Systematic review and meta-analysis for a Global Patient co-Owned Cloud (GPOC)

Cloud-based personal health records increase globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here, we present the GPOC series' Prospective Register of Systematic Reviews (PROSPERO) registered and Preferred Reporting Items Systematic and Meta-Analyses (PRISMA)-guided systematic review and meta-analysis. It examines cloud-based personal health records and factors such as data security, efficiency, privacy and cost-based measures. It is a meta-analysis of twelve relevant axes encompassing performance, cryptography and parameters based on efficiency (runtimes, key generation times), security (access policies, encryption, decryption) and cost (gas). This aims to generate a basis for further research, a GPOC sandbox model, and a possible construction of a global platform. This area lacks standard and shows marked heterogeneity. A consensus within this field would be beneficial to the development of a GPOC. A GPOC could spark the development and global dissemination of artificial intelligence in healthcare.

Big data as a value generator in decision support systems: a literature review

Purpose

This paper aims to analyze how decision support systems manage Big data to obtain value.

Design/methodology/approach

A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.

Findings

The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.

Originality/value

As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

Cloud Image Data Center for Healthcare Network in Taiwan

This paper investigates how a healthcare network in Taiwan uses a practical cloud image data center (CIDC) to communicate with its constituent hospital branches. A case study approach was used. The study was carried out in the central region of Taiwan, with four hospitals belonging to the Veterans Hospital healthcare network. The CIDC provides synchronous and asynchronous consultation among these branches. It provides storage, platforms, and services on demand to the hospitals. Any branch-client can pull up the patient's medical images from any hospital off this cloud. Patients can be examined at the branches, and the images and reports can be further evaluated by physicians in the main Taichung Veterans General Hospital (TVGH) to enhance the usage and efficiency of equipment in the various branches, thereby shortening the waiting time of patients. The performance of the CIDC over 5 years shows: (1) the total number of cross-hospital images accessed with CDC in the branches was 132,712; and (2) TVGH assisted the branches in keying in image reports using the CIDC 4,424 times; and (3) Implementation of the system has improved management, efficiency, speed and quality of care. Therefore, the results lead to the recommendation of continuing and expanding the cloud computing architecture to improve information sharing among branches in the healthcare network.

A Systematic Literature Review on Security and Privacy of Electronic Health Record Systems: Technical Perspectives

Background:

Even though many safeguards and policies for electronic health record (EHR) security have been implemented, barriers to the privacy and security protection of EHR systems persist.

Objective:

This article presents the results of a systematic literature review regarding frequently adopted security and privacy technical features of EHR systems.

Method:

Our inclusion criteria were full articles that dealt with the security and privacy of technical implementations of EHR systems published in English in peer-reviewed journals and conference proceedings between 1998 and 2013; 55 selected studies were reviewed in detail. We analysed the review results using two International Organization for Standardization (ISO) standards (29100 and 27002) in order to consolidate the study findings.

Results:

Using this process, we identified 13 features that are essential to security and privacy in EHRs. These included system and application access control, compliance with security requirements, interoperability, integration and sharing, consent and choice mechanism, policies and regulation, applicability and scalability and cryptography techniques.

Conclusion:

This review highlights the importance of technical features, including mandated access control policies and consent mechanisms, to provide patients' consent, scalability through proper architecture and frameworks, and interoperability of health information systems, to EHR security and privacy requirements.

Enabling large-scale biomedical analysis in the cloud

Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.