Nursing Home Care Quality: Insights From a Bayesian Network Approach

ArticleinThe Gerontologist 48(3):338-48 · July 2008with10 Reads
DOI: 10.1093/geront/48.3.338 · Source: PubMed
Abstract
The purpose of this research is twofold. The first purpose is to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes. The second is to provide new insight into the relationships that exist among various measures of quality and how such measures affect the overall quality of nursing home care as measured by the Observable Indicators of Nursing Home Care Quality Instrument. In contrast to many methods used for the same purpose, our method yields both qualitative and quantitative insight into nursing home care quality. We construct several Bayesian networks to study the influences among factors associated with the quality of nursing home care; we compare and measure their accuracy against other predictive models. We find the best Bayesian network to perform better than other commonly used methods. We also identify key factors, including number of certified nurse assistant hours, prevalence of bedfast residents, and prevalence of daily physical restraints, that significantly affect the quality of nursing home care. Furthermore, the results of our analysis identify their probabilistic relationships. The findings of this research indicate that nursing home care quality is most accurately represented through a mix of structural, process, and outcome measures of quality. We also observe that the factors affecting the quality of nursing home care collectively determine the overall quality. Hence, focusing on only key factors without addressing other related factors may not substantially improve the quality of nursing home care.
    • "Nursing homes with greater funding can inject greater variety into residents' schedules (Benjamin et al. 2009). Thus, underfunded nursing homes and for-profit nursing homes are the most likely to have rigid schedules (Goodson et al. 2008; Mitka 2009). These highly regulated settings increase the risk for fatalistic suicides. "
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