Research paradigms for the Internet of things in the healthcare sector:
According to Chernyshev et al. (2017), a research model to conduct research, which has been in practice for a long time, defines the research paradigm. The paradigms of the Internet of Things (IoT) are based on the making of medical devices, sensors, cameras and other medical devices that are a part of human healthcare services (Yuehong et al. 2016). The use of paradigms in the health care sector of human beings includes the interaction between things and human beings associated with the healthcare sector. These services work for enhancing the quality of life in humans and allows them to utilize resources related to healthcare. Various types of research paradigms have been devised for conducting researches based on IoT and healthcare sectors.
Epistemology can be defined as the study of knowledge and its justified beliefs. Several questions about the sufficient conditions of knowledge, sources, the structures and the limitations of the acquired knowledge (Dornberger, Inglese and Korkut 2018). According to Hamidi and Fazeli (2018), epistemology is used to devise biosensors for saliva, blood and breathing tests. This device has been created based on the Fisher-Pry model and bibliometric analysis. Biosensors are a field of significant research sector associated with the relation of IoT with healthcare services. Epistemology determines the nature of knowledge, which is associated with its meaning. This paradigm focusses on propositional knowledge, which can be justified by a declarative sentence. Belief, truth and justification are the three significant key points for epistemology.
Ontology can be defined as the philosophical study of being which is concerned with the existence of entities, the grouping of entities and their relationships with each other within a hierarchy. According to Kumar (2015), Emergency medical services (EMS) are emergency services that are dedicated to providing health care services out of the hospital. This service has been devised by the ontology paradigm of the Internet of Things based on the healthcare sector. Thus emergency medical service can be used along with biosensors in rapid blood and saliva tests to devise an effective treatment plan. Ontology is stated to be a subpart of metaphysics. This section is also relevant to spirituality and religion. Ontology has comprised of four statements:
- Every unit of the universe is made up of energy and matter.
- Every living unit is made up of consciousness.
- Every living unit has a soul.
- Every living unit has a mind.
Ontology hunts for the existing concepts behind a particular matter, and this makes it different from epistemology.
The methodology section describes the procedures used for the collection of knowledge. This is a theoretical and systematic analysis of the methods used in a research study. Methodology deals with a proper theoretical analysis of the body, principles and concepts associated with the knowledge branch of the study. However, the methodology fails to discuss the specific methods used for the study. This section discusses the kinds of the process required to be followed in a particular procedure. Every systematic study procedure has a methodology section, which states the processes to be followed get an outcome or a result for the study. For theoretical work, the research paradigm should always meet all the criteria for the methodology section. Therefore, it can be stated that methodology is required to guide the study in its way of producing a result.
This paradigm has been selected for this since it integrates the human interests to the associated study. Interpretivism does not work with a strict framework or concept as the positivism. Interpretivism works by making sense of what is observed as a reality. Internet of Things in healthcare sectors is used in order to improve human life, and interpretivism works by capturing research factors associated with human life interactions. The goal of interpretivism in the Internet of Things in healthcare sectors is to interpret the reasons for the use of different healthcare equipment and not to judge about the causes and effects. In this study, biosensors are the devices that are a result of the Internet of Things in healthcare sectors. An interpretive researcher hunts for meanings, reasons and other subjective experiences (biosensors) which are bound to the context (healthcare sector) and time. Therefore interpretivism has been selected as a research paradigm in the association of IoT with healthcare sectors.
According to Zhao, Sun and Wang (2015) use of biosensors in the human community has been derived from the use of the Internet of Things in healthcare sectors. This study also states that this research follows a particular pattern of methodology, including surveys and interviews to understand the need for biosensors in the human community. Then an opportunity space is identified where the application of IoT can be implemented. Then concepts for new products (biosensors) will be generated inside the identified space. This procedure has been followed in a recent case study involving the implementation of biosensors in testing blood and saliva of patients (Aggidis, Newman and Aggidis 2015). Finally, the prototype and testing step of the research methodology will be reached where the product of the concept will be delivered to the human community for testing and feedback. This process will allow the detection of problems in the research and needs for further improvements in the product. Another case study involves the use of biosensors for testing the quality of vegetables in a farming field (Luthra et al. 2018). This study also used the same research pattern for the implementation of the Internet of Things in the healthcare sectors. The higher the quality of the vegetables higher is the nutrient value. Therefore the above-stated research methodology has been used for this study.
Data collection methods:
The desktop research method involves the use of computers for online data collection procedures. This procedure involves searching for previous or available resources about the research topic across the Internet (Oberhauser et al. 2015). Google is the best search engine for the above research topic. This is because of the fact that this research study will be based on survey procedures and interviews. Therefor secondary data collection will involve the collection of data form Google scholar. Available information about the application of IoT in healthcare sectors will be collected from various databases and journals. Since the topic studies here are biosensors, available data will be collected about the topic from 2014 to 2018. This process of data collection is more time consuming than other primary data collection procedures. Other research studies, including interpretivism as the research paradigm in healthcare researches, also follow this data collection procedure (Dimitrov et al. 2016). However, this procedure is best for secondary data collection and not applicable for primary data collection. Desktop research mainly focuses on searching about a particular research topic (Internet of Things in healthcare sectors); however, some challenges still exist. These challenges include IoT initiatives being unsuccessful and generation of a tremendous amount of data which creates a problem in the sampling procedure (Shahid and Aneja 2017). However keeping aside all the challenges, this data collection procedure is used in this study.
This method is also a sophisticated procedure of data collection which is easy to conduct. Interview techniques can be conducted both online and offline. Online interview processes are done by interviewing the staff associated with the healthcare sectors. Most of the processes include an online questionnaire designed in a specific way to assess the participants and to collect sufficient data about the research topic (Ranney et al. 2015). The interviewing method is used for the primary data collection process since it involves direct contact with the data sources and the researcher. Since this research topic revolves around the use of the Internet of Things in healthcare sectors, therefore primary data collection is of utmost importance to avoid errors in data collection. Offline interviews involve face to face interactions with the participants for the data collection procedure. This procedure has been used in other research approaches involving the Internet of Things (Boulos and Al-Shorbaji 2014). The data collection procedure is best in performing qualitative research. Therefore it can be used for research involving interpretivism as a research paradigm.
Data analysis tools:
The data analysis procedure is defined as transforming the modeling data collected from the data collection sections to produce results. This procedure involves the use of several online and offline tools for analysis of the data. A variety of tools are available for the analysis of qualitative data since this research study involves the collection of qualitative data through the interview and desktop research methods. NVivo, MAXQDA, ATLAS. ti, CAQDS (Computer Assisted Qualitative Data Analysis Software) and other tools are also available for the study. CAQDS tool will be used for the analysis of qualitative data obtained by interview and desktop research techniques. The data evaluation and interpretation are done by sorting the materials into different groups using a hierarchical coding system. Visualization techniques are also available for picturing the data obtained in the form of results. The analysis tool has been used in other research studies involving qualitative research studies with qualitative data (Houghton et al. 2015). The Internet of Things associated with healthcare sectors is mainly based on developing medical equipment which will improve the lifestyle of human beings. One of the primary challenges in using this tool is that the grouping process is very difficult since the month of data collected is in a tremendous amount. These challenges can be recovered by using computer-based software for adding the data to the analysis tool. This condition will ensure the removal of errors while the grouping of data is performed. According to Fisher (2017), it can be stated that qualitative data collection is affected by challenges in data input which can be recovered by using proper input tools. Other data analysis tools can also be used for this study but not used here. This is because of the fact that this research study uses the biosensors and their uses in the human community as its aim. Therefore the collection of data involves several healthcare sectors using applications of the Internet of Things. As a result, this data analysis tool can be stated as perfect for this research study.
Aggidis, A.G., Newman, J.D. and Aggidis, G.A., 2015. Investigating pipeline and state of the art blood glucose biosensors to formulate next steps. Biosensors and Bioelectronics, 74, pp.243-262.
Boulos, MNK and Al-Shorbaji, N.M., 2014. On the Internet of Things, smart cities and the WHO Healthy Cities.
Chernyshev, M., Baig, Z., Bello, O. and Zeadally, S., 2017. Internet of Things (IoT): Research, simulators, and testbeds. IEEE Internet of Things Journal, 5(3), pp.1637-1647.
Dimitrov, D.V., 2016. Medical Internet of things and big data in healthcare. Healthcare informatics research, 22(3), pp.156-163.
Dornberger, R., Inglese, T. and Korkut, S., 2018. Internet of Things–A New Epistemic Object. Journal of Systemics, Cybernetics and Informatics, 15(6), pp.36-44.
Fisher, M., 2017. Qualitative computing: using software for qualitative data analysis. Routledge.
Hamidi, H. and Fazeli, K., 2018. Using Internet of Things and biosensors technology for health applications. IET Wireless Sensor Systems, 8(6), pp.260-267.
Houghton, C., Murphy, K., Shaw, D. and Casey, D., 2015. Qualitative case study data analysis: An example from practice. Nurse researcher, 22(5).
Kumar, V., 2015. Ontology based public healthcare system in Internet of Things (IoT). Procedia Computer Science, 50, pp.99-102.
Luthra, S., Mangla, S.K., Garg, D. and Kumar, A., 2018. Internet of Things (IoT) in agriculture supply chain management: A developing country perspective. In Emerging Markets from a Multidisciplinary Perspective (pp. 209-220). Springer, Cham.
Oberhauser, M., Dreyer, D., Mamessier, S., Convard, T., Bandow, D. and Hillebrand, A., 2015, August. Bridging the gap between desktop research and full flight simulators for human factors research. In International Conference on Engineering Psychology and Cognitive Ergonomics (pp. 460-471). Springer, Cham.
Ranney, M.L., Meisel, Z.F., Choo, E.K., Garro, A.C., Sasson, C. and Morrow Guthrie, K., 2015. Interview?based qualitative research in emergency care part II: Data collection, analysis and results reporting. Academic Emergency Medicine, 22(9), pp.1103-1112.
Shahid, N. and Aneja, S., 2017, February. Internet of Things: Vision, application areas and research challenges. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 583-587). IEEE.
Yuehong, Y.I.N., Zeng, Y., Chen, X. and Fan, Y., 2016. The Internet of things in healthcare: An overview. Journal of Industrial Information Integration, 1, pp.3-13.
Zhao, G., Sun, X. and Wang, X., 2015. Research on the vegetable quality safety traceability system based on Internet of Things and Biosensor. Journal of Food Safety and Quality, 6(3), pp.747-755.