were assessed. We analyzed correlations between the items included in the triage sheet and admission. We calculated risk ratios (RRs) of the items that were significantly related to admission. The RR of an item was considered its score, and the triage score was calculated by summing the individual RR scores for each patient. We performed receiver operating characteristic (ROC) analysis of admission and triage scores. Results: Among 20992 patients, 2030 patients (9.7%) were admitted to the hospital. The triage scores of all the patients ranged from 26.5 to 62.3. According to the ROC analysis, the area under the curve was 0.791 and the optimal cutoff value for the triage score was 32.7 (sensitivity: 0.74, specificity: 0.70). Discussion: Since this research was based on data from a Japanese secondary level emergency hospital in an urban area, our triage method can be adapted to the many ERs in Japan that share a similar background. The method used to develop this triage method can also be used to develop triage methods for ERs with different backgrounds.

With asthma being one of the leading causes of death in different countries, the emphasis on improving the health of asthma patients is important. While the use of smart technologies is a good approach for improving the health of asthma patients, technologies need to be connected in such a way that all components of smart health form an ecosystem. However, the components of such an ecosystem have not been identified in the current literature. The purpose of this chapter was to identify the components of a smart health ecosystem for asthma patients through a systematic literature review. A total of 28 articles met the inclusion criteria. This chapter identified the components of a smart health ecosystem for asthma patients and provided a conceptual framework. The findings of the systematic literature review are expected to inform researchers on the components required for building a smart health ecosystem for asthma patients.