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The Early Childhood Systems Collective Impact Project (ECS Collective Impact Project) will help to re-envision a truly coordinated approach to program implementation designed to advance early childhood and family well-being outcomes across federal programs that support expectant parents, children ages 0 to 8, and their families.
In recent years several researchers and child welfare agencies have begun developing predictive risk models to support child welfare decision-making. Predictive analytics is a sophisticated form of risk modeling that uses historical data to understand relationships between myriad factors to estimate a probability score for the outcome of interest.
This environmental scan, conducted from September 2016 – September 2021, examines the potential impacts of select strategies on the cost, duration, and phase transition probability associated with drug, preventive vaccine, and therapeutic complex medical device development stages.
Per Section 223(d)(7)(A) of the Protecting Access to Medicare Act (PAMA) of 2014 (Public Law 113-93), the HHS Secretary must submit to Congress an annual report on the use of funds provided under all demonstration programs conducted under this subsection, not later than one year after the date on which the first state is selected for a demonstration program under this subsection, and annually t
This report and dataset inventory identifies federally funded data linkages that may facilitate patient-centered outcomes research (PCOR) on economic outcomes for Medicare fee-for-service (FFS) beneficiaries.
People can be discharged from nursing homes for many reasons. Discharges may be a positive outcome and at an individual’s choice. In other cases, discharges may be at the direction of the facility and against the will of the resident. There are strict rules about when involuntary facility-initiated discharges (FIDs) are allowed.
Improving health equity in the United States is a priority for the Biden-Harris Administration in order to address longstanding disparities in health outcomes. Health inequities can be conceptualized and measured as drivers of differences in health outcomes.
This project focused on validating an established claims-based frailty indexes (CFI) using linked claims-EHR databases of multiple large health systems. Additionally, the project assessed and compared the EHR and claims data of these data sources to ensure sufficient data quality for frailty analysis.