Computer-Assisted Coding Explained

CAC adoption in the United States grew around the October 2015 switch from ICD-9 to ICD-10. The new code sets were far more detailed, and organizations feared coders would lose productivity. Established products come from companies such as 3M, whose health information business became part of Solventum in 2024, and Optum, among others. The workflow has four steps: Documentation flows from the EHR into the CAC system, including progress notes, operative reports, discharge summaries and results; a natural language processing engine reads the text, identifies conditions and procedures and proposes codes, each linked to the supporting phrases; the coder reviews each suggestion. They accept it, reject it, or edit it to a more specific or correct code, and add any codes the system missed; and the coder applies official guidelines, such as sequencing rules and the different inpatient and outpatient rules for uncertain diagnoses, and writes queries to physicians when documentation is unclear. CAC is different from autonomous coding. In CAC, a human validates every chart. Autonomous systems send some encounters to billing with no human review. Organizations usually judge CAC with several measures: Productivity, measured as charts or encounters per hour; coding accuracy, measured by audits; Suggestion precision and recall; Discharged-not-final-billed (DNFB) days, meaning how long discharged accounts wait for final coding; and Downstream denial rates. Three misconceptions are common. The first is that CAC removes the need for guideline expertise. In fact, coders must catch plausible but wrong suggestions. The second is that productivity gains come automatically. Poor screen design, noisy suggestions or weak training can slow coders down. The third is that high acceptance rates prove accuracy. They can also mean coders have stopped checking.