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Identification of the SARS-CoV-2 virus by RT-PCR from a nasopharyngeal swab sample is a common test for diagnosing COVID-19. However, some patients present clinical, laboratorial, and radiological evidence of COVID-19 infection with negative RT-PCR result(s). Thus, we assessed whether positive results were associated with intubation and mortality. This study was conducted in a Brazilian tertiary hospital from March to August of 2020. All patients had clinical, laboratory, and radiological diagnosis of COVID-19. They were divided into two groups: positive (+) RT-PCR group, with 2292 participants, and negative (−) RT-PCR group, with 706 participants. Patients with negative RT-PCR testing and an alternative most probable diagnosis were excluded from the study. The RT-PCR(+) group presented increased risk of intensive care unit (ICU) admission, mechanical ventilation, length of hospital stay, and 28-day mortality, when compared to the RT-PCR(−) group. A positive SARS-CoV-2 RT-PCR result was independently associated with intubation and 28 day in-hospital mortality. Accordingly, we concluded that patients with a COVID-19 diagnosis based on clinical data, despite a negative RT-PCR test from nasopharyngeal samples, presented more favorable outcomes than patients with positive RT-PCR test(s).

BACKGROUND To demonstrate the value of implementation of an artificial intelligence solution in health care service, a winning project of the Massachusetts Institute of Technology Hacking Medicine Brazil competition was implemented in an urgent care service for health care professionals at Hospital das Clínicas of the Faculdade de Medicina da Universidade de São Paulo during the COVID-19 pandemic. OBJECTIVE The aim of this study was to determine the impact of implementation of the digital solution in the urgent care service, assessing the reduction of nonvalue-added activities and its effect on the nurses’ time required for screening and the waiting time for patients to receive medical care. METHODS This was a single-center, comparative, prospective study designed according to the Public Health England guide “Evaluating Digital Products for Health.” A total of 38,042 visits were analyzed over 18 months to determine the impact of implementing the digital solution. Medical care registration, health screening, and waiting time for medical care were compared before and after implementation of the digital solution. RESULTS The digital solution automated 92% of medical care registrations. The time for health screening increased by approximately 16% during the implementation and in the first 3 months after the implementation. The waiting time for medical care after automation with the digital solution was reduced by approximately 12 minutes compared with that required for visits without automation. The total time savings in the 12 months after implementation was estimated to be 2508 hours. CONCLUSIONS The digital solution was able to reduce nonvalue-added activities, without a substantial impact on health screening, and further saved waiting time for medical care in an urgent care service in Brazil during the COVID-19 pandemic.

Background: AIRA is an AI designed to reduce the time that doctors dedicate filling out EHR, winner of the first edition of MIT Hacking Medicine held in Brazil in 2020.As a proof of concept, AIRA was implemented in administrative process before its application in a medical process. Aim: The aim of the study is to determinate the impact of AIRA by eliminating the Medical Care Registration (MCR) on Electronic Health Record (EHR) by Administrative Officer. Setting: The study was conducted at Hospital das Clinicas of the Faculty of Medicine of the University of Sao Paulo (HCFMUSP)´s employee´s care center. Methods: This is a comparative before-and-after study following the guidance “Evaluating digital health products” from Public Health England. An Artificial Intelligence named AIRA was created and implemented at CEAC (Employee Attention Center) from HCFMUSP. A total of 25,507 attendances were evaluated along 2020 for determinate AIRA´s impact. Total of MCR, time of health screening and time between the end of the screening and the beginning of medical care, were compared in the pre and post AIRA periods. Results: AIRA eliminated the need for Medical Care Registration by Administrative Officer in 92% (p

IntroductionAlthough patients’ clinical conditions were previously shown to be associated with coronavirus disease 2019 (COVID-19) severity and outcomes, their impact on hospital costs is not known. The economic evaluation of COVID-19 admissions allows the assessment of hospital costs associated with the treatment of these patients, including the main cost components and costs driven by demographic and clinical conditions. The aim of this study was to determine the COVID-19 hospitalization-related costs and their association with clinical conditions.MethodsProspective observational cohort study of the hospitalization costs of patients with COVID-19 admitted between March 30 and June 30, 2020, who were followed until discharge, death, or external transfer, using micro-costing methodology. The study was carried out in the Central Institute of the Hospital das Clinicas, affiliated with the Faculty of Medicine of the University of Sao Paulo, Brazil, which is the largest hospital complex in Latin America and was designated to exclusively admit COVID-19 patients during the pandemic response.ResultsThe average cost of the 3,254 admissions (51.7% of which involved intensive care unit (ICU) stays) was US$12,637.42. Overhead cost was the main cost component, followed by daily fixed costs and drugs. Sex, age and underlying hypertension (US$14,746.77), diabetes (US$15,002.12), obesity (US$18,941.55), cancer (US$10,315.06), chronic renal failure (US$15,377.84), and rheumatic (US$17,764.61), hematologic (US$15,908.25) and neurologic diseases (US$15,257.95) were significantly associated with higher costs. Age >69 years, RT-PCR-confirmed COVID-19, comorbidities, the use of mechanical ventilation, dialysis, or surgery, and poor outcomes remained significantly associated with higher costs after model adjustment.ConclusionKnowledge of COVID-19-associated hospital costs and their impact across different populations can aid in the development of a generalizable and comprehensive approach to hospital preparedness, decision-making and planning for future risk management. Determining the disease-associated costs is the first step in evaluating the cost-effectiveness of treatments and vaccination programs.SUMMARY BOXQuestionWhat are the COVID-19 hospitalization-related costs?FindingsIn this prospective cohort that was carried out in a single reference quaternary center designated for the treatment of severe cases of COVID-19, more than three thousand patients were included, and their costs of hospitalization were found to be directly related to the age and comorbidities. The costs were more than 50% higher in older patients, 10-24% higher in patients with comorbidities, and 24-200% higher when additional therapeutic procedures were required.MeaningDetermining the disease-associated costs is the first step in conducting future evaluations of the cost-effectiveness of treatments and vaccination programs, supporting their implementation with a comprehensive population-based approach.

Background To demonstrate the value of implementation of an artificial intelligence solution in health care service, a winning project of the Massachusetts Institute of Technology Hacking Medicine Brazil competition was implemented in an urgent care service for health care professionals at Hospital das Clínicas of the Faculdade de Medicina da Universidade de São Paulo during the COVID-19 pandemic. Objective The aim of this study was to determine the impact of implementation of the digital solution in the urgent care service, assessing the reduction of nonvalue-added activities and its effect on the nurses’ time required for screening and the waiting time for patients to receive medical care. Methods This was a single-center, comparative, prospective study designed according to the Public Health England guide “Evaluating Digital Products for Health.” A total of 38,042 visits were analyzed over 18 months to determine the impact of implementing the digital solution. Medical care registration, health screening, and waiting time for medical care were compared before and after implementation of the digital solution. Results The digital solution automated 92% of medical care registrations. The time for health screening increased by approximately 16% during the implementation and in the first 3 months after the implementation. The waiting time for medical care after automation with the digital solution was reduced by approximately 12 minutes compared with that required for visits without automation. The total time savings in the 12 months after implementation was estimated to be 2508 hours. Conclusions The digital solution was able to reduce nonvalue-added activities, without a substantial impact on health screening, and further saved waiting time for medical care in an urgent care service in Brazil during the COVID-19 pandemic.

Background: Although patients’ clinical conditions were previously shown to be associated with coronavirus disease 2019 (COVID-19) severity and outcomes, their impact on hospital costs is not known. The economic evaluation of COVID-19 admissions allows the assessment of the direct and fixed hospital costs associated with the treatment of these patients, including the main cost components and costs driven by demographic and clinical conditions. Methods: This is a prospective observational cohort study of the hospitalization costs of patients with suspected and confirmed COVID-19 admitted between March 30 and June 30, 2020, to the largest referral hospital in Sao Paulo, Brazil, and followed until discharge, death, or external transfer. The cost investigation used a micro-costing methodology. Findings: The average cost of the 3,254 admissions (51.7% of which involved intensive care unit (ICU) stays) was US$12,637.42. Overhead cost was the main cost component, followed by daily fixed costs and drugs. Sex, age and underlying hypertension (US$14,746.77), diabetes (US$15,002.12), obesity (US$18,941.55), cancer (US$10,315.06), chronic renal failure (US$15,377.84), and rheumatic (US$17,764.61), hematologic (US$15,908.25) and neurologic diseases (US$15,257.95) were significantly associated with higher costs. Age >69 years, reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19, comorbidities, the use of mechanical ventilation or dialysis, surgery and outcomes remained significantly associated with higher costs after model adjustment. Interpretation: Knowledge of COVID-19-associated hospital costs and their impact across different populations can aid in the development of a generalizable and comprehensive approach to hospital preparedness, decision-making and planning for future risk management. Determining the disease-associated costs is the first step in evaluating the cost-effectiveness of treatments and vaccination programs. Funding: This research was conducted without funding. Declaration of Interests: The authors have no conflicts of interest to declare. Ethics Approval Statement: The research protocol was approved by the institutional ethics committee (CAPPESQ: #4.107.580).

OBJECTIVES: To compare demographic/clinical/laboratory/treatments and outcomes among children and adolescents with laboratory-confirmed coronavirus disease 2019 (COVID-19). METHODS: This was a cross-sectional study that included patients diagnosed with pediatric COVID-19 (aged

Tackling the shortage: educating healthcare workers and controlling the dispensation Similar to what happened in other countries, in the first weeks of the pandemic, there was a great wastage of these items, either due to misuse or fear of scarcity that led to individual storage by some employees, leading to more scarcity for others. To address these problems, a multi-professional planning team was formed and met daily for a week, with actions concentrated on two pillars: employee education and dispensation [...]