Mukhi Shamir N
Canadian Network for Public Health Intelligence, Public Health Agency of Canada.
Online J Public Health Inform. 2016 Dec 28;8(3):e208. doi: 10.5210/ojphi.v8i3.6937. eCollection 2016.
To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats.
A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper.
KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%).
Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities.
KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.
介绍加拿大公共卫生情报网络使用基于网络的情报（KIWI）技术进行的新知识整合，并通过案例研究对KIWI技术进行初步评估。这项新技术的目的是通过监测非结构化数据源来支持监测活动，以便早期发现和了解潜在的公共卫生威胁。
由公共卫生和人畜共患/新发疾病监测领域的专家终端用户对适用于人畜共患和新发疾病的KIWI技术原型进行试点。使用地理覆盖范围、用户参与度等变量对该技术进行评估；根据基于互联网的监测系统评估指南中的高级属性进行分类。特别关注了对系统自动理解算法的评估，该算法使用了敏感性、特异性和预测值等变量。基于事件的监测评估未充分应用，因为此类评估超出了本文的范围。
KIWI进行了试点，用户参与率为85.0%，监测源内的地理覆盖范围为83.9%的国家。以人畜共患和新发疾病为重点的试点总共持续了65天，收集了3243条个人信息和2起社区报告事件进行处理。在试点的第二阶段监测了10个来源，产生了545个预期情报信号。KIWI的自动理解算法（SMA）的敏感性为63.9%（95%置信区间：60.2-67.5%），特异性为88.6%（95%置信区间：87.3-89.8%），阳性预测值为59.8%（95%置信区间：56.1-63.4%），阴性预测值为90.3%（95%置信区间：89.0-91.4%）。
文献表明需要可定制的、集成到公共卫生专业人员协作网络中并纳入国家监测活动的基于互联网的监测系统。结果表明，KIWI技术很适合应对一些提出的挑战。本研究的一个局限性是，试点参与的样本量较小，无法全面了解将KIWI整合到常规监测活动中的准备情况。
KIWI是在公共卫生专业人员已经在使用的蓬勃发展的协作平台内开发的可定制技术，作为特定学科事件监测和早期预警信号检测的工具表现良好。
