Information Management and Big Data Center, Peking University Third Hospital, Beijing, China.
Goodwill Hessian Health Technology Co. Ltd, Beijing, China.
Sci Rep. 2023 Aug 14;13(1):13202. doi: 10.1038/s41598-023-39931-8.
To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting.
为了提高医院主动发现传染病的能力，基于真实的医疗记录和知识规则，建立了基于知识的传染病监测和决策支持系统。采用中断时间序列分析方法评估系统的有效性。在系统中，通过将传染病诊断指南与文献和真实医疗记录知识图谱相结合，生成传染病监测和警报规则库。系统与电子病历系统集成，医生在书写病历时会收到各种类型的实时警报提示。采用中断时间序列分析方法，从假阳性率、规则准确率、警报有效性和漏报率等方面分析系统警报的有效性。在 12 个月的时间里，系统共分析了 4497091 份病历，共触发了 12027 次监测警报。其中，98.43%的警报是临床有效的，而 1.56%的警报是无效的，主要是由于指南生成的规则相对粗糙，导致出现了几次误报。此外，还分析了系统警报的有效性、诊断时间分布和医生报告效率。在收到警报后，89.26%的传染病病例可以在 5 分钟内由医生确诊并报告，77.6%的医生可以在 2 分钟内完成 33 项信息的填写，这比过去的时间有所减少。系统的及时提醒减少了医生漏诊的概率；中断时间序列方法的分析显示，医生漏诊率平均降低了 4.4037%。本研究提出了一种基于真实医疗记录和知识规则的基于知识的传染病决策支持系统，并验证了其有效性。该系统改善了传染病管理，提高了决策的可靠性，降低了漏报率。
