[PDF][PDF] Which patients are persistently high-risk for hospitalization
ET Chang, R Piegari, ES Wong… - Am J Manag …, 2019 - ajmc.s3.amazonaws.com
ABSTRACT OBJECTIVES: Many healthcare systems use prediction models to estimate and
manage patient-level probability of hospitalization. Patients identified as high-risk at one …
manage patient-level probability of hospitalization. Patients identified as high-risk at one …
Identifying latent subgroups of high-risk patients using risk score trajectories
ES Wong, J Yoon, RI Piegari, AMM Rosland… - Journal of general …, 2018 - Springer
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 …
identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time …
Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration
L Wang, B Porter, C Maynard, G Evans, C Bryson… - Medical care, 2013 - journals.lww.com
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 …
hospitalization have focused on population subsets, such as those with a specific clinical …
Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study
J Wong, M Taljaard, AJ Forster… - BMC health services …, 2011 - Springer
Background Clinicians informally assess changes in patients' status over time to
prognosticate their outcomes. The incorporation of trends in patient status into regression …
prognosticate their outcomes. The incorporation of trends in patient status into regression …
Predicting patients at risk for prolonged hospital stays
L Doctoroff, SJ Herzig - Medical care, 2020 - journals.lww.com
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 …
US hospitals. Predicting which medical patients are at risk for prolonged hospitalizations …
Behind the Curtain: Comparing Predictive Models Performance in 2 Publicly Insured Populations
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 …
hospitalization in 2 very different populations: Medicaid and Medicare enrollees in Maryland …
Predicting avoidable hospital events in Maryland
M Henderson, F Han, C Perman, H Haft… - Health Services …, 2022 - Wiley Online Library
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 …
Medicare fee‐for‐service (FFS) beneficiaries in Maryland. Data sources Medicare FFS …
[PDF][PDF] Enhanced risk prediction model for emergency department use and hospitalizations in patients in a primary care medical home
PY Takahashi, HC Heien, LR Sangaralingham… - Am J Manag …, 2016 - academia.edu
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 …
has become more important to providers. Existing risk-prediction models often focus on …
Predicting post‐discharge death or readmission: deterioration of model performance in population having multiple admissions per patient
C van Walraven, J Wong, AJ Forster… - Journal of evaluation …, 2013 - Wiley Online Library
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 …
commonly limit the analytical dataset to one observation per patient. Objective Measure and …
Primary care practices' ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation
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 …
fragmented and delayed care, reducing well-being and increasing health care costs. Health …