Insulin receptor signaling engages bladder urothelial defenses that limit urinary tract infection.

Urinary tract infections (UTIs) commonly afflict people with diabetes. To better understand the mechanisms that predispose diabetics to UTIs, we employ diabetic mouse models and altered insulin signaling to show that insulin receptor (IR) shapes UTI defenses. Our findings are validated in human biosamples. We report that diabetic mice have suppressed IR expression and are more susceptible to UTIs caused by uropathogenic Escherichia coli (UPEC). Systemic IR inhibition increases UPEC susceptibility, while IR activation reduces UTIs. Localized IR deletion in bladder urothelium promotes UTI by increasing barrier permeability and suppressing antimicrobial peptides. Mechanistically, IR deletion reduces nuclear factor κB (NF-κB)-dependent programming that co-regulates urothelial tight junction integrity and antimicrobial peptides. Exfoliated urothelial cells or urine samples from diabetic youths show suppressed expression of IR, barrier genes, and antimicrobial peptides. These observations demonstrate that urothelial insulin signaling has a role in UTI prevention and link IR to urothelial barrier maintenance and antimicrobial peptide expression.

Continuation of Pediatric Care after Transfer to Adult Care Among Autistic Youth Overlap of Pediatric and Adult Care.

The transition from pediatric to adult health care is a vulnerable time period for autistic adolescents and young adults (AYA) and for some autistic AYA may include a period of receiving care in both the pediatric and adult health systems. We sought to assess the proportion of autistic AYA who continued to use pediatric health services after their first adult primary care appointment and to identify factors associated with continued pediatric contact. We analyzed electronic medical record (EMR) data from a cohort of autistic AYA seen in a primary-care-based program for autistic people. Using logistic and linear regression, we assessed the relationship between eight patient characteristics and (1) the odds of a patient having ANY pediatric visits after their first adult appointment and (2) the number of pediatric visits among those with at least one pediatric visit. The cohort included 230 autistic AYA, who were mostly white (68%), mostly male (82%), with a mean age of 19.4 years at the time of their last pediatric visit before entering adult care. The majority (n = 149; 65%) had pediatric contact after the first adult visit. Younger age at the time of the first adult visit and more pediatric visits prior to the first adult visit were associated with continued pediatric contact. In this cohort of autistic AYA, most patients had contact with the pediatric system after their first adult primary care appointment.

Predicting timely transfer to adult care in a cohort of autistic adolescents and young adults.

BACKGROUND: The transition from pediatric to adult care is a challenge for autistic adolescents and young adults. Data on patient features associated with timely transfer between pediatric and adult health care are limited. Our objective was to describe the patient features associated with timely transfer to adult health care (defined as

Predicting emergency department visits among children with asthma in two academic medical systems.

Objective: To develop and validate a predictive algorithm that identifies pediatric patients at risk of asthma-related emergencies, and to test whether algorithm performance can be improved in an external site via local retraining.Methods: In a retrospective cohort at the first site, data from 26 008 patients with asthma aged 2-18 years (2012-2017) were used to develop a lasso-regularized logistic regression model predicting emergency department visits for asthma within one year of a primary care encounter, known as the Asthma Emergency Risk (AER) score. Internal validation was conducted on 8634 patient encounters from 2018. External validation of the AER score was conducted using 1313 pediatric patient encounters from a second site during 2018. The AER score components were then reweighted using logistic regression using data from the second site to improve local model performance. Prediction intervals (PI) were constructed via 10 000 bootstrapped samples.Results: At the first site, the AER score had a cross-validated area under the receiver operating characteristic curve (AUROC) of 0.768 (95% PI: 0.745-0.790) during model training and an AUROC of 0.769 in the 2018 internal validation dataset (p = 0.959). When applied without modification to the second site, the AER score had an AUROC of 0.684 (95% PI: 0.624-0.742). After local refitting, the cross-validated AUROC improved to 0.737 (95% PI: 0.676-0.794; p = 0.037 as compared to initial AUROC).Conclusions: The AER score demonstrated strong internal validity, but external validity was dependent on reweighting model components to reflect local data characteristics at the external site.

Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data.

BACKGROUND: Patient-generated health data (PGHD) captured via smart devices or digital health technologies can reflect an individual health journey. PGHD enables tracking and monitoring of personal health conditions, symptoms, and medications out of the clinic, which is crucial for self-care and shared clinical decisions. In addition to self-reported measures and structured PGHD (eg, self-screening, sensor-based biometric data), free-text and unstructured PGHD (eg, patient care note, medical diary) can provide a broader view of a patient's journey and health condition. Natural language processing (NLP) is used to process and analyze unstructured data to create meaningful summaries and insights, showing promise to improve the utilization of PGHD. OBJECTIVE: Our aim is to understand and demonstrate the feasibility of an NLP pipeline to extract medication and symptom information from real-world patient and caregiver data. METHODS: We report a secondary data analysis, using a data set collected from 24 parents of children with special health care needs (CSHCN) who were recruited via a nonrandom sampling approach. Participants used a voice-interactive app for 2 weeks, generating free-text patient notes (audio transcription or text entry). We built an NLP pipeline using a zero-shot approach (adaptive to low-resource settings). We used named entity recognition (NER) and medical ontologies (RXNorm and SNOMED CT [Systematized Nomenclature of Medicine Clinical Terms]) to identify medication and symptoms. Sentence-level dependency parse trees and part-of-speech tags were used to extract additional entity information using the syntactic properties of a note. We assessed the data; evaluated the pipeline with the patient notes; and reported the precision, recall, and F1 scores. RESULTS: In total, 87 patient notes are included (audio transcriptions n=78 and text entries n=9) from 24 parents who have at least one CSHCN. The participants were between the ages of 26 and 59 years. The majority were White (n=22, 92%), had more than one child (n=16, 67%), lived in Ohio (n=22, 92%), had mid- or upper-mid household income (n=15, 62.5%), and had higher level education (n=24, 58%). Out of 87 notes, 30 were drug and medication related, and 46 were symptom related. We captured medication instances (medication, unit, quantity, and date) and symptoms satisfactorily (precision >0.65, recall >0.77, F1>0.72). These results indicate the potential when using NER and dependency parsing through an NLP pipeline on information extraction from unstructured PGHD. CONCLUSIONS: The proposed NLP pipeline was found to be feasible for use with real-world unstructured PGHD to accomplish medication and symptom extraction. Unstructured PGHD can be leveraged to inform clinical decision-making, remote monitoring, and self-care including medical adherence and chronic disease management. With customizable information extraction methods using NER and medical ontologies, NLP models can feasibly extract a broad range of clinical information from unstructured PGHD in low-resource settings (eg, a limited number of patient notes or training data).

Invariant surface glycoprotein 65 of Trypanosoma brucei is a complement C3 receptor.

African trypanosomes are extracellular pathogens of mammals and are exposed to the adaptive and innate immune systems. Trypanosomes evade the adaptive immune response through antigenic variation, but little is known about how they interact with components of the innate immune response, including complement. Here we demonstrate that an invariant surface glycoprotein, ISG65, is a receptor for complement component 3 (C3). We show how ISG65 binds to the thioester domain of C3b. We also show that C3 contributes to control of trypanosomes during early infection in a mouse model and provide evidence that ISG65 is involved in reducing trypanosome susceptibility to C3-mediated clearance. Deposition of C3b on pathogen surfaces, such as trypanosomes, is a central point in activation of the complement system. In ISG65, trypanosomes have evolved a C3 receptor which diminishes the downstream effects of C3 deposition on the control of infection.

Characterization of Electronic Health Record Use Outside Scheduled Clinic Hours Among Primary Care Pediatricians: Retrospective Descriptive Task Analysis of Electronic Health Record Access Log Data.

BACKGROUND: Many of the benefits of electronic health records (EHRs) have not been achieved at expected levels because of a variety of unintended negative consequences such as documentation burden. Previous studies have characterized EHR use during and outside work hours, with many reporting that physicians spend considerable time on documentation-related tasks. These studies characterized EHR use during and outside work hours using clock time versus actual physician clinic schedules to define the outside work time. OBJECTIVE: This study aimed to characterize EHR work outside scheduled clinic hours among primary care pediatricians using a retrospective descriptive task analysis of EHR access log data and actual physician clinic schedules to define work time. METHODS: We conducted a retrospective, exploratory, descriptive task analysis of EHR access log data from primary care pediatricians in September 2019 at a large Midwestern pediatric health center to quantify and identify actions completed outside scheduled clinic hours. Mixed-effects statistical modeling was used to investigate the effects of age, sex, clinical full-time equivalent status, and EHR work during scheduled clinic hours on the use of EHRs outside scheduled clinic hours. RESULTS: Primary care pediatricians (n=56) in this study generated 1,523,872 access log data points (across 1069 physician workdays) and spent an average of 4.4 (SD 2.0) hours and 0.8 (SD 0.8) hours per physician per workday engaged in EHRs during and outside scheduled clinic hours, respectively. Approximately three-quarters of the time working in EHR during or outside scheduled clinic hours was spent reviewing data and reports. Mixed-effects regression revealed no associations of age, sex, or clinical full-time equivalent status with EHR use during or outside scheduled clinic hours. CONCLUSIONS: For every hour primary care pediatricians spent engaged with the EHR during scheduled clinic hours, they spent approximately 10 minutes interacting with the EHR outside scheduled clinic hours. Most of their time (during and outside scheduled clinic hours) was spent reviewing data, records, and other information in EHR.

Predicting Patient No-Shows in an Academic Pediatric Neurology Clinic.

Background: No-shows can negatively affect patient care. Efforts to predict high-risk patients are needed. Previously, our epilepsy clinic identified patients with 2 or more no-shows or late cancelations in the past 18 months as being at high risk for no-shows. Our objective was to develop a model to accurately predict the risk of no-shows among patients with epilepsy seen at our neurology clinic. Methods: Using electronic health record data, we developed a least absolute shrinkage and selection operator (LASSO)-regularized logistic regression model to predict no-shows and compared its performance with our neurology clinic's above-mentioned ad hoc rule. Results: The ad hoc rule identified 13% of patients seen at our neurology clinic as high-risk patients for no-shows and resulted in a positive predictive value of 38%. In comparison, our LASSO model resulted in a positive predictive value of 48%. Our LASSO model identified that lack of private insurance, inactive Epic MyChart, greater past no-show rates, fewer appointment changes before the appointment date, and follow-up appointments were more likely to result in no-shows. Conclusions: Our LASSO model outperformed the ad hoc rule used by our neurology clinic in predicting patients at high risk for no-shows. Social workers can use the no-show risk scores generated by our LASSO model to prioritize high-risk patients for targeted intervention to reduce no-shows at our neurology clinic.

A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study.

OBJECTIVES: Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient's condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate it with a case study on cystic fibrosis (CF). MATERIALS AND METHODS: The proposed unstructured data synthesis and information extraction pipeline extract a broad range of health information by combining rule-based approaches with pretrained deep-learning models. Particularly, we build upon the scispaCy biomedical model suite, leveraging its named entity recognition capabilities to identify and link clinically relevant entities to established ontologies such as Systematized Nomenclature of Medicine (SNOMED) and RXNORM. We then use scispaCy's syntax (grammar) parsing tools to retrieve phrases associated with the entities in medication, dose, therapies, symptoms, bowel movements, and nutrition ontological categories. The pipeline is illustrated and tested with simulated CF patient notes. RESULTS: The proposed hybrid deep-learning rule-based approach can operate over a variety of natural language note types and allow customization for a given patient or cohort. Viable information was successfully extracted from simulated CF notes. This hybrid pipeline is robust to misspellings and varied word representations and can be tailored to accommodate the needs of a specific patient, cohort, or clinician. DISCUSSION: The NLP pipeline can extract predefined or ontology-based entities from free-text PGHD, aiming to facilitate remote care and improve chronic disease management. Our implementation makes use of open source models, allowing for this solution to be easily replicated and integrated in different health systems. Outside of the clinic, the use of the NLP pipeline may increase the amount of clinical data recorded by families of CSHCN and ease the process to identify health events from the notes. Similarly, care coordinators, nurses and clinicians would be able to track adherence with medications, identify symptoms, and effectively intervene to improve clinical care. Furthermore, visualization tools can be applied to digest the structured data produced by the pipeline in support of the decision-making process for a patient, caregiver, or provider. CONCLUSION: Our study demonstrated that an NLP pipeline can be used to create an automated analysis and reporting mechanism for unstructured PGHD. Further studies are suggested with real-world data to assess pipeline performance and further implications.

A Predictive Model for Primary Care Providers to Identify Children at Greatest Risk for Early Childhood Caries.

Purpose: The purpose of this study was to create an early childhood caries (ECC) risk-screening tool that fits into the primary care provider (PCP) well-child workflow. Methods: Integrated health records were employed to develop a predictive model for infants/toddlers at ECC risk; 2,009 patients with 12-, 15-, or 18-month well-child visits and at least one dental visit were used to develop a predictive model for ECC risk at the first dental visit. Independent model validation used 880 18- to 48-month-olds at their first dental appointment after at least one well-child visit. Results: Age at the first dental visit strongly predicted caries risk (odds ratio for one-year increase in age equals 2.11; 95 percent confidence interval equals 1.80 to 2.47). Three factors predicted high-caries risk: breast feeding status, preferred language not English, and no-show rates for pediatric clinic visits greater than 20 percent. All three non-age risk factors in well-child exams prior to 18 months predicted 42 percent probability of having caries if present for the first dental visit at 18 months. If that child was not seen until four years of age for the first dental visit, the probability of high caries risk increased to 83 percent. Model performance for independent validation was very close to expected performance. Conclusions: Existing clinical documentation plus a validated predictive model enables an effective caries risk assessment within well-child visits.

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning.

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.

Measuring the impact of epilepsy on families.

OBJECTIVE: Caring for a child with illness or a child with disability impacts family in various ways. The ability to assess the impact of this care on families is one way to proactively provide the necessary support and resources for impacted families. Accordingly, the goal of the current study was to assess the impact of pediatric epilepsy on individual families in a comprehensive epilepsy clinic using a slightly modified version of the Impact on Families Scale (IFS). METHODS: Families of patients with epilepsy completed the IFS up to three times. The IFS score and the six categories (i.e., total impact, financial impact, general impact, family/social impact, coping, and sibling impact) were assessed using Student's two sample t-test to determine the differences between binary groups and Pearson's correlation to assess the associations with continuous variables. Linear regression modeling was used to develop a model to predict IFS score. RESULTS: Three hundred and forty-one patients completed the scale at one time point, 314 at two time points, and 61 at three time points. The overall impact of epilepsy on families was 109 (95% confidence interval (CI): 106-112) at time point 1, 111 (95% CI: 108-114) at time point 2, and 112 (95% CI: 105-119) at time point 3. There was no statistical difference in IFS score among the three time points. There were no associations with age or gender. Multivariable modeling using stepwise regression indicated that treatment resistance and seizure-free status were associated with IFS score. No interaction effects were identified. CONCLUSIONS: Findings from the current study suggest that the impact of epilepsy is highest for families that have children with active seizures at the time of their clinical visit and for those with children having treatment-resistant epilepsy. Although intuitive, this is the first study, to our knowledge, that has empirically verified these findings.

The Vitals Risk Index-Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System.

INTRODUCTION: Pediatric in-hospital cardiac arrests and emergent transfers to the pediatric intensive care unit (ICU) represent a serious patient safety concern with associated increased morbidity and mortality. Some institutions have turned to the electronic health record and predictive analytics in search of earlier and more accurate detection of patients at risk for decompensation. METHODS: Objective electronic health record data from 2011 to 2017 was utilized to develop an automated early warning system score aimed at identifying hospitalized children at risk of clinical deterioration. Five vital sign measurements and supplemental oxygen requirement data were used to build the Vitals Risk Index (VRI) model, using multivariate logistic regression. We compared the VRI to the hospital's existing early warning system, an adaptation of Monaghan's Pediatric Early Warning Score system (PEWS). The patient population included hospitalized children 18 years of age and younger while being cared for outside of the ICU. This dataset included 158 case hospitalizations (102 emergent transfers to the ICU and 56 "code blue" events) and 135,597 control hospitalizations. RESULTS: When identifying deteriorating patients 2 hours before an event, there was no significant difference between Pediatric Early Warning Score and VRI's areas under the receiver operating characteristic curve at false-positive rates ≤ 10% (pAUC10 of 0.065 and 0.064, respectively; P = 0.74), a threshold chosen to compare the 2 approaches under clinically tolerable false-positive rates. CONCLUSIONS: The VRI represents an objective, simple, and automated predictive analytics tool for identifying hospitalized pediatric patients at risk of deteriorating outside of the ICU setting.

The Financial Impact of Genetic Diseases in a Pediatric Accountable Care Organization.

Background: Previous studies revealed patients with genetic disease have more frequent and longer hospitalizations and therefore higher healthcare costs. To understand the financial impact of genetic disease on a pediatric accountable care organization (ACO), we analyzed medical claims from 2014 provided by Partners for Kids, an ACO in partnership with Nationwide Children's Hospital (NCH; Columbus, OH, USA). Methods: Study population included insurance claims from 258,399 children. We assigned patients to four different categories (1-A, 1-B, 2, & 3) based on the strength of genetic basis of disease. Results: We identified 22.7% of patients as category 1A or 1B- having a disease with a "strong genetic basis" (e.g., single gene diseases, chromosomal abnormalities). Total ACO paid claims in 2014 were $379M, of which $161M (42.5%) was attributed to category 1 patients. Furthermore, we identified 23.3% of patients as category 2- having a disease with a suspected genetic component or predisposition (e.g., asthma, type 1 diabetes)- whom accounted for an additional 28.6% of 2014 costs. Category 1 patients were more likely to experience at least one hospitalization compared to category 3 patients- those without genetic disease [odds ratio [OR] = 4.12; 95% confidence interval [CI] = 3.86-4.39; p < 0.0001]. Overall, category 1 patients experienced nearly five times the number of inpatient (IP) admissions and twice the number of outpatient (OP) visits compared to category 3 patients (p < 0.0001). Conclusion: Nearly half (42.5%) of healthcare paid claims cost in 2014 for this study population were accounted for by patients with single-gene diseases or chromosomal abnormalities. These findings precede and support a need for an ACO to plan for effective healthcare strategies and capitation models for children with genetic disease.

Capturing At-Home Health and Care Information for Children With Medical Complexity Using Voice Interactive Technologies: Multi-Stakeholder Viewpoint.

Digital health tools and technologies are transforming health care and making significant impacts on how health and care information are collected, used, and shared to achieve best outcomes. As most of the efforts are still focused on clinical settings, the wealth of health information generated outside of clinical settings is not being fully tapped. This is especially true for children with medical complexity (CMC) and their families, as they frequently spend significant hours providing hands-on medical care within the home setting and coordinating activities among multiple providers and other caregivers. In this paper, a multidisciplinary team of stakeholders discusses the value of health information generated at home, how technology can enhance care coordination, and challenges of technology adoption from a patient-centered perspective. Voice interactive technology has been identified to have the potential to transform care coordination for CMC. This paper shares opinions on the promises, limitations, recommended approaches, and challenges of adopting voice technology in health care, especially for the targeted patient population of CMC.

Factors Associated With Electronic Health Record Usage Among Primary Care Physicians After Hours: Retrospective Cohort Study.

BACKGROUND: There is limited published data on variation in physician usage of electronic health records (EHRs), particularly after hours. Research in this area could provide insight into the effects of EHR-related workload on physicians. OBJECTIVE: This study sought to examine factors associated with after-hours EHR usage among primary care physicians. METHODS: Electronic health records usage information was collected from primary care pediatricians in a large United States hospital. Inclusion criteria consisted solely of being a primary care physician who started employment with the hospital before the study period, so all eligible primary care physicians were included without sampling. Mixed effects statistical modeling was used to investigate the effects of age, gender, workload, normal-hour usage, week to week variation, and provider-to-provider variation on the after-hour usage of EHRs. RESULTS: There were a total of 3498 weekly records obtained on 50 physicians, of whom 22% were male and 78% were female. Overall, more EHR usage during normal work hours was associated with decreased usage after hours. The more work relative value units generated by physicians, the more time they spent interacting with EHRs after hours (ß=.04, P<.001) and overall (ie, during normal hours and after hours) (ß=.24, P<.001). Gender was associated with total usage time, with females spending more time than males (P=.03). However, this association was not observed with after-hours EHR usage. provider-to-provider variation was the largest and most dominant source of variation in after-hour EHR usage, which accounted for 52% of variance of total EHR usage. CONCLUSION: The present study found that there is a considerable amount of variability in EHR use among primary care physicians, which suggested that many factors influence after-hours EHR usage by physicians. However, provider-to-provider variation was the largest and most dominant source of variation in after-hours EHR usage. While the results are intuitive, future studies should consider the effect of EHR use variations on workload efficiency.

A hospital-based study on caregiver preferences on acute seizure rescue medications in pediatric patients with epilepsy: Intranasal midazolam versus rectal diazepam.

RATIONALE: About 20 per 100,000 children have convulsive status epilepticus every year, a life-threatening condition. Benzodiazepines are the first-line treatment for prolonged and recurrent seizures. Our study was designed to gain understanding of caregiver perception of acute seizure treatments. METHODS: Our project uses a cross-sectional survey study design using the electronic medical record and a survey at a large academic tertiary children's medical center. Subjects were patients with epilepsy prescribed intranasal (IN) midazolam and/or per rectum (PR) diazepam. The survey was administered to caregivers of children with epilepsy regarding information on the comfort, efficacy, ease of use, and time of administration for patients receiving both abortive seizure medications. Exact binomial tests were employed to determine whether or not differences in caregiver preference exist. RESULTS: One hundred and sixty responses were obtained. Incomplete and duplicate surveys were excluded, leaving 153 responses. Of those responses, 59 respondents reported administering both medications. Among parents who expressed a preference for one medication over the other, more parents felt overall greater comfort with IN midazolam compared with rectal diazepam (p = 0.0004 and p = 0.001), IN midazolam was perceived as easier to use (68%, p = 0.0038 and 74%, p = 0.0004) and more effective (87%, p < 0.0001) than rectal diazepam. Intranasal midazolam was found to be superior to rectal diazepam in several other categories as well. CONCLUSIONS: These parents of children with epilepsy report increased ease of use, comfort, and efficacy with IN midazolam as compared with rectal diazepam suggesting that a readily available form of IN midazolam would be well received in the pediatric population.

Comorbidities in Childhood Celiac Disease: A Phenome Wide Association Study Using the Electronic Health Record.

OBJECTIVES: Celiac disease (CD) is associated with a variety of extraintestinal autoimmune and inflammatory findings that manifest clinically as symptoms and comorbidities. Understanding these comorbidities may improve identification of the disease and prevent sequelae. In this study, we use an unbiased electronic health record (EHR)-based Phenome Wide Association Study (PheWAS) method to confirm known comorbidities, discover novel associations and enhance characterization of the clinical presentation of CD in children. METHODS: Data were extracted from the Nationwide Children's Hospital EHR. Confirmed CD cases (n = 433) were matched with 4330 randomly selected controls. Utilizing an EHR-based PheWAS method to analyze associations of phenotypes with CD, we conducted an unbiased screening of all International Classification of Diseases, 10th revision diagnostic codes and examined significance by performing Fisher's Exact tests. We further tested for the association between CD and 14 previously identified comorbidities in an a priori fashion. RESULTS: We found 45 International Classification of Diseases, 10th revision codes significantly associated with CD. Thirteen are known comorbidities and nine are expected symptoms of CD, thus validating our study methods. Further investigation found symptoms that characterized CD clinically and discovered a significant association between eosinophilic disorders of the esophagus and CD. Of 14 previously identified comorbidities, 8 were significantly associated with CD. CONCLUSIONS: An EHR-based PheWAS method is a powerful, efficient, and cost-effective method to screen for possible CD comorbidities and validate associations at the population level. Ours is the first PheWAS of CD to confirm a significant association of eosinophilic disorders of the esophagus with CD in a controlled study.

Healthcare Utilization Characteristics for Intranasal Midazolam Versus Rectal Diazepam.

To investigate connections between patient demographics, health care utilization, prescription use, and refills for patients using intranasal midazolam, per rectum diazepam, or both. A retrospective cohort contained patients with epilepsy prescribed intranasal midazolam, per rectum diazepam, or both. We analyzed number of emergency department visits, ambulance services, urgent care visits, and unplanned hospitalizations. A total of 5458 patients were identified. Patients on intranasal midazolam had on average 1.53 fewer emergency department visits (95% confidence interval 1.16-1.89, P < .0001), 0.29 fewer uses of ambulance services (95% confidence interval 0.17-0.41, P < .0001), and 0.60 fewer urgent care visits (95% confidence interval 0.36-0.83, P < .0001) compared to patients in the per rectum diazepam group. Patients with commercial insurance were more likely to have intranasal midazolam prescription (odds ratio = 1.73, 95% confidence interval 1.42-2.11, P < .0001). The results substantiate the cost-effective benefits of prescribing intranasal midazolam compared to per rectum diazepam because several aspects of health care utilization were decreased in those using intranasal midazolam.

Simplified Readability Metric Drives Improvement of Radiology Reports: an Experiment on Ultrasound Reports at a Pediatric Hospital.

Highly complex medical documents, including ultrasound reports, are greatly mismatched with patient literacy levels. While improving radiology reports for readability is a longstanding concern, few articles objectively measure the effectiveness of physician training for readability improvement. We hypothesized that writing styles may be evaluated using an objective two-dimensional measure and writing training could improve the writing styles of radiologists. To test it, a simplified "grade vs. length" readability metric is developed based on results from factor analysis of ten readability metrics applied to more than 500,000 radiology reports. To test the short-term effectiveness of a writing workshop, we measured the writing style improvement before and after the training. Statistically significant writing style improvement occurred as a result of the training. Although the degree of improvement varied for different measures, it is evident that targeted training could provide potential benefits to improve readability due to our statistically significant results. The simplified grade vs. length metric enables future clinical decision support systems to quantitatively guide physicians to improve writing styles through writing workshops.