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Objective: The goal of this paper is to provide a consensus review on telehealth delivery prior to and during the COVID-19 pandemic to develop a set of recommendations for designing telehealth services and tools that contribute to system resilience and equitable health. Methods: The IMIA-Telehealth Working Group (WG) members conducted a two-step approach to understand the role of telehealth in enabling global health equity. We first conducted a consensus review on the topic followed by a modified Delphi process to respond to four questions related to the role telehealth can play in developing a resilient and equitable health system. Results: Fifteen WG members from eight countries participated in the Delphi process to share their views. The experts agreed that while telehealth services before and during COVID-19 pandemic have enhanced the delivery of and access to healthcare services, they were also concerned that global telehealth delivery has not been equal for everyone. The group came to a consensus that health system concepts including technology, financing, access to medical supplies and equipment, and governance capacity can all impact the delivery of telehealth services. Conclusion: Telehealth played a significant role in delivering healthcare services during the pandemic. However, telehealth delivery has also led to unintended consequences (UICs) including inequity issues and an increase in the digital divide. Telehealth practitioners, professionals and system designers therefore need to purposely design for equity as part of achieving broader health system goals.

International Classification of Diseases (ICD) codes are used for different management activities in hospitals. Previous researches employed Machine Learning (ML) models for automatic coding to simplify the disease code assignation process; nevertheless, model performance was compromised due to problems with label imbalance and the high number of labels. In the present research, a Support Vector Machine (SVM) model for Neoplasm ICD coding was trained with a dataset previously treated by applying re-sampling methods to mitigate label imbalance issues and increase the model sensitivity. To mitigate the issue with the high number of labels, human body location information contained in the medical records and ICD code descriptions were employed to build a hierarchical model, which improved the performance of a base non-hierarchical model by up to 15%.

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Neste artigo, são analisadas as trajetórias de atendimento de pacientes na rede de atenção básica de saúde no município de São Paulo. Avaliar como é a trajetória dos pacientes do SUS, seja entre os atendimentos, seja entre as unidades de atendimento, e uma atividade importante para o entendimento é aprimoramento do sistema. Assim, neste artigo, foca-se num contexto de alta relevância social representado pela avaliação da trajetória de gestantes atendidas pelo SUS no município de São Paulo durante os anos de 2014 e 2015. São analisadas as trajetórias dessas gestantes em seus atendimentos e entre as unidades do sistema de saúde. Acredita-se que as análises apresentadas possam auxiliar em um melhor entendimento do sistema e subsidiar decisões para o aprimoramento do sistema de saúde atual.

Summary Objectives : This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances. Methods : Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth. Results : Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes. Conclusion : Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

Redes de atençãoá saúde formam um sistema complexo. Atualmente há uma tendência crescente de aplicação de métodos de análise de redes complexas para modelar e analisar tais sistemas. Essa abordagem possibilita o estudo do fluxo de atendimento dos pacientes para a identificação de padrões relevantes, a melhoria na alocação dos recursos e a promoção de melhor serviçoá população. Este é um trabalho em andamento no qual apresentamos um primeiro estudo exploratório visando a caracterização de atendimentos em uma rede de atenção à saúde pública, ponto inicial para um estudo mais amplo de fluxo de atendimentos com base na teoria de redes complexas.

Le but de cet article est de s’interesser aux moyens possibles permettant d’arriver a une diminution de ces couts, et ce, notamment, a partir de changements lies aux interventions medicales. Une des solutions preconisees a ce sujet serait la necessite d’adopter un consensus, avec comme premisse la feuille de route du «Systeme de sante durable» emise par l’Academie Suisse des Sciences Medicales.