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| Nursing home care quality: insights from a Bayesian network approach |
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Purpose: The purpose of this research is two-fold: (1) to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes and (2) 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 employed for the same purpose, our work yields both qualitative and quantitative insight into nursing home care quality.
Design & Methods: Several Bayesian networks are constructed to study the influences among factors associated with nursing home care quality, and their accuracy is compared and measured to other predictive models. Results: The best Bayesian network is found to perform better than other commonly employed 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 nursing home care quality. Furthermore, the results of our analysis identify their probabilistic relationships. Implications: 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 nursing home care quality 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. Goodson, J., Jang, W., & Rantz, M. (2008). Nursing home care quality: insights from a Bayesian network approach. The Gerontologist, 48(3), 338-348. Keywords: Nursing Home Quality, Bayesian Networks, Quality Indicators, Deficiencies, Nurse Staffing, Occupancy Rate |