EXPOSURE OF PEDIATRIC EMERGENCY PATIENTS TO IMAGING EXAMS, NOWADAYS AND IN TIMES OF COVID-19: AN INTEGRATIVE REVIEW

ABSTRACT Objective: To analyze literature data about unnecessary exposure of pediatric emergency patients to ionizing agents from imaging examinations, nowadays and during times of COVID-19. Data sources: Between April and July 2020, articles were selected using the databases: Virtual Health Library, PubMed and Scientific Electronic Library Online. The following descriptors were used: [(pediatrics) AND (emergencies) AND (diagnostic imaging) AND (medical overuse)] and [(Coronavirus infections) OR (COVID-19) AND (pediatrics) AND (emergencies) AND (diagnostic imaging)]. Inclusion criteria were articles available in full, in Portuguese or English, published from 2016 to 2020 or from 2019 to 2020, and articles that covered the theme. Articles without adherence to the theme and duplicate texts in the databases were excluded. Data synthesis: 61 publications were identified, of which 17 were comprised in this review. Some imaging tests used in pediatric emergency departments increase the possibility of developing future malignancies in patients, since they emit ionizing radiation. There are clinical decision instruments that allow reducing unnecessary exam requests, avoiding over-medicalization, and hospital expenses. Moreover, with the COVID-19 pandemic, there was a growing concern about the overuse of imaging exams in the pediatric population, which highlights the problems pointed out by this review. Conclusions: It is necessary to improve hospital staff training, use clinical decision instruments and develop guidelines to reduce the number of exams required, allowing hospital cost savings; and reducing children’s exposure to ionizing agents.

Clinical management and outcome differences between first and second waves among COVID-19 hospitalized patients: A regional prospective observational cohort

The objective was to describe the clinical characteristics and outcomes of hospitalized COVID-19 patients during the two different epidemic periods. Prospective, observational, cohort study of hospitalized COVID-19. A total of 421 consecutive patients were included, 188 during the first period (March-May 2020) and 233 in the second wave (July-December 2020). Clinical, epidemiological, prognostic and therapeutic data were compared. Patients of the first outbreak were older and more comorbid, presented worse PaO2/FiO2 ratio and an increased creatinine and D-dimer levels at hospital admission. The hospital stay was shorter (14.5[8;29] vs 8[6;14] days, p<0.001), ICU admissions (31.9% vs 13.3%, p<0.001) and the number of patients who required mechanical ventilation (OR = 0.12 [0.05–10.26]; p<0.001) were reduced. There were no significant differences in hospital and 30-day after discharge mortality (adjusted HR = 1.56; p = 0.1056) or hospital readmissions. New treatments and clinical strategies appear to improve hospital length, ICU admissions and the requirement for mechanical ventilation. However, we did not observe differences in mortality or readmissions.

Predicting next-day discharge via electronic health record access logs

Objective Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users’ granular interactions with patients’ records by communicating various semantics and has been neglected in outcome predictions. Materials and Methods This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. Results The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919–0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860–0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. Conclusion EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.

Price Transparency and Compliance With Federal Regulation for Pediatric Tonsillectomy

Objective To improve hospital price transparency, the Centers for Medicare & Medicaid Services (CMS) requires, as of January 2021, that all hospitals reveal charges for specific items and services. This analysis investigates whether otolaryngology residency–affiliated hospitals have complied with this new regulation, and it evaluates the variability in hospital-reported charges for pediatric tonsillectomy. Study Design Cross-sectional analysis. Settings Subset of hospitals affiliated with otolaryngology residency programs. Methods Hospital websites were searched to determine compliance rates with CMS guidelines by posting a price transparency tool and specific charges for Current Procedural Terminology code 42820 (tonsillectomy and adenoidectomy, <12 years old). Various charges were collected: gross charge, discounted cash price, deidentified minimum and maximum negotiated charges, hospital fees, and physician fees. Results Overall 104 unique hospitals were analyzed: 81 (78%) provided pricing data, but only 28 (27%) complied with CMS guidelines. The median reported total gross charge was $13,239 (range, $600-$41,957); deidentified minimum negotiated charge, $9222 (range, $337-$25,164); and deidentified maximum negotiated charge, $17,355 (range, $1002-$54,987). Hospital fees (median, $11,900; range, $2304-$38,831) were consistently higher than physician fees (median, $1827; range, $420-$5063). All estimates included a disclaimer stating that values likely underrepresent true prices. Conclusion Hospital compliance with the new regulation remains low, which limits efforts toward improved price transparency. There is wide variability in reported charges for pediatric tonsillectomy and adenoidectomy.

How to Improve Hospital Flows in the Context of the COVID Pandemic

In healthcare systems, the adoption of logistics 4.0 main technologies in the processes flows is essential to avoid unnecessary movements and manual work performed by people who could be performing tasks that require logical reasoning. In the context of the COVID pandemic, the adoption of new technologies to replace people in manual processes had become even more usual. This paper aims to demonstrate through simulation, the opportunities of improvement with lean manufacturing concepts and industry 4.0 technologies the hospital flows. After describing the problem and the need of improvements in hospital logistics, a literature review with concepts of Industry 4.0, Lean Manufacturing, and Logistics 4.0 will be presented. The hybrid approach used in the development of a decision aid tool that combines real data and methods of machine learning and problem-solving will be then, an example will be given for illustrating the concepts and methods elaborated.