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Pediatric sepsis is a major public health concern, and robust surveillance tools are needed to characterize its incidence, outcomes, and trends. The increasing use of electronic health records (EHRs) in the United States creates an opportunity to conduct reliable, pragmatic, and generalizable population-level surveillance using routinely collected clinical data rather than administrative claims or resource-intensive chart review. In 2015, the US Centers for Disease Control and Prevention recruited sepsis investigators and representatives of key professional societies to develop an approach to adult sepsis surveillance using clinical data recorded in EHRs. This led to the creation of the adult sepsis event definition, which was used to estimate the national burden of sepsis in adults and has been adapted into a tool kit to facilitate widespread implementation by hospitals. In July 2018, the Centers for Disease Control and Prevention convened a new multidisciplinary pediatric working group to tailor an EHR-based national sepsis surveillance approach to infants and children. Here, we describe the challenges specific to pediatric sepsis surveillance, including evolving clinical definitions of sepsis, accommodation of age-dependent physiologic differences, identifying appropriate EHR markers of infection and organ dysfunction among infants and children, and the need to account for children with medical complexity and the growing regionalization of pediatric care. We propose a preliminary pediatric sepsis event surveillance definition and outline next steps for refining and validating these criteria so that they may be used to estimate the national burden of pediatric sepsis and support site-specific surveillance to complement ongoing initiatives to improve sepsis prevention, recognition, and treatment.

We sought within an ambulatory safety study to understand if the Revised Safer Dx instrument may be helpful in identification of diagnostic missed opportunities in care of children with type 1 diabetes (T1D) and autism spectrum disorder (ASD). We reviewed two months of emergency department (ED) encounters for all patients at our tertiary care site with T1D and a sample of such encounters for patients with ASD over a 15-month period, and their pre-visit communication methods to better understand opportunities to improve diagnosis. We applied the Revised Safer Dx instrument to each diagnostic journey. We chose potentially preventable ED visits for hyperglycemia, diabetic ketoacidosis, and behavioral crises, and reviewed electronic health record data over the prior three months related to the illness that resulted in the ED visit. We identified 63 T1D and 27 ASD ED visits. Using the Revised Safer Dx instrument, we did not identify any potentially missed opportunities to improve diagnosis in T1D. We found two potential missed opportunities (Safer Dx overall score of 5) in ASD, related to potential for ambulatory medical management to be improved. Over this period, 40 % of T1D and 52 % of ASD patients used communication prior to the ED visit. Using the Revised Safer Dx instrument, we uncommonly identified missed opportunities to improve diagnosis in patients who presented to the ED with potentially preventable complications of their chronic diseases. Future researchers should consider prospectively collected data as well as development or adaptation of tools like the Safer Dx. [ABSTRACT FROM AUTHOR]

Diagnostic uncertainty is not reliably communicated to patients and caregivers. This study aims to identify barriers and facilitators to effective communication of diagnostic uncertainty, including development of potential tools and strategies for improvement, as perceived by healthcare professionals and caregivers. We completed structured interviews with providers and caregivers of hospitalized children with uncertain diagnoses (UD). The interview guides addressed barriers to communication, key components for communication of uncertainty, and qualities of effective communication. The interviews concluded with respondents prioritizing potential interventions to improve communication of uncertainty. Interviews were audio recorded, transcribed, and independently analyzed by two team members to identify common themes. Ten provider and five caregiver interviews were conducted. Common barriers to communication of uncertainty included time constraints, language barriers, and lack of clear definition of UD. Caregiver suggestions for improvement included sharing expectations of the diagnostic process and use of both written and visual communication tools. Interview respondents favored interventions of a sign summarizing the key components of diagnostic uncertainty for display in patient rooms and a structured diagnostic pause during daily rounds. We identified several potential interventions that may enhance communication of diagnostic uncertainty and better engage patients and caregivers in the diagnostic process. [ABSTRACT FROM AUTHOR]

Diagnostic uncertainty is prevalent throughout medicine and significantly impacts patient care, especially when it goes unrecognized. However, we lack a reliable clinical means of identifying uncertainty. This study evaluates the narrative discourse within clinical notes in the Electronic Health Record as a means of identifying diagnostic uncertainty. Recognizing that discourse producers use language 'semi-automatically' (Partington et al. 2013), we hypothesized that clinicians include distinct indications of uncertainty in their written assessments, which could be elucidated by linguistic analysis. Using a cohort of patients prospectively identified as having an uncertain diagnosis (UD), we conducted a detailed corpus-assisted discourse analysis. The analysis revealed a set of linguistic indicators constitutive of diagnostic uncertainty including terms of modality, register-specific terms, and linguistically identifiable clinical behaviours. This dictionary of UD indicators was thoroughly tested, and its performance was compared with a matched-control dataset. Based on the findings, we built a machine learning classification algorithm with the ability to predict UD patient cohorts with 87.0% accuracy, effectively demonstrating the feasibility of using clinical discourse to classify patients and directly impact the clinical environment. [ABSTRACT FROM AUTHOR]

Background: A quality improvement initiative at our institution resulted in a new process for prospectively identifying pediatric hospital medicine (PHM) patients with uncertain diagnoses (UD). This study describes the clinical characteristics and healthcare utilization patterns of patients with UD. Methods: This single center cross-sectional study included all PHM patients identified with UD during their admission. A structured chart review was used to abstract patient demographics, primary symptoms, discharge diagnoses, and healthcare utilization patterns, including consult service use, length of stay (LOS), escalation in care, and 30-day healthcare reutilization. Appropriate descriptive statistics were used for categorical and continuous variables. Results: This study includes 200 PHM patients identified with UD. Gastrointestinal symptoms were the primary finding in 45% of patients with UD. Consult service use was highly variable, with a range of 0–8 consult services for individual patients. The median LOS was 1.6 days and only 5% required a rapid response team evaluation. As for reutilization, 7% of patients were readmitted within 30 days. Conclusions: This descriptive study highlights the heterogeneity of patients with uncertain diagnoses. Ongoing work is needed to further understand the impact of UD and to optimize the care of these patients. [ABSTRACT FROM AUTHOR]

Importance: In the US, states enacted nonpharmaceutical interventions, including school closure, to reduce the spread of coronavirus disease 2019 (COVID-19). All 50 states closed schools in March 2020 despite uncertainty if school closure would be effective.Objective: To determine if school closure and its timing were associated with decreased COVID-19 incidence and mortality.Design, Setting, and Participants: US population-based observational study conducted between March 9, 2020, and May 7, 2020, using interrupted time series analyses incorporating a lag period to allow for potential policy-associated changes to occur. To isolate the association of school closure with outcomes, state-level nonpharmaceutical interventions and attributes were included in negative binomial regression models. States were examined in quartiles based on state-level COVID-19 cumulative incidence per 100 000 residents at the time of school closure. Models were used to derive the estimated absolute differences between schools that closed and schools that remained open as well as the number of cases and deaths if states had closed schools when the cumulative incidence of COVID-19 was in the lowest quartile compared with the highest quartile.Exposures: Closure of primary and secondary schools.Main Outcomes and Measures: COVID-19 daily incidence and mortality per 100 000 residents.Results: COVID-19 cumulative incidence in states at the time of school closure ranged from 0 to 14.75 cases per 100 000 population. School closure was associated with a significant decline in the incidence of COVID-19 (adjusted relative change per week, -62% [95% CI, -71% to -49%]) and mortality (adjusted relative change per week, -58% [95% CI, -68% to -46%]). Both of these associations were largest in states with low cumulative incidence of COVID-19 at the time of school closure. For example, states with the lowest incidence of COVID-19 had a -72% (95% CI, -79% to -62%) relative change in incidence compared with -49% (95% CI, -62% to -33%) for those states with the highest cumulative incidence. In a model derived from this analysis, it was estimated that closing schools when the cumulative incidence of COVID-19 was in the lowest quartile compared with the highest quartile was associated with 128.7 fewer cases per 100 000 population over 26 days and with 1.5 fewer deaths per 100 000 population over 16 days.Conclusions and Relevance: Between March 9, 2020, and May 7, 2020, school closure in the US was temporally associated with decreased COVID-19 incidence and mortality; states that closed schools earlier, when cumulative incidence of COVID-19 was low, had the largest relative reduction in incidence and mortality. However, it remains possible that some of the reduction may have been related to other concurrent nonpharmaceutical interventions. [ABSTRACT FROM AUTHOR]