Automated Clinical Documentation Improvement

Abstract

Complete and accurate clinical documentation in the medical record has a direct impact on the assignment of codes, more accurate levels of reimbursement, and is critical to the higher quality of patient care. This paper describes the development of a system which can automatically flag the cases if there is an opportunity of improvement in patient clinical documents. Automated Clinical Documentation Improvement (CDI) leverages the natural language processing (NLP) and contextual understanding of health record structure with additional business rules logic, helping CDI specialists identify critical documentation information that may be missing from the medical record. This results in more specific coding opportunity and better understanding of the clinical complexity for accurate reimbursement. This system helped increase CDI specialists' productivity by efficiently filtering cases which need more attention from them.