Objective: To examine the perspectives of caregivers that are not part of the antibiotic stewardship program (ASP) leadership team (eg, physicians, nurses, and clinical pharmacists), but who interact with ASPs in their role as frontline healthcare workers.

Design: Qualitative semistructured interviews.

Setting: The study was conducted in 2 large national healthcare systems including 7 hospitals in the Veterans' Health Administration and 4 hospitals in Intermountain Healthcare.

Participants: We interviewed 157 participants. The current analysis includes 123 nonsteward clinicians: 47 physicians, 26 pharmacists, 29 nurses, and 21 hospital leaders.

Methods: Interviewers utilized a semistructured interview guide based on the Consolidated Framework for Implementation Research (CFIR), which was tailored to the participant's role in the hospital as it related to ASPs. Qualitative analysis was conducted using a codebook based on the CFIR.

Results: We identified 4 primary perspectives regarding ASPs. (1) Non-ASP pharmacists considered antibiotic stewardship activities to be a high priority despite the added burden to work duties: (2) Nurses acknowledged limited understanding of ASP activities or involvement with these programs; (3) Physicians criticized ASPs for their restrictions on clinical autonomy and questioned the ability of antibiotic stewards to make recommendations without the full clinical picture; And (4) hospital leaders expressed support for ASPs and recognized the unique challenges faced by non-ASP clinical staff.

Conclusion: Further understanding these differing perspectives of ASP implementation will inform possible ways to improve ASP implementation across clinical roles.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10755145PMC
http://dx.doi.org/10.1017/ice.2023.35DOI Listing

Publication Analysis

Top Keywords

antibiotic stewardship
12
asp implementation
8
clinical
5
asps
5
qualitative evaluation
4
evaluation frontline
4
frontline clinician
4
perspectives
4
clinician perspectives
4
antibiotic
4

Similar Publications

Background: Lower respiratory tract infections (LRTIs) remain a leading cause of community-acquired and nosocomial infection in children and a common indication for antimicrobial use and intensive care admission. Determining the causative pathogen for LRTIs is difficult and traditional culture-based methods are labor- and time-intensive. Emerging molecular diagnostic tools may identify pathogens and detect antimicrobial resistance more quickly, to enable earlier targeted antimicrobial therapy.

View Article and Find Full Text PDF

Background: has recently been categorized as low-risk for AmpC β-lactamase inducible production, but research on outcomes in bacteremia by antibiotic choice is limited.

Objectives: This study examined the clinical characteristics and outcomes of patients with ceftriaxone-susceptible bacteremia who received AmpC-directed β-lactam therapy vs. narrower spectrum therapies.

View Article and Find Full Text PDF

Background: Canine gastroenteritis (CGE) is a common cause for seeking veterinary care in companion animal medicine and an area where antibiotics have been reported to be widely used. Therefore, creating relevant benchmarks for antibiotic use in CGE is important when implementing and analyzing antibiotic stewardship interventions. The aim of this paper was to describe the level and temporal trend of systemic antibiotic use for CGE in Sweden between 2020 and 2023.

View Article and Find Full Text PDF

Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.

J Infect

December 2024

Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address:

Background: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.

Methods: We used XGBoost machine learning models to predict antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021.

View Article and Find Full Text PDF

Want 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!