A few of our current projects are listed below
- Pregnancy algorithms:
- We have worked on extensive methodologies to improve validity to identify pregnancy outcome types as well as predict pregnancy start and end dates. We have linked Medicaid data to state vital statistics data to establish rich and comprehensive data sources for pregnancy research. We have developed pregnancy cohorts within automated real-world healthcare data to support valid inferences about drug effects.
- MAX/TAF:
- This project aims to evaluate the data quality and usability of the Medicaid comprehensive managed care plan of the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF).
- Signal identification using TreeScan:
- We apply TreeScan analysis, a data-mining approach, to screen thousands of maternal and infant outcomes and identify potential adverse outcomes for later causal inference studies