We observed that the top three states with higher quality-of-care are Louisiana, Nebraska, and South Dakota using either x or Ag-index to assess. The Ag-index earns the mean correlation coefficient ( = 0.86) higher than the other three metrics. Six grades on the inpatient perception of hospitalization were classified for the US states.
4.1 What this study contributes to current knowledge
We applied the metrics to rate inpatient perceptions of hospitalization experience in the US states. The principle of the bibliometric analysis disregards redundant domains and the excess scores for taking into account, which is a simple and easy way to compute metrics and categorize the classes, particularly using the Kano model for classification, which is never seen in past articles.
The quantiles methods or others [27, 28], such as the equal interval method and natural breaks (e.g., using an algorithm as k-mean [29]) can be properly used for classifying classes for entities. We particularly highlight the vital few classes labeled by the number from 1 to 3 on choropleth maps (see Figures 2 and 4), which is one of the features in this study.
Another feature is the Kano model applied to classify the entities. The reason there are no such states at the top is that the transformed domain scores (i.e., divided by 10) are less the score in Figure 3 than that score (i.e., divided by 8) in Figure 5. For instance, Louisiana in Figure 1 has the highest x-index at 7.88, but the domain score (ci) is 6.9 less than the number of x-core at 9. Another example is the District of Columbia, with x-index = 6.82, domain score at 5.8 less than the core at 6 in Figure 4. Interested readers are invited to scan the QR-code in Figures to see the details about any state. The dashboard on Google Maps is another feature that allows the public to easily understand and quickly select the high-performing states.
4.2 Strengths of this study
We recommend using the x-index to evaluate the quality-of-care for hospitals or the US states. The reason is not only the feature of invariance in Table 2, but also the suitability of three main classifications displayed in the Kano diagram. Although the Ag has higher discrimination power than the h and g-index [30], the x-index also owns the discrimination power for ranking institutional-level research achievements.
On the other hand, we usually discard hospitals with missing items in classifications of performance [16] or like the study [6] only including 58 medical groups reporting all selected measures across the four domains. We mimic the bibliometric analysis considering the core publications (or domains in the current study), like the individual-level metrics (h, g, PI, or x) that can evaluate any one with a variety of publications and citations in the past. Accordingly, many hospitals (or states) can be assessed together on a diagram, see Figures in this study.
Furthermore, many examples of disparities in health outcomes across areas, such as dengue outbreaks [31,32], disease hotspots [33], and the Global Health Observatory (GHO) maps on major health topics [34], have been presented using choropleth maps [23–25] to display. Our representation in Figures 1 and 2 on Google Maps is unique and innovative and has no precedent in the literature.
4.3 Limitations and suggestions
Despite the findings shown above, several potential limitations require further research efforts. First, the sample for this study only comprised HCAHPS survey results from 2014. As such, the top three states (Louisiana, Nebraska, and South Dakota) with higher quality-of-care cannot be generalized to other years or areas.
Second, there might be some biases when downloading HCAHPS survey results from the website. For example, it is worth further examining why the domain score for item 9 of Overall Hospital Rating in Tabel 1 is so much lower than other domains.
Third, we recommend using the Kano diagram to classify entities. There is no software [35] that can be commonly used for ordinary researchers. Our technique of plotting a Kano diagram in MS Excel for visualizing and classifying the characteristics of entities needs to be explained in detail in another future article.
Fourth, although the x-index is recommended for hospital evaluations in the classification of performance according to Table 2, other indices or approaches, such as relative thresholds or absolute values of performance, are worthy of discussions and further study.
Finally, numerous classification methods used for displaying legends on choropleth maps have been proposed before [27, 28]. We applied the quantiles method to classify six grades for US states (see Figures 1 and 2), which is also worth discussing the merits and disadvantages used for displaying choropleth maps in the future.