IntroductionRecognising prematurity is critical in order to attend to immediate needs in childbirth settings, guiding the extent of medical care provided for newborns. A new medical device has been developed to carry out the preemie-test, an innovative approach to estimate gestational age (GA), based on the photobiological properties of the newborn’s skin. First, this study will validate the preemie-test for GA estimation at birth and its accuracy to detect prematurity. Second, the study intends to associate the infant’s skin reflectance with lung maturity, as well as evaluate safety, precision and usability of a new medical device to offer a suitable product for health professionals during childbirth and in neonatal care settings.Methods and analysisResearch protocol for diagnosis, single­group, single­blinding and single­arm multicenter clinical trial with a reference standard. Alive newborns, with 24 weeks or more of pregnancy age, will be enrolled during the first 24 hours of life. Sample size is 787 subjects. The primary outcome is the difference between the GA calculated by the photobiological neonatal skin assessment methodology and the GA calculated by the comparator antenatal ultrasound or reliable last menstrual period (LMP). Immediate complications caused by pulmonary immaturity during the first 72 hours of life will be associated with skin reflectance in a nested case–control study.Ethics and disseminationEach local independent ethics review board approved the trial protocol. The authors intend to share the minimal anonymised dataset necessary to replicate study findings.Trial registration numberRBR-3f5bm5.

Background The potential of chatbots for screening and monitoring COVID-19 was envisioned since the outbreak of the disease. Chatbots can help disseminate up-to-date and trustworthy information, promote healthy social behavior, and support the provision of health care services safely and at scale. In this scenario and in view of its far-reaching postpandemic impact, it is important to evaluate user experience with this kind of application. Objective We aimed to evaluate the quality of user experience with a COVID-19 chatbot designed by a large telehealth service in Brazil, focusing on the usability of real users and the exploration of strengths and shortcomings of the chatbot, as revealed in reports by participants in simulated scenarios. Methods We examined a chatbot developed by a multidisciplinary team and used it as a component within the workflow of a local public health care service. The chatbot had 2 core functionalities: assisting web-based screening of COVID-19 symptom severity and providing evidence-based information to the population. From October 2020 to January 2021, we conducted a mixed methods approach and performed a 2-fold evaluation of user experience with our chatbot by following 2 methods: a posttask usability Likert-scale survey presented to all users after concluding their interaction with the bot and an interview with volunteer participants who engaged in a simulated interaction with the bot guided by the interviewer. Results Usability assessment with 63 users revealed very good scores for chatbot usefulness (4.57), likelihood of being recommended (4.48), ease of use (4.44), and user satisfaction (4.38). Interviews with 15 volunteers provided insights into the strengths and shortcomings of our bot. Comments on the positive aspects and problems reported by users were analyzed in terms of recurrent themes. We identified 6 positive aspects and 15 issues organized in 2 categories: usability of the chatbot and health support offered by it, the former referring to usability of the chatbot and how users can interact with it and the latter referring to the chatbot’s goal in supporting people during the pandemic through the screening process and education to users through informative content. We found 6 themes accounting for what people liked most about our chatbot and why they found it useful—3 themes pertaining to the usability domain and 3 themes regarding health support. Our findings also identified 15 types of problems producing a negative impact on users—10 of them related to the usability of the chatbot and 5 related to the health support it provides. Conclusions Our results indicate that users had an overall positive experience with the chatbot and found the health support relevant. Nonetheless, qualitative evaluation of the chatbot indicated challenges and directions to be pursued in improving not only our COVID-19 chatbot but also health chatbots in general.

The COVID-19 pandemic and the need for social distancing have created a demand for new and innovative solutions in healthcare systems worldwide. One of the strategies that have been implemented are chatbots, which can be helpful in providing reliable health information and preventing people from seeking assistance in healthcare centers and being unnecessarily exposed to the virus. In this context, although a high number of chatbots have been implemented worldwide, little has been discussed about the process and challenges in developing and implementing this technology. This paper reports on an action research, which designed a novel chatbot as a prompt response to the COVID-19 pandemic. The chatbot is intended to be a first layer of interaction with the public, performing triage of patients and providing information about COVID-19 on a large scale and without human contact. Our contribution is twofold: (i) we reflected on the development process and discuss lessons learned and recommendations to support a multidisciplinary development and evolution process of the chatbot; and (ii) we identified some interactive and technological features that can be used as a reference framework for this kind of technology. These contributions can be useful to other researchers and multidisciplinary teams facing similar challenges.

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Background Although a great number of teleconsultation services have been developed during the COVID-19 pandemic, studies assessing usability and health care provider satisfaction are still incipient. Objective This study aimed to describe the development, implementation, and expansion of a synchronous teleconsultation service targeting patients with symptoms of COVID-19 in Brazil, as well as to assess its usability and health care professionals’ satisfaction. Methods This mixed methods study was developed in 5 phases: (1) the identification of components, technical and functional requirements, and system architecture; (2) system and user interface development and validation; (3) pilot-testing in the city of Divinópolis; (4) expansion in the cities of Divinópolis, Teófilo Otoni, and Belo Horizonte for Universidade Federal de Minas Gerais faculty and students; and (5) usability and satisfaction assessment, using Likert-scale and open-ended questions. Results During pilot development, problems contacting users were solved by introducing standardized SMS text messages, which were sent to users to obtain their feedback and keep track of them. Until April 2022, the expanded system served 31,966 patients in 146,158 teleconsultations. Teleconsultations were initiated through chatbot in 27.7% (40,486/146,158) of cases. Teleconsultation efficiency per city was 93.7% (13,317/14,212) in Teófilo Otoni, 92.4% (11,747/12,713) in Divinópolis, and 98.8% (4981/5041) in Belo Horizonte (university campus), thus avoiding in-person assistance for a great majority of patients. In total, 50 (83%) out of 60 health care professionals assessed the system’s usability as satisfactory, despite a few system instability problems. Conclusions The system provided updated information about COVID-19 and enabled remote care for thousands of patients, which evidenced the critical role of telemedicine in expanding emergency services capacity during the pandemic. The dynamic nature of the current pandemic required fast planning, implementation, development, and updates in the system. Usability and satisfaction assessment was key to identifying areas for improvement. The experience reported here is expected to inform telemedicine strategies to be implemented in a postpandemic scenario.

Background Recognizing premature newborns and small-for-gestational-age (SGA) is essential for providing care and supporting public policies. This systematic review aims to identify the influence of the last menstrual period (LMP) compared to ultrasonography (USG) before 24 weeks of gestation references on prematurity and SGA proportions at birth. Methods Systematic review with meta-analysis followed the recommendations of the PRISMA Statement. PubMed, BVS, LILACS, Scopus-Elsevier, Embase-Elsevier, and Web-of-Science were searched (10–30-2022). The research question was: (P) newborns, (E) USG for estimating GA, (C) LMP for estimating GA, and (O) prematurity and SGA rates for both methods. Independent reviewers screened the articles and extracted the absolute number of preterm and SGA infants, reference standards, design, countries, and bias. Prematurity was birth before 37 weeks of gestation, and SGA was the birth weight below the p10 on the growth curve. The quality of the studies was assessed using the New-Castle-Ottawa Scale. The difference between proportions estimated the size effect in a meta-analysis of prevalence. Results Among the 642 articles, 20 were included for data extraction and synthesis. The prematurity proportions ranged from 1.8 to 33.6% by USG and varied from 3.4 to 16.5% by the LMP. The pooled risk difference of prematurity proportions revealed an overestimation of the preterm birth of 2% in favor of LMP, with low certainty: 0.02 (95%CI: 0.01 to 0.03); I2 97%). Subgroup analysis of USG biometry (eight articles) showed homogeneity for a null risk difference between prematurity proportions when crown-rump length was the reference: 0.00 (95%CI: -0.001 to 0.000; I2: 0%); for biparietal diameter, risk difference was 0.00 (95%CI: -0.001 to 0.000; I2: 41%). Only one report showed the SGA proportions of 32% by the USG and 38% by the LMP. Conclusions LMP-based GA, compared to a USG reference, has little or no effect on prematurity proportions considering the high heterogeneity among studies. Few data (one study) remained unclear the influence of such references on SGA proportions. Results reinforced the importance of qualified GA to mitigate the impact on perinatal statistics. Trial registration Registration number PROSPERO: CRD42020184646.

A pandemia do novo coronavírus tem sobrecarregado os sistemas de saúde ao limite da capacidade de atendimento. Nosso objetivo foi avaliar a eficácia de um chatbot desenvolvido para triagem de pacientes, antes de teleconsulta, para identificar sintomas de COVID-19. Sintomas informados no diálogo foram comparados com os relatados aos médicos, em um serviço de urgência. Em 96 pacientes, dispneia foi o sintoma mais frequente (16,6%) e o único que mostrou concordância moderada com a história registrada em prontuário eletrônico (Kappa=0,605). Concluindo, a tecnologia mostrou-se útil para detectar um dos sintomas graves da COVID-19, mas não foi possível evidenciar sua eficácia em relação aos sintomas menores.

O objetivo do estudo que deu origem a este artigo é propor um modelo de interface extensível (XIMEHR) para sistemas de registro eletrônico de saúde, baseados nos padrões da norma ISO 13606. A partir do conceito de Design Science, o estudo é uma resposta ao desafio à participação de usuários finais no desenvolvimento de sistemas de informação em saúde. Interfaces para prontuários eletrônicos do paciente são geradas através de um protótipo de sistema que estrutura, organiza e gerencia os conceitos clínicos. O protótipo desenvolvido foi avaliado e sua funcionalidade atendeu aos propósitos para os quais foi elaborado. Ao mesmo tempo que preserva e estrutura as informações, o modelo proposto proporcionou flexibilidade, reutilização de conceitos e permitiu a padronização do documento. Acreditamos que o produto desse estudo contribuirá para aprimorar a qualidade dos dados clínicos registrados e poderá favorecer a troca de informações entre sistemas eletrônicos utilizados na prestação de cuidado à saúde.Palavras-chave: Ciência da informação. Gestão da informação em saúde. Informática em saúde. Registros eletrônicos de saúde. Interface usuário-computador.Link: https://www.reciis.icict.fiocruz.br/index.php/reciis/article/view/1070/pdf_1070