Qualitative research methods determine the study’s reliability and denote the results’ trustworthiness. The proper investigation approach identifies the issue and analyzes circumstances influencing the research outcomes. Accordingly, focusing on the interconnection of respondents and the research methods allows scientists to receive reliable results from profound data and examination. Hence, it is vital to examine the research tools to grasp the appropriateness and relevance of data collection methods.
The article by Ayaz et al. (2018) provides information on research methods, which include searching for relevant data in various scientific databases. Scholars used digital storage such as Springer, Google Scholar, PubMed, and ScienceDirect (Ayaz et al., 2018). A systematic review and specific inclusion criteria became objectives for the research methods; the given approach relies upon statistical data presented in the research articles. Another investigation by Gohar et al. (2018) focuses on analyzing 15 models and frameworks along with scrutinizing innovative cloud-based blockchain EHR. Research methods preferred by scholars detected the adverse impact of insufficient interoperability in the healthcare system. Braunstein (2018) presents data on the negative influence of improper interoperability on the medical system and its patients.
Moreover, the author analyzes the expenditures of medical agencies on optimizing care, identifying the connection between poor healthcare structure and adverse patient outcomes. A study by Gupta et al. (2021) implies an analysis of cloud storage, focusing on the Big Data technology challenges. The research methodology of the given study is relevant for scrutinizing the impact of cloud storage on interoperability and accessibility for patients.
Another study by Svacina & Zvarova (2021) offers insight into semantic interoperability. The article’s research methodology focuses on analyzing SNOMED CT, supporting its availability and efficiency for healthcare systems (Svacina & Zvarova, 2021). Indeed, the investigation considers the only helpful tool promoting the better performance of SNOMED CT in data transmission. The study methods of Ullah et al. (2017) propose analyzing selected models, such as IoT-SIM and SPARQL, for transmitting data. This article explores the heterogeneity of devices, claiming the necessity to retransmit information to preserve its meaning. Thus, several research methods enable the abovementioned studies to analyze the issue and present findings. It is vital to note that irrespectively of research approaches, the given studies answer the research question, providing a rationale for challenges and perspectives of medical data transmission tools.
Data collection methods mentioned in the literature review are dedicated to a systematic review, analysis of sources, data analysis, and statistical overview. The reliability of information and results determines the appropriacy of statistical analyses; for example, Ayaz et al. (2018) use statistics to depict the challenges and opportunities for interoperability resources. Ullah et al. (2017) present a statistical overview of patient data, which is relevant for the interconnected studies exploring interoperability resources. Indeed, the study by Svacina & Zvarova (2021) emphasized the particular data transmission tool, which creates a bias in the analysis. All examination methods were approved of the given study objective and appeared relevant to scrutinize the issue through data collection and analysis. Indeed, several inconsistencies in research methodology can include the absence of patient-centered data; it emphasizes the advantages of analyzed interoperability tools, abandoning patient outcomes. Moreover, articles by Braunstein (2018) and Svacina & Zvarova (2021) considered exchanging medical data in large cities, which can present distorted information on interoperability tools development in smaller areas.
References
Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & Stiawan, D. (2021). The fast health interoperability resources (FHIR) standard: Systematic literature review of implementations, applications, challenges and opportunities.JMIR Medical Informatics, 9(7), 1-44.
Braunstein, M. L. (2018). Health care in the age of interoperability: the potential and challenges. IEEE pulse, 9(5), 34-36. Web.
Gohar, A., AbdelGaber, S., & Salah, M. (2021). A proposed patient-centric healthcare framework for better semantic interoperability using blockchain. International Journal of Computer Science and Information Security (IJCSIS), 19(11), 26-36.
Gupta, P., Hudnurkar, M., & Ambekar, S. (2021). Effectiveness of Blockchain to solve the interoperability challenges in Healthcare. Cardiometry, 20, 80–88. Web.
Svacina, S., & Zvarova, J. (2021). Semantic Interoperability in Medicine and Healthcare III.European Journal for Biomedical Informatics, 1 (1), 119-122.
Ullah, F., Habib, M. A., Farhan, M., Khalid, S., Durrani, M. Y., & Jabbar, S. (2017). Semantic interoperability for big data in heterogeneous IoT infrastructure for healthcare.Sustainable Cities and Society, 34, 90 96.