The current information systems for the registration and control of electronic medical records (EMR) present a series of problems in terms of the fragmentation, security, and privacy of medical information, since each health institution, laboratory, doctor, etc. has its own database and manages its own information, without the intervention of patients. This situation does not favor effective treatment and prevention of diseases for the population, due to potential information loss, misinformation, or data leaks related to a patient, which in turn may imply a direct risk for the individual and high public health costs for governments. One of the proposed solutions to this problem has been the creation of electronic medical record (EMR) systems using blockchain networks; however, most of them do not take into account the occurrence of connectivity failures, such as those found in various developing countries, which can lead to failures in the integrity of the system data. To address these problems, HealthyBlock is presented in this paper as an architecture based on blockchain networks, which proposes a unified electronic medical record system that considers different clinical providers, with resilience in data integrity during connectivity failure and with usability, security, and privacy characteristics. On the basis of the HealthyBlock architecture, a prototype was implemented for the care of patients in a network of hospitals. The results of the evaluation showed high efficiency in keeping the EMRs of patients unified, updated, and secure, regardless of the network clinical provider they consult.

This study proposes a characterization of quality attributes for applications that use augmented reality. This classification is done from the perspective of the user experience. The attribute identification was based on primary studies of the IEEE Xplore, Scopus, and ACM repositories. From an initial set of 1165 papers, 101 documents were selected. The document proposes two categories: objective and subjective. In the objective category 4 subcategories and 40 attributes were found, and in the subjective one 5 subcategories and 54 attributes were found. This is the first time that all these criteria are presented in a single document, which is the input for designing a comprehensive quality assurance tool for user experience in augmented reality.

The need for addressing geoprivacy in location based services has increased the offer of mechanisms that protect location information, however, these algorithms are not always developed to ensure the usability of the data and therefore, their adoption is not wide. In this work, a framework is presented to evaluate the effects of geoprivacy mechanisms on the quality of geodata to provide insights into how the data is affected for geospatial analysis. For this purpose, a toolkit of indices was developed to evaluate different characteristics of the data before and after a geoprivacy mechanism is implemented, providing a criterion to select one of them. The indices measure the changes in the presence of clusters through the quantification of hotspots in hotspot analysis and the difference observed in heatmaps of the concentration of the geodata. Variations in global indices like the Nearest Neighbor Index (NNI) and the orientation of the standard deviational ellipse are also measured. For demonstration, the data of crime arrests in New York was used for the month of January in 2017 and 2018. Five mechanisms were tested with different settings, resulting with the NRand-K algorithm producing fewer alterations to the reference data, preserving its initial characteristics better than the other mechanisms.

Objective: This paper aims to present Selfienemia, a novel non-invasive hemoglobin estimation mobile application working under controlled illumination conditions. Materials and methods: Mobile application was supported by a parameter optimization and curve fitting backend in the cloud to provide dynamic and device independent results based on a-priori observations and the World Health Organization’s (WHO) color scale. Thirty-four and sixty-four individuals were involved during both the mobile app training and posterior blind test respectively. Results: Selfienemia (Mobile App) was found to be suitable as a non-invasive hemoglobin estimation tool. Estimation levels showed high correlation against the gold standard invasive test. Skin pigmentation and color components were found to have specific relationships labeling individuals within one Hb concentration group. Conclusion: Selfienemia may provide an effective way to screen hemoglobin in medical shortage situations. However, it still can’t replace traditional invasive testing. Future work must include testing additional fitting techniques for illumination aware scenarios as well as quantification of skin pigmentation effects on estimation results.

In recent years, Blockchain has become a disruptivetechnology to protect the integrity of information, especially inopen and collaborative information systems. Its main advantageis the possibility to reach consensus on the new data blocks tobe added to the chain, even with anonymous actors. The mostcommon consensus mechanism is Proof of Work, but it has beenproven to be very inefficient in terms of energy spent by themembers of the blockchain. In the literature there are many othertechniques that pretend to become the new popular mechanism.However, the number of this techniques is growing too fast toreally be able to differentiate among all the options. In this work,a new characterization of consensus algorithm is proposed, thatcan be used to find families of mechanism using cluster-basedclassification. Using the Ward Method and Spearmans RankCorrelation analysis, new clusters of consensus mechanisms wereidentified. The results describe the behavioral patterns not seenbefore in the literature. In addition, some open problems ofcurrent consensus algorithms are discussed.