This project will develop a Computer-Assisted Coding (CAC) tool for ICD-10 coding for Norwegian electronic health records and specifically for the discharge letter. There are over 20 000 ICD-10 diagnosis codes for Norwegian divided into 22 chapters. The codes are hierarchical in 3 levels and each code has a textual description. One or several of these ICD-10 codes are assigned to the patient's discharge summary by the physician both for medical and for administrative purposes. The process of assigning codes is difficult and time consuming and it is also shown that up to 41 percent of the manually assigned main diagnosis maybe wrong or sometimes missing. The CAC tool will learn from previously manually coded discharges summaries, patient notes both free text and structured information such as laboratory results, blood values, etc and assign ICD-codes to unseen discharge summaries. The CAC tool will use Artificial Intelligence methods such as Natural Language Processing and Deep Learning techniques to learn and predict codes. Ranked ICD-10 code suggestions will be presented to the physician such that he or she can can select among them and assign the correct code. This will enable fast and high quality semi automatic ICD-10 coding. The CAC tool can also be used for assessing coding quality on historical data for hospital management and health authorities. The CAC tool will reduce coders workload and improve overall code quality. High-quality codes enable efficient data reuse, promoting fast knowledge generation in healthcare, thereby laying foundations for personalized medicine, more efficient health management, and, subsequently, higher quality of care. The project builds on the clinical text mining research activities started in the incubator project, NorKlinTekst (HNF1395-18), funded by Helse Nord in 2017.
Project leader: Hercules Dalianis
Category: Helse Nord RHF
Institution: Nasjonalt senter for e-helseforskning