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Background Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden. Objective (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics. Materials and Methods We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016–2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric. Results Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden. Conclusion Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.

Background Increased adoption of electronic health records (EHR) with integrated clinical decision support (CDS) systems has reduced some sources of error but has led to unintended consequences including alert fatigue. The “pop-up” or interruptive alert is often employed as it requires providers to acknowledge receipt of an alert by taking an action despite the potential negative effects of workflow interruption. We noted a persistent upward trend of interruptive alerts at our institution and increasing requests for new interruptive alerts. Objectives Using Institute for Healthcare Improvement (IHI) quality improvement (QI) methodology, the primary objective was to reduce the total volume of interruptive alerts received by providers. Methods We created an interactive dashboard for baseline alert data and to monitor frequency and outcomes of alerts as well as to prioritize interventions. A key driver diagram was developed with a specific aim to decrease the number of interruptive alerts from a baseline of 7,250 to 4,700 per week (35%) over 6 months. Interventions focused on the following key drivers: appropriate alert display within workflow, clear alert content, alert governance and standardization, user feedback regarding overrides, and respect for user knowledge. Results A total of 25 unique alerts accounted for 90% of the total interruptive alert volume. By focusing on these 25 alerts, we reduced interruptive alerts from 7,250 to 4,400 per week. Conclusion Systematic and structured improvements to interruptive alerts can lead to overall reduced interruptive alert burden. Using QI methods to prioritize our interventions allowed us to maximize our impact. Further evaluation should be done on the effects of reduced interruptive alerts on patient care outcomes, usability heuristics on cognitive burden, and direct feedback mechanisms on alert utility.

Burnout is a response to sustained job stressors manifesting as a classic triad of emotional exhaustion, depersonalization, and a sense of reduced accomplishment. With 42% of physicians demonstrating some symptoms of burnout, this has already reached epidemic proportions. The COVID-19 pandemic has only worsened this phenomenon.

Objective: To evaluate the safety of computerized physician order entry (CPOE) and associated clinical decision support (CDS) systems in electronic health record (EHR) systems at pediatric inpatient facilities in the US using the Leapfrog Group’s pediatric CPOE evaluation tool.Methods: The Leapfrog pediatric CPOE evaluation tool, a previously validated tool to assess the ability of a CPOE system to identify orders that could potentially lead to patient harm, was used to evaluate 41 pediatric hospitals over a 2-year period. Evaluation of the last available test for each institution was performed, assessing performance overall as well as by decision support category (eg, drug-drug, dosing limits). Longitudinal analysis of test performance was also carried out to assess the impact of testing and the overall trend of CPOE performance in pediatric hospitals.Results: Pediatric CPOE systems were able to identify 62% of potential medication errors in the test scenarios, but ranged widely from 23–91% in the institutions tested. The highest scoring categories included drug-allergy interactions, dosing limits (both daily and cumulative), and inappropriate routes of administration. We found that hospitals with longer periods since their CPOE implementation did not have better scores upon initial testing, but after initial testing there was a consistent improvement in testing scores of 4 percentage points per year.Conclusions: Pediatric computerized physician order entry (CPOE) systems on average are able to intercept a majority of potential medication errors, but vary widely among implementations. Prospective and repeated testing using the Leapfrog Group’s evaluation tool is associated with improved ability to intercept potential medication errors.

O RIGINAL C ONTRIBUTION Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes Son Doan, PhD, Cleo K. Maehara, MD, MMSc, Juan D. Chaparro, MD, Sisi Lu, MS, Ruiling Liu, MS, Amanda Graham, MPH, Erika Berry, Chun-Nan Hsu, PhD, John T. Kanegaye, MD, David D. Lloyd, MD, Lucila Ohno-Machado, MD, Jane C. Burns, MD, Adriana H. Tremoulet, MD, MAS, and the Pediatric Emergency Medicine Kawasaki Disease Research Group Abstract Objective: Delayed diagnosis of Kawasaki disease (KD) may lead to serious cardiac complications. We sought to create and test the performance of a natural language processing (NLP) tool, the KD-NLP, in the identification of emergency department (ED) patients for whom the diagnosis of KD should be considered. Methods: We developed an NLP tool that recognizes the KD diagnostic criteria based on standard clinical terms and medical word usage using 22 pediatric ED notes augmented by Unified Medical Language System vocabulary. With high suspicion for KD defined as fever and three or more KD clinical signs, KD-NLP was applied to 253 ED notes from children ultimately diagnosed with either KD or another febrile illness. We evaluated KD-NLP performance against ED notes manually reviewed by clinicians and compared the results to a simple keyword search. Results: KD-NLP identified high-suspicion patients with a sensitivity of 93.6% and specificity of 77.5% compared to notes manually reviewed by clinicians. The tool outperformed a simple keyword search (sensitivity = 41.0%; specificity = 76.3%). Conclusions: KD-NLP showed comparable performance to clinician manual chart review for identification of pediatric ED patients with a high suspicion for KD. This tool could be incorporated into the ED electronic health record system to alert providers to consider the diagnosis of KD. KD-NLP could serve as a model for decision support for other conditions in the ED. ACADEMIC EMERGENCY MEDICINE 2016;23:628–636 © 2016 by the Society for Academic Emergency Medicine From the Department of Biomedical Informatics, University of California (SD, CKM, JDC, CNH, LOM), San Diego, CA; the Depart- ment of Computer Science, University of Pittsburgh (SL), Pittsburgh, PA; The University of Texas Health Science Center at Hous- ton (RL), Houston, TX; Children’s Healthcare of Atlanta (AG, DDL), Atlanta, GA; the Department of Pediatrics, University of California at San Diego (EB, JTK, JCB, AHT, PEMKDRG), La Jolla, CA; Rady Children’s Hospital San Diego (JTK, JCB, AHT, PEMKDRG), San Diego, CA; and the Emory University School of Medicine (DDL), Atlanta, GA. Members of the Pediatric Emergency Medicine Kawasaki Disease Research Group included Lindsay T. Grubensky, RN, MSN, CPNP-PC, Jim R. Harley, MD, MPH, Paul Ishimine, MD, Jamie Lien, MD, Simon J. Lucio, MD, Seema Shah, MD, and Stacey Ulrich, MD. Received August 4, 2015; revision received November 29, 2015; accepted December 30, 2015. Part of this work was presented in poster format at the Pediatric Academic Societies Annual Meeting in San Diego, CA, on April This work was supported in part by the Patient-Centered Outcomes Research Institute (PCORI), contract CDRN-1306-04819 and NIH grant U54HL108460. The authors have no potential conflicts to disclose. Supervising Editor: Damon Kuehl, MD. Address for correspondence and reprints: Adriana H. Tremoulet; e-mail: atremoulet@ucsd.edu. ISSN 1069-6563 PII ISSN 1069-6563583 © 2016 by the Society for Academic Emergency Medicine doi: 10.1111/acem.12925

Introduction: Delays in hospital discharge can negatively impact patient care, bed availability, and patient satisfaction. There are limited studies examining how the electronic health record (EHR) can be used to improve discharge timeliness. This study aimed to implement an EHR discharge optimization tool (DOT) successfully and achieve a discharge before noon (DBN) percentage of 25%. Methods: We conducted a single-center quality improvement study of patients discharged from 3 pediatric hospital medicine teaching service teams at a quaternary care academic children’s hospital. The multidisciplinary team created a DOT centrally embedded within the care team standard workflow to communicate anticipated time until discharge. The primary outcome was the monthly percentage of patients discharged before noon. Secondary outcomes included provider utilization of the DOT, tool accuracy, and patient length of stay. Balancing measures were 7- and 30-day readmission rates. Results: The DBN percentage increased from 16.4% to an average of 19.3% over the 13-month intervention period (P = 0.0005). DOT utilization was measured at 87.2%, and the overall accuracy of predicting time until discharge was 75.6% (P < 0.0001). Median length of stay declined from 1.75 to 1.68 days (P = 0.0033), and there was no negative impact on 7- or 30-day readmission rates. Conclusion: This initiative demonstrated that a highly utilized and accurate discharge tool could be created in the EHR to assist medical care teams with improving DBN percentage on busy, academic teaching services.

Background: Childhood obesity is a significant problem, with prevalence rates as high as 17%. In addition to long-term health effects such as metabolic syndrome and coronary artery disease, pediatric obesity also presents immediate health and safety risks such as operative complications, difficult airways and medication dosing errors. Large-scale pediatric databases such as the Pediatric Health Information System, drawn primarily from ICD-9/10 codes, may significantly underestimate the prevalence of obesity in the pediatric inpatient and outpatient setting. One recent study in hospitalized children found that discharge ICD-9 codes underestimated the …

Parasitic diseases, previously thought to be the domain of developing nations only, are being increasingly recognized as a significant source of morbidity/mortality in developed and developing nations alike. While many parasites cause long-term low-level disease, some parasitic diseases can have severe manifestations necessitating intensive care unit admission. In this review, we focus on severe malaria (including cerebral malaria), neurocysticercosis, Strongyloides hyperinfection syndrome, American trypanosomiasis (Chagas disease), baylisascariasis and amoebic meningoencephalitis.