Forecasting Hospital Emergency Department Visits for Respiratory Illness Using Ontario's Telehealth System: An Application of Real-Time Syndromic Surveillance to Forecasting Health Services Demand

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Perry, Alexander
Forecasting , Health Services , Respiratory Illness , Surveillance , Telehealth , Syndromic Surveillance , Health Services Demand , Influenza , Parallel Cascade Identification , Fast Orthgonal Search , Subspace Identification , N4SID , State Space Models , System Identification
Background: Respiratory illnesses can have a substantial impact on population health and burden hospitals in terms of patient load. Advance warnings of the spread of such illness could inform public health interventions and help hospitals manage patient services. Previous research showed that calls for respiratory complaints to Telehealth Ontario are correlated up to two weeks in advance with emergency department visits for respiratory illness at the provincial level. Objectives: This thesis examined whether Telehealth Ontario calls for respiratory complaints could be used to accurately forecast the daily and weekly number of emergency department visits for respiratory illness at the health unit level for each of the 36 health units in Ontario up to 14 days in advance in the context of a real-time syndromic surveillance system. The forecasting abilities of three different time series modeling techniques were compared. Methods: The thesis used hospital emergency department visit data from the National Ambulatory Care Reporting System database and Telehealth Ontario call data and from June 1, 2004 to March 31, 2006. Parallel Cascade Identification (PCI), Fast Orthogonal Search (FOS), and Numerical Methods for Subspace State Space System Identification (N4SID) algorithms were used to create prediction models for the daily number of emergency department visits using Telehealth call counts and holiday/weekends as predictors. Prediction models were constructed using the first year of the study data and their accuracy was measured over the second year of data. Factors associated with prediction accuracy were examined. Results: Forecast error varied widely across health units. Prediction error increased with lead time and lower call-to-visits ratio. Compared with N4SID, PCI and FOS had significantly lower forecast error. Forecasts of the weekly aggregate number of visits showed little evidence of ability to accurately flag corresponding actual increases. However, when visits were aggregated over a four day period, increases could be flagged more accurately than chance in six of the 36 health units accounting for approximately half of the Ontario population. Conclusions: This thesis suggests that Telehealth Ontario data collected by a real-time syndromic surveillance system could play a role in forecasting health services demand for respiratory illness.
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