A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods.

Digital care management programs can reduce health care costs and improve quality of care. However, it is unclear how to target patients who are most likely to benefit from these programs ex ante, a shortcoming of current "risk score"-based approaches across many interventions. This study explores a framework to define impactability by using machine learning (ML) models to identify those patients most likely to benefit from a digital health intervention for care management.

Designing a biocontainment unit to care for patients with serious communicable diseases: a consensus statement.

In spite of great advances in medicine, serious communicable diseases are a significant threat. Hospitals must be prepared to deal with patients who are infected with pathogens introduced by a bioterrorist act (e.g.

Urinary retention during the second trimester of pregnancy: a rare cause.

Acute urinary retention during pregnancy is rare. Retention secondary to an impacted, gravid uterus is an emergency. Retroversion of the uterus, a history of pelvic inflammatory disease, and large fibroids are predisposing factors.