The digitalization of healthcare data has revolutionized clinical practice, yet Paraguay struggles with fragmented clinical data and limited interoperability. To address these challenges, we present an interoperable health information application ecosystem. The proposed solution leverages standards such as HL7 FHIR (Fast Healthcare Interoperability Resources) to enable secure and structured data exchange among stakeholders, open-source technologies to reduce costs, and an event-driven microservices architecture to ensure scalability. It integrates with existing health information systems, incorporates a proprietary health record platform for physicians and patients, and stores all data in a high-availability repository. A small-scale implementation in a Paraguayan clinic demonstrates improved data accessibility and usability for healthcare professionals, as well as greater patient engagement through enhanced access to personal medical histories.

For the training of blood pressure predictive models, it is necessary to determine the optimal number of predictors when the data set is of high dimensionality. Applying the appropriate dimensionality reduction technique according to the dataset will reduce the number of components and improve the performance of the predictive models. This work proposes the dimensionality reduction of the data set through the explorations of linear and nonlinear relationships of photoplethysmography signals by applying principal component analysis through the decomposition of Pearson and Spearman correlation matrices. The differences between the explained and cumulative variances of the principal components are minimal by applying Pearson and Spearman correlations. The predictive models trained with the first 5 principal components obtained better results for the estimation of blood pressure, with minimal loss of information with respect to the original data set.

In terms of public health policies, having all the information from the different Health Information Systems enriches the decision-making criteria, but the fact that each system has its own structure and protocols makes integrating all this information a challenge. Therefore, it is essential to have interoperability standards that enable such communication, but although several standards are available for this purpose, in order to make better use of such information, such as making inferences or integrated queries that depend on the semantics of a specific context, such as the public policies of a country, it is necessary to use modeling techniques according to the context. In this sense, this paper presents a solution for semantic interoperability between Health Information Systems, through the use of ontologies. For this purpose, we start from the definition of a Minimum Data Set for Primary Health Care in Paraguay, this Minimum Data Set is used as the basis for the development of an ontology, which for the scope of this work, and as a demonstration, the vaccination area was taken as a scenario. For the ontology validation process, the necessary syntactic interoperability was also implemented to integrate some testing systems and enable the execution of ontological queries.

The current process of searching for terminology for medical coding in health standards in the Hospital of Clinics of Paraguay, is done through coding manuals or through the internet of cell phones. This process takes a long time to be done during the medical consultation. The optimization of the current process would allow the search of encoded terminologies during the medical consultation, avoiding that the resident doctors have to perform this extra work after the workday. This work implements a medical terminology server through web services and using the text search engine library: Apache Lucene that is specialized in storage agility and data recovery. The implemented server also allows to perform successful searches of terminologies encoded in standards through friendly or familiar terms. The search time of coded terminologies using the implemented server was compared with the search time of the current process in the hospital of clinics using the internet of cell phones. Under these criteria, the terminology server responds up to 18 times faster than the current search process. In addition, it was made a comparison of the server against a search engine, called Metamorphosys. The terminology server implemented turned out to be quite competitive against Metamorphosys by presenting quite similar response times. Although both tools have a similar average response time, Metamorphosys presents some high atypical values in some algorithms that it presents as search options. The degree of satisfaction of the user in the use of the system was evaluated through a usability questionnaire called “System Usability Scale” (SUS). The evaluation rated the system as “Good” according to the Bangor.

Locating objects or people in an indoor environment quickly, accurately and at low cost is a great need nowadays and in different scenarios. Some examples are the location of products in a warehouse or the quick location of patients, medical personnel or equipment in a hospital. A location system is necessary for health care, home care, stock control or inventory. In this context, this paper presents the design and implementation of a low-cost centralized indoor location system that uses BLE technology (Bluetooth Low Energy) together with the particle filter algorithm. Experimental results show that the system can achieve an accuracy of 1.8 m, at best, with an accuracy of 74% within 3 m. The cost of the infrastructure goes hand in hand with the number of objects or people to be located.

A univocal way to identify persons is a need in Hospital Information Systems, to the point of being an essential requirement for Electronic Health Record (EHR) Systems. In Paraguay, a country with a significant rate of people without identity documents, and particularly in the Hospital de Clínicas (HC), which in general provides medical care to low-income people, this requirement must be carefully attended. This article analyzes the consequences of this fact in the management of the HC and proposes the use of an Automated Fingerprint Identification System (AFIS). In this context, we evaluate AFIS options with free software license and open source code, go into details about SourceAFIS and show experimental results. At the end, we present some future works aimed to put in practice this proposal in the HC.