Mailed feedback to primary care physicians on antibiotic prescribing for patients aged 65 years and older: pragmatic, factorial randomised controlled trial.

OBJECTIVES: To evaluate whether providing family physicians with feedback on their antibiotic prescribing compared with that of their peers reduces antibiotic prescriptions. To also identify effects on antibiotic prescribing from case-mix adjusted feedback reports and messages emphasising antibiotic associated harms. DESIGN: Pragmatic, factorial randomised controlled trial. SETTING: Primary care physicians in Ontario, Canada PARTICIPANTS: All primary care physicians were randomly assigned a group if they were eligible and actively prescribing antibiotics to patients 65 years or older. Physicians were excluded if had already volunteered to receive antibiotic prescribing feedback from another agency, or had opted out of the trial. INTERVENTION: A letter was mailed in January 2022 to physicians with peer comparison antibiotic prescribing feedback compared with the control group who did not receive a letter (4:1 allocation). The intervention group was further randomised in a 2x2 factorial trial to evaluate case-mix adjusted versus unadjusted comparators, and emphasis, or not, on harms of antibiotics. MAIN OUTCOME MEASURES: Antibiotic prescribing rate per 1000 patient visits for patients 65 years or older six months after intervention. Analysis was in the modified intention-to-treat population using Poisson regression. RESULTS: 5046 physicians were included and analysed: 1005 in control group and 4041 in intervention group (1016 case-mix adjusted data and harms messaging, 1006 with case-mix adjusted data and no harms messaging, 1006 unadjusted data and harms messaging, and 1013 unadjusted data and no harms messaging). At six months, mean antibiotic prescribing rate was 59.4 (standard deviation 42.0) in the control group and 56.0 (39.2) in the intervention group (relative rate 0.95 (95% confidence interval 0.94 to 0.96). Unnecessary antibiotic prescribing (0.89 (0.86 to 0.92)), prolonged duration prescriptions defined as more than seven days (0.85 (0.83 to 0.87)), and broad spectrum prescribing (0.94 (0.92 to 0.95)) were also significantly lower in the intervention group compared with the control group. Results were consistent at 12 months post intervention. No significant effect was seen for including emphasis on harms messaging. A small increase in antibiotic prescribing with case-mix adjusted reports was noted (1.01 (1.00 to 1.03)). CONCLUSIONS: Peer comparison audit and feedback letters significantly reduced overall antibiotic prescribing with no benefit of case-mix adjustment or harms messaging. Antibiotic prescribing audit and feedback is a scalable and effective intervention and should be a routine quality improvement initiative in primary care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04594200.

Surfacing the causal assumptions and active ingredients of healthcare quality improvement interventions: An application to primary care opioid prescribing.

Background: Efforts to maximize the impact of healthcare improvement interventions are hampered when intervention components are not well defined or described, precluding the ability to understand how and why interventions are expected to work. Method: We partnered with two organizations delivering province-wide quality improvement interventions to establish how they envisaged their interventions lead to change (their underlying causal assumptions) and to identify active ingredients (behavior change techniques [BCTs]). The interventions assessed were an audit and feedback report and an academic detailing program. Both focused on supporting safer opioid prescribing in primary care in Ontario, Canada. Data collection involved semi-structured interviews with intervention developers (n = 8) and a content analysis of intervention documents. Analyses unpacked and articulated how the interventions were intended to achieve change and how this was operationalized. Results: Developers anticipated that the feedback report would provide physicians with a clear understanding of their own prescribing patterns in comparison to others. In the feedback report, we found an emphasis on BCTs consistent with that assumption (feedback on behavior; social comparison). The detailing was designed to provide tailored support to enable physicians to overcome barriers to change and to gradually enact specific practice changes for patients based on improved communication. In the detailing materials, we found an emphasis on instructions on how to perform the behavior, for a range of behaviors (e.g., tapering opioids, treating opioid use disorder). The materials were supplemented by detailer-enacted BCTs (e.g., social support [practical]; goal setting [behavior]; review behavioral goal[s]). Conclusions: The interventions included a small range of BCTs addressing various clinical behaviors. This work provides a methodological example of how to apply a behavioral lens to surface the active ingredients, target clinical behaviors, and causal assumptions of existing large-scale improvement interventions that could be applied in other contexts to optimize effectiveness and facilitate scale and spread.

What is already known about the topic?: The causal assumptions and key components of implementation interventions are often not well described, which limits the influence of implementation science on implementation practice. What does this paper add?: This work provides an approach for surfacing the causal assumptions from intervention developers (through interviews with eight participants) and active ingredients from intervention materials, focusing on two real-world interventions already delivered at scale and designed to promote safer opioid prescribing. The analysis provides a comprehensive intervention description and reveals the extent to which final interventions align with developers' intentions. What are the implications for practice, research, or policy?: The findings provide a foundation for future work which will describe the effectiveness of these interventions (alone and in combination) and explore whether they achieve change in the intended ways, thereby providing an example of a more fulsome intervention evaluation. More broadly, our methods can be used by implementation practitioners to review and reflect on their intervention development process and support comprehensive intervention descriptions.

Prioritizing and Implementing Primary Care Performance Measures for Ontario.

In the fall of 2014, Health Quality Ontario released A Primary Care Performance Measurement Framework for Ontario. Recognizing the large number of recommended measures and the limited availability of data related to those measures, the Steering Committee for the Primary Care Performance Measurement (PCPM) initiative established a prioritization process to select two subsets of high-value performance measures - one at the system level and one at the practice level. This article describes the prioritization process and its results and outlines the initiatives that have been undertaken to date to implement the PCPM framework and to advance primary care performance measurement and reporting in Ontario. Establishing a framework for primary care measurement and prioritizing system- and practice-level measures are essential steps toward system improvement. Our experience suggests that the process of implementing a performance measurement system is inevitably non-linear and incremental.

Physician Follow-up after Hospital Discharge in Alberta and Saskatchewan.

The period immediately after discharge from hospital can potentially be high risk and a vulnerable transition point for patients. This analysis from the Canadian Institute for Health Information assessed adherence to best practices for patient follow-up in the community after hospitalization in Alberta and Saskatchewan. For three selected conditions - acute myocardial infarction, heart failure and chronic obstructive pulmonary disease - the majority of patients (77-92%) saw a physician within a month of their discharge. However, fewer patients saw a physician within the first week (35-56%).

Missed opportunity: patients who leave emergency departments without being seen.

The rate of patients who visit emergency departments (EDs) but leave before being evaluated and treated is an important indicator of ED performance. This study examines patient- and hospital-level characteristics that may increase the risk of patients leaving EDs before being seen. The data are from the National Ambulatory Care Reporting System, an administrative database, and represent 4.3 million patient visits made to 163 Ontario EDs between April 2003 and March 2004. Among these data, the proportion that left without being seen (LWBS) was 3.1% (136,805). The rate of LWBS was highest among patients aged 15 to 35 years, those with less acute conditions and facilities that handle the highest volume of patients. Facility rates were positively correlated with facility median ED length of stay, annual facility volume and percentage of inpatient admissions. Understanding patient and facility characteristics that increase rates of LWBS may inform the process of developing measures to ensure timely access to ED care for all who seek it.