Assisted Living Pilot Program

Hedrick, Susan C.; Guihan, Marylou; Chapko, Michael K.; Sullivan, Jean H.; Zhou, Xiao Hua; Manheim, Larry M.; Forsberg, Christopher W.; Mambourg, Floss J.

doi:10.1177/0898264308328929

Cited by 11 publications

(14 citation statements)

References 10 publications

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“…Regarding facility-level predictors of placement, our findings are similar to previous U.S. studies (Dobbs et al, 2006; Hedrick et al, 2009; Phillips et al, 2003; Zimmerman et al, 2005) in that only a few facility characteristics were associated with LTC placement. Specifically, residents receiving care in facilities with larger numbers of spaces (i.e., > 30 DAL or > 148 total), LPN and/or RN coverage on-site 24/7, and/or an affiliated primary care physician showed a significantly reduced risk of LTC placement over one year.…”

Section: Discussionsupporting

confidence: 90%

“…Findings regarding the availability of an RN and risk of nursing home placement have been inconsistent with one study showing a reduced risk similar to ours (Phillips et al, 2003 ); another, an increased risk (Zimmerman et al, 2005 ); and others, no association (Dobbs et al, 2006 ;Hedrick et al, 2009 ). This inconsistency may refl ect the importance of staffi ng intensity and skill mix (e.g., proportion of total directcare hours provided by licensed staff) rather than simply the presence or absence of an RN (Stearns et al, 2007 ).…”

Section: Continuedmentioning

confidence: 75%

“…Variation in the relative importance of some resident-level factors has been observed across studies likely refl ecting differences in AL sample size and characteristics (e.g., proportion with dementia) and in study measures (Gaugler et al, 2009 ;Luppa et al, 2010 ). A few studies have examined the role of AL facility characteristics (e.g., size, type, ownership, and staffi ng) as risk factors for LTC placement (Dobbs et al, 2006 ;Hedrick, Guihan, Chapko, Sullivan, & Zhou, 2009 ;Phillips et al, 2003 ;Stearns et al 2007 ;Zimmerman et al, 2005 ) although fi ndings remain unclear. A more comprehensive understanding of the role of such factors (beyond resident-level risk) is essential for ensuring empirically based decision making regarding AL practice and policy, particularly with regard to future regulations regarding staffi ng levels and mix (Polivka & Salmon, 2008 ;Stearns et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

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Predictors of Nursing Home Placement from Assisted Living Settings in Canada

et al. 2013

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We sought to estimate the incidence of long-term care (LTC) placement and to identify resident- and facility-level predictors of placement among older residents of designated assisted living (AL) facilities in Alberta, Canada. Included were 1,086 AL residents from 59 facilities. Research nurses completed interRAI-AL resident assessments and interviewed family caregivers and administrators. Predictors of placement were identified with multivariable Cox proportional hazards models. The cumulative incidence of LTC admission was 18.3 per cent by 12 months. Significantly increased risk for placement was evident for older residents and those with poor social relationships, little involvement in activities, cognitive and/or functional impairment, health instability, recent falls and hospitalizations/emergency department visits, and severe bladder incontinence. Residents from larger facilities, with an LPN and/or RN on-site 24/7 and with an affiliated primary care physician, showed lower risk of placement. Our findings highlight clinical and policy areas where targeted interventions may delay LTC admissions.

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Section: Discussionsupporting

confidence: 90%

Section: Continuedmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

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Predictors of Nursing Home Placement from Assisted Living Settings in Canada

et al. 2013

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“…The data shows that +CP communities exhibited a nearly 40% lower average hospitalization rate than the –CP communities (33.8% versus 73.6%). Several senior living outcomes studies by Zimmerman et al and Hedrick et al reported average annual hospitalization rates of 51% and 40%, respectively [ 29 , 58 ]. The +CP communities exhibited hospitalization rates that were 18% and 7% lower than the averages reported by Zimmerman and Hedrick et al, respectively [ 29 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study

Wilmink¹,

Dupey²,

Alkire³

et al. 2020

JMIR Aging

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Background Wearables and artificial intelligence (AI)–powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior’s change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors. Objective The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities. Methods Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (–CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time. Results The residents of the +CP and –CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the –CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively. Conclusions The AI-powered digital health platform provides the community staff with actionable information regarding each resident’s activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.

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“…Previous pilot studies by the VA concerning the feasibility of assisted living facilities demonstrated that they were much costlier than other types of care [17,32,36,35,46]. However, these studies were conducted on a client population that had an average age of 69.3 years whereas the median age of a returning OIF/OEF veteran is 25 years old [73].…”

Section: Rationale For the Assisted Living Neurorehabilitation Pilot mentioning

confidence: 99%

Enhanced neurorehabilitation techniques in the DVBIC Assisted Living Pilot Project

Hoffman

Shesko

Harrison

2010

NRE

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Traumatic Brain Injury has been labeled the "silent epidemic" in our current wars. Both CBO and the RAND reports predict that the costs of these injuries will be both extensive and enduring. The projected costs are based not only upon the loss contribution of these warriors to our economy, but also the long-term medical and assistive care that will be needed to support these veterans for decades to come. Thus, the primary goal of the Assisted Living Pilot Project (ALPP) at the Defense and Veterans Brain Injury Center -Johnstown (DVBIC-J) is to promote the ability of the injured warrior to move from assisted living to living independently and to be self-supporting by providing a continuum of care. To accomplish this goal the DVBIC-J ALPP is providing full set of traditional services (physical, occupational, speech, psychological/cognitive, social/familial, vocational, and spiritual), along with "cutting-edge" rehabilitative treatment technologies. These cutting-edge therapies include transdisciplinary clinical consultations, interactive patient and family counseling, and telemedicine-teleconferencing for clinical evaluations and family/significant other care participation. These services will be available to those who require assisted living through their progression to community re-entry. The ALPP also serves as a vehicle for clinical trials to investigate the effects of an enriched environment (e.g., recreational therapies, massage, multisensory stimulation, etc.) on neurorehabilitation therapy, rural telemedicine for servicemembers with traumatic brain injury, and long-term outcome measures of those who have received neurorehabilitation services at the DVBIC-J site. DVBIC-J is also developing collaborative projects with universities and private industry to create an incubator for new rehabilitation technologies. The technologies that DVBIC-J will be focusing on will include assistive technologies (to assist cognitive, physical, and communicative impairments), virtual and augmented reality simulations (for both diagnosis and treatment of TBI and PTSD), and telecommunication technologies to improve rehabilitation services to those warriors that have returned to their homes in rural areas.

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Assisted Living Pilot Program

Cited by 11 publications

References 10 publications

Predictors of Nursing Home Placement from Assisted Living Settings in Canada

Predictors of Nursing Home Placement from Assisted Living Settings in Canada

Artificial Intelligence–Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study

Enhanced neurorehabilitation techniques in the DVBIC Assisted Living Pilot Project

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