Chomutare Taridzo, Lamproudis Anastasios, Budrionis Andrius, Svenning Therese Olsen, Hind Lill Irene, Ngo Phuong Dinh, Mikalsen Karl Øyvind, Dalianis Hercules
Health Data Analytics, Norwegian Centre for E-health Research, Tromsø, Norway.
Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
JMIR Res Protoc. 2024 Mar 12;13:e54593. doi: 10.2196/54593.
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.
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.
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.
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.
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.
clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54593.
计算机辅助临床编码（CAC）工具旨在帮助临床编码人员为诸如出院小结等临床文本分配标准化编码，如国际疾病分类第十次修订本（ICD - 10）。维护这些标准化编码的完整性对于卫生系统的运作以及确保用于二次目的的数据质量至关重要。临床编码是一项容易出错且繁琐的任务，而诸如国际疾病分类第十一次修订本（ICD - 11）等现代分类系统的复杂性给实施带来了重大障碍。迄今为止，仅有少数用户研究；因此，我们对于CAC系统在减轻编码负担和提高编码整体质量方面所能发挥的作用的理解仍然有限。
用户研究的目的是生成定性和定量数据，以衡量为推荐ICD - 10编码而开发的CAC系统Easy - ICD的实用性。具体而言，我们的目标是评估我们的工具是否能够减轻临床编码人员的负担并提高编码质量。
用户研究基于交叉随机对照试验研究设计，我们在临床编码人员使用我们的CAC工具和不使用该工具时分别测量其表现。表现通过他们为简单和复杂临床文本分配编码所需的时间以及编码质量（即编码分配的准确性）来衡量。
我们期望该研究能为我们提供与手工编码流程相比，CAC系统在时间使用和编码质量方面有效性的度量。这项研究的积极结果将意味着CAC工具具有减轻医护人员负担的潜力，并将对采用基于人工智能的CAC创新以改善编码实践产生重大影响。预期结果将于2024年夏季发表。
计划开展的用户研究有望让我们更深入地了解CAC系统在实际环境中对临床编码可能产生的影响，特别是在编码时间和质量方面。此外，该研究可能会为如何有效利用当前临床文本挖掘能力提供新的见解，以期减轻临床编码人员的负担，从而降低障碍并为采用诸如新的ICD - 11等现代编码系统铺平更可持续的道路。
clinicaltrials.gov NCT06286865；https://clinicaltrials.gov/study/NCT06286865。
国际注册报告识别号（IRRID）：DERR1 - 10.2196/54593。
