Toward Improving Patient Equity in a Pediatric Emergency Department: A Framework for Implementation.

Disparities in health care delivery and health outcomes for patients in the emergency department (ED) by race, ethnicity, and language for care (REaL) are common and well documented. Addressing inequities from structural racism, implicit bias, and language barriers can be challenging, and there is a lack of data on effective interventions. We describe the implementation of a multifaceted equity improvement strategy in a pediatric ED using Kotter's model for change as a framework to identify the key drivers. The main elements included a data dashboard with quality metrics stratified by patient self-reported REaL to visualize disparities, a staff workshop on implicit bias and microaggressions, and several clinical and operational tools that highlight equity. Our next steps include refining and repeating interventions and tracking important patient outcomes, including timely pain treatment, triage assessment, diagnostic evaluations, and interpreter use, with the overall goal of improving patient equity by REaL over time. This article presents a roadmap for a disparity reduction intervention, which can be part of a multifaceted approach to address health equity in EDs.

Croup during the Coronavirus Disease 2019 Omicron Variant Surge.

We conducted a retrospective review of medical records of patients with croup seen during the coronavirus disease 2019 pandemic. Approximately 50% underwent testing for severe acute respiratory syndrome coronavirus 2. During the Delta wave, 2.8% of those tested were positive for severe acute respiratory syndrome coronavirus 2; this increased to 48.2% during the Omicron wave, demonstrating a strong correlation between the Omicron variant and croup.

askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men.

BACKGROUND: Clinical registries provide physicians with a means for making data-driven decisions but few opportunities exist for patients to interact with registry data to help make decisions. OBJECTIVE: We sought to develop a web-based system that uses a prostate cancer (CaP) registry to provide newly diagnosed men with a platform to view predicted treatment decisions based on patients with similar characteristics. DESIGN, SETTING, AND PARTICIPANTS: The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a quality improvement consortium of urology practices that maintains a prospective registry of men with CaP. We used registry data from 45 MUSIC urology practices from 2015 to 2017 to develop and validate a random forest machine learning model. After fitting the random forest model to a derivation cohort consisting of a random two-thirds sample of patients after stratifying by practice location, we evaluated the model performance in a validation cohort consisting of the remaining one-third of patients using a multiclass area under the curve (AUC) measure and calibration plots. RESULTS AND LIMITATIONS: We identified 7543 men diagnosed with CaP, of whom 45% underwent radical prostatectomy, 30% surveillance, 17% radiation therapy, 5.6% androgen deprivation, and 1.8% watchful waiting. The personalized prediction for patients in the validation cohort was highly accurate (AUC 0.81). CONCLUSIONS: Using clinical registry data and machine learning methods, we created a web-based platform for patients that generates accurate predictions for most CaP treatments. PATIENT SUMMARY: We have developed and tested a tool to help men newly diagnosed with prostate cancer to view predicted treatment decisions based on similar patients from our registry. We have made this tool available online for patients to use.