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dc.contributor.authorChomutare, Taridzo Fred
dc.contributor.authorLamproudis, Anastasios
dc.contributor.authorBudrionis, Andrius
dc.contributor.authorOlsen Svenning, Therese
dc.contributor.authorHind, Lill Irene
dc.contributor.authorNgo, Phuong Dinh
dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorDalianis, Hercules
dc.date.accessioned2024-03-22T11:53:10Z
dc.date.available2024-03-22T11:53:10Z
dc.date.issued2024-03-12
dc.description.abstractBackground: Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding.<p> <p>Objective: The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. <p>Methods: The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. <p>Results: We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence–based CAC innovations to improve coding practice. Expected results to be published summer 2024. <p>Conclusions: The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. <p>Trial Registration: clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865en_US
dc.identifier.citationChomutare, Lamproudis, Budrionis, Olsen Svenning, Hind, Ngo, Mikalsen, Dalianis. Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial. JMIR Research Protocols. 2024en_US
dc.identifier.cristinIDFRIDAID 2255233
dc.identifier.doi10.2196/54593
dc.identifier.issn1929-0748
dc.identifier.urihttps://hdl.handle.net/10037/33236
dc.language.isoengen_US
dc.publisherJMIRen_US
dc.relation.journalJMIR Research Protocols
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleImproving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trialen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)