Cloud computing has become a widely exploited research area in academia and industry. Cloud computing benefits both cloud services providers (CSPs) and consumers. The security challenges associated with cloud computing have been widely studied in the literature. This systematic literature review (SLR) is aimed to review the existing research studies on cloud computing security, threats, and challenges. This SLR examined the research studies published between 2010 and 2020 within the popular digital libraries. We selected 80 papers after a meticulous screening of published works to answer the proposed research questions. The outcomes of this SLR reported seven major security threats to cloud computing services. The results showed that data tampering and leakage were among the highly discussed topics in the chosen literature. Other identified security risks were associated with the data intrusion and data storage in the cloud computing environment. This SLR’s results also indicated that consumers’ data outsourcing remains a challenge for both CSPs and cloud users. Our survey paper identified the blockchain as a partnering technology to alleviate security concerns. The SLR findings reveal some suggestions to be carried out in future works to bring data confidentiality, data integrity, and availability.

Background: Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain. Objective: The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications. Methods: In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration. Results: The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR. Conclusions: The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future.

In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems, namely patient registration and laboratory information systems. These workflows exploit various FHIR Application programming interface (APIs) to facilitate patient-centered and cohort-based interactive analyses. We developed an FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework’s ability to generate various analytics from clinical data represented in the FHIR resources.

Firefly Algorithm (FA) mimics firefly behavior by flashing and attracts them. Firefly's global search mobility is improved for dependable global optimization using chaotic maps in this work. Investigations of benchmark problems with chaotic maps are carried out in depth. The system uses eight separate chaotic maps to fine-tune the firefly's enticing movements. By using planned chaotic transmissions instead of fixed values, the new method beats classic firefly methods. According to statistical data and the success rates of FA, the new algorithms improve the solution's performance and the reliability of global optimality.

Although Health Level Seven (HL 7) message standards (v2, v3, Clinical Document Architecture (CDA)) have been commonly adopted, there are still issues associated with them, especially the semantic interoperability issues and lack of support for smart devices (e.g., smartphones, fitness trackers, and smartwatches), etc. In addition, healthcare organizations in many countries are still using proprietary electronic health record (EHR) message formats, making it challenging to convert to other data formats—particularly the latest HL7 Fast Health Interoperability Resources (FHIR) data standard. The FHIR is based on modern web technologies such as HTTP, XML, and JSON and would be capable of overcoming the shortcomings of the previous standards and supporting modern smart devices. Therefore, the FHIR standard could help the healthcare industry to avail the latest technologies benefits and improve data interoperability. The data representation and mapping from the legacy data standards (i.e., HL7 v2 and EHR) to the FHIR is necessary for the healthcare sector. However, direct data mapping or conversion from the traditional data standards to the FHIR data standard is challenging because of the nature and formats of the data. Therefore, in this article, we propose a framework that aims to convert proprietary EHR messages into the HL7 v2 format and apply an unsupervised clustering approach using the DBSCAN (density-based spatial clustering of applications with noise) algorithm to automatically group a variety of these HL7 v2 messages regardless of their semantic origins. The proposed framework’s implementation lays the groundwork to provide a generic mapping model with multi-point and multi-format data conversion input into the FHIR. Our experimental results show the proposed framework’s ability to automatically cluster various HL7 v2 message formats and provide analytic insight behind them.

[This corrects the article DOI: 10.2196/21929.].