A estratégia da RNDS

A Estratégia de Saúde Digital é uma ambição factível e inspiracional que busca nortear e alinhar as diversas atividades e projetos públicos e privados, potencializando o poder de transformação da saúde digital no Brasil.

Ações para a adequação da RNDS à lei geral de proteção de dados

A implementação da Rede Nacional de Dados em Saúde (RNDS) iniciou-se em junho de 2019 com os objetivos de oferecer benefícios de melhoria da assistência à saúde, a partir do acesso às informações e continuidade do cuidado nos níveis de atenção, de permitir eficiência na gestão dos recursos públicos e de impulsionar a Inovação na Saúde. Em março de 2020, a abrangência e o escopo do projeto da RNDS foram direcionados ao enfrentamento da COVID-19, para o fortalecimento da resposta do sistema de saúde, monitoramento e gestão da saúde populacional, oferta de soluções para engajamento ativo do usuário no controle da epidemia e processamento do esperado número de casos da doença. Desde as primeiras ações de planejamento, governança, definição de arquitetura e de regras negociais, o DATASUS se prepara para manter a RNDS em conformidade com a Lei Geral de Proteção de Dados (LGPD), com previsão de entrada em vigor para 3 de maio de 2021, conforme Medida Provisória n° 959, de 29 de abril de 2020.

Apresentação da reunião técnica RNDS: lançamento em Alagoas

Iniciativas do Ministério da Saúde no contexto da estratégia de saúde digital. A estratégia de Saúde Digital para o Brasil trata-se do uso de recursos de Tecnologia de Informação e Comunicação(TIC) para produzir e disponibilizar informações confiáveis, sobre o estado de saúde para quem precisa no momento que precisa!

Workshop RNDS: governança e gestão da ESD28 - 10/12/020

Performance of national COVID-19 'symptom checkers': a comparative case simulation study.

OBJECTIVES: Identifying those individuals requiring medical care is a basic tenet of the pandemic response. Here, we examine the COVID-19 community triage pathways employed by four nations, specifically comparing the safety and efficacy of national online 'symptom checkers' used within the triage pathway. METHODS: A simulation study was conducted on current, nationwide, patient-led symptom checkers from four countries (Singapore, Japan, USA and UK). 52 cases were simulated to approximate typical COVID-19 presentations (mild, moderate, severe and critical) and COVID-19 mimickers (eg, sepsis and bacterial pneumonia). The same simulations were applied to each of the four country's symptom checkers, and the recommendations to refer on for medical care or to stay home were recorded and compared. RESULTS: The symptom checkers from Singapore and Japan advised onward healthcare contact for the majority of simulations (88% and 77%, respectively). The USA and UK symptom checkers triaged 38% and 44% of cases to healthcare contact, respectively. Both the US and UK symptom checkers consistently failed to identify severe COVID-19, bacterial pneumonia and sepsis, triaging such cases to stay home. CONCLUSION: Our results suggest that whilst 'symptom checkers' may be of use to the healthcare COVID-19 response, there is the potential for such patient-led assessment tools to worsen outcomes by delaying appropriate clinical assessment. The key features of the well-performing symptom checkers are discussed.

Use of public datasets in the examination of multimorbidity: Opportunities and challenges.

The interrogation of established, large-scale datasets presents great opportunities in health data science for the linkage and mining of potentially disparate resources to create new knowledge in a fast and cost-efficient manner. The number of datasets that can be queried in the field of multimorbidity is vast, ranging from national administrative and audit datasets, large clinical, technical and biological cohorts, through to more bespoke data collections made available by individual organisations and laboratories. However, with these opportunities also come technical and regulatory challenges that require an informed approach. In this review, we outline the potential benefits of using previously collected data as a vehicle for research activity. We illustrate the added value of combining potentially disparate datasets to find answers to novel questions in the field. We focus on the legal, governance and logistical considerations required to hold and analyse data acquired from disparate sources and outline some of the solutions to these challenges. We discuss the infrastructure resources required and the essential considerations in data curation and informatics management, and briefly discuss some of the analysis approaches currently used.

Toward Reducing Health Information Inequities in the Caribbean: Our Experience Building a Participatory Health Informatics Project.

Precision medicine seeks to leverage technology to improve the health for all individuals. Successful health information systems rely fundamentally on the integration and sharing of data from a range of disparate sources. In many settings, basic infrastructure inequities exist that limit the usefulness of health information systems. We discuss the work of the Yale Transdisciplinary Collaborative Center for Health Disparities focused on Precision Medicine, which aims to improve the health of people in the Caribbean and Caribbean diaspora by leveraging precision medicine approaches. We describe a participatory informatics approach to sharing data as a potential mechanism to reducing inequities in the existing data infrastructure.

Public health delivery in the information age: the role of informatics and technology.

AIM: Public health systems have embraced health informatics and information technology as a potential transformational tool to improve real-time surveillance systems, communication, and sharing of information among various agencies. Global pandemic outbreaks like Zika and Ebola were quickly controlled due to electronic surveillance systems enabling efficient information access and exchange. However, there is the need for a more robust technology to enhance adequate epidemic forecasting, data sharing, and effective communication. The purpose of this review was to examine the use of informatics and information technology tools and its impact on public health delivery. METHOD: Investigators searched six electronic databases. These were MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Complete, Cochrane Database of Systematic Reviews, COMPENDEX, Scopus, and Academic Search Premier from January 2000 to 31 March 2016. RESULTS: A total of 60 articles met the eligibility criteria for inclusion. These studies were organized into three areas as (1) definition of the term public health informatics; (2) type of public health surveillance systems and implications for public health; and (3) electronic surveillance systems functionality, capability, training, and challenges. Our analysis revealed that due to the growing expectations to provide real-time response and population-centered evidence-based public health in this information-driven age there has been a surge in informatics and information technology adoption. Education and training programs are now available to equip public health students and professionals with skills in public health informatics. However, obstacles including interoperability, data standardization, privacy, and technology transfer persist. CONCLUSION: Re-engineering the delivery of public health is necessary to meet the demands of the 21st century and beyond. To meet this expectation, public health must invest in workforce development and capacity through education and training in informatics.

Patients' intention to use online postings of ED wait times: A modified UTAUT model.

BACKGROUND: As health care becomes more reliant on technology, a better understanding of the factors that contribute to acceptance and use of technology is now critical. The Unified Theory of Acceptance and Use of Technology (UTAUT) has been applied to study a variety of technologies in different settings, and it is one of the most cited theories in Information Systems (IS) research. However, there has been limited application of UTAUT to health IT and, in particular, to patients' IT use. OBJECTIVES: The aim of this study is to adapt UTAUT to the context of patient acceptance and use of an Emergency Department (ED) wait-times website, and to empirically test the modified model and compare the results to those of the original UTAUT model. Specifically, it is proposed that there will be a significant relationship between facilitating conditions and behavioral intention. METHODS: A survey of patients in the ED of a Canadian hospital was conducted, yielding 118 completed surveys, and subsequently analyzed using Partial least squares (PLS). RESULTS: This study found that the modified UTAUT produced a substantial improvement in variance explained in behavioral intention compared to the original UTAUT (66% versus 46%). The modified-UTAUT model showed significant effects in performance expectancy (r = 0.302, p < 0.01) and facilitating conditions (r = 0.539, p < 0.001) on behavioral intention to use the website, while the effort expectancy impact was not significant. CONCLUSIONS: This study provides empirical support for the modified-UTAUT in the context of patients' intention to use an ED wait times website. Some results of this study support prior research, while some differ, such as the non-significant relationship between effort expectancy and behavioral intention and the finding that performance expectancy is not the main driver of intention to use. As proposed, facilitating conditions - having the resources necessary to view the website and having the ability to find the website - were the most important factors influencing behavioral intention. UTAUT is a key theoretical advance in IS research and by modifying it to the context of patient use, we contribute to both IS and health research.

Managing public health data: mobile applications and mass vaccination campaigns.

In response to data collection challenges during mass immunization events, Denver Public Health developed a mobile application to support efficient public health immunization and prophylaxis activities. The Handheld Automated Notification for Drugs and Immunizations (HANDI) system has been used since 2012 to capture influenza vaccination data during Denver Health's annual employee influenza campaign. HANDI has supported timely and efficient administration and reporting of influenza vaccinations through standardized data capture and database entry. HANDI's mobility allows employee work locations and schedules to be accommodated without the need for a paper-based data collection system and subsequent manual data entry after vaccination. HANDI offers a readily extensible model for mobile data collection to streamline vaccination documentation and reporting, while improving data quality and completeness.

Public Health Information Systems: Priorities and Practices for Successful Deployments.

A fast paced workshop designed for senior public health decision makers and clinical leaders implementing information systems to support delivery of public health programs. The tutorial will introduce public health information systems and provide best practices for implementing solutions related to immunization, communicable disease case management and outbreak management. Using a combination of formats, the tutorial will: • Highlight key functionality of public health information systems. • Review global crises currently exposing gaps and deficiencies in public health information. • Examine governance, planning, and implementation priorities. • Highlight considerations supporting implementations nationally and in special populations. • Provide real, actionable lessons learned to take away and apply in the real world.

Proposal for a European Public Health Research Infrastructure for Sharing of health and Medical administrative data (PHRIMA).

In Europe, health and medical administrative data is increasingly accumulating on a national level. Looking further than re-use of this data on a national level, sharing health and medical administrative data would enable large-scale analyses and European-level public health projects. There is currently no research infrastructure for this type of sharing. The PHRIMA consortium proposes to realise the Public Health Research Infrastructure for Sharing of health and Medical Administrative data (PHRIMA) which will enable and facilitate the efficient and secure sharing of healthcare data.

Public Health Intelligence: Learning From the Ebola Crisis.

Today's public health crises, as exemplified by the Ebola outbreak, lead to dramatic calls to action that typically include improved electronic monitoring systems to better prepare for, and respond to, similar occurrences in the future. Even a preliminary public health informatics evaluation of the current Ebola crisis exposes the need for enhanced coordination and sharing of trustworthy public health intelligence. We call for a consumer-centric model of public health intelligence and the formation of a national center to guide public health intelligence gathering and synthesis. Sharing accurate and actionable information with government agencies, health care practitioners, policymakers, and, critically, the general public, will mark a shift from doing public health surveillance on people to doing public health surveillance for people.

A public health decision support system model using reasoning methods.

PROBLEM: Public health programs must be based on the real health needs of the population. However, the design of efficient and effective public health programs is subject to availability of information that can allow users to identify, at the right time, the health issues that require special attention. OBJECTIVE: The objective of this paper is to propose a case-based reasoning model for the support of decision-making in public health. RESULTS: The model integrates a decision-making process and case-based reasoning, reusing past experiences for promptly identifying new population health priorities. A prototype implementation of the model was performed, deploying the case-based reasoning framework jColibri. CONCLUSIONS: The proposed model contributes to solve problems found today when designing public health programs in Colombia. Current programs are developed under uncertain environments, as the underlying analyses are carried out on the basis of outdated and unreliable data.

Better health intelligence: a new era for civil registration and vital statistics?

The impetus and opportunities for improving birth, death, and cause of death data have never been more propitious. Renewed country commitment to strengthen vital registration systems is clearly evident, supported by nascent regional coalitions of technical and development organisations. The announcement of a major new investment by Bloomberg Philanthropies to strengthen data systems and capacity in selected countries has the potential to catalyse and realise significant improvements in the availability and quality of data for health. This will require technical leadership, strategic intervention choices, strong country partnerships, and efficient delivery and management of multiple technical interventions across participating countries.

Towards an intelligent decision support system for public health surveillance - a qualitative analysis of information needs.

Public health information systems are often implemented considering the functionalities and requirements established by administrative staff or researchers, but sometimes ignoring the particular needs of decision makers. This paper describes a proposal to support the design of a Decision Support System for Public Health Surveillance in Colombia, by conducting a qualitative study to identify the real needs of people involved in decision making processes. Based on the study results, an intelligent computational component that supports Data Analysis Automation, Prediction of future scenarios and the identification of new Behavioral Patterns is proposed. The component will be implemented using the Case Based Reasoning methodology, which will be integrated as a new component of the Open Source DHIS2 Platform, enabling public health decision-making.