Coding of medical records is a requirement for billing, i.e., diagnosis codes must be included on a claim to obtain reimbursement for services from an insurer, whether a private insurance company or the government. The coding system currently used in the United States is ICD-9-CM, but its replacement with ICD-10-CM has been mandated, although the deadline has been postponed three times over the last four years. This coding also potentially creates a vast source of data for research, surveillance, and other purposes. Indeed, there is a whole body of research based on such "claims data," with one of the arguments for its use being that what this data lacks in depth or completeness is made up for by its volume .
ICD-9-CM has many limitations as a coding system. Probably its biggest limitation is that many codes cover a whole swath of diseases. In addition, its "not otherwise specified" may change over time when one of the components of not being otherwise specified becomes specified.
What does this have to do with Ebola? In ICD-9-CM, Ebola is one of many diagnoses covered by the code, 078.89 - Other specified diseases due to viruses. There are about 35 viruses that map to this code, including some common ones such as coronavirus and rotavirus. ICD-10-CM, on the other hand, has a specific code, A98.4 - Ebola virus disease.
Does this provide justification for the move to ICD-10-CM? ICD-10-CM is clearly more detailed and granular, and in fact may be excessively granular. Another concern about implementing ICD-10-CM is the cost to physicians and hospitals, which are mostly unknown although estimates vary widely [2, 3]. There is no question that ICD-9-CM falls short and that ICD-10-CM does have a specific code in the case of Ebola. But is this itself a reason justifying the move to ICD-10-CM, or are there other ways to determine from a medical record whether a diagnosis has been made?
When controversial questions arise, I always find it useful to step back and ask some questions, such as what we are trying to accomplish and whether it is the best way for doing so? There is actually a body of scientific literature that has assessed the consistency and value of coding medical records. One systematic review of United Kingdom coding studies found that coding accuracy in the UK varied widely, with a mean accuracy of 80.3% for diagnoses and 84.2% for procedures . The range of accuracy, however, was from 50.5-97.8%. Another systematic review looked at heart failure diagnoses in Canadian hospitals, finding both ICD-9 and ICD-10 coding to vary widely in sensitivity of actual diagnosis (29-89%) and kappa scores of inter-assigner agreement (0.39-0.84) . A US-based systematic review of identifying heart failure with diagnostic coding data found positive predictive value to be reasonably high (87-100%) but sensitivity to be lower . Studies of diagnosis codes for hypertension  and obesity  found low sensitivity but higher specificity.
While these studies show that coding data is imperfect, there was a time when the predominance of paper medical records meant there was no alternative to data that could be analyzed. However, we are now in an era of widespread EHR adoption, which means that there are other sources of data to document diagnoses, testing, and treatments. In the case of Ebola, we have many other possible sources of data, as described by the Centers for Disease Control.
While there is certainly no evidence that our entire medical record coding enterprise should be immediately abandoned, there is definitely a case to reassess its necessity and value in the modern EHR era. This is especially the case when we are using EHR data for so many other purposes . As with many questions, dispassionate science and analysis is the best approach to providing us with answers.
1. Ferver, K, Burton, B, et al. (2009). The use of claims data in healthcare research. The Open Public Health Journal. 2: 11-24.
2. Hartley, C and Nachimson, S (2014). The Cost of Implementing ICD‐10 for Physician Practices – Updating the 2008 Nachimson Advisors Study. Baltimore, MD, Nachimson Advisors, LLC. http://www.ama-assn.org/resources/doc/washington/icd-10-costs-for-physician-practices-study.pdf.
3. Kravis, TC, Belley, S, et al. (2014). Cost of converting small physician offices to ICD-10 much lower than previously reported. Journal of AHIMA, http://journal.ahima.org/wp-content/uploads/Week-3_PDFpost.FINAL-Estimating-the-Cost-of-Conversion-to-ICD-10_-Nov-12.pdf.
4. Burns, EM, Rigby, E, et al. (2012). Systematic review of discharge coding accuracy. Journal of Public Health. 34: 138-148.
5. Quach, S, Blais, C, et al. (2010). Administrative data have high variation in validity for recording heart failure. Canadian Journal of Cardiology. 26: e306-e312.
6. Saczynski, JS, Andrade, SE, et al. (2012). A systematic review of validated methods for identifying heart failure using administrative data. Pharmacoepidemiology and Drug Safety. 21: 129-140.
7. Tessier-Sherman, B, Galusha, D, et al. (2013). Further validation that claims data are a useful tool for epidemiologic research on hypertension. BMC Public Health. 13: 51. http://www.biomedcentral.com/1471-2458/13/51.
8. Lloyd, JT, Blackwell, SA, et al. (2014). Validity of a claims-based diagnosis of obesity among medicare beneficiaries. Evaluation and the Health Professions. Epub ahead of print.
9. Hersh, WR (2014). Healthcare Data Analytics. In Health Informatics: Practical Guide for Healthcare and Information Technology Professionals, Sixth Edition. R. Hoyt and A. Yoshihashi. Pensacola, FL, Lulu.com: 62-75.