# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
### Correlation Analysis  
In order to generate a correlation analysis between the different categorizations of the selected papers, tables were constructed to analyze each of the variables in pairs. This correlation analysis was based on the methodology developed by [PERSON] et al. [62]. With the resulting tables, it is possible to identify trends to strong interrelationships that have been widely addressed, or on the contrary, possible research niches waiting for contributions. The tables are shown in Appendix A. The values are based on a simple counts of the number of items crossing the specific subcategories. The dimensions analyzed are the study variables detailed:  
* **Paper type:** theoretical study; proposal; implementation; prototype. * **Application area:** remote care: surgery and diagnosis; real-time continuous monitoring; data transmission: smart ambulances. * **Healthcare benefits:** constant and immediate attention with intelligent systems; specialized medical care in remote areas; immediate emergency assistance. * **IoMT devices:** body sensors; teleoperated robots; artificial intelligence; wearable devices; ultra high definition cameras. * **Communication protocols:** D2D; M2M; H2M; hybrid: D2D-M2M. * **Networking technologies:** WiFi; Bluetooth; cellular network; hybrid: Bluetooth-NFC; hybrid: Bluetooth-ZigBee; hybrid: Bluetooth-WiFi; hybrid: WiFi-ZigBee-Bluetooth. * **Security protocol:** authentication; encryption; coding; not defined. With the intention of establishing a relationship between the different categorizations made of the 44 selected papers, an analysis based on the standard deviation of the count was added to the tables presented in Appendix A. Figure 14 shows the values of the standard deviation of the counts of each table presented in Appendix A. On the one hand, the higher values represent less uniform combinations of variables, i.e., with marked tendencies toward certain point combinations. On the other hand, lower values represent a more uniform distribution around the mean, without considerable variations representing drastic trends. Figure 14: Standard deviation values of different dimensions of our review. The highest result is observed in the crossing of the variables \"Healthcare benefits\" and \"Application area\", with a value of 8.25. To understand this value, we can observe that between the subclassifications \"constant and immediate attention with intelligent systems\" (healthcare benefits) and \"real-time continuous monitoring\" (application area), 23 studies cross over. Such a value is expected, since there is a logical relationship between the subcategories, where the application has a specific benefit. Leaving aside the values that imply an explicit logical relationship, high results, such as those for the relationship between \"Application area\" and \"Communication protocol\" (SD = 5.33), merit a specific analysis. In this case, there is a tendency to prefer a hybrid communication protocol: Bluetooth-WiFi, for real-time constant monitoring applications (\(n=12\)). Remote care applications mostly used the cellular network (\(n=9\)), and the next most, the hybrid Bluetooth-WiFi protocol (\(n=7\)). Such results provide a suggestion of the trends on which researchers have concentrated their efforts and can be interpreted as routes for new developments. Another way of interpreting the heat maps of the crossroads of papers is to consider those areas without overlaps as possible research niches that are waiting for contributions. Analyzing the same example above, it can be seen that the applications oriented toward information transmission use only the cellular network, without considering the possibility of inserting other long-range communication technologies, such as LoRa. With this in mind, the heat maps presented in Appendix A can be analyzed individually for trends or gaps, and in both cases, interpreted as research opportunities. Although correlation analysis shows a summary of study variables characteristics, each heat map allows a more in-depth analysis.