In an article published in Chest highlighting the continued value of ICD-10 in the big data era,2 Mark G. Weiner, MD, Assistant Dean of Informatics and professor of clinical sciences and medicine at the Temple University Lewis Katz School of Medicine in Philadelphia, Pennsylvania, points out that “much of the hate, misdirected at the coding itself, is more appropriately directed at the billing requirements tightly linked to coding, and the work of doing the coding.”2 In a way, he may be conceding that modern analytics can offer an improvement to healthcare operations and ultimately patient care. Over the past decade, advancements in natural language processing and machine learning have yielded software that can automate the process of selecting an ICD, procedural, or diagnosis-related group code by analyzing the clinical documentation.3 Autocoding can allow for even more granular data than are currently available in ICD-10. Those data are free of billing bias or user coding errors that might confound the conclusions drawn from them. The improved accuracy and reliability of software-driven solutions to medical coding could improve patient care in much the same way autonomous vehicles may someday improve safety over human drivers.3

Critics of big data argue that they are far from perfect, especially when based on electronic medical records. For example, issues like copying and pasting between clinical notes may result in the perversion of clinical findings and diagnoses that are no longer relevant.2 This type of noise in datasets can result in abnormal, unexpected, and inaccurate findings. However, these are human problems with automatable solutions. Software can be written to spot imperfections and identify issues with electronic health record data that humans would not be capable of identifying. Software algorithms could handle the coding more accurately and with less bias than humans, but it can also make ICD coding obsolete.3 Currently, we use clinical data to generate databases of ICD-10 codes, but machine learning could replace coding altogether by using those underlying data — notes and laboratory results — as the database. This is a far more granular, bias-free approach than having to recode that information into ICD-10-CM codes.

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Maybe we are asking the wrong question. Asking whether ICD-10 remains important in the era of big data is like asking whether a bicycle is still useful in the era of autonomous cars. Sure, there may be some uses for ICD-10, but there is nothing novel about it. The CMS ICD-10 implementation was yet another example of the long-standing tradition in medicine of slowly rolling out old technology as a novel tool for improving health care.

Big data are rapidly evolving, and we are underusing them. Rather than focusing on an outdated process of classification and coding, we should be asking ourselves how to improve healthcare operations in this era of automation and machine learning. We ought to seek to disrupt the status quo. New and innovated strategies aimed at a more efficient and less expensive healthcare system can help power better clinical predictions and improve clinical outcomes. In some ways, ICD-10-CM may be an improvement on an old process, but the revolution that will forever change health care is big data.

References

  1. Kaye AD, Singh V, Boswell MV, Manchikanti L. The tragedy of the implementation of ICD-10-CM as ICD_10: is the cart before the horse or is there a tragic paradox of misinformation and ignorance? Pain Physician. 2015;18(4):E485-E495.
  2. Weiner MG. POINT: Is International Statistical Classification of Diseases and Related Health Problems, 10th Revision diagnosis coding important in the era of big data? Yes [published online February 1, 2018]. Chest. doi:10.1016/j.chest.2018.01.025
  3. Liebovitz DM, Fahrenbach J. COUNTERPOINT: Is the International Statistical Classification of Diseases and Related Health Problems, 10th Revision diagnosis coding important in the era of big data? No [published online February 5, 2018]. Chest. doi:10.1016/j.chest.2018.01.034

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