PD58-06 IMPLEMENTING AND EVALUATING THE EFFICACY OF AN ACUTE CARE UROLOGY MODEL OF CARE IN A LARGE COMMUNITY HOSPITAL
Bibliographic record
Abstract
You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Quality Improvement & Patient Safety IV (PD58)1 Apr 2019PD58-06 IMPLEMENTING AND EVALUATING THE EFFICACY OF AN ACUTE CARE UROLOGY MODEL OF CARE IN A LARGE COMMUNITY HOSPITAL Abirami Kirubarajan*, Roger Buckley, Shawn Khan, Rebecca Richard, Veselina Stefanova, and Nicole Golda Abirami Kirubarajan*Abirami Kirubarajan* More articles by this author , Roger BuckleyRoger Buckley More articles by this author , Shawn KhanShawn Khan More articles by this author , Rebecca RichardRebecca Richard More articles by this author , Veselina StefanovaVeselina Stefanova More articles by this author , and Nicole GoldaNicole Golda More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557174.12474.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: The Acute Care Surgery model is quickly becoming the standard for delivering urgent surgical care across North America. While this model has gained favour in other surgical fields, it has yet to be adopted in urology practice. We implemented an Acute Care Urology (ACU) model at a large Canadian community hospital to determine the measurable impacts on safe and timely care of patients with renal colic. METHODS: In July 2016, we adopted the intervention of an ACU model through the addition of an ACU surgeon, creation of a rapid referral clinic dedicated to emergency department (ED) patient referrals, 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, time from surgical assessment to procedure, and percentage of after-hours surgeries.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. Overall satisfaction with the ACU model was 95%, and all believe there has been a positive impact on patient care. CONCLUSIONS: 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. Source of Funding: North York General Hospital Exploration Fund - $7,500.00 CAD Toronto, Canada; London, Canada; Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1030-e1030 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abirami Kirubarajan* More articles by this author Roger Buckley More articles by this author Shawn Khan More articles by this author Rebecca Richard More articles by this author Veselina Stefanova More articles by this author Nicole Golda More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".