Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.

The current COVID-19 pandemic has promoted the periodic release of several health databases aimed at discovering relationships in the data, detecting similar problems in patients, and studying the evolution of the disease. A way to exploit the data is to use visualization techniques, which can lead to the discovery of insights and patterns, as well as to guide analysis procedures to understand the data. In this paper, we present I-CovidVis, a visualization tool to explore data from interoperable healthcare systems, able to compare and navigate in both global and local perspectives. Our approach is to model data as a graph and explore its structural and temporal views. Our proposal facilitates the perception of patterns, trends, periodicity, and anomalies, resulting in faster decision making.

Several studies have been performed worldwide to improve health services using data generated by digital medical systems. The increasing volume of data generated by these systems is making the use of knowledge discovery and data analysis techniques essential to improve the quality of the health services, which are offered by the medical facilities. However, it is possible to observe a gap, in the literature, about generic and flexible vector space models (VSM) that are well adapted to handle electronic health records (EHR), requiring that each knowledge discovery effort develop their own VSM or other representation model. This restriction can turn a knowledge discovery task over clinical pathways nonviable for comparative evaluations among different methods. Targeting such scenario, we propose the Bag-of-Attributes Representation (BOAR). BOAR represents an EHR as an n-dimensional vector space. Since BOAR takes advantage of the OMOP (Observational Medical Outcomes Partnership) standard, BOAR is able to represent records retrieved from different data models. The experimental results show that BOAR is flexible and robust to representing EHR from several sources, and allows the execution and evaluation of several clustering algorithms.

Relational databases are rigid-structured data sources characterized by complex relationships among a set of relations (tables). Making sense of such relationships is a challenging problem because users must consider multiple relations, understand their ensemble of integrity constraints, interpret dozens of attributes, and draw complex SQL queries for each desired data exploration. In this scenario, we introduce a twofold methodology, we use a hierarchical graph representation to efficiently model the database relationships and, on top of it, we designed a visualization technique for rapidly relational exploration. Our results demonstrate that the exploration of databases is deeply simplified as the user is able to visually browse the data with little or no knowledge about its structure, dismissing the need for complex SQL queries. We believe our findings will bring a novel paradigm in what concerns relational data comprehension.