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SARS-CoV-2 infection (COVID-19) poses a tremendous challenge to healthcare systems across the globe. Serologic testing for SARS-CoV-2 infection in healthcare workers (HCWs) may quantify the rate of clinically significant exposure in an institutional setting and identify those HCWs who are at greatest risk.

We conducted a survey and SARS-CoV-2 serologic testing among a convenience sample of HCWs from 79 non-COVID and 3 dedicated COVID hospitals in District Srinagar of Kashmir, India. In addition to testing for the presence of SARS-CoV-2-specific immunoglobulin G (IgG), we collected information on demographics, occupational group, influenza-like illness (ILI) symptoms, nasopharyngeal reverse transcription polymerase chain reaction (RT-PCR) testing status, history of close unprotected contacts, and quarantine/travel history.

Of 7,346 eligible HCWs, 2,915 (39.7%) participated in the study. Panobinostat The overall prevalence of SARS-CoV-2-specific IgG antibodies was 2.5% (95% CI, 2.0%-3.1%), while HCWs who had ever workll have spillover effects, creating ingrained behaviors that will continue outside the hospital setting.

Our investigation suggests that infection-control practices, including a compliance-maximizing buddy system, are valuable and effective in preventing infection within a high-risk clinical setting. Universal masking, mandatory testing of patients, and residential dormitories for HCWs at COVID-19-dedicated hospitals is an effective multifaceted approach to infection control. Moreover, given that many infections among HCWs are community-acquired, it is likely that the vigilant practices in these hospitals will have spillover effects, creating ingrained behaviors that will continue outside the hospital setting.

Febrile infants aged 0 to 60 days are often hospitalized for a 36-to-48 hour observation period to rule out invasive bacterial infections (IBI). Evidence suggests that monitoring blood and cerebrospinal fluid (CSF) cultures for 24 hours may be appropriate for most infants. We aimed to decrease the average culture observation time (COT) from 38 to 30 hours among hospitalized infants 0 to 60 days old over 12 months.

This quality improvement initiative occurred at a large children’s hospital, in conjunction with development of a multidisciplinary evidence-based guideline for the management of febrile infants. We included infants aged 0 to 60 days admitted with fever without a clear infectious source. We excluded infants who had positive blood, urine, or CSF cultures within 24 hours of incubation and infants who were hospitalized for other indications (eg, bronchiolitis). Interventions included guideline dissemination, education regarding laboratory monitoring practices, standardized order sets, and near-timeged 0 to 60 days.

We implemented an observation unit and home oxygen therapy (OU-HOT) protocol at our children’s hospital during the 2010-2011 winter season to facilitate earlier discharge of children hospitalized with bronchiolitis. An earlier study demonstrated substantial reductions in inpatient length of stay and costs in the first year after implementation.

Evaluate long-term reductions in length of stay and cost.

Interrupted time-series analysis, adjusting for patient demographic factors and disease severity. Participants were children aged 3 to 24 months and hospitalized with bronchiolitis from 2007 to 2019.

OU-HOT protocol implementation.

Hospital length of stay. Process measures were the percentage of patients discharged from the OU; percentage of patients discharged with HOT. Balancing measures were 7-day hospital revisit rates; annual per-population bronchiolitis admission rates. Secondary outcomes were inflation-adjusted cost per episode of care and discharges within 24 hours.

A total of 7,116 patients met inclusion criteria. The OU-HOT protocol was associated with immediate decreases in mean length of stay (-30.6 hours; 95% CI, -37.1 to -24.2 hours) and mean cost per episode of care (-$4,181; 95% CI, -$4,829 to -$3,533). These findings were sustained for 9 years after implementation. Hospital revisit rates did not increase immediately (-1.1% immediate change; 95% CI, -1.8% to -0.4%), but a small increase in revisits was observed over time (change in slope 0.4% per season, 95% CI, 0.1%-0.8%).

The OU-HOT protocol was associated with sustained reductions in length of stay and cost, representing a promising strategy to reduce the inpatient burden of bronchiolitis.

The OU-HOT protocol was associated with sustained reductions in length of stay and cost, representing a promising strategy to reduce the inpatient burden of bronchiolitis.

Medical training programs across the country are bound to a set of work hour regulations, generally monitored via self-report.

We developed a computational method to automate measurement of intern and resident work hours, which we validated against self-report.

We included all electronic health record (EHR) access log data between July 1, 2018, and June 30, 2019, for trainees enrolled in the internal medicine training program. We inferred the duration of continuous in-hospital work hours by linking EHR sessions that occurred within 5 hours as “on-campus” work and further accounted for “out-of-hospital” work which might be taking place at home.

We compared daily work hours estimated through the computational method with self-report and calculated the mean absolute error between the two groups. We used the computational method to estimate average weekly work hours across the rotation and the percentage of rotations where average work hours exceed the 80-hour workweek.

The mean absolute error between self-reported and EHR-derived daily work hours for first- (PGY-1), second- (PGY-2), and third- (PGY-3) year trainees were 1.27, 1.51, and 1.51 hours, respectively. Using this computational method, we estimated average (SD) weekly work hours of 57.0 (21.7), 69.9 (12.2), and 64.1 (16.3) for PGY-1, PGY-2, and PGY-3 residents.

EHR log data can be used to accurately approximate self-report of work hours, accounting for both in-hospital and out-of-hospital work. Automation will reduce trainees’ clerical work, improve consistency and comparability of data, and provide more complete and timely data that training programs need.

EHR log data can be used to accurately approximate self-report of work hours, accounting for both in-hospital and out-of-hospital work. Automation will reduce trainees’ clerical work, improve consistency and comparability of data, and provide more complete and timely data that training programs need.