ABSTRACT OBJECTIVES: Many healthcare systems use prediction models to estimate and
manage patient-level probability of hospitalization. Patients identified as high-risk at one …

Objective Many healthcare systems employ population-based risk scores to prospectively
identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time …

Background: Statistical models that identify patients at elevated risk of death or
hospitalization have focused on population subsets, such as those with a specific clinical …

Background Clinicians informally assess changes in patients' status over time to
prognosticate their outcomes. The incorporation of trends in patient status into regression …

Background: Patients with prolonged hospitalizations account for 14% of all hospital days in
US hospitals. Predicting which medical patients are at risk for prolonged hospitalizations …

Methods: We compared the performance and functioning of a predictive model of avoidable
hospitalization in 2 very different populations: Medicaid and Medicare enrollees in Maryland …

Objective To develop and validate a prediction model of avoidable hospital events among
Medicare fee‐for‐service (FFS) beneficiaries in Maryland. Data sources Medicare FFS …

Objectives: With the advent of healthcare payment reform, identifying high-risk populations
has become more important to providers. Existing risk-prediction models often focus on …

Background To avoid biased estimates of standard errors in regression models, statisticians
commonly limit the analytical dataset to one observation per patient. Objective Measure and …

Background Patients with complex health care needs may suffer adverse outcomes from
fragmented and delayed care, reducing well-being and increasing health care costs. Health …