Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks
Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks
复制标题
DOI：
10.1017/ice.2018.343
复制
发表时间：
2019-03-01
影响因子：
4.5
通讯作者：
Harrison, Lee H.
中科院分区：
文献类型：
--
作者：
Sundermann, Alexander J.;Miller, James K.;Harrison, Lee H.
背景：在与医疗相关的疫情爆发期间，确定住院患者之间的传播途径可能很乏味，特别是对于住院时间复杂且多次接触的患者。电子健康记录 (EHR) 的数据挖掘有可能快速识别疑似疫情爆发患者中的常见暴露情况。方法：我们回顾性分析了 2011 年至 2016 年期间发生的 9 起医院暴发疫情，这些暴发此前已根据传播途径和细菌分离株的分子特征进行了表征。我们确定了（1）电子病历的数据挖掘识别正确传播途径的能力，（2）在疫情爆发期间多久确定了正确的传播途径，以及（3）如果系统实时运行，可以预防多少病例的爆发。结果：除了在第八名患者中检测到的涉及 >1 种传播途径的一次暴发外，第二名患者的所有暴发均确定了正确的途径。如果实时进行数据挖掘，假设在确定传播途径后 7 天内或 14 天内开始有效干预，则可以分别预防多达 40 例或 34 例感染（分别为可能可预防感染的 78% 或 66%）。结论：电子病历的数据挖掘对于确定暴发患者之间的传播途径是准确的。使用常规全基因组测序和 EHR 数据挖掘来进行疫情检测和途径归因，对该方法的前瞻性验证正在进行中。
Background: Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak. Methods: We retrospectively analyzed 9 hospital outbreaks that occurred during 2011-2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time. Results: Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively. Conclusions: Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.