Development and evaluation of a primary care antimicrobial stewardship program (PC-ASP) in Toronto, Ontario, Canada

Improving healthcare professionals' interactions with patients to tackle antimicrobial resistance: a systematic review of interventions, barriers, and facilitators

Introduction

Antimicrobial resistance (AMR) is a major public health threat. With the growing emphasis on patient-centred care/ shared decision making, it is important for healthcare professionals' (HCPs) who prescribe, dispense, administer and/or monitor antimicrobials to be adequately equipped to facilitate appropriate antimicrobial use. We systematically identified existing interventions which aim to improve HCPs interaction with patients and examined barriers and facilitators of appropriate the use of such interventions and appropriate antimicrobial use among both HCPs and patientsantimicrobial use while using these interventions.

Methods

We searched MEDLINE, EMBASE, Web of Science, Google Scholar, and internet (via Google search engine). We included primary studies, published in English from 2010 to 2023 [PROSPERO (CRD42023395642)]. The protocol was preregistered with PROSPERO (CRD42023395642). We performed quality assessment using mixed methods appraisal tool. We applied narrative synthesis and used the COM-B (Capability, Opportunity, Motivation -Behaviour) as a theoretical framework for barriers and facilitators at HCP and patient levels.

Results

Of 9,172 citations retrieved from database searches, From 4,979 citations remained after removal of duplicates. We included 59 studies spanning over 13 countries. Interventions often involved multiple components beyond HCPs' interaction with patients. From 24 studies reporting barriers and facilitators, we identified issues relating to capability (such as, knowledge/understanding about AMR, diagnostic uncertainties, awareness of interventions and forgetfulness); opportunity (such as, time constraint and intervention accessibility) and motivation (such as, patient's desire for antibiotics and fear of litigation).

Conclusion

The findings of this review should be considered by intervention designers/adopters and policy makers to improve utilisation and effectiveness.

Do family medicine residents optimally prescribe antibiotics for common infectious conditions seen in a primary care setting?

Background

Antimicrobial resistance is a worldwide phenomenon that leads to a significant number of unnecessary deaths and costly hospital admissions. More than 90% of antibiotic use happens in the community and of this, family physicians account for two-thirds of these prescriptions. Our study aims to determine whether family medicine residents are optimally trained in antibiotic prescribing for common infectious conditions seen in a primary care setting.

Methods

This study is a secondary analysis of a prior study of antimicrobial stewardship in two urban primary care clinics in central Toronto, Ontario. A total of 1099 adult patient visits were included that involved family medicine resident trainees, seen between 2015 and 2016. The main outcome measures were resident antibiotic prescription rates for each condition and expert-recommended prescribing practices, the rate prescriptions were issued as delayed prescriptions, and the use of first-line recommended narrow-spectrum antibiotics.

Results

Compared to expert-recommended prescribing rates, family medicine residents overprescribed for uncomplicated upper respiratory tract infections (URI) (5.0% [95% CI 2.2% to 9.7%] versus 0% expert recommended) and sinusitis (44.2% [95% CI 32.8% to 55.9%] versus 11%-18% expert range), and under prescribed for pneumonia (53.5% [95% CI 37.7% to 68.8%] versus 100% expert range]). Prescribing rates were within expert recommended ranges for pharyngitis (28.6% [95% CI 16.6% to 43.3%]), bronchitis (3.6% [95% CI 0% to 18.4%]), and cystitis (79.4% [95% CI 70.6% to 86.6%]).

Conclusions

The antibiotic prescribing practices of family medicine residents during their training programs indicated overprescribing of antibiotics for some common infection presentations. Further study of antibiotic prescribing in primary care training programs across Canada is recommended to determine if future family physicians are learning appropriate antibiotic prescribing practices.

Assessing the Validity of Electronic Medical Records for Identifying High Antibiotic Prescribers in Primary Care

OBJECTIVES

Electronic medical record (EMR) prescription data may identify high antibiotic prescribers in primary care. However, practitioners doubt that population differences between providers and delayed antibiotic prescriptions are adequately accounted for in EMR-derived prescription rates. This study assessed the validity of using EMR prescription data to produce antibiotic prescription rates, accounting for these factors.

METHODS

The study was a secondary analysis of antimicrobial prescriptions collected from 4 primary care clinics from 2015 to 2017. For adults with selected respiratory and urinary infections, EMR diagnostic codes, prescription data, clinical diagnoses and demographics were abstracted. Overall and delayed prescription rates were produced for EMR diagnostic codes, clinical diagnoses, by clinic, and types of infection. Direct standardization was used to adjust for case mix differences by clinic. High antibiotic prescribers, above the 75th percentile for prescriptions, were compared with low antibiotic prescribers.

RESULTS

Of 3108 EMR visits, there were 2577 (85.4%) eligible visits with a clinical diagnosis and prescription information. Overall antibiotic prescription rates were similar utilizing EMR records (31.6%) or clinical diagnoses (32.6%, P = .40). When delayed prescriptions were removed, prescribing rates were lower (22.4%, P < .01). EMR data overestimated prescribing rates for conditions where antibiotics are usually not indicated (17.7% EMR vs 7.6% clinical diagnoses, P < .001). High antibiotic prescribers saw more cases where antibiotics are usually indicated (23.4%) compared to low prescribers (16.8%; P = .001).

CONCLUSIONS

Electronic medical record prescribing rates are similar to those using clinical diagnoses overall, but overestimate prescribing by clinicians for conditions usually not needing antibiotics. EMR prescription rates do not account for delayed antibiotic prescriptions or differences in infection case-mix.