ICD-10, Diagnostic Errors, and the Payment Reforms to Improve Diagnostic Accuracy

ICD-10 Coding
ICD-10 Coding
In 2015, 1 in 20 adults in the United States experienced a diagnostic error every year; yet by the beginning of 2019, nothing had been done to improve the situation.

In October 2015, physicians across the United States transitioned from the International Statistical Classification of Diseases and Related Health Problems, Ninth Revision to the tenth revision (ICD-10-CM, the US version of the World Health Organization [WHO] ICD-10). Although the ICD-10-CM was a new concept for physicians in the United States, the international variant has been available since the early to mid-1990s. One of the main differences between the ICD-10-CM and the ICD-10 is the sheer number of diagnostic codes included in the US classification system: the ICD-10 has approximately 14,400 codes, whereas the ICD-10-CM has more than 144,000. The ICD-10-CM is so granular that it has been described as needlessly specific, absurd, and unnecessarily detailed. Some argue that this seemingly excessive detail, in theory, allows for more accurate labeling of diagnoses required both for billing and observational analysis.1

However, the value and utility of these data are highly dependent upon how accurately and effectively the classification system is used.2 As many opponents of the ICD-10 point out, in clinical practice coding is often “inconsistent, inaccurate, and incomplete.”1 In 2015, as the United States was rolling out its cumbersome version of the ICD-10, the National Academy of Sciences published a report aimed at improving diagnosis in healthcare in response to the statistic that 1 in 20 adults in the United States experiences a diagnostic error every year.3 At the beginning of 2019, nothing had yet been done to improve the situation; in fact, it may have been exacerbated by the ICD-10-CM.

The ICD-10-CM classification also does not allow clinicians to express “clinical concern” when there is insufficient, incomplete, or inconclusive evidence to support a firm diagnosis.2 Moreover, because the codes are collected for billing purposes, some argue that their use in research is “intrinsically flawed.”1,2,4 For example, if a clinically relevant ICD-10 code is not useful for billing purposes, it is likely to be left out of the coding altogether. On the other hand, a useful billing code, despite being clinically irrelevant, may prompt coders to ask physicians to add the code and amend the clinical documentation.2 Along those lines, physicians are often interrupted and forced by the electronic medical record to select a code that may not accurately represent the medical issue just so they can move on with their work. These interruptions are further exacerbated by impediments to efficient workflow such as expired code warnings, lack of coverage warnings, specificity prompts, retrospective prompts, or an inability to find the right code.2

Given the unparalleled dependency on ICD-10 coding for reimbursement, it is no surprise that diagnostic errors occur and that diagnostic accuracy is lacking. In an article published in Health Affairs, Robert Berenson, a fellow at the Urban Institute in Washington, DC, and Hardeep Singh, MD, chief of the Health Policy, Quality, and Informatics Program, Center for Innovations in Quality, Quality, Effectiveness, and Safety, Michael E. DeBakey Veterans Affairs Medical Center, and professor of medicine at Baylor College of Medicine, Houston, Texas, argue that current models fail to reward physicians for “quality or value of care.” They point out that diagnostic errors are difficult to identify in real time and current fee-for-service models exacerbate the problem by tolerating, and in some cases, even encouraging diagnostic errors.3

For example, some clinicians might take advantage of bundled payments for diagnoses requiring more extensive care, and consequently better pay, in patients with less severe illness requiring less care. Maybe a more prevalent scenario is patients with elusive diagnoses that require a more thoughtful and often a multidisciplinary approach. Current models encourage higher turnover and do not reward clinicians for taking their time to think through the diagnosis, brush up on the latest literature, and consult with colleagues. In the current assembly line, patients might pick up inaccurate diagnoses required for billing the office visits, tests, referrals, and procedures that do not accurately reflect the final conclusions. More troublesome is that current models allow for redundant testing and sometimes even inappropriate testing and procedures.

This article originally appeared on Medical Bag