What Predicts Health Care Transitions for Older Adults Following Introduction of LTSS?

Abstract

Objective: To determine predictors of health care transitions (i.e., acute care service use, transfers from lower to higher intensity services) among older adults new to long-term services and supports [LTSS]. Method: 470 new LTSS recipients followed for 24 months. Multivariable Poisson regression modeling within a generalized estimating equation framework. Results: Being male, having multiple chronic conditions, lower self-reported physical health ratings and lower quality of life ratings at baseline were associated with increased risk of health care transitions. Older adults in assisted living communities and nursing homes experienced decreases in health care transitions over time, while LTSS recipients at home had no change in risk. LTSS recipients who had orders to receive therapy, compared with those who did not, had a lower relative risk of transitions over time. Discussion: Predictors of future health care transitions support the need for LTSS providers to anticipate and monitor this risk for LTSS recipients.

Reported Needs and Depressive Symptoms Among Older Adults Entering Long-Term Services and Supports

“…Similarly, our subset had fewer individuals from the NH LTSS setting than ALCs or those receiving HCBS due to missing data at the 3-month follow-up. Findings from the parent study examining hospitalizations and other types of health care resource use found that LTSS recipients in NHs had the greatest number of resource use events at 3 months compared with LTSS recipients in ALCs or receiving services from home- and community-based organizations ( Hirschman, Toles, Hanlon, Huang, & Naylor, 2019 ). It is likely that the NH LTSS recipients who missed their follow-up interview after 3 months were hospitalized.…”

Mobile Technology Care Coordination of Long-Term Services and Support: Cluster Randomized Clinical Trial

“…Furthermore, focusing on new admissions to a care coordination program may provide new insights into the impact of the intervention. Additional studies over a longer period and additional analysis of population variables may provide better insight into care transitions (Hirschman et al, 2020;Kansagara et al, 2011;Verhaegh et al, 2014). Although Responses missing; one coordinator responded "Not Applicable" for Items 1, 4, and 5.…”

“…Furthermore, focusing on new admissions to a care coordination program may provide new insights into the impact of the intervention. Additional studies over a longer period and additional analysis of population variables may provide better insight into care transitions (Hirschman et al, 2020; Kansagara et al, 2011; Verhaegh et al, 2014). Although the cluster-randomized design in the study reported here strengthens the research design, it is possible defining clusters differently, that is, clusters of multiple provider settings and prespecifying the cluster control group care coordination approach, would further strengthen study design and outcome interpretation.…”

The 2023-2026 Hospice and Palliative Nurses Association Research Agenda

“…Secondary data analysis using machine learning and artificial intelligence research methods may uniquely contribute to understanding care transition trajectories and outcomes outside of the hospital setting. 174,175 These Journal of Hospice & Palliative Nursing www.jhpn.com cutting-edge methods make it possible to present information to clinicians in an easy-to-interpret format to support personalized clinical decision-making. 176 Moreover, machine learning and artificial intelligence may potentiate the development of dynamic, data-driven algorithms for identifying and communicating care transition risk using technology such as smartphone apps (eg, mHealth), wearable devices (eg, gait analysis for fall risk), and the interoperable exchange of shared care plans across the continuum of care using health information exchanges.…”