Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts.

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

Burnout in clinicians.

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.

Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics.

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.

Improving Early Discharges With an Electronic Health Record Discharge Optimization Tool.

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.

Reducing Interruptive Alert Burden Using Quality Improvement Methodology.

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.

Two key cathepsins, TgCPB and TgCPL, are targeted by the vinyl sulfone inhibitor K11777 in in vitro and in vivo models of toxoplasmosis.

Although toxoplasmosis is one of the most common parasitic infections worldwide, therapeutic options remain limited. Cathepsins, proteases that play key roles in the pathogenesis of toxoplasmosis and many other protozoan infections, are important potential therapeutic targets. Because both TgCPB and TgCPL play a role in T. gondii invasion, we evaluated the efficacy of the potent, irreversible vinyl sulfone inhibitor, K11777 (N-methyl-piperazine-Phe-homoPhe-vinylsulfone-phenyl). The inhibitor's toxicity and pharmacokinetic profile have been well-studied because of its in vitro and in vivo activity against a number of parasites. We found that it inhibited both TgCPB (EC50 = 114 nM) and TgCPL (EC50 = 71 nM) in vitro. K11777 also inhibited invasion of human fibroblasts by RH tachyzoites by 71% (p = 0.003) and intracellular replication by >99% (p<0.0001). In vivo, a single dose of K11777 led to 100% survival of chicken embryos in an model of acute toxoplasmosis (p = 0.015 Cox regression analysis). Therefore, K11777 shows promise as a novel therapeutic agent in the treatment of toxoplasmosis, and may prove to be a broadly effective anti-parasitic agent.

Low Infection-Related Re-Admission Rates in a Retrospective of 4725 Children with Appendicitis Using a Clinical Pathway in a Tertiary Care Pediatric Center.

BACKGROUND: Standardization of antibiotic management of appendicitis in tertiary care pediatric centers has been associated with improved outcomes. Rady Children's Hospital-San Diego implemented an appendicitis clinical pathway in 2005. We evaluated infection-related re-admission risk factors since 2010, when an electronic medical record was established, with the aim to optimize the clinical pathway. METHODS: Between January 2010 and August 2015, 4725 children with a diagnosis of appendicitis were evaluated for demographic data, pathology diagnoses, culture results, and inpatient and oral step-down antibiotic therapy regimens. From children originally admitted for appendicitis, those who were re-admitted with infection were compared with those who were not re-admitted for infection. The populations were controlled by severity of infection using a pathology-defined appendicitis severity scale: Grade 0, no appendicitis; grade 1, simple acute appendicitis with gross and microscopic evidence of inflammation, but no perforation; grade 2, gangrenous/necrotizing/micro-perforated appendicitis with subserosal or serosal exudate, but no frank or visually appreciated perforation; and grade 3, frank perforation. RESULTS: Of 4725 children (total population, TP) admitted with a diagnosis of appendicitis, only 199 (4.2%) were re-admitted, with 125 of these admissions for infection (2.65% of the TP). Age, race/ethnicity, language preference, and body mass index were not found to correlate with re-admission for infection. Length of stay significantly differed between the no infection-related re-admission population and infection-related re-admission population (3.02 vs. 4.03 d, p < 0.001). There was a trend toward higher infection-re-admission rates as the pathology grade increased (odds ratio grade 1 vs. grade 3 = 2.28, 95% confidence interval 1.03, 5.03). CONCLUSIONS: Infection-related re-admission rates for children on the clinical pathway in our institution were infrequent. The greater association of all-cause and infection-related re-admission rates with pathology grade suggest that defining appendicitis by pathology and clinical severity may provide an evidence-based scoring system to support clinical observation in the use and duration of antibiotic therapy.

How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets.

BACKGROUND: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Twitter is a microblog platform that allows users to post their own personal messages (tweets), including their expressions about feelings and actions related to stress and stress management (eg, relaxing). While Twitter is increasingly used as a source of data for understanding mental health from a population perspective, the specific issue of stress-as manifested on Twitter-has not yet been the focus of any systematic study. OBJECTIVE: The objective of our study was to understand how people express their feelings of stress and relaxation through Twitter messages. In addition, we aimed at investigating automated natural language processing methods to (1) classify stress versus nonstress and relaxation versus nonrelaxation tweets, and (2) identify first-hand experience-that is, who is the experiencer-in stress and relaxation tweets. METHODS: We first performed a qualitative content analysis of 1326 and 781 tweets containing the keywords "stress" and "relax," respectively. We then investigated the use of machine learning algorithms-in particular naive Bayes and support vector machines-to automatically classify tweets as stress versus nonstress and relaxation versus nonrelaxation. Finally, we applied these classifiers to sample datasets drawn from 4 cities in the United States (Los Angeles, New York, San Diego, and San Francisco) obtained from Twitter's streaming application programming interface, with the goal of evaluating the extent of any correlation between our automatic classification of tweets and results from public stress surveys. RESULTS: Content analysis showed that the most frequent topic of stress tweets was education, followed by work and social relationships. The most frequent topic of relaxation tweets was rest & vacation, followed by nature and water. When we applied the classifiers to the cities dataset, the proportion of stress tweets in New York and San Diego was substantially higher than that in Los Angeles and San Francisco. In addition, we found that characteristic expressions of stress and relaxation varied for each city based on its geolocation. CONCLUSIONS: This content analysis and infodemiology study revealed that Twitter, when used in conjunction with natural language processing techniques, is a useful data source for understanding stress and stress management strategies, and can potentially supplement infrequently collected survey-based stress data.

National trends in safety performance of electronic health record systems in children's hospitals.

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.

Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes.

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.

Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.

Auranofin is highly efficacious against Toxoplasma gondii in vitro and in an in vivo experimental model of acute toxoplasmosis.

BACKGROUND: The mainstay of toxoplasmosis treatment targets the folate biosynthetic pathways and has not changed for the last 50 years. The activity of these chemotherapeutic agents is restricted to one lifecycle stage of Toxoplasma gondii, they have significant toxicity, and the impending threat of emerging resistance to these agents makes the discovery of new therapies a priority. We now demonstrate that auranofin, an orally administered gold containing compound that was FDA approved for treatment of rheumatoid arthritis, has activity against Toxoplasma gondii in vitro (IC50 = 0.28 µM) and in vivo (1 mg/kg). METHODS/PRINCIPAL FINDINGS: Replication within human foreskin fibroblasts of RH tachyzoites was inhibited by auranofin. At 0.4 µM, auranofin inhibited replication, as measured by percent infected fibroblasts at 24 hrs, (10.94% vs. 24.66% of controls; p = 0.0003) with no effect on parasite invasion (16.95% vs. 12.91% p = 0.4331). After 18 hrs, 62% of extracellular parasites treated with auranofin were non-viable compared to control using an ATP viability assay (p = 0.0003). In vivo, a previously standardized chicken embryo model of acute toxoplasmosis was used. Fourteen day old chicken embryos were injected through the chorioallantoic vein with 1×104 tachyzoites of the virulent RH strain. The treatment group received one dose of auranofin at the time of inoculation (1 mg/kg estimated body weight). On day 5, auranofin-treated chicken embryos were 100% protected against death (p = 0.0002) and had a significantly reduced parasite load as determined by histopathology, immunohistochemistry and by the number of parasites quantified by real-time PCR. CONCLUSIONS: These results reveal in vitro and in vivo activity of auranofin against T. gondii, suggesting that it may be an effective alternative treatment for toxoplasmosis.

Parasitic disease in the pediatric intensive care unit - A review of severe manifestations of pediatric parasitic disease.

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.