ICD-10, or the International Classification of Diseases 10th Revision, is used to collect morbidity and mortality information for populations all over the world. As a late adopter of the classification, the United States has its own specialized version of this clinical coding system, ICD-10-CM/PCS. The U.S. also uses this classification, as do many other countries around the world, for billing purposes.
The way it works is that physicians take down the patients’ stories in the medical records, in a format called unstructured clinical narrative, and then that information is condensed into a few lines of structured information, the ICD codes. This ends up being just the most germane conditions of the patient, and usually only those that will be relevant for reimbursement of services. Very few providers actually submit more ICD codes than necessary when translating the unstructured narrative into structured codes. Providers can actually be penalized if they do apply more ICD codes than necessary as this can affect the reimbursement they receive. This makes the structured data an incomplete, and therefore, inaccurate picture of the patient as a whole.
Traditionally, clinical coders or the practitioners themselves will condense that narrative into the codes. When a clinical coder conducts the process, a manual review of the chart is necessary to pick out the pertinent aspects of the patient story, and translate them into ICD codes. This manual review process means coders are often alternating between many different documents which increases the likelihood of human error.
Even with 68,000 different ICD-10-CM codes demarcating diseases and health conditions, not all are represented, and even fewer are captured completely. The massive expansion in the number of ICD-10-CM codes, compared with just 13,000 in ICD-9-CM, is the result of an attempt to introduce more granularity (more detail) into what can be captured as structured data. Because of the way that ICD-10 is designed to be used, this necessarily means listing all of the permutations of additional context and giving each one a unique code. Even so, there are still aspects of conditions that impact clinical decision making which cannot be represented within ICD-10. These are temporal context (‘history of’ or ‘present’ conditions), severity and acuity, laterality, anatomical representation (‘distal’, ‘anterior’), frequency (‘nightly’, ‘every six hours’, ‘once or twice a week’), just to name a few. An example of a non-specific, ICD-10 code that is meant to capture any of the myriad of digestive system diseases and conditions is “Personal history of other diseases of the digestive system,” Z87.19. While, SNOMED has over 300 variations of digestive conditions that can then be specified by severity, history, laterality and frequency.
De-code patient stories
Adding more codes to capture more detail seems like a good idea but the inevitable explosion of the number of possible codes to choose from makes the whole scheme harder to use. There are just too many possibilities within the unstructured clinical narrative to be accounted for and to translate into structured data. Or are there?
SNOMED CT is a standardized nomenclature of medicine and clinical terms (hence the name), which can be thought of as the language of medicine that is used within healthcare to structure clinical narrative. It is the most comprehensive and precise clinical terminology available in the world today. And while, even SNOMED cannot capture every single possibility, with nearly 1.5 million different relational combinations, it comes a lot closer than ICD-10-CM. There are 349,473 core concepts, that’s over five times as many ICD-10-CM codes for conditions, but much more important than the total number of codes is the fact that the concepts can be combined together to create structured information for ideas that are not actually listed in the terminology. SNOMED CT can actually represent billions of possible ideas precisely because it doesn’t have to list all the possible permutations like ICD-10-CM does.
Put it into context
Combining these concepts to create contextual meaning, is called post-coordination. By using post-coordinated SNOMED CT concepts, very specific and very complete depictions of patients’ stories can be created as structured data. Learning to use SNOMED CT in a fully post-coordinated way is undoubtedly harder than simply searching for individual concepts, which is why few health-care system providers have attempted to implement it. The good news, however, is that Clinithink have removed the barrier to effective, richly-detailed structured data generation by developing a powerful CNLP (Clinical Natural Language Processing) engine that takes unstructured clinical narrative and turns it into structured data utilizing post-coordinated SNOMED CT concepts.
Today, finding patients that match complex clinical trial eligibility criteria using structured data is rife with complications and inaccuracies. The ICD-10 code just can’t describe the patients’ conditions, diseases and current state well enough to positively identify them to match most of the more complex protocols which are becoming more prevalent.
But what if you could use post-coordinated SNOMED data instead to match patients? CLiX ENRICH for Clinical Trials does just this. By utilizing the unique CNLP engine, combined with an easy-to-use, built-in querying module, CLiX ENRICH makes it possible to define the specific criteria required for a clinical trial (or any other type of audit or analysis) and then search through the automatically-generated structured data from millions of clinical records in just a few hours and find precisely the right individuals for any given scenario.
About the Author
Dr Richard Gain, MD joined Clinithink in August 2011. He manages the team of clinical terminologists who work to further develop and maintain the products as well as providing operational and training capability. He brings a wealth of experience of clinical terminologies and coding schemes as well as the practical deployment of clinical information technology. Dr. Gain is also an active member of the SNOMED CT UK Edition committee.