Increasing Emergency Department Patient Navigation And Buprenorphine Use: A Model For Low-Barrier Treatment.

Emergency departments (EDs) in the US treat large numbers of people with opioid use disorder (OUD), but most health systems do not equip EDs to provide OUD care. CA Bridge has supported the implementation of low-threshold, ED-initiated medications for OUD bundled with harm reduction and patient navigation in more than 80 percent of California EDs. Using grant reporting and California controlled substances prescribing data, we assessed CA Bridge implementation from July 2022 through December 2023, as well as 2022 California statewide ED buprenorphine prescribing.

Predicting near-complete pathological response to (chemo)radiotherapy in patients with rectal cancer: A federated learning study.

Background: Organ preservation in patients with locally advanced rectal cancer has attracted interest due to improved quality of life and functional outcomes compared with total mesorectal excision. Hence, patients who achieve clinical complete response (cCR) after (chemo)radiotherapy are offered a watch-and-wait strategy. Those who are likely to fall short of the strict criteria of cCR and only achieve near complete response (NCR) might benefit from radiation boosting to avoid surgery.

Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence-Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium.

Background: Atherosclerotic cardiovascular disease poses a heavy burden on the population's health and health care costs. Identifying apparently healthy individuals at risk of developing cardiovascular diseases using clinical prediction models raises awareness, facilitates shared decision-making, and supports tailored management of disease prevention. In the CARRIER project, a personalized cardiovascular risk management (CVRM) eCoach approach is cocreated, in which identified individuals receive education, guidance, and monitoring to prevent atherosclerotic cardiovascular disease through existing interventions.

Artificial intelligence in breast cancer radiotherapy: Insights from the Toolbox Consortium Delphi study.

Artificial intelligence (AI) is being incorporated in several breast cancer care domains, including for radiation therapy (RT). Herein we provide a review about AI for the management and planning of RT for breast cancer, which is part of the Toolbox-3 project's multidisciplinary Delphi study, including a literature review of studies related to the topic raised by the Delphi questionnaire. Our review shows that available evidence mainly consists of small single institutional studies, often at least partly supported by commercial companies.