HSR&D Home » Research » CDA 19-120 – HSR&D Study
Helping VA optimize its long-term care services
Josephine C. Jacobs PhD MSc
Palo Alto, CA
Funding Period: July 2020 - December 2025
AbstractBy 2023, it is expected that the number of VHA enrollees aged 65 and over will increase from 4.1 million to 4.7 million. To meet the growing demand for long-term care services, VA has attempted to expand its home and community-based services (HCBS) through measures such as the 1999 Millennium Health Care and Benefits Act (the Millennium Act). These expansion efforts were based on the premise that HCBS provide care in Veterans’ setting of choice for a lower cost than in institutional settings and with comparable outcomes. Since passing the Millennium Act, however, VA still lags significantly behind other health systems with respect to rebalancing its long-term care expenditures away from institutional care and towards HCBS. VA’s 21 percentage point increase in the proportion of its long-term care expenditures spent on HCBS between 1999 and 2016 (from 5% to 26%) can be compared to Medicaid’s 42 percentage point increase over the same period (from 15% o 57%). VA needs to examine the empirical evidence to understand why this transformation remains elusive. A health system’s ability to rebalance towards HCBS is determined by a combination of patient, system, and family level factors. Precise patient targeting, local home health market conditions, and adequate supply of and support for informal caregivers all contribute to how successful health systems will be in rebalancing towards HCBS. However, these factors remain under-explored in the VA context – in part due to gaps in VA’s structured data and in part due to the limited application of methods that enable these types of analyses. My long-term goal is to become an independent investigator focused on leading research initiatives that help VA to achieve its long-term care rebalancing aims and to fill these gaps in the existing evidence base. The proposed research will strengthen VA’s knowledge of how patient, system, and family level factors are affecting its rebalancing efforts. Specifically, the research aims of this CDA-2 are to: 1) use natural language processing to extract patient functional status from free-text notes and use the constructed measures to improve prediction of Veterans’ one-year risk of institutionalization; 2) build a geospatial database of VA and VA-contracted home health providers and conduct analyses evaluating the association between distance to and market supply of home health agencies and long-term care utilization patterns; and 3) quantify the impact of informal care receipt on VA health care utilization and costs. I will achieve these aims by receiving mentorship and training in natural language processing, risk adjustment, geospatial econometrics, and causal modeling. These new skills will contribute to my overall career development and, in collaboration with my mentors and operational partners, enable me to submit two merit review proposals focused on developing enhanced HCBS patient targeting tools and improved caregivers supports. They will also enable me to submit an application for a partnered evaluation initiative with the Office of Connected Care aimed at developing a geospatial tool to help regional offices efficiently identify prospective partners for new home health service contracts. Overall, this CDA will help me to become an independent investigator focused on leading research initiatives that help VA achieve its long-term care rebalancing aims. The results of this CDA project will be relevant to Veterans, their caregivers, and VA policy makers involved in allocating long-term care funding and will be an innovative contribution to the broader literature on the determinants of successful HCBS expansion strategies.
NIH Reporter Project Information: https://reporter.nih.gov/project-details/9951207
DRA: Aging, Older Veterans' Health and Care, Health Systems
DRE: TRL - Applied/Translational
Keywords: Care Coordination, Career Development, Caregiving, Decision Support
MeSH Terms: None at this time.