The issue of harnessing machine learning (ML) algorithms for the prediction of adverse
medical events is important considering the prevalence of vast repositories of patient-level …

Hospital readmission shortly after discharge threatens the quality of patient care and leads
to increased medical care costs. In the United States, hospitals with high readmission rates …

Background: An accurate estimate of expected survival time assists people near the end of
life to make informed decisions about their medical care. Objectives: Use advanced machine …

Background The term “data science” encompasses several methods, many of which are
considered cutting edge and are being used to influence care processes across the world …

In preparation for value-based care (VBC) reimburse-ment and risk contracting, a significant
proportion of health systems and physicians in the United States are engaging in population …

Objective We report on our experience of deploying a continuous remote patient monitoring
(CRPM) study soft launch with structured cascading and escalation pathways on heart …

Health analytics frequently involve tasks to predict outcomes of care. A foundational
predictor of clinical outcomes is the medical diagnosis (Dx). The most used expression of …

BACKGROUND COVID‐19 represents a grave risk to residents in skilled nursing facilities
(SNFs). OBJECTIVE To determine whether establishment of an appropriate‐use committee …

Background Heart failure (HF) is a prevalent chronic disease and is associated with
increases in mortality and morbidity. HF is a leading cause of hospitalizations and …

The Cascade-HF protocol is a Continuous Remote Patient Monitoring (CRPM) study at a
major health system in the United States to reduce Heart Failure (HF)-related …