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Making Medicaid Data More Accessible Through Common Data Models and FHIR APIs

Basic Details
Date Posted
Status
In progress
Description

This is a joint agency project involving the Food and Drug Administration (FDA) and National Institutes of Health/ National Library of Medicine (NIH/NLM) that addresses the Patient-Centered Outcomes Research (PCOR) priority to expand data capacity or data infrastructure for conducting research that informs decisions about the effectiveness of health interventions used in the Medicaid and Children's Health Insurance Programs.

This project aims to develop and publish open source code to format T-MSIS Research Identifiable File (RIFs) to the Sentinel CDM in tandem with NLM’s formatting of the T-MSIS RIFs to the OMOP CDM. Data quality will be characterized using a harmonized Data Quality Assessment Framework for electronic healthcare data that was developed in a previously funded OS-PCORTF project. To illustrate the benefits of CDM transformation, open source tools will be used to conduct several studies of public health importance, such as studies of maternal mortality and adherence to clinical guidelines for perinatal testing. Additionally, the project will assess the feasibility of using Fast Healthcare Interoperability Resources (FHIR) APIs (Application Programming Interfaces) to link Electronic Health Record (EHR) data to T-MSIS claims data. Lastly, the project will ensure stakeholder engagement and sustainability via a Technical Expert Panel (TEP) to provide guidance on the data quality metrics, the PCOR demonstration projects, and development of a series of training materials. The goal of the training materials is to ensure findings are optimally disseminated to the Medicaid research community to lead to greater utilization of the CDM translation code and use of T-MSIS for patient-centered outcomes research in Sentinel and OMOP ecosystems.

This project covers several major tasks:

Task 1: Develop open source code to format the T-MSIS data into the Sentinel and Observational Medical Outcomes Partnership (OMOP) Common Data Models (CDMs)

Task 2: Leverage the ASPE funded data quality metrics model to characterize each CDM-formatted version

Task 3: Develop open-source code to create a mother-infant linkage in the Sentinel CDM to support several Sentinel analyses on maternal health

Task 4: Conduct one or more demonstration studies assessing the harms and benefits of an aspect of maternal health using the transformed T-MSIS dataset and Sentinel analytic tools. The FDA-led workgroup will include participation from CDC/NCHHSTP, CDC/NCBDDD, NIH/NICHD and HRSA

Task 5: Evaluate the feasibility of implementing FHIR APIs to link T-MSIS data with EHR data

Task 6: Engage stakeholders and create sustainability by:

  • Establishing a TEP with relevant expertise in the Medicaid program, T-MSIS data, or patient-centered outcomes research to provide non-binding guidance from an end-user’s perspective on the data quality metrics selected in Task 2,  potential PCOR demonstration projects using the new T-MSIS dataset in Task 4, and the structure of the training materials
  • Developing a webinar series to train Medicaid researchers on the new data transformation tools and disseminate major project findings
Deliverable(s) (5)
Task 1: The Centers for Medicare and Medicaid Services (CMS): Medicaid and Children’s Health Insurance Program (CHIP) Claims in Sentinel Common Data Model (SCDM) Format Task 3: A Mother-Infant Linkage Using CMS Medicaid and CHIP Claims in the Sentinel Common Data Model (SCDM) Format Task 4: (Protocol) Prenatal and Congenital Syphilis in the US: Characterizing Screening and Treatment Task 5: (White Paper) Feasibility of Using the Fast Healthcare Interoperability Resources (FHIR) Specification to Enable EHR Data Linkage with Administrative Claims, with T-MSIS Data as a Motivating Example Task 6: Transformed Medicaid Statistical Information System (T-MSIS) Training Webinar Series
Information
Time Period
August 1, 2021 – September 29, 2024
Population / Cohort
Low-income younger adults and children; mother-infant data
Data Source(s)
SCDM-formatted Transformed Medicaid Statistical Information System (T-MSIS) utilization and claims data from Medicaid and the Medicaid Children’s Health Insurance Program (CHIP).
Related Links
Related Link(s)
Transformed Medicaid Statistical Information System (T-MSIS)
ASPE Project Page
Observational Health Data Sciences and Informatics OMOP Common Data Model
Presentation: Variation in Mother-Infant Linkage Rates by Jurisdiction in U.S. Medicaid Data
Presentation: Diversifying the FDA’s Sentinel System with Rigorous Quality Inclusion Rules for the U.S. Medicaid Population
Presentation: Untangling National Medicaid Data: 30 Data Quality Metrics to Support Maternal Health Studies in Two Common Data Models
Presentation: Prenatal Syphilis in the US: Characterizing Screening and Treatment During Pregnancy in Publicly and Commercially Insured Individuals
Workgroup Leader(s)

Judith C. Maro, PhD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

David Money, RPh., PMH; Efe Eworuke, Ph.D., MSc; Division of Epidemiology II, Office of Pharmacovigilance and Epidemiology, Office of Surveillance and Epidemiology, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, MD;

Sarah Dutcher, PhD, Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland

Workgroup Member(s)

Patricia Bright, MSPH, PhD; Jamila Mwidau, RN, BSN, MPH; Lucia Menegussi, BSN, MS, MSL; Jamal T. Jones, PhD, MPH; Terrence Lee, PhD, MPH; Office of Surveillance and Epidemiology, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, MD

Brad Hammill, DrPh; Steven J. Lippmann, PhD; Michael Stagner; Jessica E. Pritchard, PhD Department of Population Health Sciences, Duke University School of Medicine, Durham, NC

Christine Halbig, MPH; Laura Shockro, Katie Shapiro, Malcolm Rucker, MS; Lauren Zichittella, MS Alexander Mai; Daniel Kiernan; Suzanne Carter, PhD, MS, MBA; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA

Robert Rosofsky, MA; Health Information Systems Consulting LLC, Milton, MA