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Objectives: The Canadian Network for Public Health Intelligence (CNPHI) is a secure, web-based scientific informatics and biosurveillance platform that leverages disparate public health information resources and expertise for the direct benefit of local, regional and national decision makers. CNPHI fosters collaboration and consultation through innovation in disease surveillance, intelligence exchange, research and response to protect, promote and support public health. The objective of this article is to present the CNPHI 'on the go' mobile application, and to discuss preliminary evaluation of the technology. The mobile application is intended to enable rapid mobile data collection using both online and offline modes supporting various stages of surveillance and response through the extension of data collection and analysis to the mobile environment., Methods: Two needs assessment meetings were held with stakeholders representing individuals from government, academia and research institutions, to inform the development of the CNPHI "on the go" mobile application. An initial version of the mobile technology (an "app") was developed and piloted by end-users with expertise in the field. Two focused pilots were conducted to test the technology: Pilot 1: 17-7-2017 to 21-11-2017 (6 participants); Pilot 2: 25-7-2017 to 15-9-2017 (2 participants). An initial consultation was held with the project leads to identify data elements for mobile data collection. A custom data collection form was designed using CNPHI's Web Data technology for each pilot, which was then made available through the mobile app. The technology was assessed using feedback received during each pilot as well as through a survey that was conducted at the conclusion of pilots., Results: Pilot participants reported that the mobile technology allowed seamless data collection, data management and rapid information sharing. Participants also reported that the entire process was seamless, simple, efficient, and that fewer steps were required for data collection and management. Further, significant efficiencies were gained by directly entering information using the mobile app without having to transfer handwritten information into an electronic database. An overall positive experience was reported by participants from both pilots., Discussion: Literature suggests that traditional methods of surveillance and data collection using a paper based methodology pose many challenges such as data loss and duplication, difficulty in managing the database, and lack of timely access to the data. Accurate and rapid access is critical for public health professionals in order to effectively make decisions and respond to public health emergencies. Results show that the CNPHI "on the go" app is well poised to address some of the suggested challenges. A limitation of this study was that sample size for pilot participation was small for capturing overall feedback on the readiness of the technology for integration into regular surveillance activities and response procedures., Conclusions: CNPHI "on the go" is a customizable technology developed within an already thriving collaborative CNPHI platform used by public health professionals, and performs well as a tool for rapid data collection and secure information sharing.

Objectives: To review user signal rating activity within the Canadian Network for Public Health Intelligence's (CNPHI's) Knowledge Integration using Web-based Intelligence (KIWI) technology by answering the following questions: (1) who is rating, (2) how are users rating, and (3) how well are users rating?, Methods: KIWI rating data was extracted from the CNPHI platform. Zoonotic & Emerging program signals with first rating occurring between January 1, 2016 and December 31, 2017 were included. Krippendorff's alpha was used to estimate inter-rater reliability between users. A z-test was used to identify whether users tended to rate within 95% confidence interval (versus outside) the average community rating., Results: The 37 users who rated signals represented 20 organizations. 27.0% (n = 10) of users rated ≥10% of all rated signals, and their inter-rater reliability estimate was 72.4% (95% CI: 66.5-77.9%). Five users tended to rate significantly outside of the average community rating. An average user rated 58.4% of the time within the signal's 95% CI. All users who significantly rated within the average community rating rated outside the 95% CI at least once., Discussion: A diverse community of raters participated in rating the signals. Krippendorff's Alpha estimate revealed moderate reliability for users who rated ≥10% of signals. It was observed that inter-rater reliability increased for users with more experience rating signals., Conclusions: Diversity was observed between user ratings. It is hypothesized that rating diversity is influenced by differences in user expertise and experience, and that the number of times a user rates within and outside of a signal's 95% CI can be used as a proxy for user expertise. The introduction of a weighted rating algorithm within KIWI that takes this into consideration could be beneficial.

Purpose: To validate the utility and effectiveness of a standardized tool for prioritization of information sources for early detection of diseases., Methods: The tool was developed with input from diverse public health experts garnered through survey. Ten raters used the tool to evaluate ten information sources and reliability among raters was computed. The Proc mixed procedure with random effect statement and SAS Macros were used to compute multiple raters' Fleiss Kappa agreement and Kendall's Coefficient of Concordance., Results: Ten disparate information sources evaluated obtained the following composite scores: ProMed 91%; WAHID 90%; Eurosurv 87%; MediSys 85%; SciDaily 84%; EurekAl 83%; CSHB 78%; GermTrax 75%; Google 74%; and CBC 70%. A Fleiss Kappa agreement of 50.7% was obtained for ten information sources and 72.5% for a sub-set of five sources rated, which is substantial agreement validating the utility and effectiveness of the tool., Conclusion: This study validated the utility and effectiveness of a standardized criteria tool developed to prioritize information sources. The new tool was used to identify five information sources suited for use by the KIWI system in the CEZD-IIR project to improve surveillance of infectious diseases. The tool can be generalized to situations when prioritization of numerous information sources is necessary.

Background: Emergency Department (ED) overcrowding is an important healthcare issue facing increasing public and regulatory scrutiny in Canada and around the world. Many approaches to alleviate excessive waiting times and lengths of stay have been studied. In theory, optimal ED patient flow may be assisted via balancing patient loads between EDs (in essence spreading patients more evenly throughout this system). This investigation utilizes simulation to explore "Crowdinforming" as a basis for a process control strategy aimed to balance patient loads between six EDs within a mid-sized Canadian city., Methods: Anonymous patient visit data comprising 120,000 ED patient visits over six months to six ED facilities were obtained from the region's Emergency Department Information System (EDIS) to (1) determine trends in ED visits and interactions between parameters; (2) to develop a process control strategy integrating crowdinforming; and, (3) apply and evaluate the model in a simulated environment to explore the potential impact on patient self-redirection and load balancing between EDs., Results: As in reality, the data available and subsequent model demonstrated that there are many factors that impact ED patient flow. Initial results suggest that for this particular data set used, ED arrival rates were the most useful metric for ED 'busyness' in a process control strategy, and that Emergency Department performance may benefit from load balancing efforts., Conclusions: The simulation supports the use of crowdinforming as a potential tool when used in a process control strategy to balance the patient loads between EDs. The work also revealed that the value of several parameters intuitively expected to be meaningful metrics of ED 'busyness' was not evident, highlighting the importance of finding parameters meaningful within one's particular data set. The information provided in the crowdinforming model is already available in a local context at some ED sites. The extension to a wider dissemination of information via an Internet web service accessible by smart phones is readily achievable and not a technological obstacle. Similarly, the system could be extended to help direct patients by including future estimates or predictions in the crowdinformed data. The contribution of the simulation is to allow for effective policy evaluation to better inform the public of ED 'busyness' as part of their decision making process in attending an emergency department. In effect, this is a means of providing additional decision support insights garnered from a simulation, prior to a real world implementation.