technique. We explored coupling  Machine Learning and advanced Natural Language Processing (NLP) to holistically review the entire patient record, and found the approach worked well. This allowed us to move past surface-level documentation improvements constrained to specificity and capture the full spectrum of possible discrepancies between documentation and clinical reality. 

Q: What underlying technology does Cognitive Emulation rely on, and how is it different from legacy solutions on the market?

LE: Iodine’s combination of machine learning and NLP allows it to make separate judgments about the clinical state of the patient versus what is documented. This is a more statistical approach and does not rely on specific hard factors to determine if the patient’s symptoms are above or below certain markers. Instead, it calculates a statistical likelihood that a documentation opportunity exists based on clinical evidence. We let the technology find the patterns and connections between the data rather than arbitrarily defining rules ourselves.

Q: What is a marker-based or rules-based approach, and how is Iodine different?

JM: Rules- or marker-based solutions require either the client or the software authors to define what each condition means, so there is not a lot of flexibility. These approaches follow “if, then” logic and make simple yes or no decisions based on whether inputs are above or below certain values. The problem with this strategy is that one could spend forever iterating on those thresholds manually and still get poor results. You lose a lot of nuance in the data because what is apparently useful might actually be missing a large subset of important features in the models. Machine learning algorithms are able to find these features on their own and do so automatically, which takes the guesswork and years of iteration out of the picture.

 Q: How is Iodine’s Cognitive Emulation approach impacting healthcare?

JM: The biggest advantage of Iodine’s Cognitive Emulation approach is that it is able to leverage the experiences and knowledge of physicians, coders, and CDI Specialists (CDIS) across the country into one product. Iodine’s very broad data set allows it to define new and interesting features that are helpful for predictions, which you would not be able to do if you were just coming up with rules on your own. In this way, Iodine helps hospitals work with the resources they have to capture more opportunities for documentation improvement. With Iodine’s vast dataset, experience, and clinical expertise, we are able to build products that drive revenue integrity and labor optimization. So many functions in a hospital require guessing or people to spend time doing unproductive work. The sky’s the limit for what Iodine can do for health systems with our machine learning models. 

LE: There are a lot of patients in the hospital at any given time, and their medical records are each constantly being updated with new data. The job of a CDIS is to survey all of the patients all of the time. With an infinite amount of CDIS you would review every case every day, but this is unrealistic without the help of technology. Iodine does the job of filtering and prioritizing, emulating what the CDIS would have been doing, but constantly and for every case, so that CDI teams are spending their time on the cases that most require human review. This allows them to do more productive work, not just searching through all of the cases to find the ones to isolate for review. If you think of CDI work as searching for needles in haystacks, queries would be the needles and the hay would be all of the cases that do not require a review. Iodine removes most of the hay so the needles can easily be identified.

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