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P074: Improving urology care in the emergency department through implementation of an Acute Care Urology model
- A. Kirubarajan, R. Buckley, S. Khan, R. Richard, V. Stefanova, A. Chin, N. Golda
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- Journal:
- Canadian Journal of Emergency Medicine / Volume 21 / Issue S1 / May 2019
- Published online by Cambridge University Press:
- 02 May 2019, p. S90
- Print publication:
- May 2019
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- Article
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Introduction: Renal colic is one of the most common presentations to the emergency department (ED), and often requires complex interdisciplinary collaboration between emergency physicians and urology surgeons. Previous literature has shown that adoption of interdisciplinary rapid referral clinics can improve both timeliness of care and patient outcomes. However, these Acute Care Surgery models have not yet been commonly adopted for urology care in the ED. Methods: In July 2016, we adopted the intervention of an Acute Care Urology (ACU) model through the creation of a rapid referral clinic dedicated to ED patient referrals, the addition of an ACU surgeon, and enhanced use of daytime OR blocks. We conducted a manual chart review of 579 patients presenting to the ED with a complaint of renal colic. Patient data was collected in two separate time periods to analyze trends before implementation of the ACU model (pre-intervention, September - November 2015), to examine the model's impact (post-intervention, September - November 2016). Secondary methods of evaluation included a survey of 20 ED physicians to capture subjective feedback through Likert scale data. Results: Of the evaluated 579 patients with a complaint of renal colic,194 patients were discharged from ED with an diagnosis of obstructing kidney stone and were referred to urology for outpatient care. The ED-to-clinic time was significantly lower for those in the ACU model (p <0.001). The mean time to clinic was 15.76 days (SD = 15.47, range 1-93) pre-intervention versus 4.17 days (SD = 2.33, range = 1-12) post-intervention. Furthermore, the ACU clinic allowed significantly more patients to be referred for outpatient care (p = 0.0004). There was also higher likelihood that patients would successfully obtain an appointment following referral (p = 0.0055). Decreasing trends were shown in mean ED wait time, in addition to time from assessment to procedure. Results of the qualitative survey were overwhelmingly positive. All 20 surveyed ED physicians were more confident that outpatients would be seen in a timely manner (85% strongly agree, 15% agree). Qualitative feedback included the belief that follow-up is more accessible, that ED physicians are less likely to page the on-call urologist, and that they are able to discharge patients sooner. Conclusion: The ACU model for patients with renal colic may be beneficial in reducing ED-to-clinic time, ensuring proper follow-up after ED diagnosis, and improving patient care within the ED.
Chapter 20 - Weather and seasonal climate forecasts using the superensemble approach
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- By T. N. Krishnamurti, Department of Meteorology, Florida State University, Tallahassee, T. S. V. Vijaya Kumar, Department of Meteorology, Florida State University, Tallahassee, Won-Tae Yun, Department of Meteorology, Florida State University, Tallahassee, Arun Chakraborty, Department of Meteorology, Florida State University, Tallahassee, Lydia Stefanova, Department of Meteorology, Florida State University, Tallahassee
- Edited by Tim Palmer, Renate Hagedorn
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- Book:
- Predictability of Weather and Climate
- Published online:
- 03 December 2009
- Print publication:
- 27 July 2006, pp 532-560
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- Chapter
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Summary
In this chapter we present a short overview of the Florida State University (FSU) superensemble methodology for weather and seasonal climate forecasts and cite some examples on application for hurricanes, numerical weather prediction (NWP) and seasonal climate forecasts. This is a very powerful method for producing a consensus forecast from a suite of multimodels and the use of statistical algorithms. The message conveyed here is that the superensemble reduces the errors considerably compared with those of the member models and of the ensemble mean. This is based on results from several recent publications, where varieties of skill scores such as anomaly correlation, root-mean-square (rms) errors and threat scores have been examined. The improvements in several categories such as seasonal climate prediction from coupled atmosphere–ocean multimodels and NWP forecasts for precipitation exceed those of the best models in a consistent manner and are more accurate compared with the ensemble mean. It is difficult to state, soon after a forecast is made, as to which among the member models would have the highest skill. The superensemble is very consistent in this regard and is thus more reliable. In this study, we show walk-through tables that illustrate the workings of the superensemble for a hurricane track and heavy rain forecast for a flooding event. A number of features of the superensemble – number of training days, behaviour as the number of models increased, reduction of systematic errors and use of a synthetic superensemble – illustrate the strength of this new forecast experience.