BACKGROUND: Hypertension (HTN) is a complex condition with significant heterogeneity in presentation and treatment response. Identifying distinct subphenotypes of HTN may improve our understanding of its underlying mechanisms and guide more precise t...
BACKGROUND: Kidney transplantation is the preferred treatment for end-stage kidney disease (ESKD) in the United States, yet access and outcomes vary by insurance type, race, and socioeconomic status. This systematic review synthesizes U.S.-based evid...
AIMS: To develop machine-learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers o...
OBJECTIVE: To develop a machine learning (ML) algorithm capable of identifying children at risk of out-of-home placement among a Medicaid-insured population.
JDR clinical and translational research
Jun 14, 2024
OBJECTIVES: To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions.
BACKGROUND: People with intellectual and developmental disabilities (IDD) were disproportionately affected by the COVID-19 pandemic. Predicting COVID-19 infection has been difficult.
Journal of managed care & specialty pharmacy
May 25, 2021
Medication therapy management (MTM) and comprehensive medication management (CMM) have been practiced by clinical pharmacists as a predominantly manual activity with interventions documented in a record-keeping system. Program evaluations, largely b...
Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with h...
BACKGROUND: Robotic prostatectomy is a costly new technology, but the costs may be offset by changes in treatment patterns. The net effect of this technology on Medicaid spending has not been assessed.
BACKGROUND: Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans.
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