Demystifying Quality Metrics and Unveiling the True Measure of Quality of Care in Nursing Homes
Abstract
The increasing elderly population in the United States, coupled with the increasing average life expectancy, presents a significant challenge in delivering quality nursing home services while managing complex resident needs. Examining the landscape of nursing home quality unveils that its evolution was triggered by pivotal events, notably the emergence of the Minimum Data Set (MDS) following the Omnibus Budget Reconciliation Act (OBRA) of 1987. The MDS, a comprehensive clinical assessment tool, forms the basis for various Quality Indicators (QIs) and Quality Measures (QMs) crucial for identifying areas of improvement in nursing home care. QIs/QMs have become integral in the evaluation of nursing home quality and have subsequently contributed to the development and implementation of the Nursing Home Compare (NHC) public reporting system and the Center for Medicare and Medicaid Services (CMS) Five-Star Quality Rating System. However, concerns have been raised regarding the efficacy of the CMS Five-Star Rating System as a reliable quality measure. Challenges, such as inconsistency of MDS reporting, significant variations in standard errors when comparing across facilities, and ascertainment bias, contribute to the ongoing debate surrounding the reliability of the CMS Five-Star Rating System as an indicator of quality. To measure the effectiveness of the current quality metrics in capturing the true quality of nursing homes, chapter 4 of this dissertation adopts the Donabedian's Structure-Process-Outcome (SPO) framework to classify QIs/QMs (see Table 4.1) and examines the correlation between QIs/QMs and COVID-19 outcomes. Establishing which QIs/QMs are well-correlated with COVID-19 outcomes will enable us to recommend them as the true quality metrics of nursing homes. This information will be invaluable for patients and their families in making informed decisions when selecting a nursing home. It is also recognized that optimal health, functioning, and Quality-Of-Life (QOL) are achieved when favorable conditions exist in all areas of one’s life. Therefore, chapter 5 of this dissertation also considers the influence of social determinants of health (SDOH) on nursing home performance, acknowledging the broader context in which these facilities operate. The Kaiser Family Foundation (KFF) framework was used for classifying Social Determinants Of Health (SDOH) attributes (see Table 5.1). To test whether it is possible to predict the performance and outcomes of nursing homes in case of a pandemic, we examine the correlation among the current quality metrics, SDOH attributes, and COVID-19 outcomes. Developing a predictive model to preemptively address pandemics will allow us to proactively prepare, minimizing the potential adverse effects and mitigating their impact. Finally, the performance of nursing homes is also dependent upon their proximity to important places such as nursing schools, hospitals, etc. In chapter 6 of this dissertation, proximity of nursing homes to physical locations will be mapped using Geographic Information Systems (GIS). This will allow us to understand whether physical locations had an influence on the COVID-19 outcomes.