Interrater Reliability, Accuracy, and Triage Time Pre- and Post-implementation of a Real-Time Electronic Triage Decision-Support Tool.

STUDY OBJECTIVE: The electronic Canadian Triage and Acuity Scale (eCTAS) is a real-time electronic triage decision-support tool designed to improve patient safety and quality of care by standardizing the application of the Canadian Triage and Acuity Scale (CTAS). The objective of this study is to determine interrater agreement of triage scores pre- and post-implementation of eCTAS. METHODS: This was a prospective, observational study conducted in 7 emergency departments (EDs), selected to represent a mix of triage documentation practices, hospital types, and patient volumes. A provincial CTAS auditor observed triage nurses in the ED pre- and post-implementation of eCTAS and assigned an independent CTAS score in real time. Research assistants independently recorded triage time. Interrater agreement was estimated with κ statistics with 95% confidence intervals (CIs). RESULTS: A total of 1,491 individual triage assessments (752 pre-eCTAS, 739 post-implementation) were audited during 42 7-hour triage shifts (21 pre-eCTAS, 21 post-implementation). Exact modal agreement was achieved for 567 patients (75.4%) pre-eCTAS compared with 685 patients (92.7%) triaged with eCTAS. With the auditor's CTAS score as the reference, eCTAS significantly reduced the number of patients over-triaged (12.0% versus 5.1%; Δ 6.9; 95% CI 4.0 to 9.7) and under-triaged (12.6% versus 2.2%; Δ 10.4; 95% CI 7.9 to 13.2). Interrater agreement was higher with eCTAS (unweighted κ 0.89 versus 0.63; quadratic-weighted κ 0.93 versus 0.79). Median triage time was 312 seconds (n=3,808 patients) pre-eCTAS and 347 seconds (n=3,489 patients) with eCTAS (Δ 35 seconds; 95% CI 29 to 40 seconds). CONCLUSION: A standardized, electronic approach to performing triage assessments improves both interrater agreement and data accuracy without substantially increasing triage time.

Strategic analytics: towards fully embedding evidence in healthcare decision-making.

Cancer Care Ontario (CCO) has implemented multiple information technology solutions and collected health-system data to support its programs. There is now an opportunity to leverage these data and perform advanced end-to-end analytics that inform decisions around improving health-system performance. In 2014, CCO engaged in an extensive assessment of its current data capacity and capability, with the intent to drive increased use of data for evidence-based decision-making. The breadth and volume of data at CCO uniquely places the organization to contribute to not only system-wide operational reporting, but more advanced modelling of current and future state system management and planning. In 2012, CCO established a strategic analytics practice to assist the agency's programs contextualize and inform key business decisions and to provide support through innovative predictive analytics solutions. This paper describes the organizational structure, services and supporting operations that have enabled progress to date, and discusses the next steps towards the vision of embedding evidence fully into healthcare decision-making.

The use of syndromic surveillance for decision-making during the H1N1 pandemic: a qualitative study.

BACKGROUND: Although an increasing number of studies are documenting uses of syndromic surveillance by front line public health, few detail the value added from linking syndromic data to public health decision-making. This study seeks to understand how syndromic data informed specific public health actions during the 2009 H1N1 pandemic. METHODS: Semi-structured telephone interviews were conducted with participants from Ontario's public health departments, the provincial ministry of health and federal public health agency to gather information about syndromic surveillance systems used and the role of syndromic data in informing specific public health actions taken during the pandemic. Responses were compared with how the same decisions were made by non-syndromic surveillance users. RESULTS: Findings from 56 interviews (82% response) show that syndromic data were most used for monitoring virus activity, measuring impact on the health care system and informing the opening of influenza assessment centres in several jurisdictions, and supporting communications and messaging, rather than its intended purpose of early outbreak detection. Syndromic data had limited impact on decisions that involved the operation of immunization clinics, school closures, sending information letters home with school children or providing recommendations to health care providers. Both syndromic surveillance users and non-users reported that guidance from the provincial ministry of health, communications with stakeholders and vaccine availability were driving factors in these public health decisions. CONCLUSIONS: Syndromic surveillance had limited use in decision-making during the 2009 H1N1 pandemic in Ontario. This study provides insights into the reasons why this occurred. Despite this, syndromic data were valued for providing situational awareness and confidence to support public communications and recommendations. Developing an understanding of how syndromic data are utilized during public health events provides valuable evidence to support future investments in public health surveillance.