PD58-06 IMPLEMENTING AND EVALUATING THE EFFICACY OF AN ACUTE CARE UROLOGY MODEL OF CARE IN A LARGE COMMUNITY HOSPITAL
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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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».