Using Discrete Event Simulation to Design and Assess an AI-aided Workflow for Same-day Diagnostic Testing of Women Undergoing Breast Screening.

The process of patients waiting for diagnostic examinations after an abnormal screening mammogram is inefficient and anxiety-inducing. Artificial intelligence (AI)-aided interpretation of screening mammography could reduce the number of recalls after screening. We proposed a same-day diagnostic workup to alleviate patient anxiety by employing an AI-aided interpretation to reduce unnecessary diagnostic testing after an abnormal screening mammogram. However, the potential unintended consequences of introducing this workflow in a high-volume breast imaging center are unknown. Using discrete event simulation, we observed that implementing the AI-aided screening mammogram interpretation and same-day diagnostic workflow would reduce daily patient volume by 4%, increase the time a patient would be at the clinic by 24%, and increase waiting times by 13-31%. We discuss how changing the hours of operation and introducing new imaging equipment and personnel may alleviate these negative impacts.

School Masking Policies and Secondary SARS-CoV-2 Transmission.

OBJECTIVES: Throughout the COVID-19 pandemic, masking has been a widely used mitigation practice in kindergarten through 12th grade (K-12) school districts to limit within-school transmission. Prior studies attempting to quantify the impact of masking have assessed total cases within schools; however, the metric that more optimally defines effectiveness of mitigation practices is within-school transmission, or secondary cases. We estimated the impact of various masking practices on secondary transmission in a cohort of K-12 schools. METHODS: We performed a multistate, prospective, observational, open cohort study from July 26, 2021 to December 13, 2021. Districts reported mitigation practices and weekly infection data. Districts that were able to perform contact tracing and adjudicate primary and secondary infections were eligible for inclusion. To estimate the impact of masking on secondary transmission, we used a quasi-Poisson regression model. RESULTS: A total of 1 112 899 students and 157 069 staff attended 61 K-12 districts across 9 states that met inclusion criteria. The districts reported 40 601 primary and 3085 secondary infections. Six districts had optional masking policies, 9 had partial masking policies, and 46 had universal masking. In unadjusted analysis, districts that optionally masked throughout the study period had 3.6 times the rate of secondary transmission as universally masked districts; and for every 100 community-acquired cases, universally masked districts had 7.3 predicted secondary infections, whereas optionally masked districts had 26.4. CONCLUSIONS: Secondary transmission across the cohort was modest (<10% of total infections) and universal masking was associated with reduced secondary transmission compared with optional masking.

Using control charts to understand community variation in COVID-19.

Decision-makers need signals for action as the coronavirus disease 2019 (COVID-19) pandemic progresses. Our aim was to demonstrate a novel use of statistical process control to provide timely and interpretable displays of COVID-19 data that inform local mitigation and containment strategies. Healthcare and other industries use statistical process control to study variation and disaggregate data for purposes of understanding behavior of processes and systems and intervening on them. We developed control charts at the county and city/neighborhood level within one state (California) to illustrate their potential value for decision-makers. We found that COVID-19 rates vary by region and subregion, with periods of exponential and non-exponential growth and decline. Such disaggregation provides granularity that decision-makers can use to respond to the pandemic. The annotated time series presentation connects events and policies with observed data that may help mobilize and direct the actions of residents and other stakeholders. Policy-makers and communities require access to relevant, accurate data to respond to the evolving COVID-19 pandemic. Control charts could prove valuable given their potential ease of use and interpretability in real-time decision-making and for communication about the pandemic at a meaningful level for communities.