Zhongguo cuzhong zazhi (Nov 2024)
急性脑梗死诊断编码临床决策支持系统的研究与应用 Research and Application of Clinical Decision Support System for Acute Cerebral Infarction Diagnosis Coding
Abstract
目的 研发急性脑梗死诊断编码临床决策支持系统（clinical decision support system，CDSS），并在具体临床应用环境中评价其对提高疾病编码的精准度是否具有积极作用。 方法 基于中国缺血性卒中亚型，建立诊断、病因、发病机制、责任血管、国际疾病分类（international classification of diseases，ICD）第11次修订版（ICD-11）知识库，开发ICD第10次修订版（ICD-10）急性脑梗死诊断编码CDSS。回顾性纳入首都医科大学附属北京天坛医院应用CDSS前（2017年1—6月）急性脑梗死（I63类目）患者主要诊断编码作为对照组，应用CDSS后（2023年1—6月）急性脑梗死患者主要诊断编码作为干预组。比较两组编码体现急性脑梗死病因与责任血管双轴心分类的情况，评估CDSS对编码精准度的影响。 结果 本研究研发了急性脑梗死诊断编码CDSS并集成入临床电子病历系统，临床医师可自主选择应用。验证过程共纳入1847例住院急性脑梗死患者，对照组849例，干预组998例。干预组I63类目诊断编码中体现病因与责任血管双轴心分类率高于对照组（93.99% vs. 9.42%，P＜0.001）。 结论 急性脑梗死诊断编码CDSS是一种融合临床实践与ICD-10分类规则的新编码模式，较传统编码方法精准度高。同时，CDSS为临床医师提供了编码自学工具，也可为获取病种诊疗质量控制数据提供帮助。 Abstract: Objective Develop a clinical decision support system (CDSS) for the diagnosis coding of acute cerebral infarction, and evaluate whether it can play a positive role on improving the accuracy of disease coding in specific clinical application environment. Methods Based on the Chinese ischemic stroke sub-classification, a CDSS for international classification of diseases (ICD)-10 clinical modification was developed by establishing a knowledge base for diagnosis, etiology, pathogenesis, responsible vessels, and ICD-11. The primary diagnosis coding of patients with acute cerebral infarction (I63), who were hospitalized at Beijing Tiantan Hospital, Capital Medical University before applying the CDSS (January to June 2017), was retrospectively included as the control group. Conversely, the primary diagnosis coding of patients with acute cerebral infarction admitted after the application of the CDSS (January to June 2023) was included as the intervention group. This study compared the diagnosis coding between the two groups to reflect the biaxial classification of acute cerebral infarction etiology and responsible vessels, thereby evaluating the effect of the CDSS on coding accuracy. Results This study developed the diagnosis coding CDSS for acute cerebral infarction, which was integrated into the electronic medical record system and was applied by clinicians of their own choice. A total of 1847 hospitalized patients with acute cerebral infarction were included in the validation process, including 849 in the control group and 998 in the intervention group. The rate of biaxial classification of etiology and responsible vessels in I63 diagnosis coding in the intervention group was higher than that in the control group (93.99% vs. 9.42%, P<0.001). Conclusions The CDSS for acute cerebral infarction diagnosis coding is a new coding model that integrates clinical practice with ICD-10 classification rules, which is more accurate than traditional coding methods. At the same time, CDSS provides a coding self-learning tool for clinicians, and it also provides help in obtaining quality control data for disease diagnosis and treatment.
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