scholarly journals Returns to Operating Room After Neurosurgical Procedures in a Tertiary Care Academic Medical Center: Implications for Health Care Policy and Quality Improvement

Neurosurgery ◽  
2018 ◽  
Vol 84 (6) ◽  
pp. E392-E401
Author(s):  
Panagiotis Kerezoudis ◽  
Amy E Glasgow ◽  
Mohammed Ali Alvi ◽  
Robert J Spinner ◽  
Fredric B Meyer ◽  
...  
Keyword(s):  
Health Care ◽  
Quality Improvement ◽  
Operating Room ◽  
Health Care Policy ◽  
Medical Center ◽  
Academic Medical Center ◽  
Tertiary Care ◽  
Neurosurgical Procedures ◽  
Care Policy ◽  
Academic Medical

Successful strategies for the reduction of operating room turnover times in a tertiary care academic medical center

2014 ◽  
Vol 187 (2) ◽  
pp. 403-411 ◽  
Author(s):  
Bhavani S. Kodali ◽  
Dennie Kim ◽  
Ronald Bleday ◽  
Hugh Flanagan ◽  
Richard D. Urman
Keyword(s):  
Operating Room ◽  
Medical Center ◽  
Academic Medical Center ◽  
Tertiary Care ◽  
Academic Medical

Using an Electronic Perioperative Documentation Tool to Identify Returns to Operating Room (ROR) in a Tertiary Care Academic Medical Center

2017 ◽  
Vol 43 (3) ◽  
pp. 138-145 ◽  
Author(s):  
Robert R. Cima ◽  
Sarah R. Dhanorker ◽  
Christopher L. Ostendorf ◽  
Mfonabasi Ntekpe ◽  
Raghu V. Mudundi ◽  
...  
Keyword(s):  
Operating Room ◽  
Medical Center ◽  
Academic Medical Center ◽  
Tertiary Care ◽  
Academic Medical ◽  
Documentation Tool

scholarly journals Clinical Team Distribution and Antibiotic Use Patterns at a Tertiary-Care Academic Medical Center

2020 ◽  
Vol 41 (S1) ◽  
pp. s168-s169
Author(s):  
Rebecca Choudhury ◽  
Ronald Beaulieu ◽  
Thomas Talbot ◽  
George Nelson
Keyword(s):  
Medical Center ◽  
Academic Medical Center ◽  
Tertiary Care ◽  
Antibiotic Use ◽  
Care Hospital ◽  
Hospital Units ◽  
Antibiotic Utilization ◽  
Academic Medical ◽  
Level Data ◽  
Utilization Data

Background: As more US hospitals report antibiotic utilization to the CDC, standardized antimicrobial administration ratios (SAARs) derived from patient care unit-based antibiotic utilization data will increasingly be used to guide local antibiotic stewardship interventions. Location-based antibiotic utilization surveillance data are often utilized given the relative ease of ascertainment. However, aggregating antibiotic use data on a unit basis may have variable effects depending on the number of clinical teams providing care. In this study, we examined antibiotic utilization from units at a tertiary-care hospital to illustrate the potential challenges of using unit-based antibiotic utilization to change individual prescribing. Methods: We used inpatient pharmacy antibiotic use administration records at an adult tertiary-care academic medical center over a 6-month period from January 2019 through June 2019 to describe the geographic footprints and AU of medical, surgical, and critical care teams. All teams accounting for at least 1 patient day present on each unit during the study period were included in the analysis, as were all teams prescribing at least 1 antibiotic day of therapy (DOT). Results: The study population consisted of 24 units: 6 ICUs (25%) and 18 non-ICUs (75%). Over the study period, the average numbers of teams caring for patients in ICU and non-ICU wards were 10.2 (range, 3.2–16.9) and 13.7 (range, 10.4–18.9), respectively. Units were divided into 3 categories by the number of teams, accounting for ≥70% of total patient days present (Fig. 1): “homogenous” (≤3), “pauciteam” (4–7 teams), and “heterogeneous” (>7 teams). In total, 12 (50%) units were “pauciteam”; 7 (29%) were “homogeneous”; and 5 (21%) were “heterogeneous.” Units could also be classified as “homogenous,” “pauciteam,” or “heterogeneous” based on team-level antibiotic utilization or DOT for specific antibiotics. Different patterns emerged based on antibiotic restriction status. Classifying units based on vancomycin DOT (unrestricted) exhibited fewer “heterogeneous” units, whereas using meropenem DOT (restricted) revealed no “heterogeneous” units. Furthermore, the average number of units where individual clinical teams prescribed an antibiotic varied widely (range, 1.4–12.3 units per team). Conclusions: Unit-based antibiotic utilization data may encounter limitations in affecting prescriber behavior, particularly on units where a large number of clinical teams contribute to antibiotic utilization. Additionally, some services prescribing antibiotics across many hospital units may be minimally influenced by unit-level data. Team-based antibiotic utilization may allow for a more targeted metric to drive individual team prescribing.Funding: NoneDisclosures: None

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