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Introduction. Many Emergency Departments (ED) publish wait times; however, the patient perspective in what information is requested and the quantity of information to post is limited. Methods. We conducted a mixed-methods study at a tertiary care academic center. First, we conducted focus groups of 7 patients. We then generated themes following content analysis to create a patient survey. We administered in-person surveys to patients in ED waiting rooms at sites randomized for survey administration. We used preassigned shifts utilized for even patient perspective representation of the 24 hours-a-day/7 days-a-week service. We included waiting room patients over 18 years of age and excluded patients directly referred to a specialty service or who did not speak French or English. We analyzed survey data using descriptive statistics. Results. We identified nine dominant focus group themes: wait time definition, wait time notification, communication, education, patient expectations, utilization of the ED, patient behaviour, physical comfort, and patient empowerment. Of the 240 patient questionnaires administered, 81.3% of respondents wanted to know ED wait times before hospital arrival hospital and 90.8% wanted ED wait times posted in the waiting room. Website (46.7%) was the most popular choice for publishing wait times outside the ED. Within the ED, patients had no preference regarding display modality, if times were displayed (39.6%). Overall, 76.7% stated that their satisfaction with the ED would be improved if wait times were posted. Conclusion. ED patients strongly supported having access to wait time information. Patients believed having wait time information will have a positive impact on their overall ED satisfaction.

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days. Time-series models have traditionally been used in econometrics to develop financial models, but have been adapted in other fields, such as health informatics. This study uses a time-series approach to assess whether these impressions are valid. Methods: The daily volume of patients presenting to four emergency departments (ED) at the Nova Scotia Health Authority from Jan 2010 to May 2015 were analyzed to assess for the effect of previous volumes on future volumes. Parameters were selected using the auto-correlation (ACF) and partial auto-correlation functions (PACF) for a Seasonal Auto-regressive Integrated Moving Average (SARIMA) model. The Box-Jenkins statistic was assessed for model suitability. To assess for accuracy, a forecast of the model was evaluated with a year of volumes set aside for testing. Results: The EDs saw an average of 365.1 patients per day, with a minimum of 188 patients and a maximum of 479. The increasing trend in volumes consistent with the increasing number of ED presentations nation-wide was detrended using linear regression. There was a significant correlation in ACF with the previous day (ρ 1 = 0.297). A seasonal, periodic trend was seen weekly. Significant correlations occurred annually (ρ 365 = 0.279) and at 29 days (ρ 29 = 0.339), consistent with the lunar cycle. A seasonal model was postulated incorporating an auto-regressive (AR) coefficient, and a moving average (MA) coefficient for the previous day's volume. An AR and MA seasonal coefficient were each incorporated using the weekly period. When using the model on the test data, the model predicted 4 more patient presentations on average than the true value, with 90% of the values within 37 presentations of the true volume. The Box-Jenkins statistic was non-significant, indicating no problems with model specification. Conclusion: The volume of patients presenting to an ED system is correlated with that of the previous day. A weekly seasonal variation was confirmed. Auto-correlations also occur annually and possibly associated with the lunar cycle. Previous ED volumes may be useful in forecasting patient volumes. The time-series approach may discover further ways to predict ED volumes. Keywords: crowding, time-series, forecasting P018 A prospective diagnostic support tool for the differentiation of abdominal pain in the adult emergency department population M.B. Butler, MSc, T. Kenney, PhD, H. Gu, PhD, A. Carter, MD, S. Ling, MSc; Dalhousie University, Halifax, NS Introduction: The chaotic environment of the emergency department has a deleterious effect on clinical judgement. The diagnosis of abdominal pathology is difficult to differentiate. There are also many diagnoses that could be considered abdominal in nature, exacerbating the task of diagnosing these patients. We propose a novel machinelearning method, Hierarchical Structured Models (HSMs), to provide an adjunct to clinician judgement, that provides a ranking of the probabilities of a patient having each of 39 abdominal pathologies, using only variables at the tria...

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