One strength of the BN approach is its ability to provide both qualitative and quantitative insight into the multidimensional makeup of nursing home care quality. Accordingly, we examine BN3 in more detail. The qualitative insight gained from BN3’s structure depicts how various factors interact with each other to produce certain levels of nursing home care quality. We obtain further understanding of these interactions from the quantitative analysis of BN3’s parameters. Considered together, the quantitative and qualitative aspects of the BN afford a more complete perspective of the QOC in nursing homes. We conclude this section by highlighting the differences between BN methodology and the methods employed in other studies.
The structure of BN3 (see ) indicates that the overall quality of nursing home care is tightly related to five quality measures: certified nurse assistant staffing levels (CNAhrs), occupancy rate (occupancy), prevalence of bedfast residents (QI17), prevalence of daily physical restraints (QI26), and number of deficiencies (deficiencies). The direction of the arc between QOC and deficiencies implies that the overall QOC is indicative of the number of deficiencies issued to a facility. This direction of causation makes sense from an evaluation perspective (i.e., the number of deficiencies results from the QOC provided). Conversely, the arc direction between QOC and the remaining variables implies that CNAhrs, occupancy, QI17, and QI26 each influence the overall quality.
A further analysis of BN3’s structure reveals that it is composed of structural, process, and outcome measures. As illustrated in , CNAhrs and occupancy provide structural quality measures, QI26 provides a process measure, QI17 supplies an outcome measure, and the variable of deficiencies encompasses both process and outcome measures of nursing home care quality. The inclusion of quality measures from each group provides a more complete view of the QOC in nursing facilities.
Insights from the structure of BN3 also have significant practical implications because the identification of factors that directly influence the overall quality of nursing home care provides direction for nursing home administrators.
We obtain further understanding of how the quality of nursing home care is influenced by the aforementioned factors through an examination of the parameters of BN3. To illustrate how the parameters provide insight into the QOC, we present excerpts from the conditional probability tables of BN3 in .
Conditional Probability Table Excerpts
The first portion of inspects the relationship between the overall quality of nursing home care (QOC) and the prevalence of bedfast residents (QI17). To facilitate this examination, we hold the levels of CNAhrs, occupancy, and QI26 constant while we vary the level of QI17. As shown, the probability that the overall quality of nursing home care is above average decreases with an increased likelihood of poor levels of QI17. In other words, the probability of above average QOC declines as the instances of bedfast residents increase. This is an important observation for nursing home administrators seeking to improve the overall QOC. The magnitude of decline is not severe when the level of QI17 is average, and hence, relatively good overall quality of nursing home care can be maintained as long as the level of QI17 is average or good.
We examine the relationship between certified nurse assistant staffing levels and overall QOC in the second portion of . As before, we hold the other variables at a constant level while we allow the level of CNAhrs to vary. Similar to the relationship between QI17 and QOC, decreased CNA staffing levels significantly shift the probability distribution for the overall QOC. Unlike the previous case, it is imperative to have good CNA staffing levels to keep above average QOC.
The last portion of reveals an interesting observation: The occupancy rate has no affect on the overall QOC when QI17, CNAhrs, and QI26 are at their lowest levels. This is apparent from the repetitive probability distribution of QOC as occupancy moves from high to average to low. Therefore, despite a facility’s occupancy rate, one can achieve little or no QOC improvement without first addressing the prevalence of bedfast residents, the prevalence of daily physical restraints, and certified nurse assistant staffing levels. When other factors such as QI17, CNAhrs, and QI26 are held at their middle and upper levels (as opposed to their lower levels in the preceding scenario), the distribution for QOC changes when the occupancy rate is varied (not shown in ).
The analysis just presented demonstrates the complicated nature of nursing home care quality. Although it is evident that some factors have a greater influence on the overall QOC, the effect of other factors is not readily apparent. Knowledge of such interdependencies has important implications for QOC improvement. Nursing home administrators should recognize the relative significance of each factor influencing the overall quality of nursing home care. Accordingly, attention should not only be given to key factors but also to closely related factors in an effort to further improve the overall QOC. In other words, focusing on only one key factor while ignoring closely related factors may not result in the desired outcome.
We conclude this section by highlighting the differences between our method and the methods utilized in other studies. As we discussed in the Background section, many studies have been conducted that focus on specific factors influencing nursing home care quality. The methods employed in these studies include ordinary least-squares regression, two-stage and three-stage least-squares regression, Tobit multivariate techniques, logistic regression, and various experimental designs. Studies devoted to aggregating information to measure the overall QOC in nursing homes have used additional methods. These methods include conceptual frameworks, analysis of deficiencies, hierarchical decomposition, factor analysis, path analysis, cluster analysis, structural equation modeling, QI rank and classification techniques, and methods utilizing on-site surveys.
In contrast to methods used in previous studies, this work relies on BN methodology. This methodology is fundamentally different from the other approaches. We use BN methodology in this study for two purposes. First, it serves as a method to aggregate various indicators of quality to predict an overall measure of nursing home care quality. Second, it is used to gain qualitative and quantitative insight into nursing home care quality through an analysis of BN structure and parameters. Thus, this research makes contributions to both types of studies addressed in the Background section.
The analysis and results presented in this article provide researchers, administrators, and policy makers alike with valuable insight into the multidimensional nature of the quality of nursing home care. Consideration of the new perspective afforded by the BN approach, in combination with previous research in this area, will yet provide heretofore unattained insight and understanding. Toward this end, the salient points of this research include the following.
First, a more accurate representation of nursing home care quality is obtained through a mix of structural, process, and outcome measures of quality. Moreover, sole consideration of variables belonging to any of these three categories is not as powerful in describing nursing home care quality as is their collective influence.
Second, a BN framework for nursing home care quality provides both qualitative and quantitative explanations of the factors that influence the overall QOC in nursing homes. Consequently, previously unattained insight into the quality of nursing home care delivery can be afforded through a BN approach.
Third, nursing home administrators seeking to improve the overall QOC should first consider ensuring good staffing levels of certified nurse assistants, decreasing the prevalence of bedfast residents, and reducing the prevalence of daily physical restraints. In addition to improving quality, such efforts are likely to result in a decreased number of deficiencies.
Fourth, 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.
Future research with BNs may provide additional utility when they are used in conjunction with a larger data set. This would provide more data for structure and parameter learning, which may in turn yield a model that provides more in-depth insights into nursing home care quality. Furthermore, a larger data set would allow for more extensive testing of the networks.
In addition to augmenting the data set, future efforts may benefit from considering further measures for the overall QOC. In this research, we used the OIQ for this purpose. Although the OIQ has provided valuable insights into the QOC in nursing homes, other measures may offer further insight and may yield more accurate results. Despite the need for some improvements in future research, the positive implications of assessing the QOC in nursing homes through a Bayesian network—as illustrated in this analysis—outweigh any limitations or difficulties encountered in this research.