OBJECTIVES: To examine the incidence of, variations in, and costs of potentially avoidable hospitalizations (PAHs) of nursing home (NH) residents at the end of life and to identify the association between NH characteristics and a facility-level quality measure (QM) for PAH. DESIGN: Retrospective. SETTING: Hospitalizations originating from NHs. PARTICIPANTS: Long-term care NH residents who died in 2007. MEASUREMENTS: A risk-adjusted QM was constructed for PAH. A Poisson regression model was used to predict the count of PAH given residents' risk factors. For each facility, the QM was defined as the difference between the observed facility-specific rate (per 1,000 person-years) of PAH (O) and the expected risk-adjusted rate (E). A logistic regression model with state fixed-effects was then fit to examine the association between facility characteristics and the likelihood of having higher-than-expected rates of PAH (O-E > 0). QM values greater than 0 indicate worse-than-average quality. RESULTS: Almost 50% of hospital admissions for NH residents in their last year of life were for potentially avoidable conditions, costing Medicare $1 billion. Five conditions were responsible for more than 80% of PAHs. PAH QM across facilities showed significant variation (mean 12.0 ± 142.3 per 1,000 person-years, range -399.48 to 398.09 per 1,000 person-years). Chain and hospital-based facilities were more likely to exhibit better performance (O-E < 0). Facilities with higher nursing staffing were more likely to have better performance, as were facilities with higher skilled staff ratio, those with nurse practitioners or physician assistants, and those with on-site X-ray services. CONCLUSION: Variations in facility-level PAHs suggest that a potential for reducing hospital admissions for these conditions may exist. Presence of modifiable facility characteristics associated with PAH performance could help us formulate interventions and policies for reducing PAHs at the end of life.
OBJECTIVES: To examine the incidence of, variations in, and costs of potentially avoidable hospitalizations (PAHs) of nursing home (NH) residents at the end of life and to identify the association between NH characteristics and a facility-level quality measure (QM) for PAH. DESIGN: Retrospective. SETTING: Hospitalizations originating from NHs. PARTICIPANTS: Long-term care NH residents who died in 2007. MEASUREMENTS: A risk-adjusted QM was constructed for PAH. A Poisson regression model was used to predict the count of PAH given residents' risk factors. For each facility, the QM was defined as the difference between the observed facility-specific rate (per 1,000 person-years) of PAH (O) and the expected risk-adjusted rate (E). A logistic regression model with state fixed-effects was then fit to examine the association between facility characteristics and the likelihood of having higher-than-expected rates of PAH (O-E > 0). QM values greater than 0 indicate worse-than-average quality. RESULTS: Almost 50% of hospital admissions for NH residents in their last year of life were for potentially avoidable conditions, costing Medicare $1 billion. Five conditions were responsible for more than 80% of PAHs. PAH QM across facilities showed significant variation (mean 12.0 ± 142.3 per 1,000 person-years, range -399.48 to 398.09 per 1,000 person-years). Chain and hospital-based facilities were more likely to exhibit better performance (O-E < 0). Facilities with higher nursing staffing were more likely to have better performance, as were facilities with higher skilled staff ratio, those with nurse practitioners or physician assistants, and those with on-site X-ray services. CONCLUSION: Variations in facility-level PAHs suggest that a potential for reducing hospital admissions for these conditions may exist. Presence of modifiable facility characteristics associated with PAH performance could help us formulate interventions and policies for reducing PAHs at the end of life.
Authors: Ellen F Binder; Robin L Kruse; Ashley K Sherman; Richard Madsen; Steven C Zweig; Ralph D'Agostino; David R Mehr Journal: J Gerontol A Biol Sci Med Sci Date: 2003-01 Impact factor: 6.053
Authors: Daniel S Stadler; Brant J Oliver; Jennifer G Raymond; George F Routzhan; Ellen A Flaherty; James E Stahl; John A Batsis; Stephen J Bartels Journal: J Am Med Dir Assoc Date: 2019-05-06 Impact factor: 4.669
Authors: Orna Intrator; Edward Alan Miller; Emily Gadbois; Joseph Kofi Acquah; Rajesh Makineni; Denise Tyler Journal: Health Serv Res Date: 2015-11-13 Impact factor: 3.402