Search

Introduction: Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic. Methods: In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation. Results: The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores. Discussion and conclusions: The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.

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).

OBJECTIVES: A good health care does not only depend on good medical practice, but also needs great management of its resources, which are generally short. In this sense, PROAHSA has been training new health managers since 1972. With the arrival of the COVID-19 pandemic, it was clear that medicine will go through a new phase, where telehealth will be present in this “Improved Normal”. This report is about how a pilot teleconsultation study was carried out for HCFMUSP patients through the Scrum-like framework. It is to deploy a pilot of remote assistance involving a doctor and a patient in the Ambulatory of Hepatology and Liver Transplantation of HCFMUSP. METHODS: We applied the Scrum-like framework to carry out this work with an interdisciplinary multifunctionality team. RESULTS: A full telemedicine service flow was implemented within eight weeks using existing infrastructure and resources implementing the Scrum methodology. Twenty-three teleconsultations were scheduled and eight guides built. CONCLUSION: Scrum framework has a great potential to improve the training of students and to conclude pilot projects.

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.

The rapid spread of the coronavirus pandemic worldwide warrants a huge transformation in the health assistance system and patient care. Most countries affected by coronavirus disease (COVID-19) are in lockdown, with restrictions on the movement of people and in services to prevent rapid transmission that may consequently lead to a health system collapse. The disease is known to cause severe acute respiratory syndrome (SARS), especially in the elderly population and in patients with comorbidities. Patients with chronic liver diseases can [...]

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).

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.

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 [...]

OBJECTIVES: We designed a cohort study to describe characteristics and outcomes of patients with coronavirus disease (COVID-19) admitted to the intensive care unit (ICU) in the largest public hospital in Sao Paulo, Brazil, as Latin America becomes the epicenter of the pandemic. METHODS: This is the protocol for a study being conducted at an academic hospital in Brazil with 300 adult ICU beds dedicated to COVID-19 patients. We will include adult patients admitted to the ICU with suspected or confirmed COVID-19 during the study period. The main outcome is ICU survival at 28 days. Data will be collected prospectively and retrospectively by trained investigators from the hospital’s electronic medical records, using an electronic data capture tool. We will collect data on demographics, comorbidities, severity of disease, and laboratorial test results at admission. Information on the need for advanced life support and ventilator parameters will be collected during ICU stay. Patients will be followed up for 28 days in the ICU and 60 days in the hospital. We will plot Kaplan-Meier curves to estimate ICU and hospital survival and perform survival analysis using the Cox proportional hazards model to identify the main risk factors for mortality. ClinicalTrials.gov: NCT04378582. RESULTS: We expect to include a large sample of patients with COVID-19 admitted to the ICU and to be able to provide data on admission characteristics, use of advanced life support, ICU survival at 28 days, and hospital survival at 60 days. CONCLUSIONS: This study will provide epidemiological data about critically ill patients with COVID-19 in Brazil, which could inform health policy and resource allocation in low- and middle-income countries.