Motivated by the need to reduce the cost of patient transport to health centers, the authors designed a prototype national telemedicine network in Brazil. As a result of this project, we now have an agile, easy-to-use, high-quality telemedicine network that provides greater access to patient data and helps in medical decisions, cutting unnecessary costs to the state and benefiting the population as a whole.

Aims: This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results. Method: An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied. Results: Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs. Conclusion: It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.

The increasing adoption of the Internet of things and cloud computing in recent years provided the increasing development and improvement of various well-known approaches, such as the ambient assisted living approach. The merging of Internet of things and cloud brought about the so-called CloudIoT paradigm. CloudIoT intends to extend both technologies to make possible developing the next generation of smart environments, such as healthcare applications. New healthcare applications demand an increasing capacity of resources for storing, processing, and transmitting data. Looking at this scenario, along with the growing number of devices connected to the Internet of things, we must consider providing mechanisms to mitigate the excessive data offloading on the network, the latency between nodes, and even the unnecessary waste of computing power. In this article, we present an efficient and effective CloudIoT-based healthcare architecture for ambient assisted living environments. The innovation of our approach lies on the use of a game theory approach (by means of a stochastic search algorithm) to improve efficiency and latency of the CloudIoT network. This proposal aims to provide better availability levels to the whole environment. Experiments performed through simulation have shown us a remarkable improvement of network parameters, by applying a stochastic search algorithm called Gur game, when compared to a baseline application.

The development of the Internet of Things (IoT) paradigm and its significant spread as an affordable data source has brought many challenges when pursuing efficient data collection, distribution, and storage. Since such hierarchical logical architecture can be inefficient and costly in many cases, Data Reduction (DR) solutions have arisen to allow data preprocessing before actual transmission. To increase DR performance, researchers are using Artificial Intelligence (AI) techniques and models toward reducing sensed data volume. AI for DR on the edge is investigated in this study in the form of a Systematic Literature Review (SLR) encompassing major issues such as data heterogeneity and AI-based techniques to reduce data, architectures, and contexts of usage. An SLR is conducted to map the state of the art in this area, highlighting the most common challenges and potential research trends in addition to a proposed taxonomy.