@Article{info:doi/10.2196/12341，作者=“杨成义与陈，雷玉与周，万琳与李，袁江与罗玉生”，标题=“基于国家流感样疾病发病率和多家医院电子病历的流感综合监测框架设计与评价”，期刊=“J Med Internet Res”，年=“2019”，月=“Feb”，日=“01”，卷=“21”，号=“2”，页=“e12341”，关键词=“流感;流行;流感监测;电子疾病监测;电子病历;电子健康记录;公共卫生"，摘要="背景:流感是全世界死亡的主要原因，给个人和社区造成重大经济损失。因此，流感流行的早期预测和干预措施对于降低该病的死亡率和发病率至关重要。与其他国家类似，台湾疾病预防控制中心(TWCDC)实施了流感监测和报告系统，主要依靠卫生保健提供者报告的流感样疾病(ILI)数据，对流感流行进行早期预测。然而，这些监测和报告系统在预测方面至少有两周的延迟，表明需要改进。 Objective: We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. Methods: First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. Results: Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables---TMUHcS-RITP and TMUHcS-IMU---showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. Conclusions: Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations. ", issn="1438-8871", doi="10.2196/12341", url="//www.mybigtv.com/2019/2/e12341/", url="https://doi.org/10.2196/12341", url="http://www.ncbi.nlm.nih.gov/pubmed/30707099" }