The National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC) lacks a rigorous enrollment audit process, unlike other collaborative networks. Most centers require individual families to consent to participate. It is unknown whether there is variation across centers or biases in enrollment.

We used the Pediatric Cardiac Critical Care Consortium (PC4) registry to assess enrollment rates in NPC-QIC for those centers participating in both registries using indirect identifiers (date of birth, date of admission, gender, and center) to match patient records. All infants born 1/1/2018–12/31/2020 and admitted 30 days of life were eligible. In PC4, all infants with a fundamental diagnosis of hypoplastic left heart or variant or who underwent a surgical or hybrid Norwood or variant were eligible. Standard descriptive statistics were used to describe the cohort and center match rates were plotted on a funnel chart.

Of 898 eligible NPC-QIC patients, 841 were linked to 1,114 eligible PC4 patients (match rate 75.5%) in 32 centers. Match rates were lower in patients of Hispanic/Latino ethnicity (66.1%, p = 0.005), and those with any specified chromosomal abnormality (57.4%, p = 0.002), noncardiac abnormality (67.8%, p = 0.005), or any specified syndrome (66.5%, p = 0.001). Match rates were lower for patients who transferred to another hospital or died prior to discharge. Match rates varied from 0 to 100% across centers.

It is feasible to match patients between the NPC-QIC and PC4 registries. Variation in match rates suggests opportunities for improvement in NPC-QIC patient enrollment.

Coronavirus disease (COVID-19) has been identified as an acute respiratory illness leading to severe acute respiratory distress syndrome. As the disease spread, demands on health care systems increased, specifically the need to expand hospital capacity. Alternative care hospitals (ACHs) have been used to mitigate these issues; however, establishing an ACH has many challenges. The goal of this session was to perform systems testing, using a simulation-based evaluation to identify areas in need of improvement.

Four simulation cases were designed to depict common and high acuity situations encountered in the ACH, using a high technology simulator and standardized patient. A multidisciplinary observer group was given debriefing forms listing the objectives, critical actions, and specific areas to focus their attention. These forms were compiled for data collection.

Logistical, operational, and patient safety issues were identified during the simulation and compiled into a simulation event report. Proposed solutions and protocol changes were made in response to the identified issues.

Simulation was successfully used for systems testing, supporting efforts to maximize patient care and provider safety in a rapidly developed ACH. The simulation event report identified operational deficiencies and safety concerns directly resulting in equipment modifications and protocol changes.

Nudging in microbiology is an antimicrobial stewardship strategy to influence decision making through the strategic reporting of microbiology results while preserving prescriber autonomy. The purpose of this scoping review was to identify the evidence that demonstrates the effectiveness of nudging strategies in susceptibility result reporting to improve antimicrobial use.

A search for studies in Ovid MEDLINE, Embase, PsycINFO, and All EBM Reviews was conducted. All simulated and vignette studies were excluded. Two independent reviewers were used throughout screening and data extraction.

Of a total of 1,346 citations screened, 15 relevant studies were identified. Study types included pre- and postintervention (n = 10), retrospective cohort (n = 4), and a randomized controlled trial (n = 1). Most studies were performed in acute-care settings (n = 13), and the remainder were in primary care (n = 2). Most studies used a strategy to alter the default antibiotic choices on the antibiotic report. All studies reported at least 1 outcome of antimicrobial use: utilization (n = 9), appropriateness (n = 7), de-escalation (n = 2), and cost (n = 1). Moreover, 12 studies reported an overall benefit in antimicrobial use outcomes associated with nudging, and 4 studies evaluated the association of nudging strategy with subsequent antimicrobial resistance, with 2 studies noting overall improvement.

The number of heterogeneous studies evaluating the impact of applying nudging strategies to susceptibility result reports is small; however, most strategies do show promise in altering prescriber’s antibiotic selection. Selective and cascade reporting of targeted agents in a hospital setting represent the majority of current research. Gaps and opportunities for future research identified from our scoping review include performing prospective randomized controlled trials and evaluating other approaches aside from selective reporting.

It is unclear which pediatric disaster triage (PDT) strategy yields the best accuracy or best patient outcomes.

We conducted a cross-sectional analysis on a sample of emergency medical services providers from a prospective cohort study comparing the accuracy and triage outcomes for 2 PDT strategies (Smart and JumpSTART) and clinical decision-making (CDM) with no algorithm. Participants were divided into cohorts by triage strategy. We presented 10-victim, multi-modal disaster simulations. A Delphi method determined patients’ expected triage levels. We compared triage accuracy overall and for each triage level (RED/Immediate, YELLOW/Delayed, GREEN/Ambulatory, BLACK/Deceased).

There were 273 participants (71 JumpSTART, 122 Smart, and 81 CDM). There was no significant difference between Smart triage and CDM. When JumpSTART triage was used, there was greater accuracy than with either Smart (P<0.001; OR [odds ratio]: 2.03; interquartile range [IQR]: 1.30, 3.17) or CDM (P=0.02; OR: 1.76; IQR: 1.10, 2.82). JumpSTART outperformed Smart for RED patients (P=0.05; OR: 1.48; IQR: 1.01,2.17), and outperformed both Smart (P<0.001; OR: 3.22; IQR: 1.78,5.88) and CDM (P<0.001; OR: 2.86; IQR: 1.53,5.26) for YELLOW patients. Furthermore, JumpSTART outperformed CDM for BLACK patients (P=0.01; OR: 5.55; IQR: 1.47, 20.0).

Our simulation-based comparison suggested that JumpSTART triage outperforms both Smart and CDM. JumpSTART outperformed Smart for RED patients and CDM for BLACK patients. For YELLOW patients, JumpSTART yielded more accurate triage results than did Smart triage or CDM. (Disaster Med Public Health Preparedness. 2016;10:253–260)