Research Suggests Use of Billing Codes to Find Diagnoses Leads to Large Amount of Misidentifications

New research suggests that the “use of billing codes in big data sets to find diagnoses can result in up to two-thirds of cases being mistakenly identified.”

Databases often used for medical research, like from CMS or the National Inpatient Survey, “typically rely on ambulatory billing codes to identify diseases or medical procedures, but their accuracy is rarely verified in publications that rely on this data.” These findings specifically focused on hernia diagnoses, but “reliance on billing codes in research reports can lead to similar discrepancies with other diseases or conditions.”

The researchers examined records for 1.36 million patients, “of whom 41,700 were diagnosed with hernias based on the coding.” However, the researchers had corresponding images for 28,600 of those code-based diagnosed patients, and only 36% of them actually had hernias, or about 10,000 of the 41,700.

This discrepancy possibly stems from “physicians basing their coding on the clinical problem for which they initially examined the patient and not on what they ultimately found. For instance, a patient visit for a possible hernia will be coded as a hernia in the record and remains that way even if that initial diagnosis is ruled out during subsequent examination.”

The researchers write that this highlights a “fundamental weakness in using administrative data for disease identification. … Encounter coding occurs because a diagnosis is considered, and not necessarily proven. We found that reliance on billing codes for hernia identification could result in 2/3 of cases being erroneously identified. This issue exists beyond hernia, highlighting a serious limitation in using administrative data for clinical research.”