Aslam A, Walker L, Abaho M, Cant H, O'Connell M, Abuzour A S, Hama L, Schofield P, Mair F S, Ruddle R A, Popoola O, Sperrin M, Tsang J Y, Shantsila E, Gabbay M, Clegg A, Woodall A A, Buchan I, Relton S D
Information School, University of Sheffield, Sheffield, UK.
Institute of Health Sciences, Faculty of Medicine & Health, University of Leeds, Leeds, UK.
BMC Med Res Methodol. 2025 May 24;25(1):138. doi: 10.1186/s12874-025-02541-1.
Codelists play a crucial role in ensuring accurate and standardized communication within healthcare. However, preparation of high-quality codelists is a rigorous and time-consuming process. The literature focuses on transparency of clinical codelists and overlooks the utility of automation. METHODS (AUTOMATED FRAMEWORK DESIGN AND USE-CASE: DYNAIRX): Here we present a Codelist Generation Framework that can automate generation of codelists with minimal input from clinical experts. We demonstrate the process using a specific project, DynAIRx, producing appropriate codelists and a framework allowing future projects to take advantage of automated codelist generation. Both the framework and codelist are publicly available. DynAIRx is an NIHR-funded project aiming to develop AIs to help optimise prescribing of medicines in patients with multiple long-term conditions. DynAIRx requires complex codelists to describe the trajectory of each patient, and the interaction between their conditions. We promptly generated 214 codelists for DynAIRx using the proposed framework and validated them with a panel of experts, significantly reducing the amount of time required by making effective use of automation.
The framework reduced the clinician time required to validate codes, automatically shrunk codelists using trusted sources and added new codes for review against existing codelists. In the DynAIRx case study, a codelist of 14000 codes required only 7-9 hours of clinician's time in the end (while existing methods takes months), and application of the automation framework reduced the workload by >80%.
This work examines current methodologies for codelist development and the challenges associated with ensuring transparency and reproducibility. A key benefit of this approach is its emphasis on automation and reliance on trusted sources, which significantly lowers the workload, minimizes human error, and saves substantial time, particularly the time needed from clinical experts.
代码集在确保医疗保健领域内准确和标准化的沟通方面发挥着关键作用。然而，高质量代码集的编制是一个严谨且耗时的过程。文献主要关注临床代码集的透明度，而忽视了自动化的效用。
方法（自动化框架设计与用例：DynAIRx）：在此，我们展示了一个代码集生成框架，该框架能够在临床专家投入最少的情况下自动生成代码集。我们通过一个特定项目DynAIRx演示了这一过程，生成了合适的代码集以及一个能让未来项目利用代码集自动生成功能的框架。该框架和代码集均已公开。DynAIRx是一个由英国国家卫生研究院（NIHR）资助的项目，旨在开发人工智能以帮助优化患有多种长期病症患者的用药处方。DynAIRx需要复杂的代码集来描述每位患者的病程以及他们病情之间的相互作用。我们使用所提出的框架为DynAIRx迅速生成了214个代码集，并由一组专家进行了验证，通过有效利用自动化显著减少了所需时间。
该框架减少了验证代码所需的临床医生时间，利用可靠来源自动精简代码集，并添加新代码以供对照现有代码集进行审查。在DynAIRx案例研究中，一个包含14000个代码的代码集最终仅需临床医生7 - 9小时（而现有方法需要数月时间），并且自动化框架的应用将工作量减少了80%以上。
这项工作审视了当前代码集开发的方法以及确保透明度和可重复性所面临的挑战。这种方法的一个关键优势在于其对自动化的强调以及对可靠来源的依赖，这显著降低了工作量，将人为错误降至最低，并节省了大量时间，尤其是临床专家所需的时间。
