**6.11 Chatterjee and Armentano**

Chatterjee and Armentano identified several issues, such as the availability of a live data connection and the security structure of a system, which prompted them to

**59**

**6.12 Gupta et al.**

**Figure 18.**

*IOT Service Utilisation in Healthcare*

*DOI: http://dx.doi.org/10.5772/intechopen.86014*

*Schematic diagram of the IoT-based remote treatment model [10].*

develop a system for a smart medical environment that provides ubiquitous services [10]. Specifically, they proposed a model with an inclusive approach for applying IoT in a smart medical environment that provides ubiquitous services. This model virtually stores patient data and makes them ubiquitously accessible to the concerned healthcare personnel in order to be shared. Another important aspect of using these data lies in the design of an intelligent clinical decision support system that can help doctors when delivering treatment. However, Chatterjee and Armentano failed to address the requirements for adopting IoT and only focused on the inclusion of technologies in the healthcare sector, thereby limiting the generalisability of the factors that they proposed for different types of hospitals in various countries [10]. The schematic diagram of

Gupta et al. examined the design and implementation of an IoT-based health monitoring system for emergency medical services [65]. This system demonstrates the flexible collection, integration and interoperation of IoT data that can provide support to emergency medical services. Their proposed model allows users to improve health-related risks and reduce healthcare costs by collecting, recording, analysing and sharing large amounts of data in real time. This system uses smart sensors that collect and send raw data to a database server where they are further analysed and statistically maintained to be used by medical experts. The results are deployed and tested on a patient whose personal details are inputted into a Web portal. This patient is then connected to a health monitoring system that includes a heart rate sensor and a temperature sensor. However, Gupta et al. did not consider in their work some factors in the organisational and system domain as identified in the literature review. They also did not consider the actual examination of healthcare professionals [65]. The

The aforementioned models/framework for IoT use in healthcare can be classified based on the technological, system and individual aspects as summarised in **Table 2**. In sum, most studies on IoT use in healthcare have some limitations related to their context of use, antecedents of implementation and need of use. Moreover, these studies have only focused on specific domains to achieve certain needs for using IoT in the healthcare context. Their models/frameworks are only designed for certain circumstances and environments related to the context and needs for which they are developed. Meanwhile, very few researchers have examined the actual

their IoT-based remote treatment model is summarised in **Figure 18**.

proposed health monitoring system is illustrated in **Figure 19**.

*Internet of Things (IoT) for Automated and Smart Applications*

**58**

**Figure 17.**

**Figure 16.**

*Reference framework [63].*

*HMIS framework [64].*

**6.11 Chatterjee and Armentano**

problems faced by users when interacting with the system. However, Pir et al. did not test the applicability of this framework for users from a specific hospital [64].

Chatterjee and Armentano identified several issues, such as the availability of a live data connection and the security structure of a system, which prompted them to

Their proposed HMIS framework is presented in **Figure 17**.

**Figure 18.** *Schematic diagram of the IoT-based remote treatment model [10].*

develop a system for a smart medical environment that provides ubiquitous services [10]. Specifically, they proposed a model with an inclusive approach for applying IoT in a smart medical environment that provides ubiquitous services. This model virtually stores patient data and makes them ubiquitously accessible to the concerned healthcare personnel in order to be shared. Another important aspect of using these data lies in the design of an intelligent clinical decision support system that can help doctors when delivering treatment. However, Chatterjee and Armentano failed to address the requirements for adopting IoT and only focused on the inclusion of technologies in the healthcare sector, thereby limiting the generalisability of the factors that they proposed for different types of hospitals in various countries [10]. The schematic diagram of their IoT-based remote treatment model is summarised in **Figure 18**.
