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Objectives: Exercise stress echocardiograms (stress echos) are overused, whereas exercise stress electrocardiograms (stress ECGs) can be an appropriate, lower‐cost substitute. In this post hoc, mixed methods evaluation, we assessed an initiative promoting value‐based, guideline‐concordant ordering practices in primary care (PC) and cardiology clinics. Methods: Change in percent of stress ECGs ordered of all exercise stress tests (stress ECGs and echos) was calculated between three periods: baseline (January 2019–February 2020); Period 1 with reduced stress ECG report turnaround time + PC‐targeted education (began June 2020); and Period 2 with the addition of electronic health record‐based alternative alert (AA) providing point‐of‐care clinical decision support. The AA was deployed in two of five PC clinics in July 2020, two additional PC clinics in January 2021, and one of four cardiology clinics in February 2021. Nineteen primary care providers (PCPs) and five cardiologists were interviewed in Period 2. Results: Clinicians reported reducing ECG report turnaround time was crucial for adoption. PCPs specifically reported that value‐based education helped change their practice. In PC, the percent of stress ECGs ordered increased by 38% ± 6% (SE) (p < 0.0001) from baseline to Period 1. Most PCPs identified the AA as the most impactful initiative, yet stress ECG ordering did not change (6% ± 6%; p = 0.34) between Periods 1 and 2. In contrast, cardiologists reportedly relied on their expertise rather than AAs, yet their stress ECGs orders increased from Period 1 to 2 to a larger degree in the cardiology clinic with the AA (12% ± 5%; p = 0.01) than clinics without the AA (6% ± 2%; p = 0.01). The percent of stress ECGs ordered was higher in Period 2 than baseline for both specialties (both p < 0.0001). Conclusions: This initiative influenced ordering behaviour in PC and cardiology clinics. However, clinicians' perceptions of the initiative varied between specialties and did not always align with the observed behaviour change. [ABSTRACT FROM AUTHOR]

Introduction: Tracking adaptations during implementation can help assess and interpret outcomes. The framework for reporting adaptations and modifications‐expanded (FRAME) provides a structured approach to characterize adaptations. We applied the FRAME across multiple health services projects, and developed an analytic approach to assess the impact of adaptations. Methods: Mixed methods analysis of research diaries from seven quality improvement (QI) and research projects during the early stages of the COVID‐19 pandemic. Using the FRAME as a codebook, discrete adaptations were described and categorized. We then conducted a three‐step analysis plan: (1) calculated the frequency of adaptations by FRAME categories across projects; (2) qualitatively assessed the impact of adaptations on project goals; and (3) qualitatively assessed relationships between adaptations within projects to thematically consolidate adaptations to generate more explanatory value on how adaptations influenced intervention progress and outcomes. Results: Between March and July 2020, 42 adaptations were identified across seven health services projects. The majority of adaptations related to training or evaluation (52.4%) with the goal of maintaining the feasibility (66.7%) of executing projects during the pandemic. Five FRAME constructs offered the most explanatory benefit to assess the impact of adaptations on program and evaluation goals, providing the basis for creating an analytic approach dubbed the "A‐FRAME," analysis of FRAME data. Using the A‐FRAME, the 42 adaptations were consolidated into 17 succinct adaptations. Two QI projects discontinued altogether. Intervention adaptations related to staffing, training, or delivery, while evaluation adaptations included design, recruitment, and data collection adjustments. Conclusions: By sifting qualitative data about adaptations into the A‐FRAME, implementers and researchers can succinctly describe how adaptations affect interventions and their evaluations. The simple and concise presentation of information using the A‐FRAME matrix can help implementers and evaluators account for the influence of adaptations on program outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Many healthcare delivery systems have developed clinician‐led quality improvement (QI) initiatives but fewer have also developed in‐house evaluation units. Engagement between the two entities creates unique opportunities. Stanford Medicine funded a collaboration between their Improvement Capability Development Program (ICDP), which coordinates and incentivizes clinician‐led QI efforts, and the Evaluation Sciences Unit (ESU), a multidisciplinary group of embedded researchers with expertise in implementation and evaluation sciences. Aim: To describe the ICDP‐ESU partnership and report key learnings from the first 2 y of operation September 2019 to August 2021. Methods: Department‐level physician and operational QI leaders were offered an ESU consultation to workshop design, methods, and overall scope of their annual QI projects. A steering committee of high‐level stakeholders from operational, clinical, and research perspectives subsequently selected three projects for in‐depth partnered evaluation with the ESU based on evaluability, importance to the health system, and broader relevance. Selected project teams met regularly with the ESU to develop mixed methods evaluations informed by relevant implementation science frameworks, while aligning the evaluation approach with the clinical teams' QI goals. Results: Sixty and 62 ICDP projects were initiated during the 2 cycles, respectively, across 18 departments, of which ESU consulted with 15 (83%). Within each annual cycle, evaluators made actionable, summative findings rapidly available to partners to inform ongoing improvement. Other reported benefits of the partnership included rapid adaptation to COVID‐19 needs, expanded clinician evaluation skills, external knowledge dissemination through scholarship, and health system‐wide knowledge exchange. Ongoing considerations for improving the collaboration included the need for multi‐year support to enable nimble response to dynamic health system needs and timely data access. Conclusion: Presence of embedded evaluation partners in the enterprise‐wide QI program supported identification of analogous endeavors (eg, telemedicine adoption) and cross‐cutting lessons across QI efforts, clinician capacity building, and knowledge dissemination through scholarship. [ABSTRACT FROM AUTHOR]

Background and Objectives De-normalization of smoking as a public health strategy may create shame and isolation in vulnerable groups unable to quit. To examine the nature and impact of smoking stigma, we developed the Internalized Stigma of Smoking Inventory (ISSI), tested its validity and reliability, and explored factors that may contribute to smoking stigma. Methods We evaluated the ISSI in a sample of smokers with mental health diagnoses ( N = 956), using exploratory and confirmatory factor analysis, and assessed construct validity. Results Results reduced the ISSI to eight items with three subscales: smoking self-stigma related to shame, felt stigma related to social isolation, and discrimination experiences. Discrimination was the most commonly endorsed of the three subscales. A multivariate generalized linear model predicted 21-30% of the variance in the smoking stigma subscales. Self-stigma was greatest among those intending to quit; felt stigma was highest among those experiencing stigma in other domains, namely ethnicity and mental illness-based; and smoking-related discrimination was highest among women, Caucasians, and those with more education. Discussion and Conclusion Smoking stigma may compound stigma experiences in other areas. Aspects of smoking stigma in the domains of shame, isolation, and discrimination were related to modeled stigma responses, particularly readiness to quit and cigarette addiction, and were found to be more salient for groups where tobacco use is least prevalent. Scientific Significance The ISSI measure is useful for quantifying smoking-related stigma in multiple domains. (Am J Addict 2015;24:410 -418) [ABSTRACT FROM AUTHOR]