Automating the overburdened clinical coding system: challenges and next steps

Artificial intelligence (AI) and natural language processing (NLP) have found a highly promising application in automated clinical coding (ACC), an innovation that will have profound impacts on the clinical coding industry, billing and revenue management, and potentially clinical care itself. Dong et al. recently analyzed the technical challenges of ACC and proposed future directions. Primary challenges for ACC exist at the technological and implementation levels; clinical documents are redundant and complex, code sets like the ICD-10 are rapidly evolving, training sets are not comprehensive of codes, and ACC models have yet to fully capture the logic and rules of coding decisions. Next steps include interdisciplinary collaboration with clinical coders, accessibility and transparency of datasets, and tailoring models to specific use cases.

Automated summary

Artificial intelligence and natural language processing have a promising application in automated clinical coding, which will have significant impacts on the clinical coding industry, billing and revenue management, and potentially clinical care. However, there are challenges at the technological and implementation levels, including redundant and complex clinical documents, rapidly evolving code sets, incomplete training sets, and the need to capture coding decision logic and rules. Next steps include interdisciplinary collaboration with clinical coders, accessible and transparent datasets, and tailoring models to specific use cases.