Data Overview
A hallmark of the Division of Health Policy and Economics is our utilization of large, population-level data to conduct research. The Division takes pride in our existing shared resources that our faculty and students can access. Two of these rich data sources include Medicare and Medicaid claims data. Medicare claims data includes a breadth of health information, including beneficiary demographic and enrollment information, Medicare fee-for-service claims data including all parts A, B, and D files, and Medicare Advantage encounter data, including claims for six care settings: inpatient, skilled nursing facilities, home health, institutional outpatient, carrier, and durable medical equipment. The Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) include data on Medicaid and the Children’s Health Insurance Program (CHIP) enrollment, demographics, service utilization (inpatient hospital, long-term care, other services, including outpatient, and pharmacy claims), and payments from all US states.
Medicaid Claims Data
The Division of Health Policy and Economics currently houses one of the largest repositories of new-generation national Medicaid claims data. The T-MSIS Analytic Files (TAF), first released in 2019, include demographic and eligibility information for all Medicaid enrollees, as well as detailed data on inpatient, outpatient, and prescription drug utilization and spending. These files represent a significant improvement in quality and usability over the predecessor Medicaid Analytic eXtract (MAX) files. The TAF data are highly complex, however, with varying quality across data elements, states, and time. In an effort to catalyze policy-relevant research using TAF, Weill Cornell Medicine became a founding member of the Medicaid Data Learning Network, a national collaborative of academic research teams designed to develop best practices for working with the data.
Researchers at Weill Cornell Medicine have used TAF data to examine variation in access to primary care in the Medicaid program, segregation of care for Medicaid enrollees, the effects of Medicaid’s transportation benefit on health outcomes, and the implications of recent policy changes in Medicaid designed to improve access to treatments for substance use disorder and mental health care.
Post-Acute/Long-Term Care (LTC) Research
Multiple data sources are used in the division for research on individuals who receive post-acute and long-term care in nursing homes. These include Minimum Data Set (MDS) assessments and Medicare fee-for-service claims. MDS assessments are federally mandated for all nursing home residents, regardless of payer, and contain data elements on demographic information, diagnoses, measures of both physical and cognitive functional status, and process measures of care. The assessments can be merged with Medicare claims for inpatient and outpatient services, as well as the Medicare Beneficiary Summary File (MBSF). The MBSF includes additional resident characteristics such as chronic and disabling conditions as well as the reason for Medicare entitlement and current enrollment status.
HPE researchers frequently use the “Long Term Care: Facts on Care in the US” (LTCfocus) database for research on nursing homes. LTCfocus is a free online data source hosted by Brown University. It includes facility characteristics for nearly all nursing homes in the US. Measures in LTCFocus are constructed from MDS assessments, the Certification and Survey Provider Enhanced Reporting (CASPER) System, in addition to Medicare enrollment and claims data aggregated to the facility level. LTCFocus also includes county-level market data from the Area Health Resources File. LTCFocus can be merged with MDS assessments and Medicare claims using CMS Certification Numbers.
Beneficiaries Dually Eligible for Medicare & Medicaid
Over 12 million Medicare beneficiaries are dually enrolled in Medicaid. These individuals, usually called dual eligibles, represent one of the most vulnerable patient populations due to their complex medical, social, and behavioral health conditions. Despite their great policy and clinical importance, data limitations have been a major challenge to conducting high quality research about dual eligibles. Most research has only been able to use Medicare data to examine primary, acute, and post-acute care among dual eligibles, without considering long-term services and supports or intensive behavioral health treatment covered by Medicaid.
Researchers in the Division of Health Policy and Economics have developed a linked Medicare-Medicaid dataset for dual eligibles. This dataset encompasses the totality of care of dual eligibles covered by both Medicare and Medicaid (Exhibit 1). Both Medicare fee-for-service and Medicare Advantage beneficiaries are included in this dataset. HPE faculty are using this dataset to study various important health policy topics, including dual eligible special needs plans and use of home and community-based services at the end of life among dual eligibles.
Medicare Claims & Nursing Home Acquisition/Ownership Data
The Health Economics Financing and Transparency Initiative (HEFTI) aims to increase transparency and informed decision-making within the nursing home sector. By consolidating various datasets, including proprietary information on ownership structures and public data on quality, costs, financials, and enforcement, HEFTI will offer a comprehensive view of nursing home operations. This initiative stands to significantly impact policymakers, state regulators, and researchers by providing a unified platform for analyzing and comparing nursing home performance, enabling targeted improvements in care quality and regulatory compliance.
HEFTI's platform is designed not just to aggregate data, but to make it intuitively accessible through user-friendly interfaces, facilitating straightforward analyses. By allowing users to track changes in ownership, evaluate quality metrics, and assess financial information across different timelines and geographic regions, HEFTI promotes a deeper understanding of the factors influencing nursing home quality. This, in turn, supports more nuanced policy development and regulation, offering a robust tool for stakeholders aiming to enhance the accountability and transparency of nursing home care nationwide.