Postpartum hemorrhage in electronic health records: risk factors at admission and in-hospital occurrence.

Objective: Postpartum hemorrhage (PPH) is the leading cause of maternal death globally. Therefore, prevention strategies have been created. The study aimed to evaluate the occurrence of PPH and its risk factors after implementing a risk stratification at admission in a teaching hospital. Methods: A retrospective cohort involving a database of SISMATER® electronic medical record. Classification in low, medium, or high risk for PPH was performed through data filled out by the obstetrician-assistant. PPH frequency was calculated, compared among these groups and associated with the risk factors. Results: The prevalence of PPH was 6.8%, 131 among 1,936 women. Sixty-eight (51.9%) of them occurred in the high-risk group, 30 (22.9%) in the medium-risk and 33 (25.2%) in the low-risk group. The adjusted-odds ratio (OR) for PPH were analyzed using a confidence interval (95% CI) and was significantly higher in who presented multiple pregnancy (OR 2.88, 95% CI 1.28 to 6.49), active bleeding on admission (OR 6.12, 95% CI 1.20 to 4.65), non-cephalic presentation (OR 2.36, 95% CI 1.20 to 4.65), retained placenta (OR 9.39, 95% CI 2.90 to 30.46) and placental abruption (OR 6.95, 95% CI 2.06 to 23.48). Vaginal delivery figured out as a protective factor (OR 0.58, 95% CI 0.34 to 0.98). Conclusion: Prediction of PPH is still a challenge since its unpredictable factor arrangements. The fact that the analysis did not demonstrate a relationship between risk category and frequency of PPH could be attributable to the efficacy of the strategy: Women classified as "high-risk" received adequate medical care, consequently.

The role of chest X-ray in the diagnosis of neonatal respiratory distress syndrome: a systematic review concerning low-resource birth scenarios.

BACKGROUND: Access to diagnostic tools like chest radiography (CXR) is challenging in resource-limited areas. Despite reduced reliance on CXR due to the need for quick clinical decisions, its usage remains prevalent in the approach to neonatal respiratory distress syndrome (NRDS). OBJECTIVES: To assess CXR's role in diagnosing and grading NRDS severity compared to current clinical features and laboratory standards. METHODS: A review of studies with NRDS diagnostic criteria was conducted across six databases (MEDLINE, EMBASE, BVS, Scopus-Elsevier, Web of Science, Cochrane) up to 3 March 2023. Independent reviewers selected studies, with discrepancies resolved by a senior reviewer. Data were organised into descriptive tables to highlight the use of CXR and clinical indicators of NRDS. RESULTS: Out of 1,686 studies screened, 23 were selected, involving a total of 2,245 newborns. All selected studies used CXR to diagnose NRDS, and 21 (91%) applied it to assess disease severity. While seven reports (30%) indicated that CXR is irreplaceable by other diagnostic tools for NRDS diagnosis, 10 studies (43%) found that alternative methods surpassed CXR in several respects, such as severity assessment, monitoring progress, predicting the need for surfactant therapy, foreseeing Continuous Positive Airway Pressure failure, anticipating intubation requirements, and aiding in differential diagnosis. CONCLUSION: CXR remains an important diagnostic tool for NRDS. Despite its continued use in scientific reports, the findings suggest that the study's outcomes may not fully reflect the current global clinical practices, especially in low-resource settings where the early NRDS approach remains a challenge for neonatal survival.Trial registration: PROSPERO number CRD42022336480.

Main findings: Access to diagnostic tools like chest radiography is challenging in resource-limited areas, yet its usage persists in the management of neonatal respiratory distress syndrome despite a decreased dependency due to the imperative for swift clinical decisions.Added knowledge: Despite its continued significance in scientific literature, the usage of chest radiography as a diagnostic tool for neonatal respiratory distress syndrome may not entirely reflect current global clinical practices, particularly in low-resource settings where early management of neonatal respiratory distress syndrome poses a challenge for neonatal survival.Global health impact for policy and action: The results underscore the necessity of guidelines for the utilisation of chest radiography to minimise unnecessary ionising radiation exposure while ensuring timely access to critical clinical information for appropriate newborn care.

Pre-eclampsia in low and middle-income settings: What are the barriers to improving perinatal outcomes and evidence-based recommendations?

OBJECTIVE: To discuss the points that still challenge low- and middle-income countries (LMICs) and strategies that have been studied to help them overcome these issues. METHODS: Narrative review addressing 20 years of articles concerning pre-eclampsia morbidity and mortality in LMICs. We summarized evidence-based strategies to overcome the challenges in order to reduce the pre-eclampsia impact on perinatal outcomes. RESULTS: Pre-eclampsia is the first or second leading cause in the ranking of avoidable causes of maternal death, and approximately 16% of all maternal deaths are attributable to eclampsia and pre-eclampsia. Considering the social and economic contexts, it represents a major public health concern, and prevention and early detection of pre-eclampsia seem to be a major challenge. Reducing maternal mortality related to hypertensive disturbances depends on public policies to manage these preventable conditions. Early and continuous recognition of signs of severity related to hypertensive disorders during pregnancy and childbirth, self-monitoring of symptoms and blood pressure, as well as preventive approaches such as aspirin and calcium, and magnesium sulfate, are lifesaving procedures that have not yet reached a universal scale. CONCLUSION: This review provides a vision of relevant points to support pregnant women in overcoming the constraints to healthcare access in LMICs, and strategies that can be applied in primary prenatal care units.

Registro de saúde na APS: educação permanente para enfermeiros, médicos da equipe de saúde da família, cirurgiões-dentistas e equipes multiprofissionais / Health record in PHC: ongoing education for nurses, family health team doctors, dental surgeons and multidisciplinary teams

O Sistema Único de Saúde tem enfrentado grandes desafios relacionados à formação, qualificação e organização do processo de trabalho dos profissionais de saúde. Neste momento, com a incorporação da Estratégia e-SUS Atenção Primária à Saúde, alinhada à Estratégia de Saúde Digital do Brasil, já em um patamar de cobertura bastante avançada de municípios com prontuário eletrônico, vivenciamos um cenário de qualificação permanente no uso de tecnologias digitais e uma frente promissora no enfrentamento dos obstáculos para prover saúde para todos e fixar profissionais em áreas mais afastadas dos grandes centros urbanos e nas regiões de maior vulnerabilidade social. No entanto, prover recursos tecnológicos não basta; é preciso estar atento aos profissionais que, de fato, fazem a transformação digital acontecer. Nesse sentido, a Secretaria de Atenção Primária à Saúde coordena uma agenda positiva e apresenta um projeto de retomada da construção do SUS com base na inovação tecnológica centrada nas pessoas. E, dentre as diversas estratégias, implanta um projeto nacional de educação permanente em saúde digital, que potencializa o uso qualificado, ético e responsável do Sistema e-SUS APS e da estratégia como um todo. O Educa e-SUS APS oferece trajetórias formativas para a adequação do perfil profissional e técnico às demandas e necessidades do cuidado primário, mediado por tecnologia e fortalecido pelo conjunto de inovações que compõem a saúde digital. Acreditamos que, quando capacitada e motivada, a força de trabalho da APS será capaz de transformar os mais diversos cenários do cuidado em dados, analisar as informações geradas e utilizá-las no aprimoramento do cuidado à saúde das pessoas, da gestão e transparência de suas ações. Além disso, poderá contribuir para ampliar a cobertura e a resolutividade da diversa carteira de serviços da APS, com o emprego de recursos de comunicação à distância, incorporados aos protocolos de cuidado. É o comprometimento de cada um, aliado à educação permanente para consolidação das boas práticas em saúde digital, que trará mudanças significativas na tomada de decisão baseada em informação confiável e oportuna, fomentando as melhores soluções para enfrentar os grandes desafios da atenção à saúde no país.

O sistema E-SUS APS para profissionais de saúde: educação permanente para enfermeiros, médicos da equipe de saúde da família e equipes multiprofissionais / The E-SUS APS system for healthcare professionals: continuing education for nurses, physicians of the family health team, and multidisciplinary teams

O Sistema Único de Saúde tem enfrentado grandes desafios relacionados à formação, qualificação e organização do processo de trabalho dos profissionais de saúde. Neste momento, com a incorporação da Estratégia e-SUS Atenção Primária à Saúde, alinhada à Estratégia de Saúde Digital do Brasil, já em um patamar de cobertura bastante avançada de municípios com com prontuário eletrônico, vivenciamos um cenário de qualificação permanente no uso de tecnologias digitais e uma frente promissora no enfrentamento dos obstáculos para prover saúde para todos e fixar profissionais em áreas mais afastadas dos grandes centros urbanos e nas regiões de maior vulnerabilidade social. No entanto, prover recursos tecnológicos não basta; é preciso estar atento aos profissionais que, de fato, fazem a transformação digital acontecer. Nesse sentido, a Secretaria de Atenção Primária à Saúde coordena uma agenda positiva e apresenta um projeto de retomada da construção do SUS com base na inovação tecnológica centrada nas pessoas. E, dentre as diversas estratégias, implanta um projeto nacional de educação permanente em saúde digital, que potencializa o uso qualificado, ético e responsável do Sistema e-SUS APS e da estratégia como um todo. O Educa e-SUS APS oferece trajetórias formativas para a adequação do perfil profissional e técnico às demandas e necessidades do cuidado primário, mediado por tecnologia e fortalecido pelo conjunto de inovações que compõem a saúde digital. Acreditamos que, quando capacitada e motivada, a força de trabalho da APS será capaz de transformar os mais diversos cenários do cuidado em dados, analisar as informações geradas e utilizá-las no aprimoramento do cuidado à saúde das pessoas, da gestão e transparência de suas ações. Além disso, poderá contribuir para ampliar a cobertura e a resolutividade da diversa carteira de serviços da APS, com o emprego de recursos de comunicação à distância, incorporados aos protocolos de cuidado. É o comprometimento de cada um, aliado à educação permanente para consolidação das boas práticas em saúde digital, que trará mudanças significativas na tomada de decisão baseada em informação confiável e oportuna, fomentando as melhores soluções para enfrentar os grandes desafios da atenção à saúde no país

Respiratory distress syndrome prediction at birth by optical skin maturity assessment and machine learning models for limited-resource settings: a development and validation study.

Background: A handheld optical device was developed to evaluate a newborn's skin maturity by assessing the photobiological properties of the tissue and processing it with other variables to predict early neonatal prognosis related to prematurity. This study assessed the device's ability to predict respiratory distress syndrome (RDS). Methods: To assess the device's utility we enrolled newborns at childbirth in six urban perinatal centers from two multicenter single-blinded clinical trials. All newborns had inpatient follow-up until 72 h of life. We trained supervised machine learning models with data from 780 newborns in a Brazilian trial and provided external validation with data from 305 low-birth-weight newborns from another trial that assessed Brazilian and Mozambican newborns. The index test measured skin optical reflection with an optical sensor and adjusted acquired values with clinical variables such as birth weight and prenatal corticoid exposition for lung maturity, maternal diabetes, and hypertensive disturbances. The performance of the models was evaluated using intrasample k-parts cross-validation and external validation in an independent sample. Results: Models adjusting three predictors (skin reflection, birth weight, and antenatal corticoid exposure) or five predictors had a similar performance, including or not maternal diabetes and hypertensive diseases. The best global accuracy was 89.7 (95% CI: 87.4 to 91.8, with a high sensitivity of 85.6% (80.2 to 90.0) and specificity of 91.3% (95% CI: 88.7 to 93.5). The test correctly discriminated RDS newborns in external validation, with 82.3% (95% CI: 77.5 to 86.4) accuracy. Our findings demonstrate a new way to assess a newborn's lung maturity, providing potential opportunities for earlier and more effective care. Trial registration: RBR-3f5bm5 (online access: http://www.ensaiosclinicos.gov.br/rg/RBR-3f5bm5/), and RBR-33mjf (online access: https://ensaiosclinicos.gov.br/rg/RBR-33rnjf/).

Assessment of the Implementation of a Chatbot-Based Screening for Burnout and COVID-19 Symptoms Among Residents During the Pandemic.

Background: Early identification of COVID-19 symptoms and burnout among residents is essential for proper management. Digital assistants might help in the large-scale screening of residents. Objective: To assess the implementation of a chatbot for tele-screening emotional exhaustion and COVID-19 among residents at a hospital in Brazil. Methods: From August to October 2020, a chatbot sent participants' phones a daily question about COVID-19 symptoms and a weekly question about emotional exhaustion. After 8 weeks, the residents answered the Maslach Burnout Inventory-Human Services Survey (MBI-HSS). The primary outcome was the reliability of the chatbot in identifying suspect cases of COVID-19 and burnout. Results: Among the 489 eligible residents, 174 (35.6%) agreed to participate. The chatbot identified 61 positive responses for COVID-19 symptoms, and clinical suspicion was confirmed in 9 residents. User error in the first weeks was the leading cause (57.7%, 30 of 52) of nonconfirmed suspicion. The chatbot failed to identify 3 participants with COVID-19 due to nonresponse. Twelve of 118 (10.2%) participants who answered the MBI-HSS were characterized as having burnout by the MBI-HHS. Two of them were identified as at risk by the chatbot and 8 never answered the emotional exhaustion screening question. Conversely, among the 19 participants identified as at risk for emotional exhaustion by the chatbot, 2 (10.5%) were classified with burnout, and 5 (26.3%) as overextended based on MBI-HHS scores. Conclusions: The chatbot was able to identify residents suspected of having COVID-19 and those at risk for burnout. Nonresponse was the leading cause of failure in identifying those at risk.

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth.

Introduction: A new medical device was previously developed to estimate gestational age (GA) at birth by processing a machine learning algorithm on the light scatter signal acquired on the newborn's skin. The study aims to validate GA calculated by the new device (test), comparing the result with the best available GA in newborns with low birth weight (LBW). Methods: We conducted a multicenter, non-randomized, and single-blinded clinical trial in three urban referral centers for perinatal care in Brazil and Mozambique. LBW newborns with a GA over 24 weeks and weighing between 500 and 2,500 g were recruited in the first 24 h of life. All pregnancies had a GA calculated by obstetric ultrasound before 24 weeks or by reliable last menstrual period (LMP). The primary endpoint was the agreement between the GA calculated by the new device (test) and the best available clinical GA, with 95% confidence limits. In addition, we assessed the accuracy of using the test in the classification of preterm and SGA. Prematurity was childbirth before 37 gestational weeks. The growth standard curve was Intergrowth-21st, with the 10th percentile being the limit for classifying SGA. Results: Among 305 evaluated newborns, 234 (76.7%) were premature, and 139 (45.6%) were SGA. The intraclass correlation coefficient between GA by the test and reference GA was 0.829 (95% CI: 0.785-0.863). However, the new device (test) underestimated the reference GA by an average of 2.8 days (95% limits of agreement: -40.6 to 31.2 days). Its use in classifying preterm or term newborns revealed an accuracy of 78.4% (95% CI: 73.3-81.6), with high sensitivity (96.2%; 95% CI: 92.8-98.2). The accuracy of classifying SGA newborns using GA calculated by the test was 62.3% (95% CI: 56.6-67.8). Discussion: The new device (test) was able to assess GA at birth in LBW newborns, with a high agreement with the best available GA as a reference. The GA estimated by the device (test), when used to classify newborns on the first day of life, was useful in identifying premature infants but not when applied to identify SGA infants, considering current algohrithm. Nonetheless, the new device (test) has the potential to provide important information in places where the GA is unknown or inaccurate.

Usability of Telehealth Systems for Noncommunicable Diseases in Primary Care From the COVID-19 Pandemic Onward: Systematic Review.

BACKGROUND: During the COVID-19 pandemic, telehealth was expanded without the opportunity to extensively evaluate the adopted technology's usability. OBJECTIVE: We aimed to synthesize evidence on health professionals' perceptions regarding the usability of telehealth systems in the primary care of individuals with noncommunicable diseases (NCDs; hypertension and diabetes) from the COVID-19 pandemic onward. METHODS: A systematic review was performed of clinical trials, prospective cohort studies, retrospective observational studies, and studies that used qualitative data collection and analysis methods published in English, Spanish, and Portuguese from March 2020 onward. The databases queried were MEDLINE, Embase, BIREME, IEEE Xplore, BVS, Google Scholar, and grey literature. Studies involving health professionals who used telehealth systems in primary care and managed patients with NCDs from the COVID-19 pandemic onward were considered eligible. Titles, abstracts, and full texts were reviewed. Data were extracted to provide a narrative qualitative evidence synthesis of the included articles. The risk of bias and methodological quality of the included studies were analyzed. The primary outcome was the usability of telehealth systems, while the secondary outcomes were satisfaction and the contexts in which the telehealth system was used. RESULTS: We included 11 of 417 retrieved studies, which had data from 248 health care professionals. These health care professionals were mostly doctors and nurses with prior experience in telehealth in high- and middle-income countries. Overall, 9 studies (82%) were qualitative studies and 2 (18%) were quasiexperimental or multisite trial studies. Moreover, 7 studies (64%) addressed diabetes, 1 (9%) addressed diabetes and hypertension, and 3 (27%) addressed chronic diseases. Most studies used a survey to assess usability. With a moderate confidence level, we concluded that health professionals considered the usability of telehealth systems to be good and felt comfortable and satisfied. Patients felt satisfied using telehealth. The most important predictor for using digital health technologies was ease of use. The main barriers were technological challenges, connectivity issues, low computer literacy, inability to perform complete physical examination, and lack of training. Although the usability of telehealth systems was considered good, there is a need for research that investigates factors that may influence the perceptions of telehealth usability, such as differences between private and public services; differences in the level of experience of professionals, including professional experience and experience with digital tools; and differences in gender, age groups, occupations, and settings. CONCLUSIONS: The COVID-19 pandemic has generated incredible demand for virtual care. Professionals' favorable perceptions of the usability of telehealth indicate that it can facilitate access to quality care. Although there are still challenges to telehealth, more than infrastructure challenges, the most reported challenges were related to empowering people for digital health. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021296887; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=296887. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.21801/ppcrj.2022.82.6.

Influence of different methods for calculating gestational age at birth on prematurity and small for gestational age proportions: a systematic review with meta-analysis.

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.

Mechanical ventilation and death in pregnant patients admitted for COVID-19: a prognostic analysis from the Brazilian COVID-19 registry score.

BACKGROUND: The assessment of clinical prognosis of pregnant COVID-19 patients at hospital presentation is challenging, due to physiological adaptations during pregnancy. Our aim was to assess the performance of the ABC2-SPH score to predict in-hospital mortality and mechanical ventilation support in pregnant patients with COVID-19, to assess the frequency of adverse pregnancy outcomes, and characteristics of pregnant women who died. METHODS: This multicenter cohort included consecutive pregnant patients with COVID-19 admitted to the participating hospitals, from April/2020 to March/2022. Primary outcomes were in-hospital mortality and the composite outcome of mechanical ventilation support and in-hospital mortality. Secondary endpoints were pregnancy outcomes. The overall discrimination of the model was presented as the area under the receiver operating characteristic curve (AUROC). Overall performance was assessed using the Brier score. RESULTS: From 350 pregnant patients (median age 30 [interquartile range (25.2, 35.0)] years-old]), 11.1% had hypertensive disorders, 19.7% required mechanical ventilation support and 6.0% died. The AUROC for in-hospital mortality and for the composite outcome were 0.809 (95% IC: 0.641-0.944) and 0.704 (95% IC: 0.617-0.792), respectively, with good overall performance (Brier = 0.0384 and 0.1610, respectively). Calibration was good for the prediction of in-hospital mortality, but poor for the composite outcome. Women who died had a median age 4 years-old higher, higher frequency of hypertensive disorders (38.1% vs. 9.4%, p < 0.001) and obesity (28.6% vs. 10.6%, p = 0.025) than those who were discharged alive, and their newborns had lower birth weight (2000 vs. 2813, p = 0.001) and five-minute Apgar score (3.0 vs. 8.0, p < 0.001). CONCLUSIONS: The ABC2-SPH score had good overall performance for in-hospital mortality and the composite outcome mechanical ventilation and in-hospital mortality. Calibration was good for the prediction of in-hospital mortality, but it was poor for the composite outcome. Therefore, the score may be useful to predict in-hospital mortality in pregnant patients with COVID-19, in addition to clinical judgment. Newborns from women who died had lower birth weight and Apgar score than those who were discharged alive.

Evaluating User Experience With a Chatbot Designed as a Public Health Response to the COVID-19 Pandemic in Brazil: Mixed Methods Study.

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.

Panorama da Saúde Digital na APS: educação permanente para enfermeiros, médicos da equipe de saúde da família, cirurgiões-dentistas e equipes multiprofissionais / Panorama of Digital Health in PHC: continuing education for nurses, family health team doctors, dental surgeons and multidisciplinary teams

O Sistema Único de Saúde tem enfrentado grandes desafios relacionados à formação, qualificação e organização do processo de trabalho dos profissionais de saúde. Neste momento, com a incorporação da Estratégia e-SUS Atenção Primária à Saúde, alinhada à Estratégia de Saúde Digital do Brasil, já em um patamar de cobertura bastante avançada de municípios com com prontuário eletrônico, vivenciamos um cenário de qualificação permanente no uso de tecnologias digitais e uma frente promissora no enfrentamento dos obstáculos para prover saúde para todos e fixar profissionais em áreas mais afastadas dos grandes centros urbanos e nas regiões de maior vulnerabilidade social. No entanto, prover recursos tecnológicos não basta; é preciso estar atento aos profissionais que, de fato, fazem a transformação digital acontecer. Nesse sentido, a Secretaria de Atenção Primária à Saúde coordena uma agenda positiva e apresenta um projeto de retomada da construção do SUS com base na inovação tecnológica centrada nas pessoas. E, dentre as diversas estratégias, implanta um projeto nacional de educação permanente em saúde digital, que potencializa o uso qualificado, ético e responsável do Sistema e-SUS APS e da estratégia como um todo. O Educa e-SUS APS oferece trajetórias formativas para a adequação do perfil profissional e técnico às demandas e necessidades do cuidado primário, mediado por tecnologia e fortalecido pelo conjunto de inovações que compõem a saúde digital. Acreditamos que, quando capacitada e motivada, a força de trabalho da APS será capaz de transformar os mais diversos cenários do cuidado em dados, analisar as informações geradas e utilizá-las no aprimoramento do cuidado à saúde das pessoas, da gestão e transparência de suas ações. Além disso, poderá contribuir para ampliar a cobertura e a resolutividade da diversa carteira de serviços da APS, com o emprego de recursos de comunicação à distância, incorporados aos protocolos de cuidado. É o comprometimento de cada um, aliado à educação permanente para consolidação das boas práticas em saúde digital, que trará mudanças significativas na tomada de decisão baseada em informação confiável e oportuna, fomentando as melhores soluções para enfrentar os grandes desafios da atenção à saúde no país.

Anais do I Simpósio CI-IA Saúde da UFMG / Proceedings of the 1st CI-IA Health Symposium at UFMG

Anualmente o Centro promove um evento com o objetivo principal reunir os pesquisadores, profissionais e alunos, tanto do setor público como privado, para promover um amplo debate de ideias, fundamentos e aplicações relacionados ao uso da Inteligência Artificial na área da Saúde. O evento deste ano contou com 120 participantes e ocorreu no dia 25 de setembro de 2023 no Instituto de Ciências Exatas no Campus Pampulha da Universidade Federal de Minas Gerais. Ao todo, o evento contou com a submissão e avaliação de 26 resumos científicos (short papers) e as revisões de pares foram realizadas com o apoio de 11 revisores voluntários. Ao final da avaliação, foram selecionados para publicação 22 trabalhos, que foram apresentados ao público no formato de pôsteres digitais. Os trabalhos mais bem avaliados foram convidados para apresentação oral no evento e receberam certificados de menção honrosa. A programação do evento contou com mesas redondas onde os projetos de pesquisas apoiados pelo Centro foram apresentados e foram debatidos as competências necessárias para a utilização da Inteligência Artificial na área da Saúde, além da palestra proferida pelo professor Ricardo Cruz-Correia, Universidade do Porto, que abordou os desafios e oportunidades da IA generativa para o ensino e pesquisa em Saúde.

Every year, the Center promotes an event with the main objective of bringing together researchers, professionals and students, both from the public and private sectors, to promote a broad debate of ideas, foundations and applications related to the use of Artificial Intelligence in the area of ​​Health. This year's event featured with 120 participants and took place on September 25, 2023 at the Institute of Exact Sciences in Pampulha Campus of the Federal University of Minas Gerais. In total, the event included the submission and evaluation of 26 scientific summaries (short papers) and the Peer reviews were carried out with the support of 11 volunteer reviewers. At the end of the assessment, they were 22 works were selected for publication, which were presented to the public in poster format digital. The best evaluated works were invited for oral presentation at the event and received certificates of honorable mention. The event's program included round tables where research projects supported by the Center were presented and the necessary skills for using the Artificial Intelligence in the area of ​​Health, in addition to the lecture given by professor Ricardo Cruz-Correia, University of Porto, which addressed the challenges and opportunities of generative AI for teaching and research in Health.

Introdução à análise de dados em saúde com Python / Introduction to healthcare data analysis with Python

Este eBook foi elaborado no contexto do curso de capacitação Introdução à Análise de Dados em Saúde com Python ofertado pelo Centro de Inovação em Inteligência Artificial para Saúde. O curso tem como objetivo introduzir o estudo exploratório de bases de dados de saúde, com a utilização do Python. Neste eBook, procura-se apresentar uma abordagem preliminar à Ciência de Dados, que explora e descreve um conjunto de dados com técnicas da estatística descritiva e inferencial por meio da linguagem de programação Python. O público alvo que pretende-se atingir caracteriza-se por profissionais de saúde, alunos de graduação e pós-graduação, docentes e pesquisadores da área das ciências da saúde, exatas ou demais interessados em utilizar os recursos computacionais para análise de bases de dados em saúde. A linguagem Python tem se destacado como uma ferramenta poderosa para análise de dados em saúde, possuindo uma ampla gama de bibliotecas e recursos, o Python pode ser usado para limpar, processar, analisar e visualizar dados de saúde. Além disso, a comunidade de utilizadores da linguagem Python é muito colaborativa, com muitos recursos disponíveis, incluindo documentação, tutoriais e fóruns de suporte. O conteúdo foi agrupado em conceitos iniciais sobre a utilização dos dados em saúde, introdução ao Python para utilização de dados, conceitos de limpeza e tratamento de dados, aplicação da estatística descritiva com os sumários estatísticos e gráficos, técnicas de amostragens, aplicação da estatística inferencial com os testes de hipótese, de associação, de médias, de medianas e correlações, além de explorar a estilização de gráficos.

Synchronous Teleconsultation and Monitoring Service Targeting COVID-19: Leveraging Insights for Postpandemic Health Care.

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.

Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial.

BACKGROUND: Early access to antenatal care and high-cost technologies for pregnancy dating challenge early neonatal risk assessment at birth in resource-constrained settings. To overcome the absence or inaccuracy of postnatal gestational age (GA), we developed a new medical device to assess GA based on the photobiological properties of newborns' skin and predictive models. OBJECTIVE: This study aims to validate a device that uses the photobiological model of skin maturity adjusted to the clinical data to detect GA and establish its accuracy in discriminating preterm newborns. METHODS: A multicenter, single-blinded, and single-arm intention-to-diagnosis clinical trial evaluated the accuracy of a novel device for the detection of GA and preterm newborns. The first-trimester ultrasound, a second comparator ultrasound, and data regarding the last menstrual period (LMP) from antenatal reports were used as references for GA at birth. The new test for validation was performed using a portable multiband reflectance photometer device that assessed the skin maturity of newborns and used machine learning models to predict GA, adjusted for birth weight and antenatal corticosteroid therapy exposure. RESULTS: The study group comprised 702 pregnant women who gave birth to 781 newborns, of which 366 (46.9%) were preterm newborns. As the primary outcome, the GA as predicted by the new test was in line with the reference GA that was calculated by using the intraclass correlation coefficient (0.969, 95% CI 0.964-0.973). The paired difference between predicted and reference GAs was -1.34 days, with Bland-Altman limits of -21.2 to 18.4 days. As a secondary outcome, the new test achieved 66.6% (95% CI 62.9%-70.1%) agreement with the reference GA within an error of 1 week. This agreement was similar to that of comparator-LMP-GAs (64.1%, 95% CI 60.7%-67.5%). The discrimination between preterm and term newborns via the device had a similar area under the receiver operating characteristic curve (0.970, 95% CI 0.959-0.981) compared with that for comparator-LMP-GAs (0.957, 95% CI 0.941-0.974). In newborns with absent or unreliable LMPs (n=451), the intent-to-discriminate analysis showed correct preterm versus term classifications with the new test, which achieved an accuracy of 89.6% (95% CI 86.4%-92.2%), while the accuracy for comparator-LMP-GA was 69.6% (95% CI 65.3%-73.7%). CONCLUSIONS: The assessment of newborn's skin maturity (adjusted by learning models) promises accurate pregnancy dating at birth, even without the antenatal ultrasound reference. Thus, the novel device could add value to the set of clinical parameters that direct the delivery of neonatal care in birth scenarios where GA is unknown or unreliable. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-027442.

COVID-19: Distance learning to empower educators and health assistants in rural areas.

BACKGROUND: Educators and health assistants can act as key players in controlling the pandemic. In general, they are respected by the community, especially in rural areas, and can help guide and raise the population's awareness of preventive measures. The objective of this paper is to evaluate the results in knowledge progress of an e-learning course offered to educators and health assistants by a public university and to analyze the profile and satisfaction of the participants. MATERIALS AND METHODS: A single group pre- and post-test design based on a questionnaire, interventional, study was conducted in the period from April 20th to June 20th. All participants of the course answered sociodemographic and satisfaction questionnaire and a pre- and post-test. Data were descriptively arranged and regarding the distribution and comparison of means and frequency, paired t-test for group comparisons. P < 0.05 was statistically significant. Data were collected from the Moodle® teaching platform, without identifying the participants. RESULTS: A total of 674 participants were enrolled in the five groups, and 583 concluded the course (86.5%). The reasons for those who did not access the entire course (n = 47) were: Lack of time, difficulty in accessing the internet, and lack of experience with distance learning courses. On the evaluation of the course platform, from the tutors in general and the degree of satisfaction in several questions, participants marked maximum grades. The comparison between pre- and post-proficiency scores showed increased proficiency of the enrolled groups (P < 0.0001). CONCLUSIONS: The course contributed to the training of health assistants and educators, preparing them to act in a participatory way in the prevention and control of the pandemic. The course was well evaluated and there was a progression of knowledge by the participants.

A feasibility analysis of digital interface to transfer information during doctors' handovers / Uma análise de viabilidade de interface digital para transferência de informações durante a passagem dos médicos / Análisis de viabilidad de la interfaz digital para apoyar los traspasos de turnos medicos

Objective: To report the pre-deployment analysis of a digital system to transfer patient information during physicians' obstetric shift sessions. Methods: A literature review explored evidence concerning electronic handover applications in hospitals. A survey met local approaches used to shift changing and the expectations of managers' stakeholders. To explore local practices, we analyzed a sample 251 obstetric handovers. Finally, requirements for the system were listed, and end-users evaluated mockups of the proposed design. Results: From the literature review, easy-to-use and integration with existing systems were the most critical requests to achieve user adherence. The main system requirement was using the hospital infrastructure to ensure full access to the current medical record. Mockup validation by end-users pinpointed items to improve a complete implementation and the positive acceptance of prefilled structured entries. Conclusions: There are blockages to overcome deficits in the quality of the information in clinical handovers to safely transfer patient care between doctors' shifts.

Objetivo: Relatar a análise pré-implantação de um sistema para transferência de dados clínicos durante as sessões de plantão obstétrico. Métodos: Uma revisão de literatura explorou evidências sobre sistemas hospitalares em uso. Um questionário levantou abordagens empregadas e as expectativas das partes interessadas. Para explorar as práticas, analisamos uma amostra de 251 transferências de plantão. Os requisitos para o sistema foram listados e os usuários finais avaliaram protótipos de interface. Resultados: A partir da revisão da literatura, a facilidade de uso e a integração com sistemas eletrônicos existentes foram os quesitos mais críticos para alcançar a adesão dos usuários. O principal requisito de sistema foi utilizar a infraestrutura do hospital para garantir o acesso ao prontuário eletrônio. Na validação das interfaces, identificaram-se itens de melhoria antes da implementação plena e uma aceitação de entradas estruturadas, pré-preenchidas. Conclusões: Há desafios para superar déficits na qualidade da informação clínica trocada em sessões de transferência de plantões médicos, para garantir a segurança do atendimento ao paciente.

Objetivo: Reportar el análisis previo al despliegue de un sistema digital para transferir información de pacientes durante las sesiones de turno de médicos obstétricos. Métodos: Uma revisión de literatura explorou evidencias sobre sistemas hospitalares em uso. Um questionário levantou abordagens empregadas e as expectativas das partes interessadas. Para explorar las prácticas, analizamos una muestra de 251 transferencias de planta. Los requisitos para el sistema de listados y los usuarios finales para evaluar los prototipos de la interfaz. Resultados: partir de la revisión de la literatura, la facilidad de uso y la integración de los sistemas electrónicos existentes para las preguntas más críticas para alcanzar el objetivo de los usuarios. O principal requisito de sistema para utilizar una infraestructura del hospital para garantizar o acesso ao prontuário eletrônio. Na validação das interfaces, identificaram-se itens de melhoria antes da implementação plena e uma aceitação de entradas estruturadas, pré-preenchidas. Conclusiones: Há desafios para superar los déficits en la calidad de la información clínica trocada en sesiones de transferencia de plantas médicas, para garantizar una atención segura al paciente.

Association between the chronology of gestation and the morphometrical skin characteristics at childbirth: a development of predictive model.

OBJECTIVE: The structural maturation of the skin is considered a potential marker of pregnancy dating. This study investigated the correlation between the morphometrical skin characteristics with the pregnancy chronology to propose models for predicting gestational age. METHODS: A cross-sectional analysis selected 35 corpses of newborns. The biopsy was performed up to 48 hours after death in the periumbilical abdomen, palm and sole regions. Pregnancy chronology was based on the obstetric ultrasound before 14 weeks. The dimensions of the skin layers, area of glands and connective fibrous tissue were measured with imaging software support. Univariate and multivariate regression models on morphometric values were used to predict gestational age. RESULTS: Gestational age at birth ranged from 20.3 to 41.2 weeks. Seventy-one skin specimens resulted in the analysis of 1183 digital histological images. The correlation between skin thickness and gestational age was positive and strong in both regions of the body. The highest univariate correlation between gestational age and skin thickness was using the epidermal layer dimensions, in palm (r=0.867, p<0.001). The multivariate modelling with the thickness of the abdominal epidermis, the dermis and the area of the sebaceous glands adjusted had the highest correlation with gestational age (r=0.99, p<0.001). CONCLUSION: The thickness of the protective epidermal barrier is, in itself, a potential marker of pregnancy dating. However, sets of values obtained from skin morphometry enhanced the estimation of the gestational age. Such findings may support non-invasive image approaches to estimate pregnancy dating with various clinical applications.