# Helping VA optimize its long-term care services

> **NIH VA IK2** · VETERANS ADMIN PALO ALTO HEALTH CARE SYS · 2023 · —

## Abstract

By 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 a...

## Key facts

- **NIH application ID:** 10689724
- **Project number:** 5IK2HX002860-04
- **Recipient organization:** VETERANS ADMIN PALO ALTO HEALTH CARE SYS
- **Principal Investigator:** JOSEPHINE JACOBS
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2023
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2020-07-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10689724

## Citation

> US National Institutes of Health, RePORTER application 10689724, Helping VA optimize its long-term care services (5IK2HX002860-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10689724. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*