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Study Objective: The workload of clinical documentation contributes to health care costs and professional burnout. The advent of generative artificial intelligence language models presents a promising solution. The perspective of clinicians may contribute to effective and responsible implementation of such tools. This study sought to evaluate 3 uses for generative artificial intelligence for clinical documentation in pediatric emergency medicine, measuring time savings, effort reduction, and physician attitudes and identifying potential risks and barriers., Methods: This mixed-methods study was performed with 10 pediatric emergency medicine attending physicians from a single pediatric emergency department. Participants were asked to write a supervisory note for 4 clinical scenarios, with varying levels of complexity, twice without any assistance and twice with the assistance of ChatGPT Version 4.0. Participants evaluated 2 additional ChatGPT-generated clinical summaries: a structured handoff and a visit summary for a family written at an 8th grade reading level. Finally, a semistructured interview was performed to assess physicians' perspective on the use of ChatGPT in pediatric emergency medicine. Main outcomes and measures included between subjects' comparisons of the effort and time taken to complete the supervisory note with and without ChatGPT assistance. Effort was measured using a self-reported Likert scale of 0 to 10. Physicians' scoring of and attitude toward the ChatGPT-generated summaries were measured using a 0 to 10 Likert scale and open-ended questions. Summaries were scored for completeness, accuracy, efficiency, readability, and overall satisfaction. A thematic analysis was performed to analyze the content of the open-ended questions and to identify key themes., Results: ChatGPT yielded a 40% reduction in time and a 33% decrease in effort for supervisory notes in intricate cases, with no discernible effect on simpler notes. ChatGPT-generated summaries for structured handoffs and family letters were highly rated, ranging from 7.0 to 9.0 out of 10, and most participants favored their inclusion in clinical practice. However, there were several critical reservations, out of which a set of general recommendations for applying ChatGPT to clinical summaries was formulated., Conclusion: Pediatric emergency medicine attendings in our study perceived that ChatGPT can deliver high-quality summaries while saving time and effort in many scenarios, but not all., (Copyright © 2024 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Competing Interests: Funding Support and Author Disclosures The authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Background: The management of the SARS-CoV-2 pandemic depends amongst other factors on disease prevalence in the general population. The gap between the true rate of infection and the detected rate of infection may vary, especially between sub-groups of the population. Identifying subpopulations with high rates of undetected infection can guide authorities to direct resource distribution in order to improve health equity., Methods: A cross-sectional epidemiological survey was conducted between April and July 2021 in the Pediatric Emergency Department of the Shaare Zedek Medical Center, Jerusalem, Israel. We compared three categories: unconfirmed disease (UD), positive serology test result with no history of positive PCR; confirmed disease (CD), history of a positive PCR test result, regardless of serology test result; and no disease (ND), negative serology and no history of PCR. These categories were applied to local prevailing subpopulations: ultra-orthodox Jews (UO), National Religious Jews (NRJ), secular Jews (SJ), and Muslim Arabs (MA)., Results: Comparing the different subpopulations groups, MAs and UOs had the greatest rate of confirmed or unconfirmed disease. MA had the highest rate of UD and UO had the highest rate of CD. UD significantly correlated with ethnicity, with a low prevalence in NRJ and SJ. UD was also associated with larger family size and housing density defined as family size per number of rooms., Conclusion: This study highlights the effect of ethnicity on disease burden. These findings should serve to heighten awareness to disease burden in weaker populations and direct a suitable prevention program to each subpopulation's needs. Early awareness and possible intervention may lower morbidity and mortality., (© 2024. W. Montague Cobb-NMA Health Institute.)

Background: Maldistribution of pulmonary blood flow in patients with congenital heart disease impacts exertional performance and pulmonary artery growth. Currently, measurement of relative pulmonary perfusion can only be performed outside the catheterization laboratory. We sought to develop a tool for measuring relative lung perfusion using readily available fluoroscopy sequences., Methods: A retrospective cohort study was conducted on patients with conotruncal anomalies who underwent lung perfusion scans and subsequent cardiac catheterizations between 2011 and 2022. Inclusion criteria were nonselective angiogram of pulmonary vasculature, oblique angulation ≤20°, and an adequate view of both lung fields. A method was developed and implemented in 3D Slicer's SlicerHeart extension to calculate the amount of contrast that entered each lung field from the start of contrast injection and until the onset of levophase. The predicted perfusion distribution was compared with the measured distribution of pulmonary blood flow and evaluated for correlation, accuracy, and bias., Results: In total, 32% (79/249) of screened studies met the inclusion criteria. A strong correlation between the predicted flow split and the measured flow split was found ( R 2 =0.83; P <0.001). The median absolute error was 6%, and 72% of predictions were within 10% of the true value. Bias was not systematically worse at either extreme of the flow distribution. The prediction was found to be more accurate for either smaller and younger patients (age 0-2 years), for right ventricle injections, or when less cranial angulations were used (≤20°). In these cases (n=40), the prediction achieved R 2 =0.87, median absolute error of 5.5%, and 78% of predictions were within 10% of the true flow., Conclusions: The current study demonstrates the feasibility of a novel method for measuring relative lung perfusion using conventional angiograms. Real-time measurement of lung perfusion at the catheterization laboratory has the potential to reduce unnecessary testing, associated costs, and radiation exposure. Further optimization and validation is warranted., Competing Interests: Disclosures None.

Objective: To determine whether data-driven family histories (DDFH) derived from linked EHRs of patients and their parents can improve prediction of patients' 10-year risk of diabetes and atherosclerotic cardiovascular disease (ASCVD)., Materials and Methods: A retrospective cohort study using data from Israel's largest healthcare organization. A random sample of 200 000 subjects aged 40-60 years on the index date (January 1, 2010) was included. Subjects with insufficient history (<1 year) or insufficient follow-up (<10 years) were excluded. Two separate XGBoost models were developed-1 for diabetes and 1 for ASCVD-to predict the 10-year risk for each outcome based on data available prior to the index date of January 1, 2010., Results: Overall, the study included 110 734 subject-father-mother triplets. There were 22 153 cases of diabetes (20%) and 11 715 cases of ASCVD (10.6%). The addition of parental information significantly improved prediction of diabetes risk (P < .001), but not ASCVD risk. For both outcomes, maternal medical history was more predictive than paternal medical history. A binary variable summarizing parental disease state delivered similar predictive results to the full parental EHR., Discussion: The increasing availability of EHRs for multiple family generations makes DDFH possible and can assist in delivering more personalized and precise medicine to patients. Consent frameworks must be established to enable sharing of information across generations, and the results suggest that sharing the full records may not be necessary., Conclusion: DDFH can address limitations of patient self-reported family history, and it improves clinical predictions for some conditions, but not for all, and particularly among younger adults., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Evaluate Piccolo and ADOII devices for transcatheter patent ductus arteriosus (PDA) closure. Piccolo has smaller retention discs reducing risk of flow disturbance but residual leak and embolization risk may increase., Methods: Retrospective review of all patients undergoing PDA closure with an Amplatzer device between January 2008 and April 2022 in our institution. Data from the procedure and 6 months follow-up were collected., Results: 762 patients, median age 2.6 years (range 0-46.7) years and median weight 13 kg (range 3.5-92) were referred for PDA closure. Overall, 758 (99.5%) had successful implantation: 296 (38.8%) with ADOII, 418 (54.8%) with Piccolo, and 44 (5.8%) with AVPII. The ADOII patients were smaller than the Piccolo patients (15.8 vs. 20.5 kg, p  < 0.001) and with larger PDA diameters (2.3 vs. 1.9 mm, p  < 0.001). Mean device diameter was similar for both groups. Closure rate at follow-up was similar for all devices ADOII 295/296 (99.6%), Piccolo 417/418 (99.7%), and AVPII 44/44 (100%). Four intraprocedural embolizations occurred during the study time period: two ADOII and two Piccolo. Following retrieval the PDA was closed with an AVPII in two cases, ADOI in one case and with surgery in the fourth case. Mild stenosis of the left pulmonary artery (LPA) occurred in three patients with ADOII devices (1%) and one patient with Piccolo device (0.2%). Severe LPA stenosis occurred in one patient with ADOII (0.3%) and one with AVPII device (2.2%)., Conclusions: ADOII and Piccolo are safe and effective for PDA closure with a tendency to less LPA stenosis with Piccolo. There were no cases of aortic coarctation related to a PDA device in this study., Competing Interests: EB is a proctor for Abbott Vascular. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Bruckheimer, Steiner, Barak-Corren, Slanovic, Levinzon, Lowenthal, Amir, Dagan and Birk.)

Growing evidence has shown that applying machine learning models to large clinical data sources may exceed clinician performance in suicide risk stratification. However, many existing prediction models either suffer from "temporal bias" (a bias that stems from using case-control sampling) or require training on all available patient visit data. Here, we adopt a "landmark model" framework that aligns with clinical practice for prediction of suicide-related behaviors (SRBs) using a large electronic health record database. Using the landmark approach, we developed models for SRB prediction (regularized Cox regression and random survival forest) that establish a time-point (e.g., clinical visit) from which predictions are made over user-specified prediction windows using historical information up to that point. We applied this approach to cohorts from three clinical settings: general outpatient, psychiatric emergency department, and psychiatric inpatients, for varying prediction windows and lengths of historical data. Models achieved high discriminative performance (area under the Receiver Operating Characteristic curve 0.74-0.93 for the Cox model) across different prediction windows and settings, even with relatively short periods of historical data. In short, we developed accurate, dynamic SRB risk prediction models with the landmark approach that reduce bias and enhance the reliability and portability of suicide risk prediction models., Competing Interests: Declaration of Competing Interest Dr. Smoller is a member of the the Scientific Advisory Board of Sensorium Therapeutics (with equity), and has received grant support from Biogen, Inc. He is PI of a collaborative study of the genetics of depression and bipolar disorder sponsored by 23andMe for which 23andMe provides analysis time as in-kind support but no payments., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Several recent studies have applied machine learning techniques to develop risk algorithms that predict subsequent suicidal behavior based on electronic health record data. In this study we used a retrospective cohort study design to test whether developing more tailored predictive models-within specific subpopulations of patients-would improve predictive accuracy. A retrospective cohort of 15,117 patients diagnosed with multiple sclerosis (MS), a diagnosis associated with increased risk of suicidal behavior, was used. The cohort was randomly divided into equal sized training and validation sets. Overall, suicidal behavior was identified among 191 (1.3%) of the patients with MS. A Naïve Bayes Classifier model was trained on the training set to predict future suicidal behavior. With 90% specificity, the model detected 37% of subjects who later demonstrated suicidal behavior, on average 4.6 years before the first suicide attempt. The performance of a model trained only on MS patients was better at predicting suicide in MS patients than that a model trained on a general patient sample of a similar size (AUC of 0.77 vs. 0.66). Unique risk factors for suicidal behavior among patients with MS included pain-related codes, gastroenteritis and colitis, and history of smoking. Future studies are needed to further test the value of developing population-specific risk models., Competing Interests: NO authors have competing interests., (Copyright: © 2023 Barak-Corren et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Objectives: To determine the rate of serious-bacterial-infections (SBI) in young ex-premature infants with fever, and to develop a risk-stratification algorithm for these patients., Methods: A retrospective cohort study including all infants who presented to the pediatric emergency department (ED) of a tertiary-care university-hospital between 2010 and 2020 with fever (≥38°C), were born prematurely (<37-weeks), had post-conception age of <52-weeks, and had available blood, urine, or CSF cultures. The rates of SBI by age-of-birth and age-at-visit were calculated and compared to a cohort of matched full-term controls., Results: The study included a total of 290 ex-premature cases and 290 full-term controls. There were 11 cases (3.8%) with an invasive bacterial infection (IBI) of either bacteremia, meningitis or both and only six controls (2.1%) with IBI ( p  = 0.32). Over 28-days chronologic-age, there were 10 (3.6%) IBIs among cases and no IBIs among the controls ( p  = 0.02). There were eight (3%) cases and three (1%) controls with IBI who were well-appearing on physical examination ( p  = 0.19). All eight well-appearing ex-premature infants were under 60-days adjusted-age, seven of whom (88%) were also under 28-days adjusted-age. There were 28 (10.6%) cases and 34 (12%) controls with urinary tract infection (UTI) ( p  = 0.5). Among cases under 60-days adjusted-age, urinalysis was not reliable to exclude UTI (50% negative)., Conclusions: Well-appearing ex-preterm infants have a significant risk for IBI until the adjusted age of 28-days and for UTI until the adjusted age of 60-days. Further studies are needed to evaluate the approach to fever in this unique population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Barak-Corren, Elizur, Yuval, Burstyn, Deri, Schwartz, Megged and Toker.)

Objective: Routine use of central venous access is needed in children undergoing open heart surgery for pressure monitoring and inotrope infusion. We sought to evaluate the efficiency and safety of routine use of transthoracic intracardiac lines (ICLs) in patients undergoing cardiac surgery and to compare them to patients who have been previously treated with traditional central venous lines (non-ICLs)., Methods: Retrospective review of charts of all patients who underwent cardiac surgery and had an ICL inserted in the operating room. Case control matching was done with similar patient in which ICL was not inserted. Patients characteristics, diagnosis, operative, and intensive care data were collected for each patient and analyzed., Results: A total number of 376 patient records were reviewed (198 ICL patients and 178 non-ICL patients). Umbilical line and non-ICL durations were longer in the non-ICL group. ICL duration was the longest of all lines, averaging 12.87 ± 10.82 days. The necessity for multiple line insertions (˃2 insertions) was significantly higher in the non-ICL group, with a relative risk ratio of 3.24 (95% confidence interval: 1.617-6.428). There was no statistical difference of infections rate and line complications between the two groups., Conclusion: ICLs are safe in infants undergoing cardiac surgery and can be kept in place for a long period of time with a low rate of line complications and infection. Routine use of ICLs reduces the number of central venous catheter placement in this complex patient population., (© 2022 Wiley Periodicals LLC.)

Temporal association between BNT162b2 mRNA COVID-19 vaccine and myocarditis (PCVM) has been reported. We herein present early and 6-month clinical follow-up and cardiac magnetic resonance imaging (CMR) of patients with PVCM. A retrospective collection of data from 15 patients with PCVM and abnormal CMR was performed. Clinical manifestation, laboratory data, hospitalizations, treatment protocols, and imaging studies were collected early (up to 2 months) and later. In nine patients, an additional CMR evaluation was performed 6 months after diagnosis. PCVM was diagnosed in 15 patients, mean age 17 ± 1 (median 17.2, range 14.9-19 years) years, predominantly in males. Mean time from vaccination to onset of symptoms was 4.4 ± 6.7 (median 3, range 0-28) days. All patients had CMR post diagnosis at 4 ± 3 (median 3, range 1-9) weeks, 4/5 patients had hyper enhancement on the T2 sequences representing edemaQuery, and 12 pathological Late glandolinium enhancement. A repeat scan performed after 5-6 months was positive for scar formation in 7/9 patients. PCVM is a rare complication, affecting predominantly males and appearing usually within the first week after administration of the second dose of the vaccine. It usually is a mild disease, with clinical resolution with anti-inflammatory treatment. Late CMR follow up demonstrated resolution of the edema in all patients, while some had evidence of residual myocardial scarring., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: Brain imaging for suspected significant head injuries in pediatric emergency departments is an important and time-sensitive procedure. The use of sedation to successfully complete imaging can be limited due to young age and other injury related factors. Using a non-pharmacological method using feeding and swaddling can be used. This may obviate the need for sedation but can be time consuming., Methods: A retrospective study of all children undergoing brain imaging for head injury during the years 2016-2021. Use of sedation, time to completion and imaging findings were compared., Results: Of 281 children requiring brain imaging, 268 (95.4%) were completed using the feed and swaddle method. Time to imaging completion was similar between sedation and feed and swaddle groups (85.5 min vs. 86 min). Abnormal findings on imaging were found in 186 (69.4%) in the feed and swaddle group and in 10 (77%) of the sedation group. No adverse events were seen in the sedation group., Conclusion: Using the feed and swaddle method can help lower the need for sedation in the under 1 year age group with a successful and timely completion of brain imaging., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background & Aims: Studies have shown decreased response to coronavirus disease 2019 (COVID-19) vaccinations in some populations. In addition, it is possible that vaccine-triggered immune activation could trigger immune dysregulation and thus exacerbate inflammatory bowel diseases (IBD). In this population-based study we used the epi-Israeli IBD Research Nucleus validated cohort to explore the effectiveness of COVID-19 vaccination in IBD and to assess its effect on disease outcomes., Methods: We included all IBD patients insured in 2 of the 4 Israeli health maintenance organizations, covering 35% of the population. Patients receiving 2 Pfizer-BioNTech BNT162b2 vaccine doses between December 2020 and June 2021 were individually matched to non-IBD controls. To assess IBD outcomes, we matched vaccinated to unvaccinated IBD patients, and response was analyzed per medical treatment., Results: In total, 12,109 IBD patients received 2 vaccine doses, of whom 4946 were matched to non-IBD controls (mean age, 51 ± 16 years; median follow-up, 22 weeks; interquartile range, 4-24). Fifteen patients in each group (0.3%) developed COVID-19 after vaccination (odds ratio, 1; 95% confidence interval, 0.49-2.05; P = 1.0). Patients on tumor necrosis factor (TNF) inhibitors and/or corticosteroids did not have a higher incidence of infection. To explore IBD outcomes, 707 vaccinated IBD patients were compared with unvaccinated IBD patients by stringent matching (median follow-up, 14 weeks; interquartile range, 2.3-20.4). The risk of exacerbation was 29% in the vaccinated patients compared with 26% in unvaccinated patients (P = .3)., Conclusions: COVID-19 vaccine effectiveness in IBD patients is comparable with that in non-IBD controls and is not influenced by treatment with TNF inhibitors or corticosteroids. The IBD exacerbation rate did not differ between vaccinated and unvaccinated patients., (Copyright © 2022. Published by Elsevier Inc.)

Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p < 0.001), likely due to the RF model's ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts., (© 2022. The Author(s).)

Importance: Half of the people who die by suicide make a health care visit within 1 month of their death. However, clinicians lack the tools to identify these patients., Objective: To predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems., Design, Setting, and Participants: This prognostic study assessed the 1-month and 6-month risk of suicide attempts among 1818 patients presenting to an ED between February 4, 2015, and March 13, 2017, with psychiatric problems. Data analysis was performed from May 1, 2020, to November 19, 2021., Main Outcomes and Measures: Suicide attempts 1 and 6 months after presentation to the ED were defined by combining data from electronic health records (EHRs) with patient 1-month (n = 1102) and 6-month (n = 1220) follow-up surveys. Ensemble machine learning was used to develop predictive models and a risk score for suicide., Results: A total of 1818 patients participated in this study (1016 men [55.9%]; median age, 33 years [IQR, 24-46 years]; 266 Hispanic patients [14.6%]; 1221 non-Hispanic White patients [67.2%], 142 non-Hispanic Black patients [7.8%], 64 non-Hispanic Asian patients [3.5%], and 125 non-Hispanic patients of other race and ethnicity [6.9%]). A total of 137 of 1102 patients (12.9%; weighted prevalence) attempted suicide within 1 month, and a total of 268 of 1220 patients (22.0%; weighted prevalence) attempted suicide within 6 months. Clinicians' assessment alone was little better than chance at predicting suicide attempts, with externally validated area under the receiver operating characteristic curve (AUC) of 0.67 for the 1-month model and 0.60 for the 6-month model. Prediction accuracy was slightly higher for models based on EHR data (1-month model: AUC, 0.71; 6 month model: AUC, 0.65) and was best using patient self-reports (1-month model: AUC, 0.76; 6-month model: AUC, 0.77), especially when patient self-reports were combined with EHR and/or clinician data (1-month model: AUC, 0.77; and 6 month model: AUC, 0.79). A model that used only 20 patient self-report questions and an EHR-based risk score performed similarly well (1-month model: AUC, 0.77; 6 month model: AUC, 0.78). In the best 1-month model, 30.7% (positive predicted value) of the patients classified as having highest risk (top 25% of the sample) made a suicide attempt within 1 month of their ED visit, accounting for 64.8% (sensitivity) of all 1-month attempts. In the best 6-month model, 46.0% (positive predicted value) of the patients classified at highest risk made a suicide attempt within 6 months of their ED visit, accounting for 50.2% (sensitivity) of all 6-month attempts., Conclusions and Relevance: This prognostic study suggests that the ability to identify patients at high risk of suicide attempt after an ED visit for psychiatric problems improved using a combination of patient self-reports and EHR data.

Objective: Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions., Materials and Methods: We propose a temporally enhanced variant of the random forest (RF) model-Omni-Temporal Balanced Random Forests (OT-BRFs)-that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs., Results: Temporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them., Discussion: We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Several approaches exist today for developing predictive models across multiple clinical sites, yet there is a lack of comparative data on their performance, especially within the context of EHR-based prediction models. We set out to provide a framework for prediction across healthcare settings. As a case study, we examined an ED disposition prediction model across three geographically and demographically diverse sites. We conducted a 1-year retrospective study, including all visits in which the outcome was either discharge-to-home or hospitalization. Four modeling approaches were compared: a ready-made model trained at one site and validated at other sites, a centralized uniform model incorporating data from all sites, multiple site-specific models, and a hybrid approach of a ready-made model re-calibrated using site-specific data. Predictions were performed using XGBoost. The study included 288,962 visits with an overall admission rate of 16.8% (7.9-26.9%). Some risk factors for admission were prominent across all sites (e.g., high-acuity triage emergency severity index score, high prior admissions rate), while others were prominent at only some sites (multiple lab tests ordered at the pediatric sites, early use of ECG at the adult site). The XGBoost model achieved its best performance using the uniform and site-specific approaches (AUC = 0.9-0.93), followed by the calibrated-model approach (AUC = 0.87-0.92), and the ready-made approach (AUC = 0.62-0.85). Our results show that site-specific customization is a key driver of predictive model performance., (© 2021. The Author(s).)

Aim: We evaluated the prevalence of paediatric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections using antibody testing and characterised antibody titres by time from exposure., Methods: This was a single-centre, prospective, cross-sectional cohort study. Patients under 18 years old were eligible to participate if they attended the paediatric emergency department at the tertiary Shaare Zedek Medical Center, Jerusalem, Israel, from 18 October 2020 to 12 January 2021 and required blood tests or intravenous access. SARS-CoV-2 seropositivity and antibody levels were tested by a dual-assay model., Results: The study comprised 1138 patients (56% male) with a mean age of 4.4 years (interquartile range 1.3-11.3). Anti-SARS-CoV-2 antibodies were found in 10% of the patients. Seropositivity increased with age and 41% of seropositive patients had no known exposure. Children under 6 years of age had higher initial antibody levels than older children, followed by a steeper decline. The seropositivity rate did not vary during the study, despite schools re-opening. The findings suggest that children's immunity may start falling 4 months after the initial infection., Conclusion: Immunity started falling after just 4 months, and re-opening schools did not affect infection rates. These findings could aid decisions about vaccinating paediatric populations and school closures., (©2021 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.)

Objective: To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician-computer models., Materials and Methods: A single-center prospective observational study in a tertiary pediatric hospital in Boston, Massachusetts, United States. Nine emergency department (ED) attending physicians participated in the study. Physicians predicted the likelihood of admission for patients in the ED whose hospitalization disposition had not yet been decided. In parallel, a random-forest computer model was developed to predict hospitalizations from the ED, based on data available within the first hour of the ED encounter. The model was tested on the same cohort of patients evaluated by the participating physicians., Results: 198 pediatric patients were considered for inclusion. Six patients were excluded due to incomplete or erroneous physician forms. Of the 192 included patients, 54 (28%) were admitted and 138 (72%) were discharged. The positive predictive value for the prediction of admission was 66% for the clinicians, 73% for the computer model, and 86% for a hybrid model combining the two. To predict admission, physicians relied more heavily on the clinical appearance of the patient, while the computer model relied more heavily on technical data-driven features, such as the rate of prior admissions or distance traveled to hospital., Discussion: Computer-generated predictions of patient disposition were more accurate than clinician-generated predictions. A hybrid prediction model improved accuracy over both individual predictions, highlighting the complementary and synergistic effects of both approaches., Conclusion: The integration of computer and clinician predictions can yield improved predictive performance., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Clinical decision-making is complex and requires the integration of multiple sources of information. Physicians tend to over-rely on objective measures, despite the lack of supportive evidence in many cases. We sought to test if pediatricians over-rely on C-reactive protein (CRP) results when managing a child with suspected pneumonia., Methods: A nationwide decision-making experiment was conducted among 337 pediatricians in Israel. Each participant was presented with two detailed vignettes of a child with suspected pneumonia, each with a chest X-ray (CXR) taken from a real-life case of viral pneumonia. Participants were randomly assigned to one of three groups: Controls-where no lab tests were provided, and two intervention groups where the vignettes also noted a high or a low CRP value, in varying orders. Between-participant and within-participant analyses were conducted to study the effect of CRP on CXR interpretation. The three groups were presented with identical medical history, vital signs, findings on physical examination, blood count, and CXR., Results: Three-hundred and one pediatricians (89.3% of those approached) completed the study. Pediatricians were 60%-90% more likely to diagnose viral pneumonia as bacterial when presented with high CRP levels versus low CRP levels, despite the identical clinical data and CXR (62% vs. 39% and 58% vs. 31% of physicians; p = .002). Accordingly, they were 60%-90% more likely to prescribe antibiotics in these cases (86% vs. 53% and 78% vs. 41% of physicians; p < .001)., Conclusions: CRP by itself may modify the way in which pediatricians interpret a CXR, leading to the overprescription of antibiotics., (© 2021 Wiley Periodicals LLC.)

Aim: To measure the prognostic value of C-reactive protein (CRP) and its ability to predict pneumonia-associated complications., Methods: A 3.75-years retrospective cohort analysis of all paediatric emergency department visits with a discharge diagnosis of pneumonia. Visits where CRP was not measured or with a discharge diagnosis of viral pneumonia were excluded. The following five outcomes were studied: hospitalisation, presence of parapneumonic effusion (PPE), placement of a chest drain, admission to paediatric intensive care unit (PICU) and bacteremia. A multivariate model was constructed and validated using k-fold cross-validation., Results: During the study time period, there were 2561 visits for pneumonia, of which 810 were included in our analysis. The median age of included children was 3.2 years (range 0.2-17.7). Overall, 38.8% visits ended in hospitalisation, 2.2% required admission to PICU, 15.2% were complicated by a PPE of which 28% required the placement of a chest drain. Statistically significant association was found between CRP levels and each of these outcomes (P < .001). Incorporating CRP within a multivariate prediction model provided an area under the curve of up to 0.96., Conclusion: CRP can be a useful prognostic marker when evaluating a patient with suspected bacterial pneumonia and could help the paediatrician in identifying patients needing closer follow-up., (© 2020 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.)

Objectives: To determine the prevalence of features of viral illness in a national sample of visits involving children tested for group A Streptococcus pharyngitis. Additionally, we sought to derive a decision rule to identify patients with features of viral illness who were at low risk of having group A Streptococcus and for whom laboratory testing might be avoided., Study Design: Retrospective validation study using data from electronic health records of patients 3-21 years old evaluated for sore throat in a national network of retail health clinics (n = 67 127). We determined the prevalence of features of viral illness in patients tested for group A Streptococcus and developed a decision tree algorithm to identify patients with features of viral illness at low risk (<15%) of having group A Streptococcus., Results: Overall, 54% of patients had features of viral illness. Among patients with features of viral illness, those without tonsillar exudates who were 11 years or older and either lacked cervical adenopathy or had cervical adenopathy and lacked fever were identified as at low risk for group A Streptococcus according to the decision rule. This group comprised 34% of patients with features of viral illness, or 19% of all patients tested for group A Streptococcus infection., Conclusions: Our findings provide an objective way to identify patients with features of viral illness who are at low risk of having group A Streptococcus. Improved identification such patients at low risk of group A Streptococcus could improve appropriate testing and antibiotic prescribing for pharyngitis., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: In order to examine the potential clinical value of integrating family history information directly from the electronic health records of patients' family members, the electronic health records of individuals in Clalit Health Services, the largest payer/provider in Israel, were linked with the records of their parents., Methods: We describe the results of a novel approach for creating data-derived family history information for 2 599 575 individuals, focusing on three chronic diseases: asthma, cardiovascular disease (CVD) and diabetes., Results: In our cohort, there were 256 598 patients with asthma, 55 309 patients with CVD and 66 324 patients with diabetes. Of the people with asthma, CVD or diabetes, the percentage that also had a family history of the same disease was 22.0%, 70.8% and 70.5%, respectively., Conclusions: Linking individuals' health records with their data-derived family history has untapped potential for supporting diagnostic and clinical decision-making., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.)

Importance: Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings., Objective: To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems., Design, Setting, and Participants: For this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019., Main Outcomes and Measures: The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site., Results: Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%]) included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance., Conclusions and Relevance: Across 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect the risk of suicidal behavior in unselected patients. This approach could facilitate the development of clinical decision support tools that inform risk reduction interventions.

Acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with significant mortality, morbidity and increased risk for further exacerbations. Therefore, appropriate measures for prevention of further exacerbations should be initiated before discharge. Unfortunately, this opportunity for treatment review and change in disease course is often missed. We designed a decision support tool to automatically generate discharge recommendations for COPD patients based on the Global Initiative for Chronic Obstructive Lung Disease (GOLD) report. A pre- and post-intervention study was conducted including data from 24 months before and 18 months after the implementation of the tool. The rate of adherence of the discharge recommendations to the report was measured. Overall, 536 patients were included in the pre-intervention cohort and 367 in the intervention cohort. Demographic and clinical features were similar between the two groups. After introduction of the tool, the percentage of patients discharged with long-acting medications increased from 42% to 84%, recommendations for smoking cessation increased from 32% to 91%, for vaccination from 13% to 92%, and for follow-up visit in a pulmonology clinic from 72% to 98%. Of the patients given prescriptions for long-acting bronchodilators, 54% purchased these after discharge versus 20% of the patients without such prescriptions. Decision-support tools can significantly improve adherence to guidelines among patients discharged after hospitalization due to Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) and potentially improve their clinical course.

Background: A recently published, large prospective study showed unexpectedly high prevalence of acute pulmonary embolism (APE) among patients hospitalized for syncope. In such a case, a high incidence of recurrent pulmonary embolism is expected among patients who were discharged without APE workup., Objectives: To determine the incidence of symptomatic APE among patients hospitalized for a first episode of syncope and discharged without APE workup or anticoagulation., Methods: This retrospective cohort study included patients hospitalized at Rambam Health Care Campus between January 2006 and February 2017 with a primary admission diagnosis of syncope, who were not investigated for APE and were not taking anticoagulants. The patients were followed up for up to three years after discharge. The occurrence of venous thromboembolism (VTE) during the follow-up period was documented., Results: The median follow-up duration was 33 months. 1,126 subjects completed a three-year follow-up. During this period, 38 patients (3.38%) developed VTE, 17 (1.51%) of them had APE. The cumulative incidence of VTE and APE was 1.9% (95% CI 1.3%-2.5%) and 0.9% (95% CI 0.4%-1.3%) respectively. Only seven subjects developed APE during the first year of follow-up. The median times from the event of syncope to the development of APE and VTE were 18 and 19 months respectively., Conclusions: The cumulative incidence of VTE during a three-year follow-up period after an episode of syncope is low. In the absence of clinical suspicion, a routine diagnostic workup for APE in patients with syncope cannot be recommended.

Introduction: One of the factors contributing to ED crowding is the lengthy delay in transferring an admitted patient from the ED to an inpatient department (ie, boarding time). An earlier start of the admission process using an automatic hospitalisation prediction model could potentially shorten these delays and reduce crowding., Methods: Clinical, operational and demographic data were retrospectively collected on 80 880 visits to the ED of Rambam Health Care Campus in Haifa, Israel, from January 2011 to January 2012. Using these data, a logistic regression model was developed to predict patient disposition (hospitalisation vs discharge) at three progressive time points throughout the ED visit: within the first 10 min, within an hour and within 2 hours. The algorithm was trained on 50% of the data (n=40 440) and tested on the remaining 50%., Results: During the study time period, 58 197 visits ended in discharge and 22 683 in hospitalisation. Within 1 hour of presentation, our model was able to predict hospitalisation with a specificity of 90%, sensitivity of 94% and an AUCof 0.97. Early clinical decisions such as testing for calcium levels were found to be highly predictive of hospitalisations. In the Rambam ED, the use of such a prediction system would have the potential to save more than 250 patient hours per day., Conclusions: Data collected by EDs in electronic medical records can be used within a progressive modelling framework to predict patient flow and improve clinical operations. This approach relies on commonly available data and can be applied across different healthcare settings., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Background and Objectives: Emergency departments (EDs) in the United States are overcrowded and nearing a breaking point. Alongside ever-increasing demand, one of the leading causes of ED overcrowding is the boarding of hospitalized patients in the ED as they await bed placement. We sought to develop a model for early prediction of hospitalizations, thus enabling an earlier start for the placement process and shorter boarding times., Methods: We conducted a retrospective cohort analysis of all visits to the Boston Children's Hospital ED from July 1, 2014 to June 30, 2015. We used 50% of the data for model derivation and the remaining 50% for validation. We built the predictive model by using a mixed method approach, running a logistic regression model on results generated by a naive Bayes classifier. We performed sensitivity analyses to evaluate the impact of the model on overall resource utilization., Results: Our analysis comprised 59 033 patient visits, of which 11 975 were hospitalized (cases) and 47 058 were discharged (controls). Using data available within the first 30 minutes from presentation, our model identified 73.4% of the hospitalizations with 90% specificity and 35.4% of hospitalizations with 99.5% specificity (area under the curve = 0.91). Applying this model in a real-time setting could potentially save the ED 5917 hours per year or 30 minutes per hospitalization., Conclusions: This approach can accurately predict patient hospitalization early in the ED encounter by using data commonly available in most electronic medical records. Such early identification can be used to advance patient placement processes and shorten ED boarding times., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2017 by the American Academy of Pediatrics.)

Objective: The purpose of this article was to determine whether longitudinal historical data, commonly available in electronic health record (EHR) systems, can be used to predict patients' future risk of suicidal behavior., Method: Bayesian models were developed using a retrospective cohort approach. EHR data from a large health care database spanning 15 years (1998-2012) of inpatient and outpatient visits were used to predict future documented suicidal behavior (i.e., suicide attempt or death). Patients with three or more visits (N=1,728,549) were included. ICD-9-based case definition for suicidal behavior was derived by expert clinician consensus review of 2,700 narrative EHR notes (from 520 patients), supplemented by state death certificates. Model performance was evaluated retrospectively using an independent testing set., Results: Among the study population, 1.2% (N=20,246) met the case definition for suicidal behavior. The model achieved sensitive (33%-45% sensitivity), specific (90%-95% specificity), and early (3-4 years in advance on average) prediction of patients' future suicidal behavior. The strongest predictors identified by the model included both well-known (e.g., substance abuse and psychiatric disorders) and less conventional (e.g., certain injuries and chronic conditions) risk factors, indicating that a data-driven approach can yield more comprehensive risk profiles., Conclusions: Longitudinal EHR data, commonly available in clinical settings, can be useful for predicting future risk of suicidal behavior. This modeling approach could serve as an early warning system to help clinicians identify high-risk patients for further screening. By analyzing the full phenotypic breadth of the EHR, computerized risk screening approaches may enhance prediction beyond what is feasible for individual clinicians.

Tracking the progress of vaccination campaigns is a challenging and important public health need. Examining a recent Polio outbreak in the Middle East, we show that novel methods utilizing online search trends have great potential to provide a real-time, reliable proxy for vaccination rates over space and time., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Management of medical images increasingly involves the need for integration with a variety of information systems. To address this need, we developed Content Management Offering (CMO), a platform for medical image management supporting interoperability through compliance with standards. CMO is based on the principles of service-oriented architecture, implemented with emphasis on three areas: clarity of business process definition, consolidation of service configuration management, and system scalability. Owing to the flexibility of this platform, a small team is able to accommodate requirements of customers varying in scale and in business needs. We describe two deployments of CMO, highlighting the platform's value to customers. CMO represents a flexible approach to medical image management, which can be applied to a variety of information technology challenges in healthcare and life sciences organizations.