A non-intrusive and reactive architecture to support real-time ETL processes in data warehousing environments.

Nowadays, organizations are very interested to gather data for strategic decision-making. Data are disposable in operational sources, which are distributed, heterogeneous, and autonomous. These data are gathered through ETL processes, which occur traditionally in a pre-defined time, that is, once a day, once a week, once a month or in a specific period of time. On the other hand, there are special applications for which data needs to be obtained in a faster way and sometimes even immediately after the data are generated in the operation data sources, such as health systems and digital agriculture. Thus, the conventional ETL process and the disposable techniques are incapable of making the operational data delivered in real-time, providing low latency, high availability, and scalability. As our proposal, we present an innovative architecture, named Data Magnet, to cope with real-time ETL processes. The experimental tests performed in the digital agriculture domain using real and synthetic data showed that our proposal was able to deal in real-time with the ETL process. The Data Magnet provided great performance, showing an almost constant elapsed time for growing data volumes. Besides, Data Magnet provided significant performance gains over the traditional trigger technique.

Implementing an Integrated Large-Scale Clinical Information System for ISSSTE's Hospital Network in Mexico.

The Institute for Security and Social Services for State Workers (ISSSTE) is a large public provider of health care services that serve around 13.2 million Mexican government workers and their families. To attain process efficiencies, cost reductions, and improvement of the quality of diagnostic and imaging services, ISSSTE was set out in 2019 to create a digital filmless medical image and report management system. A large-scale clinical information system (CIS), including radiology information system (RIS), picture archiving and communication system (PACS), and clinical data warehouse (CDW) components, was implemented at ISSSTE's network of forty secondary- and tertiary-level public hospitals, applying global HL-7 and Digital Imaging and Communications in Medicine (DICOM) standards. In just 5 months, 40 hospitals had their endoscopy, radiology, and pathology services functionally interconnected within a national CIS and RIS/PACS on secure private local area networks (LANs) and a secure national wide area network (WAN). More than 2 million yearly studies and reports are now in digital form in a CDW, securely stored and always available. Benefits include increased productivity, reduced turnaround times, reduced need for duplicate exams, and reduced costs. Functional IT solutions allow ISSSTE hospitals to leave behind the use of radiographic film and printed medical reports with important cost reductions, as well as social and environmental impacts, leading to direct improvement in the quality of health care services rendered. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s42399-020-00713-2) contains supplementary material, which is available to authorized users.

IoMT Platform for Pervasive Healthcare Data Aggregation, Processing, and Sharing Based on OneM2M and OpenEHR.

Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).

Factors Associated with Increased Adoption of a Research Data Warehouse.

The increased demand of clinical data for the conduct of clinical and translational research incentivized repurposing of the University of Arkansas for Medical Sciences' enterprise data warehouse (EDW) to meet researchers' data needs. The EDW was renamed the Arkansas Clinical Data Repository (AR-CDR), underwent content enhancements, and deployed a self-service cohort estimation tool in late of 2016. In an effort to increase adoption of the AR-CDR, a team of physician informaticist and information technology professionals conducted various informational sessions across the UAMS campus to increase awareness of the AR-CDR and the informatics capabilities. The restructuring of the data warehouse resulted in four-fold utilization increase of the AR-CDR data services in 2017. To assess acceptance rates of the AR-CDR and quantify outcomes of services provided, Everett Rogers' diffusion of innovation (DOI) framework was applied, and a survey was distributed. Results show the factors that had impact on increased adoption were: presence of physician informaticist to mediate interactions between researchers and analysts, data quality, communication with and engagement of researchers, and the AR-CDR's team responsiveness and customer service mindset.

AD-SISCOLO: a decision-support tool to aid the management of a cervical cancer screening program

AbstractIntroduction: This paper aims to develop a data warehouse (AD-SISCOLO) in order to support the management of the cervical cancer screening program in the municipality of Rio de Janeiro/Brazil. As a part of the management process, the program managers of the municipality perform tedious manual work in order to calculate a series of performance indicators and then take decisions based on them. Methods AD-SISCOLO was implemented using the Pentaho BI Suite Business Intelligence Platform and the MySQL database management system. The indicators to be calculated and visualized in the tool were based on the municipal data of the cytopathology and histopathology tests from January 2012 until December 2014, which was obtained from the Information System of Cervical Cancer (SISCOLO) after a record linkage process. The follow-up indicators were based on a simplified version of the Brazilian guidelines for the cervical cancer screening. Results AD-SISCOLO allows the visualization of a set of test-based and follow-up indicators from different views and dimensions, which enable managers to monitor all the phases of the screening process and to identify the process' failures. Conclusions Compared with the current available environments in Brazil, AD-SISCOLO is unique in its visualization of the follow-up indicators of groups of women, according to their test results and age. Thereby it provides presentation flexibility to suit the program manager's needs.

Geminivirus data warehouse: a database enriched with machine learning approaches.

BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic losses worldwide. Geminiviruses are divided into nine genera, according to their insect vector, host range, genome organization, and phylogeny reconstruction. Using rolling-circle amplification approaches along with high-throughput sequencing technologies, thousands of full-length geminivirus and satellite genome sequences were amplified and have become available in public databases. As a consequence, many important challenges have emerged, namely, how to classify, store, and analyze massive datasets as well as how to extract information or new knowledge. Data mining approaches, mainly supported by machine learning (ML) techniques, are a natural means for high-throughput data analysis in the context of genomics, transcriptomics, proteomics, and metabolomics. RESULTS: Here, we describe the development of a data warehouse enriched with ML approaches, designated geminivirus.org. We implemented search modules, bioinformatics tools, and ML methods to retrieve high precision information, demarcate species, and create classifiers for genera and open reading frames (ORFs) of geminivirus genomes. CONCLUSIONS: The use of data mining techniques such as ETL (Extract, Transform, Load) to feed our database, as well as algorithms based on machine learning for knowledge extraction, allowed us to obtain a database with quality data and suitable tools for bioinformatics analysis. The Geminivirus Data Warehouse (geminivirus.org) offers a simple and user-friendly environment for information retrieval and knowledge discovery related to geminiviruses.

Integrating Brazilian health information systems in order to support the building of data warehouses

AbstractIntroductionThis paper's aim is to develop a data warehouse from the integration of the files of three Brazilian health information systems concerned with the production of ambulatory and hospital procedures for cancer care, and cancer mortality. These systems do not have a unique patient identification, which makes their integration difficult even within a single system.MethodsData from the Brazilian Public Hospital Information System (SIH-SUS), the Oncology Module for the Outpatient Information System (APAC-ONCO) and the Mortality Information System (SIM) for the State of Rio de Janeiro, in the period from January 2000 to December 2004 were used. Each of the systems has the monthly data production compiled in dbase files (dbf). All the files pertaining to the same system were then read into a corresponding table in a MySQL Server 5.1. The SIH-SUS and APAC-ONCO tables were linked internally and with one another through record linkage methods. The APAC-ONCO table was linked to the SIM table. Afterwards a data warehouse was built using Pentaho and the MySQL database management system.ResultsThe sensitivities and specificities of the linkage processes were above 95% and close to 100% respectively. The data warehouse provided several analytical views that are accessed through the Pentaho Schema Workbench.ConclusionThis study presented a proposal for the integration of Brazilian Health Systems to support the building of data warehouses and provide information beyond those currently available with the individual systems.

Tetanus, diphtheria, acellular pertussis vaccine during pregnancy: pregnancy and infant health outcomes.

OBJECTIVE: To assess pregnancy and birth outcomes in infants born to women who did or did not receive tetanus, diphtheria, acellular pertussis (Tdap) vaccine during pregnancy. STUDY DESIGN: Retrospective cohort. Pregnant women 12-45 years of age who received Tdap at Intermountain Healthcare facilities and their infants were identified and compared with mother-infant pairs without documented Tdap from May 2005 through August 2009. Primary measures included pregnancy outcomes and infant health outcomes at birth through 12 months. RESULTS: From 162,448 pregnancies we identified 138 women (0.08%) with documented Tdap administration during pregnancy (cases); 552 pregnant women without documented Tdap were randomly selected as controls. Of 138 immunized women, 63% received Tdap in the first trimester and 37% after. Tdap was given most commonly as wound prophylaxis. The incidence of spontaneous or elective abortion was no greater in Tdap cases than in controls. There were no significant differences in preterm delivery, gestational age, or birth weight between groups. One or more congenital anomaly was identified in 3.7% (95% CI 1.2%-8.5%) of case infants and 4.4% (95% CI 2.7%-6.5%) of control infants (P = .749). In infants born to women receiving Tdap during pregnancy, 3.6% (0.8%-10.2%) had International Classification of Diseases, Ninth Revision, Clinical Modification diagnoses consistent with complex chronic conditions within 12 months compared with 10.4% (95% CI 7.2%-14.4%) of infants of controls (P = .054). CONCLUSIONS: Documented Tdap administration during pregnancy was uncommon and occurred most often in the first trimester as prophylaxis following trauma. No increase in adverse outcomes was identified in infants born to women receiving Tdap compared with infants of controls.

Methodological Guidelines for Reducing the Complexity of Data Warehouse Development for Transactional Blood Bank Systems.

Over time, data warehouse (DW) systems have become more difficult to develop because of the growing heterogeneity of data sources. Despite advances in research and technology, DW projects are still too slow for pragmatic results to be generated. Here, we address the following question: how can the complexity of DW development for integration of heterogeneous transactional information systems be reduced? To answer this, we proposed methodological guidelines based on cycles of conceptual modeling and data analysis, to drive construction of a modular DW system. These guidelines were applied to the blood donation domain, successfully reducing the complexity of DW development.

Procedimiento para el desarrollo de un sistema de inteligencia de negocios en la gestión de ensayos clínicos en el Centro de Inmunología Molecular / Procedure for development of a business intelligence system in the clinical trials management at the Center of Molecular Immunology

La presente investigación surgió como parte de la colaboración que existe entre la Universidad de las Ciencias Informáticas y el Centro de Inmunología Molecular. El objetivo fue desarrollar un procedimiento que contribuyera al almacenamiento y análisis de los ensayos clínicos y que facilitara la aplicación integral de la inteligencia de negocios en esta actividad. Se realizó una propuesta de procedimiento para conducir el desarrollo de soluciones de inteligencia de negocios en el centro. El procedimiento fue evaluado a partir del método de experto Delphi y se obtuvo el resultado de Muy adecuado. Se contó además con un aval del centro cliente, donde se valoró de satisfactorio el trabajo realizado. La implementación de este procedimiento permitirá almacenar toda la información que se gestiona, de manera íntegra y estándar, con lo que se logrará viabilizar los análisis estadísticos que se necesitan realizar por parte de los especialistas de la institución

Present paper appeared as part of cooperation existing between the Information Sciences University and the Center of Molecular Immunology. The aim was to develop a procedure contributing to storage and analysis of clinical trials and to integral application of business intelligence service in this activity. A procedure proposal was made to manage the development of solutions in business intelligence in our center. Procedure was assessed from the method of the Delphi expert method obtaining a result qualified as very appropriate. Implementation of this procedure will allows storing all information managed in an integral and standard way, thus achieving to make viable the statistic analyses needed to be performed by the specialists of the institution

Uso de informações de saúde para suporte à decisão: uma metodologia focada no consumidor da informação / Use of health information for decision support: a methodology focused on consumer information

O objetivo deste trabalho é apresentar os passos necessários para a construção de data marts / data warehouses como uma solução para um ambiente de suporte à decisão. Seu foco não é, entretanto, o aprofundamento de cada uma das etapas deste método (uma vez que existe uma ampla literatura sobre o assunto), mas realçar a importância do envolvimento do usuário – aqui denominado consumidor da informação – durante o processo de criação e manutenção destes data marts / data warehouses. O consumidor da informação é chamado a participar de um plano integrado com os membros das áreas detentoras do conhecimento necessário para a construção destas soluções de suporte à decisão, incluindo a área de tecnologia da informação. A disseminação das informações contidas nestes bancos de dados para os diversos tipos de consumidores da informação (com diferentes recursos de hardware, software e humanos disponíveis), e sua aplicação no controle social, também são discutidas aqui.

The purpose of this work is to present the required steps for the construction of data marts / data warehouses as a solution for a decision support environment. However, its focus is not to go deeper in each of the steps of this method (since there is a broadliterature about the subject), but to highlight the importance of the user – here called information consumer – engagement during the process of creating and maintaining these data marts / data warehouses. The information consumer is called to take part in an integrated plan together with the members of the areas who own the necessary knowledge to build these decision support solutions, including the information technology (IT) area. The spread of the informations contained in these databases to the different types of information consumers (with different hardware, software and humanresources available), and its application in social control, is also discussed here.