Building consensus on common features and interoperability use cases for community health information systems: a Delphi study.

INTRODUCTION: Information systems for community health have become increasingly sophisticated and evidence-based in the last decade and they are now the most widely used health information systems in many low-income and middle-income countries. This study aimed to establish consensus regarding key features and interoperability priorities for community health information systems (CHISs). METHODS: A Delphi study was conducted among a systematically selected panel of CHIS experts. This impressive pool of experts represented a range of leading global health institutions, with gender and regional balance as well as diversity in their areas of expertise. Through five rounds of iterative surveys and follow-up interviews, the experts established a high degree of consensus. We supplemented the Delphi study findings with a series of focus group discussions with 10 community health worker (CHW) leaders. RESULTS: CHISs today are expected to adapt to a wide range of local contextual requirements and to support and improve care delivery. While once associated with a single role type (CHWs), these systems are now expected to engage other end users, including patients, supervisors, clinicians and data managers. Of 30 WHO-classified digital health interventions for care providers, experts identified 23 (77%) as being important for CHISs. Case management and care coordination features accounted for more than one-third (14 of 37, 38%) of the core features expected of CHISs today, a higher proportion than any other category. The highest priority use cases for interoperability include CHIS to health management information system monthly reporting and CHIS to electronic medical record referrals. CONCLUSION: CHISs today are expected to be feature-rich, to support a range of user roles in community health systems, and to be highly adaptable to local contextual requirements. Future interoperability efforts, such as CHISs in general, are expected not only to move data efficiently but to strengthen community health systems in ways that measurably improve care.

A cross-sectional survey on awareness of cancer risk factors, information sources and health behaviors for cancer prevention in Japan.

Due to recent increases in cancer burden worldwide, we investigated current awareness of cancer risk factors and the association between information sources and health behaviors for cancer prevention in Japan. A nationwide representative sample aged 20 years or older (563 men and 653 women) responded to a questionnaire as part of a population-based survey in December 2018. Tobacco smoking (55.7% of the mean attributable fraction of cancer risk overall) and cancer-causing infection (52.0%) were regarded more highly than other lifestyle factors as causes of cancer (obesity [36.6%], physical inactivity [31.9%], unbalanced diet [30.9%], and alcohol consumption [26.2%]). The association between information sources and health behaviors for cancer prevention was evaluated using a logistic regression model. The websites of public institutions, and health professionals were associated with a broad range of health behaviors including improving diet, exercise, cancer screening/health check-up, and abstinence from smoking/drinking. Among sources of print media, positive associations were observed between books and improving diet/exercise, brochures and cancer screening/health check-up, and advertisements and abstinence from smoking/drinking. A strategic health communication approach that utilizes various information sources and delivery channels is needed to inform the public about cancer prevention and to motivate risk-reducing behaviors in the population.

Gobernanza de datos en salud: ética, privacidad y seguridad: parte 2

La gobernanza de datos es un requisito en el actual entorno de los sistemas de salud, en el cual las demandas ciudadanas en cuanto al acceso a los servicios de salud son temas prioritarios en las agendas de salud de los países latinoamericanos. Ahora que las organizaciones relacionadas a la salud, tienen la oportunidad de captar cantidades inmensas de datos internos y externos, estructurados y no estructurados, necesitan una disciplina que les permita maximizar su valor, gestionar riesgos y reducir costes. En este webinar, discutimos uno de los dominios claves dentro de la gobernanza de datos en salud como es la privacidad, seguridad y uso ético de los mismos, como uno de los temas más sensibles de actualidad y que han puesto en jaque a muchos de los sistemas de salud durante la pandemia de COVID-19.

Gobernanza de datos en salud: ética, privacidad y seguridad: parte 1

La gobernanza de datos es un requisito en el actual entorno de los sistemas de salud, en el cual las demandas ciudadanas en cuanto al acceso a los servicios de salud son temas prioritarios en las agendas de salud de los países latinoamericanos. Ahora que las organizaciones relacionadas a la salud, tienen la oportunidad de captar cantidades inmensas de datos internos y externos, estructurados y no estructurados, necesitan una disciplina que les permita maximizar su valor, gestionar riesgos y reducir costes. En este webinar, discutimos uno de los dominios claves dentro de la gobernanza de datos en salud como es la privacidad, seguridad y uso ético de los mismos, como uno de los temas más sensibles de actualidad y que han puesto en jaque a muchos de los sistemas de salud durante la pandemia de COVID-19.

Gobernanza de datos: estándares y buenas prácticas para la gestión de datos en salud: parte 2

La gestión de datos de salud es un factor fundamental para garantizar que los datos se recolectan, validan, transfieren, almacenan y resguardan de forma estandarizada, utilizando las mejores prácticas. Es esencial implementar procesos correctos para que los usuarios de los datos estén seguros de que los mismos son confiables, accesibles y actualizados. La gestión de datos de salud incluye, además, actividades relacionadas con la planificación, implementación, desarrollo y control de la información generada por una organización de salud, una región o un país. La computación en la nube es la tendencia que está siendo adoptada por las instituciones de salud y los gobiernos en los países de la región latinoamericana y, con ello, nuevos desafíos y oportunidades para la gobernanza de los datos.

Gobernanza de datos: estándares y buenas prácticas para la gestión de datos en salud: parte 1

La gestión de datos de salud es un factor fundamental para garantizar que los datos se recolectan, validan, transfieren, almacenan y resguardan de forma estandarizada, utilizando las mejores prácticas. Es esencial implementar procesos correctos para que los usuarios de los datos estén seguros de que los mismos son confiables, accesibles y actualizados. La gestión de datos de salud incluye, además, actividades relacionadas con la planificación, implementación, desarrollo y control de la información generada por una organización de salud, una región o un país. La computación en la nube es la tendencia que está siendo adoptada por las instituciones de salud y los gobiernos en los países de la región latinoamericana y, con ello, nuevos desafíos y oportunidades para la gobernanza de los datos.

Gobernanza de datos en salud: interoperabilidad y estándares

Dentro del marco de la gobernanza de datos, es necesario la adopción de arquitecturas empresariales que nos permitan tener un marco referencial para el abordaje de los distintos dominios en la gestión de los datos. La construcción de una infoestructura sólida es fundamentada en la integración de estándares y buenas prácticas que permitan a las organizaciones de salud mejora el ciclo de los datos: recolección, integración, transferencia, interoperabilidad y uso de los datos para la toma de decisiones, de una forma óptima, con calidad y seguridad.Por lo anterior, la adopción de estándares para la codificación de los datos de salud (Por ejemplo CIE 9,10 y 11), así como para la interoperabilidad de datos (FHIR/HL7 por ejemplo) y marcos de intercambio en salud en general (OpenHIE y otros más), se tornan fundamentales para que la infoestructura del sistema de información para la salud en el país funcione eficientemente con una arquitectura de salud pública robusta y acorde a los retos de la transformación digital del sector salud.

Salud digital basada en código abierto: parte 1

El código abierto es una filosofía de trabajo y colaboración seguida por los miembros de la comunidad de open source. Esta filosofía se basa en la libertad intelectual y en sus principios fundamentales: transparencia, colaboración, suministro, inclusión y comunidad. El intercambio de ideas y software desarrollado por dichas comunidades ha fomentado el avance creativo, científico y tecnológico en sectores como: educación, gobierno, leyes, salud y manufactura. Este movimiento creó una instancia para que los miembros de la comunidad global colaboren, compartan y se ayuden entre sí para lograr tanto objetivos personales como comerciales a través del código fuente. En el mercado de salud digital, los dos métodos más comunes son proporcionar servicios, mantenimiento y desarrollo, y recibir fondos de terceros, como fundaciones. En América Latina no existe una amplia difusión del uso del Software de código abierto en salud, aunque esa tendencia parece estar cambiando en los últimos años, especialmente en el campo de la salud pública a través de la adopción de soluciones como DHIS2 y Commcare, y para la gestión clínica de pacientes como OpenEMR, GNU Solidario, DCM4CHEE, entre otros. En este webinar organizado por la Red Centroamericana de Informática en Salud, conocimos algunas experiencias en la adopción de Software de código abierto para la salud en América Latina, y se discutió sobre los principales retos y oportunidades que estas intervenciones representan para la aceleración de la transformación digital en salud.

Salud digital basada en código abierto: parte 2

El código abierto es una filosofía de trabajo y colaboración seguida por los miembros de la comunidad de open source. Esta filosofía se basa en la libertad intelectual y en sus principios fundamentales: transparencia, colaboración, suministro, inclusión y comunidad. El intercambio de ideas y software desarrollado por dichas comunidades ha fomentado el avance creativo, científico y tecnológico en sectores como: educación, gobierno, leyes, salud y manufactura. Este movimiento creó una instancia para que los miembros de la comunidad global colaboren, compartan y se ayuden entre sí para lograr tanto objetivos personales como comerciales a través del código fuente. En el mercado de salud digital, los dos métodos más comunes son proporcionar servicios, mantenimiento y desarrollo, y recibir fondos de terceros, como fundaciones. En América Latina no existe una amplia difusión del uso del Software de código abierto en salud, aunque esa tendencia parece estar cambiando en los últimos años, especialmente en el campo de la salud pública a través de la adopción de soluciones como DHIS2 y Commcare, y para la gestión clínica de pacientes como OpenEMR, GNU Solidario, DCM4CHEE, entre otros. En este webinar organizado por la Red Centroamericana de Informática en Salud, conocimos algunas experiencias en la adopción de Software de código abierto para la salud en América Latina, y se discutió sobre los principales retos y oportunidades que estas intervenciones representan para la aceleración de la transformación digital en salud.

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.

Maturity assessment of Kenya's health information system interoperability readiness.

BACKGROUND: The use of digital technology in healthcare promises to improve quality of care and reduce costs over time. This promise will be difficult to attain without interoperability: facilitating seamless health information exchange between the deployed digital health information systems (HIS). OBJECTIVE: To determine the maturity readiness of the interoperability capacity of Kenya's HIS. METHODS: We used the HIS Interoperability Maturity Toolkit, developed by MEASURE Evaluation and the Health Data Collaborative's Digital Health and Interoperability Working Group. The assessment was undertaken by eHealth stakeholder representatives primarily from the Ministry of Health's Digital Health Technical Working Group. The toolkit focused on three major domains: leadership and governance, human resources and technology. RESULTS: Most domains are at the lowest two levels of maturity: nascent or emerging. At the nascent level, HIS activities happen by chance or represent isolated, ad hoc efforts. An emerging maturity level characterises a system with defined HIS processes and structures. However, such processes are not systematically documented and lack ongoing monitoring mechanisms. CONCLUSION: None of the domains had a maturity level greater than level 2 (emerging). The subdomains of governance structures for HIS, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for operations and maintenance of hardware attained higher maturity levels. These findings are similar to those from interoperability maturity assessments done in Ghana and Uganda.

Importancia del uso de sistemas de información en la automatización de historiales clínicos, una revisión sistemática / Importance of Use of Information Systems in the Automation of Medical Records, a Systematic Review

Los sistemas de información en los servicios de salud han contribuido en los procesos de automatización de historiales clínicos, desempeñando un papel importante en la atención médica. El objetivo de esta revisión ha sido identificar la importancia de los sistemas de información para la automatización de historiales clínicos y las herramientas usadas para su implementación. Se revisaron artículos de revistas indexadas en base de datos bibligráficas como: IEEE Digital Library, ScienceDirect, Scielo, Google Scholar con la finalidad de tener una mejor clasificación de información que aportara al desarrollo del contenido estudiado. Se identificó que los sistemas de información mejoran la comunicación médico-paciente, aceleran procesos de atención médica, reducen costos y tiempo. Los sistemas de información son importantes para la automatización de historiales clínicas, garantizado mejoras en el proceso de atención al paciente en los establecimientos de salud(AU)

Information systems in health services have contributed to the automation of medical records, playing an important role in medical care. The objective of this review was to identify the importance of information systems for the automation of medical records and the tools used for their implementation. Articles from journals indexed in bibliographic databases such as: IEEE Digital Library, ScienceDirect, Scielo, Google Scholar have been reviewed in order to have a better classification of information that contributes to the improvement of our interest topic. It has been identified that these information systems increase doctor-patient communication, speed up medical care processes, reduce costs and time. Information systems are important for the automation of medical records, guaranteeing advances in the patient care process in health establishments(AU)

Intelligent machines and mental health in the era of COVID-19.

The idea of a network of small devices that would be able to connect each other, appeared in the early 80s. In a prophetic article, Mark Weiser,1 described such a connection, that it is now known under the term of Internet of Things (IoT). In a broadest sense, the term IoT encompasses everything connected to the internet, but it is increasingly being used to define objects that "talk" to each other, creating a network from simple sensors to smartphones and wearables connected. During the recent years this network of communicating devices has been combined with other technological achievements, and particularly with the Virtual Reality (VR)2 and the Artificial Intelligence (AI).3 The emerge of COVID-19 pandemic in 2019, resulted to the poor response and healthcare failures of many countries globally.4 One of the main reasons for such a failure, was the inability of accurate data collection from different sources. Apparently, it was the first time, humanity realized the need for massive amounts of heterogeneous data to be collected, interpreted, and shared. Amid the ongoing COVID-19 pandemic, several innovators and public authorities are looking to leverage IoT tools to reduce the burden on the healthcare systems.5 Mental health is one of the areas that seems to benefit the most of such technologies. A significant decrease of the total amount of ER visits and a dramatic increase of internet access from the patients and care givers along to the development of applications for mental health issues, followed the outbreak of SARS-CoV-2.6 Such technologies proved to be efficient to help mentally ill patients and pioneer the path in the future. Probably the most obvious use of these emerged technologies is the improvement of the telehealth options. Patients who suffer from mental illness face significant problems towards the continuity of care during the crisis.7 Nonetheless, they usually have other health problems, that deprive them from an equitable health care provision. Improved telehealth platforms can give them a single point access to address all their problems. The use of electronic health records can reduce the fragmentary health services and improve the outcome.8 However, this is only the beginning. The COVID-19 crisis and the subsequent social isolation, to reduce both the contamination and the spread of the disease, highlighted the necessity for providing accurate and secure diagnoses and treatments from a safe distance. Virtual reality combined with IoT and AI technologies seem to be a reliable alternative to the classic physical and mental examination and treatment in many areas of mental and neurological diseases.2 These novel techniques can spot the early signs and detect mental illnesses with high accuracy. However, caution and more work are required to bridge the space between these recently thrived technologies and mental health care.7 It is worth mentioning, that internet-oriented health care procedures can also help to reduce the gaps caused by the stigma of mental illness. For example, the development of AI chatbots (an application used to chat directly with a human) can alleviate the fears of judgment of the help seeking persons and provide the professionals with a supplemental support toward improved services to their patients.9 A final remark for conclusion. Humanity is more and more depended to the "intelligent" machines. However, we must not forget that we humans are responsible to set the rules of such co-existence.

[Health Data Hub: Why and how?] / Le Health Data Hub (suite) - Pourquoi ? Comment ?

TITLE: Le Health Data Hub (suite) - Pourquoi ? Comment ? ABSTRACT: Dans le monde de la recherche et de la santé publique, un consensus existe pour considérer que les données de santé constituent une ressource extrêmement précieuse pour de multiples usages, et qu'il convient d'en faciliter l'accès et le partage. Dans ce domaine, la France dispose de nombreux atouts, notamment de bases de données d'ampleur et de richesse sans doute uniques. Depuis quelques années, les pouvoirs publics ont pris conscience des enjeux autour de ces données et ont mis en place un dispositif technique, légal et réglementaire pour y faire face : le Système national des données de santé (SNDS) accompagné de la Plateforme des données de santé (PDS), plus communément appelée Health Data Hub (HDH). Cette plateforme est une infrastructure officiellement créée par un arrêté ministériel du 30 novembre 2019, destinée à faciliter l'accès et l'utilisation des données de santé afin de favoriser la recherche. On ne peut qu'applaudir une telle initiative qui constitue un progrès majeur et ouvre de nombreuses perspectives pour la recherche et la santé publique. Cependant, tel qu'il est conçu, le HDH pose divers problèmes qui amènent à questionner ses orientations actuelles.

Maternal and neonatal data collection systems in low- and middle-income countries for maternal vaccines active safety surveillance systems: A scoping review.

BACKGROUND: Most post-licensure vaccine pharmacovigilance in low- and middle-income countries (LMICs) are passive reporting systems. These have limited utility for maternal immunization pharmacovigilance in LMIC settings and need to be supplemented with active surveillance. Our study's main objective was to identify existing perinatal data collection systems in LMICs that collect individual information on maternal and neonatal health outcomes and could be developed to inform active safety surveillance of novel vaccines for use during pregnancy. METHODS: A scoping review was performed following the Arksey and O'Malley six-stage approach. We included studies describing electronic or mixed paper-electronic data collection systems in LMICs, including research networks, electronic medical records, and custom software platforms for health information systems. Medline PubMed, EMBASE, Global Health, Cochrane Library, LILACS, Bibliography of Asian Studies (BAS), and CINAHL were searched through August 2019. We also searched grey literature including through Google and websites of existing relevant perinatal data collection systems, as well as contacted authors of key studies and experts in the field to validate the information and identify additional sources of relevant unpublished information. RESULTS: A total of 11,817 records were identified. The full texts of 264 records describing 96 data collection systems were assessed for eligibility. Eight perinatal data collection systems met our inclusion criteria: Global Network's Maternal Newborn Health Registry, International Network for the Demographic Evaluation of Populations and their Health; Perinatal Informatic System; Pregnancy Exposure Registry & Birth Defects Surveillance; SmartCare; Open Medical Record System; Open Smart Register Platform and District Health Information Software 2. These selected systems were qualitatively characterized according to seven different domains: governance; system design; system management; data management; data sources, outcomes and data quality. CONCLUSION: This review provides a list of active maternal and neonatal data collection systems in LMICs and their characteristics as well as their outreach, strengths, and limitations. Findings could potentially help further understand where to obtain population-based high-quality information on outcomes to inform the conduct of maternal immunization active vaccine safety surveillance activities and research in LMICs.

An Education Framework for Effective Implementation of a Health Information System: Scoping Review.

BACKGROUND: To optimize their use of a new Health Information System (HIS), supporting health care providers require effective HIS education. Failure to provide this education can significantly hinder an organization's HIS implementation and sustainability efforts. OBJECTIVE: The aim of this review is to understand the most effective educational strategies and approaches to enable health care providers to optimally use an HIS. METHODS: Ovid MEDLINE, Ovid Embase, EBSCO Cumulative Index to Nursing and Allied Health Literature, and EBSCO Education Resources Information Center were searched to identify relevant papers. Relevant studies were systematically reviewed and analyzed using a qualitative thematic analysis approach. RESULTS: Of the 3539 studies screened, 17 were included for data extraction. The literature on the most effective approaches to enable health care providers to optimally use an HIS emphasized the importance of investing in engaging and understanding learners in the clinical context, maximizing the transfer of learning to care, and designing continuous and agile evaluation to meet the emerging demands of the clinical environment. CONCLUSIONS: This review supports the advancement of a new HIS learning framework that organizational leaders and educators can use to guide HIS education design and development. Future research should examine how this framework can be translated into practice.

Estudo da qualidade dos Dados do Painel COVID-19 para crianças, adolescente e jovens, Espírito Santo ­ Brasil, 2020 / A study assessing the quality of the COVID-19 Panel Data for children, adolescents and young individuals, Espírito Santo ­ Brazil, 2020 / Estudio de calidad de datos del Panel COVID-19 para niños, adolescentes y jóvenes, Espírito Santo ­ Brasil, 2020

Objetivo: descrever a completude dos dados e avaliar a qualidade do Banco de dados do Painel COVID-19 no Espírito Santo em 2020, quanto à completude de suas variáveis, bem como analisar a confirmação da doença e sua evolução por crianças, adolescentes e jovens. Métodos: estudo descritivo exploratório. A completude no preenchimento da ficha no Painel COVID-19 foi classificada como excelente (menos de 5% de preenchimento incompleto), bom (5% a 10%), regular (10% a 20%), ruim (20% a 50%) ou muito ruim (50% ou mais). Resultados: observou-se qualidade regular para o critério de confirmação (16%), ruim para a classificação da doença (44%) e status de notificação (30%) e muito ruim para a evolução (79%). Quanto às variáveis epidemiológicas, destaca-se a variável raça/cor da pele com completude regular (17%). Conclusão e implicações para a prática: é necessário educação permanente dos profissionais para o preenchimento dos dados de forma correta. Tratando-se de uma pandemia por um vírus novo, esses dados devem estar disponíveis imediatamente, e com qualidade para que medidas de controle possam ser adotadas

Objective: to describe the completeness of the data and evaluate the quality of the COVID-19 Panel Database in Espírito Santo in 2020, as to the completeness of its variables, as well as to analyze the confirmation of the disease and its evolution by children, adolescents and young people. Methods: exploratory descriptive study. Completeness of filling in the form on the COVID-19 Panel was classified as excellent (less than 5% incomplete), good (5% to 10%), fair (10% to 20%), poor (20% to 50%) or very bad (50% or more). Results: regular quality was observed for the confirmation criterion (16%), poor for the classification of the disease (44%) and notification status (30%) and very poor for the evolution (79%). Regarding the epidemiological variables, the race-skin color variable with regular completeness (17%) stands out. Conclusion and implications for the practice: permanent education of professionals is necessary to fill in the data correctly. in the case of a pandemic due to a new virus, these data must be available immediately, and with quality so that control measures can be adopted

Objetivo: describir la exhaustividad de los datos y evaluar la calidad de la Base de Datos Panel COVID-19 en Espírito Santo en 2020, en cuanto a la exhaustividad de sus variables, así como analizar la confirmación de la enfermedad y su evolución en niños, adolescentes y jóvenes. Métodos: estudio descriptivo exploratorio. La exhaustividad al completar el formulario en el Panel COVID-19 se clasificó como excelente (menos del 5% incompleto), buena (5% a 10%), regular (10% a 20%), deficiente (20% a 50%) o muy mala (50% o más). Resultados: se observó calidad regular para el criterio de confirmación (16%), mala para la clasificación de la enfermedad (44%) y estado de notificación (30%) y muy mala para la evolución (79%). En cuanto a las variables epidemiológicas, se destaca la variable raza-color de piel con exhaustividad regular (17%). Conclusión e implicaciones para la práctica: es necesaria la formación permanente de los profesionales para completar correctamente los datos. En el caso de una pandemia por un nuevo virus, estos datos deben estar disponibles de manera inmediata y con calidad para que se puedan adoptar medidas de control