Introduction: A 2015 survey of members of the American Urological Association found the majority of urologists (62.7%) work with an advanced practice provider. The aim of this study was to analyze direct cost savings of inpatient advanced practice provider hiring focusing on direct cost, length of stay, readmission rate and discharge planning.
Methods: Data from a single tertiary care referral center was collected between 2015 Q (quarter) 1 to 2018 Q2. Seven quarters before inpatient advanced practice provider implementation (2015 Q1 to 2016 Q3) were compared to 7 subsequent quarters after initiation of inpatient nurse practitioner program (2016 Q4 to 2018 Q2). A total of 1,190 major urological cases were performed during this time (178 cystectomies, 663 nephrectomies, 349 prostatectomies). Factors influenced by inpatient advanced practice provider such as length of stay, discharge before 11 a.m. and total direct cost were analyzed before advanced practice provider and after advanced practice provider. Mann-Whitney U and chi-square tests were used for continuous and categorical data, respectively.
Results: Advanced practice provider inclusion as part of a urology health care team showed an overall decreased length of stay (p=0.042) with an increase in discharges before 11 a.m. (p <0.001). There was no significant change in direct cost (p=0.89) combined for all 3 groups, but direct cost in the cystectomy group was significantly lower (p=0.048). There was a statistically significant improvement in discharge time for nephrectomy (p=0.039) and cystectomy (p=0.02) after advanced practice provider program initiation.
Conclusions: Inclusion of an advanced practice provider within inpatient setting was shown to consistently improve length of stay and direct cost for cystectomy care.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/UPJ.0000000000000143 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
MXene-based composite photocatalysts for efficient degradation of antibiotics in wastewater.
Sci Rep
December 2024
Department of Nano-Chemical Engineering, Faculty of Advanced Technologies, Shiraz University, Shiraz, Iran.
MXene-based (nano)materials have recently emerged as promising solutions for antibiotic photodegradation from aquatic environments, yet they are limited by scalability, stability, and selectivity challenges in practical settings. We formulated FeO-SiO/MXene ternary nano-photocomposites via coupled wet impregnation and sonochemistry approach for optimised tetracycline (TC) removal (the second most used antibiotic worldwide) from water using response surface methodology-central composite design (RSM-CCD). The photocatalysts containing various loading of FeO/SiO (5-45 wt%) on the MXene with a range of calcination temperatures (300-600 °C) via RSM optimisation were synthesised, characterised regarding crystallinity properties, surface morphology, binding energy, and light absorption capability, and analysed for TC degradation efficiency.
View Article and Find Full Text PDFSynergizing quantum techniques with machine learning for advancing drug discovery challenge.
Sci Rep
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
The Quantum Computing for Drug Discovery Challenge, held at the 42nd International Conference on Computer-Aided Design (ICCAD) in 2023, was a multi-month, research-intensive competition. Over 70 teams from more than 65 organizations from 12 different countries registered, focusing on the use of quantum computing for drug discovery. The challenge centered on designing algorithms to accurately estimate the ground state energy of molecules, specifically OH+, using quantum computing techniques.
View Article and Find Full Text PDFAn efficient method for identifying surface damage in hydraulic concrete buildings.
Sci Rep
December 2024
Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures.
View Article and Find Full Text PDFA secure and efficient blockchain enabled federated Q-learning model for vehicular Ad-hoc networks.
Sci Rep
December 2024
Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, Iraq.
Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained.
View Article and Find Full Text PDFProvisional Prosthetic Outcome when Using Photogrammetry for Complete Arch Oral Implants: A Report of 111 Patient Treatments.
Oral Maxillofac Surg Clin North Am
December 2024
Private Practice, Brooklyn, NY, USA.
Complete arch oral implant treatment using photogrammetry was studied in 77 patients with 111 arches focusing on digitization of clinical records and optimization of the provisional. The aim of the study was to test the hypothesis that patient satisfaction with the provisional prosthesis during the first 4 months of care determined workflow efficiency and success. Digitization of 8 clinical records was done including centric relation, vertical dimension, esthetics, occlusion, inter-arch space, abutment selection, abutment capture, and soft tissue scan.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!