Public Health Informatics and the Perioperative Physician: Looking to the Future.

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Building a More Diverse Public Health Informatics Workforce: Preliminary Results.

The US public health infrastructure has been historically underfunded, a condition that was exacerbated by the COVID-19 pandemic. This was especially noted in the area of public health informatics. It was also acknowledged that the lack of a diverse public health workforce made it more difficult to address biases and disparities effectively. In 2021 the Office of the National Coordinator awarded $73 million to 10 awardees to develop public health informatics and technology (PHIT) workforce training. The Gaining Equity in Training for Public Health Informatics and Technology (GET PHIT) award utilizes various methods to train and engage minority and underserved populations in the field of public health informatics. Evaluations of the bootcamps and internships to date have shown generally positive results, both in terms of skills acquired and overall experiences. These results indicate that integrating the fields of public health and data science in non-degree, short-term experiences can have positive outcomes.

Broad Trends in Public Health and Epidemiology Informatics.

OBJECTIVES: The objective of this study is to highlight innovative research and contemporary trends in the area of Public Health and Epidemiology Informatics (PHEI). METHODS: Following a similar approach to last year's edition, a meticulous search was conducted on PubMed (with keywords including topics related to Public Health, Epidemiological Surveillance and Medical Informatics), examining a total of 2,022 scientific publications on Public Health and Epidemiology Informatics (PHEI). The resulting references were thoroughly examined by the three section editors. Subsequently, 10 papers were chosen as potential candidates for the best paper award. These selected papers were then subjected to peer-review by six external reviewers, in addition to the section editors and two chief editors of the IMIA yearbook of medical informatics. Each paper underwent a total of five reviews. RESULTS: Out of the 539 references retrieved from PubMed, only two were deemed worthy of the best paper award, although four papers had the potential to qualify in total. The first best paper by pertains to a study about the need for a new annotation framework due to inadequacies in existing methods and resources. The second paper elucidates the use of Weibo data to monitor the health of Chinese urbanites. The correlation between air pollution and health sensing was measured via generalized additive models. CONCLUSIONS: One of the primary findings of this edition is the dearth of studies identified for the PHEI section, which represents a significant decline compared to the previous edition. This is particularly surprising given that the post-COVID period should have led to an increased use of information and communication technology for public health issues.

An anti-infodemic virtual center for the Americas

[ABSTRACT]. The Pan American Health Organization/ World Health Organization (PAHO/WHO) Anti-Infodemic Virtual Center for the Americas (AIVCA) is a project led by the Department of Evidence and Intelligence for Action in Health, PAHO and the Center for Health Informatics, PAHO/WHO Collaborating Center on Information Systems for Health, at the University of Illinois, with the participation of PAHO staff and consultants across the region. Its goal is to develop a set of tools—pairing AI with human judgment—to help ministries of health and related health institutions respond to infodemics. Public health officials will learn about emerging threats detected by the center and get recommendations on how to respond. The virtual center is structured with three parallel teams: detection, evidence, and response. The detection team will employ a mixture of advanced search que- ries, machine learning, and other AI techniques to sift through more than 800 million new public social media posts per day to identify emerging infodemic threats in both English and Spanish. The evidence team will use the EasySearch federated search engine backed by AI, PAHO’s knowledge management team, and the Librar- ian Reserve Corps to identify the most relevant authoritative sources. The response team will use a design approach to communicate recommended response strategies based on behavioural science, storytelling, and information design approaches.

[RESUMEN]. El centro virtual contra la infodemia para la Región de las Américas de la Organización Panamericana de la Salud/Organización Mundial de la Salud (OPS/OMS) es un proyecto liderado por el Departamento de Eviden- cia e Inteligencia para la Acción en la Salud de la OPS y el Center for Health Informatics de la Universidad de Illinois, centro colaborador de la OPS/OMS en sistemas de información para la salud, con la participación de personal y consultores de la OPS en toda la Región. Su objetivo es crear un conjunto de herramientas que combinen inteligencia artificial (IA) y los criterios humanos para apoyar a los ministerios de salud y las instituciones relacionadas con la salud en la respuesta a la infodemia. Los funcionarios de salud pública reci- birán formación sobre las amenazas emergentes detectadas por el centro y recomendaciones sobre cómo abordarlas. El centro virtual está estructurado en tres equipos paralelos: detección, evidencia y respuesta. El equipo de detección empleará una combinación de consultas mediante búsqueda avanzada, aprendizaje automático y otras técnicas de IA para evaluar más de 800 millones de publicaciones nuevas en las redes sociales al día con el fin de detectar amenazas emergentes en el ámbito de la infodemia tanto en inglés como en español. El equipo de evidencia hará uso del motor de búsqueda federado EasySearch y, con el apoyo de la IA, el equipo de gestión del conocimiento de la OPS y la red Librarian Reserve Corps, determinará cuáles son las fuentes autorizadas más pertinentes. El equipo de respuesta utilizará un enfoque vinculado al diseño para difundir las estrategias recomendadas sobre la base de las ciencias del comportamiento, la narración de historias y el diseño de la información.

[RESUMO]. O Centro Virtual Anti-Infodemia para as Américas (AIVCA, na sigla em inglês) da Organização Pan-Ameri- cana da Saúde/Organização Mundial da Saúde (OPAS/OMS) é um projeto liderado pelo Departamento de Evidência e Inteligência para a Ação em Saúde da OPAS e pelo Centro de Informática em Saúde da Uni- versidade de Illinois, EUA (Centro Colaborador da OPAS/OMS para Sistemas de Informação para a Saúde), com a participação de funcionários e consultores da OPAS de toda a região. Seu objetivo é desenvolver um conjunto de ferramentas — combinando a inteligência artificial (IA) com o discernimento humano — para ajudar os ministérios e instituições de saúde a responder às infodemias. As autoridades de saúde pública aprenderão sobre as ameaças emergentes detectadas pelo centro e obterão recomendações sobre como responder. O centro virtual está estruturado com três equipes paralelas: detecção, evidência e resposta. A equipe de detecção utilizará consultas de pesquisa avançada, machine learning (aprendizagem de máquina) e outras técnicas de IA para filtrar mais de 800 milhões de novas postagens públicas nas redes sociais por dia, a fim de identificar ameaças infodêmicas emergentes em inglês e espanhol. A equipe de evidência usará o mecanismo de busca federada EasySearch, com apoio de IA, da equipe de gestão de conhecimento da OPAS e do Librarian Reserve Corps (LRC), para identificar as fontes abalizadas mais relevantes. A equipe de resposta usará uma abordagem de design para comunicar estratégias de resposta recomendadas com base em abordagens de ciência comportamental, narração de histórias e design da informação.

Estratégia e-sus atenção primária em consultórios na rua: análise de um processo de educação permanente / E-sus strategy primary care in street offices: evaluation of a continuing education process / Estrategia e-sus atención primaria en oficinas de calle: evaluación de un proceso de educación continua

Objetivo: Analisar o uso da estratégia e-SUS Atenção Primária pelas equipes de Consultório na Rua, após processo de educação permanente. Métodos: Estudo descritivo-exploratório de abordagem qualitativa do tipo pesquisa-intervenção, realizado com 23 profissionais de três Consultórios na Rua da Região Centro-Oeste do Brasil em 2016. A intervenção consistiu em um seminário teórico-prático sobre a estratégia e-SUS Atenção Primária, com avaliação mediada por grupos focais. Os dados foram submetidos à Análise de Conteúdo, modalidade Temática, com auxílio do software ATLAS.ti. Resultados: Houve reflexões sobre a necessidade de transformação da prática profissional e valorização dos registros eletrônicos de saúde das atividades realizadas pelas equipes para elevar a qualidade da assistência e dar mais visibilidade ao trabalho empreendido pelos profissionais. Passaram a incorporar a estratégia e-SUS Atenção Primária nos processos de trabalho dos serviços de forma gradual. Conclusão: O processo de educação permanente proporcionou aos profissionais e gestores um espaço de reflexão e ressignificação da prática profissional em relação aos registros eletrônicos de saúde, sensibilizando-os para a importância da informatização nos processos de trabalho. Ao longo do processo interventivo, ficou evidenciado que os participantes foram mobilizados quanto à compreensão e atitudes em relação à estratégia e-SUS Atenção Primária no seu cotidiano. (AU)

Objective: To analyze the use of the e-SUS Primary Care strategy by the Street Clinic teams, after a continuing education process. Methods: Descriptive-exploratory study with a qualitative research-intervention approach carried out with 23 professionals from three clinics in Rua da Centro-Oeste do Brasil in 2016. The intervention consisted of a theoretical-practical seminar on the e-SUS Primary Care strategy with assessment mediated by focus groups. Data were submitted to Content Analysis, Thematic modality, with the help of the ATLAS.ti software. Results: There were reflections on the need to transform professional practice and value electronic health records of the activities carried out by the teams to raise the quality of care and give more visibility to the work undertaken by professionals. They began to gradually incorporate the e-SUS Primary Care strategy into the work processes of the services. Conclusion: The continuing education process provided professionals and managers with a space for reflection and resignification of professional practice in relation to electronic health records, making them aware of the importance of computerization in work processes. Throughout the intervention process, it was evident that the participants were mobilized regarding their understanding and attitudes towards the e-SUS Primary Care strategy in their daily lives. (AU)

Objectivo: Analizar el uso de la estrategia de Atención Primaria e-SUS por los equipos de Clínica de Calle, luego de un proceso de educación continua. Métodos: Estudio descriptivo-exploratorio con enfoque de investigación-intervención cualitativo realizado con 23 profesionales de tres oficinas de la Rua da Centro-Oeste de Brasil en 2016. La intervención consistió en un seminario teórico-práctico sobre la estrategia e-SUS de Atención Primaria con evaluación mediada por grupos focales. Los datos fueron sometidos a Análisis de Contenido, modalidad Temática, con la ayuda del software ATLAS.ti. Resultados: Se reflexionó sobre la necesidad de transformar la práctica profesional y valorar la historia clínica electrónica de las actividades que realizan los equipos para elevar la calidad de la atención y dar más visibilidad al trabajo realizado por los profesionales. Comenzaron a incorporar gradualmente la estrategia de Atención Primaria e-SUS en los procesos de trabajo de los servicios. Conclusión: El proceso de formación continua brindó a los profesionales y directivos un espacio de reflexión y resignificación del ejercicio profesional en relación a la historia clínica electrónica, sensibilizándolos sobre la importancia de la informatización en los procesos de trabajo. A lo largo del proceso de intervención, se evidenció que los participantes se movilizaron en cuanto a su comprensión y actitudes hacia la estrategia de Atención Primaria e-SUS en su vida diaria. (AU)

Germina: herramienta para la detección automática de agrupaciones de casos de COVID-19 / Germina: tool for automatic detection of clusters of COVID-19 cases

La pandemia de COVID-19 evidenció que la vigilancia epidemiológica no disponía de recursos para responder a los aumentos de casos ni a los brotes. La alta transmisibilidad comunitaria entre la población escolar en la ciudad de Barcelona al inicio de la sexta ola tensionó la unidad de vigilancia de COVID-19 de la ciudad. Mediante metodología SCRUM se desarrolló e implementó Germina, una herramienta informática capaz de capturar, armonizar, integrar, almacenar, analizar y visualizar diariamente datos de múltiples fuentes de información. Germina permite identificar agrupaciones de tres o más casos escolares y calcular indicadores epidemiológicos. La implementación de Germina facilitó la respuesta epidemiológica a la sexta ola de COVID-19 en el ámbito escolar en Barcelona. Esta herramienta es aplicable a otros ámbitos de exposición y a otras enfermedades transmisibles. El uso de herramientas informáticas automatizadas, como Germina, mejora los sistemas de vigilancia epidemiológica y apoya la toma de decisiones basada en la evidencia.(AU)

The COVID-19 pandemic showed that epidemiological surveillance was under-resourced to respond to increases in cases and outbreaks. The high community transmissibility among the school population in the city of Barcelona at the beginning of the sixth wave strained the local COVID-19 surveillance unit. Using SCRUM methodology, Germina was developed and implemented as a software tool capable of capturing, harmonizing, integrating, storing, analysing and visualizing data from multiple information sources on a daily basis. Germina identifies clusters of three or more school cases and calculates epidemiological indicators. The implementation of Germina facilitated the epidemiological response to the sixth wave of COVID-19 in the school setting in the city of Barcelona. This tool is transferable to other exposure settings and communicable diseases. The use of automated informatics tools such, as Germina, improves epidemiological surveillance systems and supports evidence-based decision making.(AU)

Novelty in Public Health and Epidemiology Informatics.

OBJECTIVES: To highlight novelty studies and current trends in Public Health and Epidemiology Informatics (PHEI). METHODS: Similar to last year's edition, a PubMed search of 2021 scientific publications on PHEI has been conducted. The resulting references were reviewed by the two section editors. Then, 11 candidate best papers were selected from the initial 782 references. These papers were then peer-reviewed by selected external reviewers. They included at least two senior researchers, to allow the Editorial Committee of the 2022 IMIA Yearbook edition to make an informed decision for selecting the best papers of the PHEI section. RESULTS: Among the 782 references retrieved from PubMed, two were selected as the best papers. The first best paper reports a study which performed a comprehensive comparison of traditional statistical approaches (e.g., Cox Proportional Hazards models) vs. machine learning techniques in a large, real-world dataset for predicting breast cancer survival, with a focus on explainability. The second paper describes the engineering of deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images. CONCLUSION: Overall, from this year edition, we observed that the number of studies related to PHEI has decreased. The findings of the two studies selected as best papers on the topic suggest that a significant effort is still being made by the community to compare traditional learning methods with deep learning methods. Using multimodality datasets (images, texts) could improve approaches for tackling public health issues.

NTD Health: An electronic medical record system for neglected tropical diseases / NTD Health: un sistema de historias clínicas electrónicas para enfermedades tropicales desatendidas

Introduction: The use of technological resources to support processes in health systems has generated robust, interoperable, and dynamic platforms. In the case of institutions working with neglected tropical diseases, there is a need for specific customizations of these diseases. Objectives: To establish a medical record platform specialized in neglected tropical diseases which could facilitate the analysis of treatment evolution in patients, as well as generate more accurate data about various clinical aspects. Materials and methods: A set of requirements to develop state of the art forms, concepts, and functionalities to include neglected tropical diseases were compiled. An OpenMRS distribution (version 2.3) was used as reference to build the platform, following the recommended guidelines and shared-community modules. Results: All the customized information was developed in a platform called NTD Health, which is web-based and can be upgraded and improved by users without technological barriers. Conclusions: The electronic medical record system can become a useful tool for other institutions to improve their health practices as well as the quality of life for neglected tropical disease patients, simplifying the customization of healthcare systems able to interoperate with other platforms.

Introducción. El uso de recursos tecnológicos destinados a apoyar procesos en los sistemas de salud ha generado plataformas sólidas, interoperables y dinámicas. En el caso de las instituciones que trabajan con enfermedades tropicales desatendidas, existe la necesidad de personalizaciones específicas en las herramientas de uso médico. Objetivos. Establecer una plataforma para historias clínicas especializada en enfermedades tropicales desatendidas, con el fin de facilitar el análisis de la evolución del tratamiento de los pacientes, además de generar datos más precisos sobre diversos aspectos clínicos. Materiales y métodos. Se compiló un conjunto de requisitos para implementar formularios, conceptos y funcionalidades que permitan incluir enfermedades tropicales desatendidas. Se utilizó una distribución de OpenMRS (versión 2.3) como referencia para construir la plataforma, siguiendo las pautas recomendadas y módulos compartidos por la comunidad. Resultados. Toda la información personalizada se implementó en una plataforma llamada NTD Health, la cual se encuentra almacenada en la web y los usuarios pueden actualizarla y mejorarla sin barreras tecnológicas. Conclusiones. El sistema de historias clínicas electrónicas puede convertirse en una herramienta útil para que otras instituciones mejoren sus prácticas en salud, así como la calidad de vida de los pacientes con enfermedades tropicales desatendidas, simplificando la personalización de los sistemas de salud capaces de interoperar con otras plataformas.

Towards the Adoption of Novel Visualizations in Public Health.

Visualizations form an important part of public health informatics (PHI) communications. Visualizing data facilitates discussion, aids understanding, makes patterns apparent, promotes analysis, and fosters recall. How rare are novel visualizations in the PHI literature? In Phase 1, we used a rapid review methodology to test the commonness of the Sankey diagram in the PHI theory literature via an automated text search for key terms. In Phase 2, we prototype an uncommon chart type. A total of 27 relvant papers were searched and a computer-generated Sankey diagram was prototyped. PHI professionals have access to visualization tools emerging from social media and niche systems. PHI literature underutilizes uncommon visualizations requiring programming expertise. The authors advocate for: multi-disciplinary teamwork, technical education, the use of open visualization tools, and further adoption of visualization for public health professionals.

Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics.

Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical for public health, especially during the COVID-19 pandemic, as institutions and government agencies rely on death reports to formulate responses to communicable diseases. Unfortunately, determining the causes of death is challenging even for experienced physicians. The novel coronavirus and its variants may further complicate the task, as physicians and experts are still investigating COVID-related complications. To assist physicians in accurately reporting causes of death, an advanced Artificial Intelligence (AI) approach is presented to determine a chronically ordered sequence of conditions that lead to death (named as the causal sequence of death), based on decedent's last hospital discharge record. The key design is to learn the causal relationship among clinical codes and to identify death-related conditions. There exist three challenges: different clinical coding systems, medical domain knowledge constraint, and data interoperability. First, we apply neural machine translation models with various attention mechanisms to generate sequences of causes of death. We use the BLEU (BiLingual Evaluation Understudy) score with three accuracy metrics to evaluate the quality of generated sequences. Second, we incorporate expert-verified medical domain knowledge as constraints when generating the causal sequences of death. Lastly, we develop a Fast Healthcare Interoperability Resources (FHIR) interface that demonstrates the usability of this work in clinical practice. Our results match the state-of-art reporting and can assist physicians and experts in public health crisis such as the COVID-19 pandemic.

Public health surveillance in the U.S. Department of Veterans Affairs: evaluation of the Praedico surveillance system.

BACKGROUND: Early threat detection and situational awareness are vital to achieving a comprehensive and accurate view of health-related events for federal, state, and local health agencies. Key to this are public health and syndromic surveillance systems that can analyze large data sets to discover patterns, trends, and correlations of public health significance. In 2020, Department of Veterans Affairs (VA) evaluated its public health surveillance system and identified areas for improvement. METHODS: Using the Centers for Disease Control and Prevention (CDC) Guidelines for Evaluating Public Health Surveillance Systems, we assessed the ability of the Praedico Surveillance System to perform public health surveillance for a variety of health issues and evaluated its performance compared to an enterprise data solution (VA Corporate Data Warehouse), legacy surveillance system (VA ESSENCE) and a national, collaborative syndromic surveillance platform (CDC NSSP BioSense). RESULTS: Review of system attributes found that the system was simple, flexible, and stable. Representativeness, timeliness, sensitivity, and Predictive Value Positive were acceptable but could be further improved. Data quality issues and acceptability present challenges that potentially affect the overall usefulness of the system. CONCLUSIONS: Praedico is a customizable surveillance and data analytics platform built on big data technologies. Functionality is straightforward, with rapid query generation and runtimes. Data can be graphed, mapped, analyzed, and shared with key decision makers and stakeholders. Evaluation findings suggest that future development and system enhancements should focus on addressing Praedico data quality issues and improving user acceptability. Because Praedico is designed to handle big data queries and work with data from a variety of sources, it could be enlisted as a tool for interdepartmental and interagency collaboration and public health data sharing. We suggest that future system evaluations include measurements of value and effectiveness along with additional organizations and functional assessments.

Salud pública digital: avances y retos en América Latina: parte 2

Salud pública digital: avances y retos en América Latina: parte 1

An Evaluation of Syndromic Surveillance-Related Practices Among Selected State and Local Health Agencies.

CONTEXT: Syndromic surveillance consists of the systematic collection and use of near real-time data about health-related events for situational awareness and public health action. As syndromic surveillance programs continue to adopt new technologies and expand, it is valuable to evaluate these syndromic surveillance systems and practices to ensure that they meet public health needs. OBJECTIVE: This assessment's aim is to provide recent information about syndromic surveillance systems and practice characteristics among a group of state and local health departments. DESIGN/SETTING: Information was obtained between November 2017 and June 2018 through a telephone survey using an Office of Management and Budget-approved standardized data collection tool. Participants were syndromic surveillance staff from each of 31 state and local health departments participating in the National Syndromic Surveillance Program funded by the Centers for Disease Control and Prevention. Questions included jurisdictional experience, data sources and analysis systems used, syndromic system data processing characteristics, data quality verification procedures, and surveillance activities conducted with syndromic data. MEASURES: Practice-specific information such as types of systems and data sources used for syndromic surveillance, data quality monitoring, and uses of data for public health situational awareness (eg, investigating occurrences of or trends in diseases). RESULTS: The survey analysis revealed a wide range of experiences with syndromic surveillance. Participants reported the receipt of data daily or more frequently. Emergency department data were the primary data source; however, other data sources are being integrated into these systems. All health departments routinely monitored data quality. Syndromes of highest priority across the respondents for health events monitoring were influenza-like illness and drug-related syndromes. However, a wide variety of syndromes were reported as priorities across the health departments. CONCLUSION: Overall, syndromic surveillance was relevantly integrated into the public health surveillance infrastructure. The near real-time nature of the data and its flexibility to monitor different types of health-related issues make it especially useful for public health practitioners. Despite these advances, syndromic surveillance capacity, locally and nationally, must continue to evolve and progress should be monitored to ensure that syndromic surveillance systems and data are optimally able to meet jurisdictional needs.

Assessing Public Health Workforce Informatics Competencies: A Study of 3 District Health Departments in Georgia.

CONTEXT: Despite the increased recognition of the importance of having informatics-competent public health professionals, the competency level of the public health workforce in public health informatics (PHI) has not been examined extensively in the literature. OBJECTIVE: The purpose of this study was to assess public health workforce informatics competencies in select Georgia health districts and determine the correlates of PHI proficiency. METHODS: This study is based on a cross-sectional quantitative study design. We conducted an online self-administered survey of employees from 3 selected district health departments to assess proficiency in foundational PHI competency domains. Three hundred thirty-three respondents completed the survey, with a response rate of 32.5%. A gap score was calculated as a proxy to identify informatics training needs. A path analysis was conducted to assess the relationships among contextual factors and foundational PHI competency domains. RESULTS: The public health employees participating in this study reported relatively high proficiency in foundational PHI competency. Psychometric testing of the competency assessment instrument revealed 2 foundational informatics competency domains-effective information technology (IT) use and effective use of information. The effective use of IT mediated the relationship between employee-level factors of age and past informatics training and the effective use of information. CONCLUSION: The study highlights the importance of improving the ability of public health professionals to leverage IT and information to advance population health. Periodic assessment of staff PHI competencies can help proactively identify competency gaps and address needs for additional training. Short assessment tools, such as presented in this study, can be validated and used for such assessments.

O uso da inteligência artificial como ferramenta de apoio à gestão das ações em saúde na Secretaria de Estado da Saúde de Goiás / The use of artificial intelligence as a tool to support the management of health actions in the Goiás State Health Department

Introdução: A área de Tecnologia da Informação da Secretaria de Estado da Saúde de Goiás com o intuito de desenvolver soluções tecnológicas para apoio à tomada de decisão iniciou em 2010 o projeto na área de Business Intelligence que engloba, dentre outras ações, a construção de um data warehouse que agrega dados dos sistemas transacionais em conjunto com a elaboração de painéis de monitoramento que permitem uma visão integrada do todo para subsidiar a alta gestão e áreas técnicas da Secretaria de Estado da Saúde de Goiás (SES-GO), garantindo assim, decisões mais assertivas e a elaboração de políticas públicas mais pertinentes e fundamentadas. Este projeto evoluiu e atualmente as áreas de Ciência de Dados e Big Data também compõem área de analytics da SES-GO. Objetivo: o principal objetivo deste artigo é apresentar a plataforma FLINK que foi desenvolvida vislumbrando a democratização de dados e indicadores de saúde da Secretaria de Estado da Saúde de Goiás e compartilhar o processo de construção e implantação da mesma. Metodologia: Trata-se de um estudo de abordagem qualitativa, do tipo relato de experiência, no qual os autores relatam a construção e funcionalidades da plataforma FLINK. Considerações finais: A plataforma FLINK representa a efetivação da transparência da gestão pública no estado de Goiás em dados de saúde. A versatilidade e a flexibilidade do mesmo possibilita a melhoria das ações de saúde e beneficia todos os usuários do SUS

Introduction: The servers in the Business Intelligence (BI) area of the State Department of Health of Goiás, in order to develop technological solutions to support decision-making, started in 2010 to build a data warehouse that would aggregate data from traditional systems, and of monitoring panels, allowing a more integrated view of the whole, to support the senior management and technical areas of State Department of Health of Goiás (SES-GO), thus ensuring more assertive decisions and the development of more relevant and grounded public policies. Objective: The main objective of this article is to present the flink plataform that was developed with a view to the democratization of data and health status indicators of the Goiás state health department and to share the process of construction and implementation of the same. Method: This is a study with a qualitative approach, of the experience report type, in which the authors report the construction and functionalities of the FLINK plataform. Final considerations: FLINK represents the effectiveness of transparency in public management in the state of Goiás. Its versatility and flexibility enables the improvement of health actions and benefits all SUS users

Comparison of Five Methods to Estimate the Parameters for the Three-Parameter Lindley Distribution with Application to Life Data.

We have studied one of the most common distributions, namely, Lindley distribution, which is an important continuous mixed distribution with great ability to represent different systems. We studied this distribution with three parameters because of its high flexibility in modelling life data. The parameters were estimated by five different methods, namely, maximum likelihood estimation, ordinary least squares, weighted least squares, maximum product of spacing, and Cramér-von Mises. Simulation experiments were performed with different sample sizes and different parameter values. The different methods were compared on the generated data by mean square error and mean absolute error. In addition, we compared the methods for real data, which represent COVID-19 data in Iraq/Anbar Province.