Benefits of MRI Acceleration Techniques in Increasing Productivity and Urgent Patient Access to MRI at a Large Academic Medical Center

Background Obtaining wrist radiographs prior to surgeon evaluation may be wasteful for patients ultimately diagnosed with de Quervain tendinopathy (DQT). Questions/Purpose Our primary question was whether radiographs directly influence treatment of patients presenting with DQT. A secondary question was whether radiographs influence the frequency of injection and surgical release between cohorts with and without radiographs evaluated within the same practice. Patients and Methods Patients diagnosed with DQT by fellowship-trained hand surgeons at an urban academic medical center were identified retrospectively. Basic demographics and radiographic findings were tabulated. Clinical records were studied to determine whether radiographic findings corroborated history or physical examination findings, and whether management was directly influenced by radiographic findings. Frequencies of treatment with injection and surgery were separately tabulated and compared between cohorts with and without radiographs. Results We included 181 patients (189 wrists), with no differences in demographics between the 58% (110 wrists) with and 42% (79 wrists) without radiographs. Fifty (45%) of imaged wrists demonstrated one or more abnormalities; however, even for the 13 (12%) with corroborating history and physical examination findings, wrist radiography did not directly influence a change in management for any patient in this series. No difference was observed in rates of injection or surgical release either upon initial presentation, or at most recent documented follow-up, between those with and without radiographs. No differences in frequency, types, or total number of additional simultaneous surgical procedures were observed for those treated surgically. Conclusion Wrist radiography does not influence management of patients presenting DQT. Level of Evidence This is a level III, diagnostic study.

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

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.695 ◽

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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