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John Paul Cook

Why ICD codes matter to data professionals

The International Classifications of Diseases (ICD) is a global standard administered and copyrighted by the World Health Organization (WHO). The 10th revision, ICD-10 is the current revision. Some countries adapt the WHO standard. In the United States, the National Center for Health Statistics (NCHS) has made two modifications to ICD-10 known as ICD-10-CM (Coordination and Maintenance) and ICD-10-PCS (Procedure Coding System). ICD-10 was originally scheduled to replace ICD-9 as the U.S. standard on October 1, 2013, but the implementation date was changed to October 1, 2014.

Systems must be modified to accommodate this new standard. Schemas must be changed, mappings between old and new must take place. Much work is yet to be done, which is why implementation was delayed by a full year.

The granularity and specificity of ICD-10 codes has been ridiculed by the press. It is helpful to understand ICD-10 instead of laugh at it if you want to win ICD conversion business. Code W22.02XA is the code for “walked into a lamppost, initial encounter” and code W22.02XD is the code for “walked into a lamppost, subsequent encounter”. In clinical parlance, an initial encounter is the first time the patient seeks treatment. Followup visits about the same condition are known as subsequent encounters. The term “subsequent encounter” has nothing to do with how many times a patient has walked into a lamppost, which some people have misinterpreted.

The number of codes increased by an order of magnitude when going from ICD-9 to ICD-10. Data professionals should be able to understand and appreciate the intention behind having more specificity to diagnosis codes. If only W22 (striking against a stationary object) was used, the data would be too vague to help with prevention efforts. Just for sake of discussion, let’s consider what if the data showed that most people who walk into something actually walk into lampposts instead of walls (W22.01XA) and furniture (W22.03XA). In this contrived example, it might be in the public interest to see if it would be cost effective to develop a lamppost injury prevention program (just kidding trying to make a point about data mining).

Fine grained data lends itself to analysis. Federal payment programs (Medicaid and Medicare) and private insurance companies are already scrutinizing diagnosis codes and withholding payments in some cases. If a patient without an infection is admitted to a hospital and contracts an infection while being treated in the hospital (this is called a nosocomial infection), payment for treating the infection is likely to be denied. This gives hospitals an incentive to be more diligent in preventing infections.

Having more detail gives hospitals and practitioners an improved ability to indicate the extra complexity of a case and get reimbursed at a higher rate. This could give providers an incentive to convert to ICD-10 perhaps even ahead of the October 1, 2014 deadline. More codes means more data, which can lead to projects to expand and optimize systems to accommodate the impending data explosion.

Published Sunday, August 11, 2013 3:59 PM by John Paul Cook

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Ian Yates said:

Here in Australia we've been using ICD10 for a while without a lot of fuss.

The number of trained clinical coders is, unfortunately, decreasing :(

There's generally a bit of misunderstanding between the ICD10 procedure codes, diagnosis codes and how that rolls up in to a DRG.  Then there's the effect that has on revenue for the case depending on the health fund involved.  It's not too big an area to understand and I'm glad we're not writing software in the US health system where, based on my limited observations, the interactions between patient, surgeon, hospital, fund, etc are significantly more complicated.

August 12, 2013 7:34 PM

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About John Paul Cook

John Paul Cook is a database and Azure specialist in Houston. He previously worked as a Data Platform Solution Architect in Microsoft's Houston office. Prior to joining Microsoft, he was a SQL Server MVP. He is experienced in SQL Server and Oracle database application design, development, and implementation. He has spoken at many conferences including Microsoft TechEd and the SQL PASS Summit. He has worked in oil and gas, financial, manufacturing, and healthcare industries. John is also a Registered Nurse currently studying to be a psychiatric nurse practitioner. Contributing author to SQL Server MVP Deep Dives and SQL Server MVP Deep Dives Volume 2. Connect on LinkedIn

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