Trajectories of care and outcomes of Veterans receiving home-based primary care.

BACKGROUND: Veterans Affairs (VA) home-based primary care (HBPC) provides comprehensive longitudinal care to patients with complex, chronic disabling disease. While enrollment is associated with lower hospitalization rates and costs, detailed trajectories have not been well described. METHODS: We performed a longitudinal descriptive study of patients newly enrolled in VA HBPC in fiscal year (FY) 2015. We extracted demographics, comorbidities, functional status, and social supports from VA and Medicare data and examined patterns of care and clinical outcomes, including hospital, nursing home (NH), hospice use and mortality from FY2015-2017. We present results using descriptive statistics, alluvial plots, and heat maps. RESULTS: We identified 10,571 HBPC enrollees in FY2015; mean age was 77.7. HBPC patients commonly had chronic medical conditions with high self-management burden (e.g., diabetes 48.2%) and disabling conditions such as dementia (39.3%). Over half had ≥2 deficits in activities of daily living, 46% had caregivers with functional limitations or no caregiver, and 25% resided in a socially deprived area. Patients experienced variable care trajectories. Mean time enrolled in HBPC was 331 days, 8.3% of patients were discharged after 3 months, and 22.8% stayed enrolled for over 2 years. Institutional health care use declined in the 6 months after initial enrollment: (e.g., hospital: 41%-25%, NH: 34%-11%). At 2 years, 36% of patients had died; among decedents, 58% received hospice and 72% died in a non-institutional setting. In the last 180 days of life, 84% of time was spent at home, and once enrolled in hospice, 97% of time was spent outside of institutional care. CONCLUSIONS: HBPC patients experience highly variable care trajectories but on average have reductions in acute care use and spend a majority of time in non-institutional settings. These data allow for a nuanced understanding of HBPC, providing a platform for monitoring, evaluating, and improving program function.

Better performance for right-skewed data using an alternative gamma model.

BACKGROUND: The Maximum Likelihood Estimator (MLE) for parameters of the gamma distribution is commonly used to estimate models of right-skewed variables such as costs, hospital length of stay, and appointment wait times in Economics and Healthcare research. The common specification for this estimator assumes the variance is proportional to the square of the mean, which underlies estimation and specification tests. We present a specification in which the variance is directly proportional to the mean. METHODS: We used simulation experiments to investigate finite sample results, and we used United States Department of Veterans Affairs (VA) healthcare cost data as an empirical example comparing the fit and predictive ability of the models. RESULTS: Simulation showed the MLE based on a correctly specified alternative has less parameter bias, lower standard errors, and less skewness in distribution than a misspecified standard model. The application to VA healthcare cost data showed the alternative specification can have better R square, smaller root mean squared error, and smaller mean residuals within deciles of predicted values. CONCLUSIONS: The alternative gamma specification can be a useful alternative to the standard specification for estimating models of right-skewed continuous variables.

Measuring the unmet needs of American military Veterans and their caregivers: Survey protocol of the HERO CARE survey.

BACKGROUND: Empowering Veterans to age in place is a Department of Veterans Affairs priority. Family or unpaid caregivers play an important role in supporting Veterans to achieve this goal. Effectively meeting the needs of Veterans and caregivers requires identifying unmet needs and relevant gaps in resources to address those needs. METHODS: Using a modified Socio-Ecological Model, we developed a prospective longitudinal panel design survey. We randomly selected 20,000 community-dwelling Veterans enrolled in the Veterans Health Administration (VHA), across five VHA sites. We oversampled Veterans with a higher predicted 2-year long-term institutional care (LTIC) risk. Veterans were mailed a packet containing a Veteran survey and a caregiver survey, to be answered by their caregiver if they had one. The Veteran survey assessed the following health-related domains: physical, mental, social determinants of health, and caregiver assistance. Caregivers completed questions regarding their demographic factors, caregiving activities, impact of caregiving, use of VA and non-VA services, and caregiver support resources. Follow-up surveys will be repeated twice at 12-month intervals for the same respondents. This article describes the HERO CARE survey protocol, content, and response rates. RESULTS: We received responses from 8,056 Veterans and 3,579 caregivers between July 2021 and January 2022, with 95.6% being received via mail. Veteran respondents were mostly males (96.5%), over 65 years of age (94.9%), married (55.0%), Non-Hispanic White (75.2%), and residing in urban areas (80.7%). CONCLUSIONS: This longitudinal survey is unique in its comprehensive assessment of domains relevant to older Veterans stratified by LTIC risk and their caregivers, focusing on social determinants, caregiver support, and the use of caregiver support resources. Survey data will be linked to Centers for Medicare & Medicaid Services and VA data. The results of this study will inform better planning of non-institutional care services and policy for Veterans and their caregivers.

Comparison of Claims-Based Frailty Indices in U.S. Veterans 65 and Older for Prediction of Long-Term Institutionalization and Mortality.

BACKGROUND: Frailty is increasingly recognized as a useful measure of vulnerability in older adults. Multiple claims-based frailty indices (CFIs) can readily identify individuals with frailty, but whether 1 CFI improves prediction over another is unknown. We sought to assess the ability of 5 distinct CFIs to predict long-term institutionalization (LTI) and mortality in older Veterans. METHODS: Retrospective study conducted in U.S. Veterans ≥65 years without prior LTI or hospice use in 2014. Five CFIs were compared: Kim, Orkaby (Veteran Affairs Frailty Index [VAFI]), Segal, Figueroa, and the JEN-FI, grounded in different theories of frailty: Rockwood cumulative deficit (Kim and VAFI), Fried physical phenotype (Segal), or expert opinion (Figueroa and JFI). The prevalence of frailty according to each CFI was compared. CFI performance for the coprimary outcomes of any LTI or mortality from 2015 to 2017 was examined. Because Segal and Kim include age, sex, or prior utilization, these variables were added to regression models to compare all 5 CFIs. Logistic regression was used to calculate model discrimination and calibration for both outcomes. RESULTS: A total of 3 million Veterans were included (mean age 75, 98% male participants, 80% White, and 9% Black). Frailty was identified for between 6.8% and 25.7% of the cohort with 2.6% identified as frail by all 5 CFIs. There was no meaningful difference between CFIs in the area under the receiver operating characteristic curve for LTI (0.78-0.80) or mortality (0.77-0.79). CONCLUSIONS: Based on different frailty constructs, and identifying different subsets of the population, all 5 CFIs similarly predicted LTI or death, suggesting each could be used for prediction or analytics.

Validation of a Multivariate Prediction Model of the Clinical Progression of Alzheimer's Disease in a Community-Dwelling Multiethnic Cohort.

BACKGROUND: The major aims of the three Predictors Studies have been to further our understanding of Alzheimer's disease (AD) progression sufficiently to predict the length of time from disease onset to major disease outcomes in individual patients with AD. OBJECTIVES: To validate a longitudinal Grade of Membership (L-GoM) prediction algorithm developed using clinic-based, mainly white patients from the Predictors 2 Study in a statistically representative community-based sample of Hispanic (N = 211) and non-Hispanic (N = 62) older adults (with 60 males and 213 females) from the Predictors 3 Study and extend the algorithm to mild cognitive impairment (MCI). METHODS: The L-GoM model was applied to data collected at the initial Predictors 3 visit for 150 subjects with AD and 123 with MCI. Participants were followed annually for up to seven years. Observed rates of survival and need for full-time care (FTC) were compared to those predicted by the algorithm. RESULTS: Initial MCI/AD severity in Predictors 3 was substantially higher than among clinic-based AD patients enrolled at the specialized Alzheimer's centers in Predictors 2. The observed survival and need for FTC followed the L-GoM model trajectories in individuals with MCI or AD, except for N = 32 subjects who were initially diagnosed with AD but reverted to a non-AD diagnosis on follow-up. CONCLUSION: These findings indicate that the L-GoM model is applicable to community-dwelling, multiethnic older adults with AD. They extend the use of the model to the prediction of outcomes for MCI. They also justify release of our L-GoM calculator at this time.

Peer-to-Patient-Aligned Care Team (Peer-to-PACT; P2P), a Peer-Led Home Visit Intervention Program for Targeting and Improving Long-term Care Services and Support for Veterans With High Needs and High Risk: Protocol for a Mixed Methods Feasibility Study.

BACKGROUND: Keeping older veterans with high needs and high risk (HNHR) who are at risk of long-term institutional care safely in their homes for as long as possible is a Department of Veterans Affairs priority. Older veterans with HNHR face disproportionate barriers and disparities to engaging in their care, including accessing care and services. Veterans with HNHR often have poor ability to maintain health owing to complicated unmet health and social needs. The use of peer support specialists (peers) is a promising approach to improving patient engagement and addressing unmet needs. The Peer-to-Patient-Aligned Care Team (Peer-to-PACT; P2P) intervention is a multicomponential home visit intervention designed to support older veterans with HNHR to age in place. Participants receive a peer-led home visit to identify unmet needs and home safety risks aligned with the age-friendly health system model; care coordination, health care system navigation, and linking to needed services and resources in collaboration with their PACT; and patient empowerment and coaching using Department of Veterans Affairs whole health principles. OBJECTIVE: The primary aim of this study is to evaluate the preliminary effect of the P2P intervention on patient health care engagement. The second aim is to identify the number and types of needs and unmet needs as well as needs addressed using the P2P needs identification tool. The third aim is to evaluate the feasibility and acceptability of the P2P intervention delivered over 6 months. METHODS: We will use a quantitative-qualitative convergent mixed methods approach to evaluate the P2P intervention outcomes. For our primary outcome, we will conduct an independent, 2-tailed, 2-sample t test to compare the means of the 6-month pre-post differences in the number of outpatient PACT encounters between the intervention and matched comparison groups. Qualitative data analysis will follow a structured rapid approach using deductive coding as well as the Consolidated Framework for Implementation Research. RESULTS: Study enrollment began in July 2020 and was completed in March 2022. Our sample size consists of 114 veterans: 38 (33.3%) P2P intervention participants and 76 (66.7%) matched comparison group participants. Study findings are expected to be published in late 2023. CONCLUSIONS: Peers may help bridge the gap between PACT providers and veterans with HNHR by evaluating veterans' needs outside of the clinic, summarizing identified unmet needs, and developing team-based solutions in partnership with the PACT. The home visit component of the intervention provides eyes in the home and may be a promising and innovative tool to improve patient engagement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46156.

Defining a taxonomy of Medicare-funded home-based clinical care using claims data.

BACKGROUND: As more Americans age in place, it is critical to understand care delivery in the home. However, data on the range of home-based services provided by Medicare is limited. We define a taxonomy of clinical care in the home funded through fee-for-service Medicare and methods to identify receipt of those services. METHODS: We analyzed Fee-for-service (FFS) Medicare claims data from a nationally-representative cohort of older adults, the National Health and Aging Trends Study (NHATS), to identify home-based clinical care. We included 6,664 NHATS enrollees age ≥ 70 and living in the community, observed an average of 3 times each on claims-linked NHATS surveys. We examined provider and service type of home-based clinical care to identify a taxonomy of 5 types: home-based medical care (physician, physician assistant, or nurse practitioner visits), home-based podiatry, skilled home health care (SHHC), hospice, and other fee-for-service (FFS) home-based care. We further characterized home-based clinical care by detailed care setting and visit types. RESULTS: From 2011-2016, 17.8%-20.8% of FFS Medicare beneficiaries age ≥ 70 received Medicare-funded home-based clinical care. SHHC was the most common service (12.8%-16.1%), followed by other FFS home-based care (5.5%-6.5%), home-based medical care (3.2%-3.9%), and hospice (2.6%-3.0%). Examination of the other-FFS home-based care revealed imaging/diagnostics and laboratory testing to be the most common service. CONCLUSIONS: We define a taxonomy of clinical care provided in the home, serving 1 in 5 FFS Medicare beneficiaries. This approach can be used to identify and address research and clinical care gaps in home-based clinical care delivery.

The Program of All-Inclusive Care for the Elderly: An Update after 25 Years of Permanent Provider Status.

PACE is the gold standard for community-based integrated care. Over the 25 years as permanent provider status by Centers for Medicare and Medicaid Services, it has evolved in design and grown in numbers served. We review the evidence base, history, and future direction of PACE.

Outpatient Care Fragmentation and Acute Care Utilization in Veterans Affairs Home-Based Primary Care.

Importance: Veterans Affairs (VA) Home-Based Primary Care (HBPC) provides comprehensive, interdisciplinary primary care at home to patients with complex, chronic, disabling disease, but little is known about care fragmentation patterns and consequences among these patients. Objective: To examine outpatient care fragmentation patterns and subsequent acute care among HBPC-engaged patients at high risk of hospitalization or death. Design, Setting, and Participants: This retrospective cohort study included VA patients aged at least 65 years who were enrolled in the VA and Medicare, whose risk of hospitalization or death was in the top 10%, and who had at least 4 outpatient visits between October 1, 2013, and September 30, 2014. HBPC engagement was defined as having at least 2 HBPC encounters between July 1, 2014, and September 30, 2014. Data were analyzed from March 2020 to March 2022. Exposures: Two indices of outpatient care fragmentation: practitioner count and the Usual Provider Continuity Index (UPC), based on VA and non-VA health care use from October 1, 2013, to September 30, 2014. All care delivered by HBPC clinicians was analyzed as coming from a single practitioner. Main Outcomes and Measures: Emergency department (ED) visits and hospitalizations for ambulatory care sensitive conditions (ACSC) from VA records and Medicare claims from October 1, 2014, to September 30, 2015. Results: Among 8908 identified HBPC patients, 8606 (96.6%) were male, 1562 (17.5%) were Black, 249 (2.8%) were Hispanic, 6499 (73.0%) were White, 157 (1.8%) were other race or ethnicity, and 441 (5.0%) had unknown race or ethnicity; the mean (SD) age was 80.0 (9.02) years; patients had a mean (SD) of 11.25 (3.87) chronic conditions, and commonly had disabling conditions such as dementia (38.8% [n = 3457]). In adjusted models, a greater number of practitioners was associated with increased odds of an ED visit (adjusted odds ratio [aOR], 1.05 [95% CI, 1.03-1.07]) and hospitalization for an ACSC (aOR, 1.04 [95% CI, 1.02-1.06]), whereas more concentrated care with a higher UPC was associated with reduced odds of these outcomes (highest vs lowest tertile of UPC: aOR for ED visit, 0.77 [95% CI, 0.67-0.88], aOR for ACSC hospitalization, 0.78 [95% CI, 0.68-0.88]). Conclusions and Relevance: Among patients in HBPC, fragmented care was associated with more ED visits and ACSC hospitalizations. These findings suggest that consolidating or coordinating fragmented care may be a target for reducing preventable acute care.

What matters when it comes to measuring Age-Friendly Health System transformation.

Thousands of health systems are now recognized as "Age-Friendly Health Systems," making this model one of the most widely disseminated - and most promising- models to redesign care delivery for older adults. Sustaining these gains will require demonstrating the impact on care delivery and outcomes of older adults. We propose a new measurement model to more tightly link Age-Friendly Health System transformation to outcomes within each "M" (What Matters, Medications, Mobility, and Mentation). We evaluated measures based on the following characteristics: (1) conceptual responsiveness to changes brought about by practicing "4Ms" care; (2) degree to which they represent outcomes that matter to older adults; and (3) how they can be feasibly, reliably, and validly measured. We offer specific examples of how novel measures are currently being used where available. Finally, we present measures that could capture system-level effects across "M"s. We tie these suggestions together into a conceptual measurement model for AFHS transformation, with the intent to spur discussion, debate, and iterative improvement in measures over time.

Successful Discharge of Short Stay Veterans from VA Community Living Centers.

The Veterans Health Administration (VHA) long-term care rebalancing initiative encouraged VA Community Living Centers (CLCs) to shift from long-stay custodial-focused care to short-stay skilled and rehabilitative care. Using all VA CLC admissions during 2007-2010 categorized as needing short-stay rehabilitation or skilled nursing care, we assessed the patient and facility rates of successful discharge to the community (SDC) of these short-stay Veterans. We found large variation in inter- as well as intra- facility SDC rates across the rehabilitation and skilled nursing short-stay cohorts. We discuss how our results can help guide VHA policy directed at delivering high-quality short-stay CLC care for Veterans.

Development and validation of a prediction model for actionable aspects of frailty in the text of clinicians' encounter notes.

OBJECTIVE: Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes. MATERIALS AND METHODS: We used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nutritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV). RESULTS: We manually annotated 155 952 sentences from 326 patients. Elastic net regression had the best performance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49-0.54) followed by random forests (SBS 0.49, 95% CI 0.47-0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37-0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%-56.6%) at a sensitivity of 80%. DISCUSSION: Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models. CONCLUSIONS: NLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.

"I felt useless": a qualitative examination of COVID-19's impact on home-based primary care providers in New York.

Research on professional burnout during the pandemic has focused on hospital-based health care workers. This study examined the psychological impact of the pandemic on home-based primary care (HBPC) providers. We interviewed 13 participants from six HBPC practices in New York City including medical/clinical directors, program managers, nurse practitioners, and social workers and analyzed the transcripts using inductive qualitative analysis approach. HBPC providers experienced emotional exhaustion and a sense of reduced personal accomplishment. They reported experiencing grief of losing many patients at once and pressure to adapt to changing circumstances quickly. They also reported feeling guilty for failing to protect their patients and reduced confidence in their professional expertise. Strategies to combat burnout included shorter on-call schedules, regular condolence meetings to acknowledge patient deaths, and peer support calls. Our study identifies potential resources to improve the well-being and reduce the risk of burnout among HBPC providers.