Machine Learning-Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents.

Selective serotonin reuptake inhibitors (SSRI) are the first-line pharmacologic treatment for anxiety and depressive disorders in children and adolescents. Many patients experience side effects that are difficult to predict, are associated with significant morbidity, and can lead to treatment discontinuation. Variation in SSRI pharmacokinetics could explain differences in treatment outcomes, but this is often overlooked as a contributing factor to SSRI tolerability. This study evaluated data from 288 escitalopram-treated and 255 sertraline-treated patients ≤ 18 years old to develop machine learning models to predict side effects using electronic health record data and Bayesian estimated pharmacokinetic parameters. Trained on a combined cohort of escitalopram- and sertraline-treated patients, a penalized logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval (CI): 0.66-0.88), with 0.69 sensitivity (95% CI: 0.54-0.86), and 0.82 specificity (95% CI: 0.72-0.87). Medication exposure, clearance, and time since the last dose increase were among the top features. Individual escitalopram and sertraline models yielded an AUROC of 0.73 (95% CI: 0.65-0.81) and 0.64 (95% CI: 0.55-0.73), respectively. Post hoc analysis showed sertraline-treated patients with activation side effects had slower clearance (P = 0.01), which attenuated after accounting for age (P = 0.055). These findings raise the possibility that a machine learning approach leveraging pharmacokinetic data can predict escitalopram- and sertraline-related side effects. Clinicians may consider differences in medication pharmacokinetics, especially during dose titration and as opposed to relying on dose, when managing side effects. With further validation, application of this model to predict side effects may enhance SSRI precision dosing strategies in youth.

Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study.

BACKGROUND AND OBJECTIVES: Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation. METHODS: In this multicenter, prospective, longitudinal cohort study, random forest models were validated at a pediatric epilepsy center consisting of 2 hospitals and 14 outpatient neurology clinic sites and an adult epilepsy center with 2 hospitals and 27 outpatient neurology clinic sites. The models used neurology visit notes, EEG and MRI reports, visit patterns, hospitalizations, and medication, laboratory, and procedure orders to identify candidates for surgery. The models were trained on historical data up to May 10, 2019. Patients with an ICD-10 diagnosis of epilepsy who visited from May 11, 2019, to May 10, 2020, were screened by the algorithm and assigned surgical candidacy scores. The primary outcome was area under the curve (AUC), which was calculated by comparing scores from patients who underwent epilepsy surgery before November 10, 2020, against scores from nonsurgical patients. Nonsurgical patients' charts were reviewed to determine whether patients with high scores were more likely to be missed surgical candidates. Delay to surgery was defined as the time between the first visit that a surgical candidate was identified by the algorithm and the date of the surgery. RESULTS: A total of 5,285 pediatric and 5,782 adult patients were included to train the ML algorithms. During the study period, 41 children and 23 adults underwent resective epilepsy surgery. In the pediatric cohort, AUC was 0.91 (95% CI 0.87-0.94), positive predictive value (PPV) was 0.08 (0.05-0.10), and negative predictive value (NPV) was 1.00 (0.99-1.00). In the adult cohort, AUC was 0.91 (0.86-0.97), PPV was 0.07 (0.04-0.11), and NPV was 1.00 (0.99-1.00). The models first identified patients at a median of 2.1 years (interquartile range [IQR]: 1.2-4.9 years, maximum: 11.1 years) before their surgery and 1.3 years (IQR: 0.3-4.0 years, maximum: 10.1 years) before their presurgical evaluations. DISCUSSION: ML algorithms can identify surgical candidates earlier in the disease course. Even at specialized epilepsy centers, there is room to shorten the time to surgery. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with epilepsy who require resective surgery from those who do not.

Social-environmental phenotypes of rapid cystic fibrosis lung disease progression in adolescents and young adults living in the United States.

Background: Cystic fibrosis (CF) is a genetic disease but is greatly impacted by non-genetic (social/environmental and stochastic) influences. Some people with CF experience rapid decline, a precipitous drop in lung function relative to patient- and/or center-level norms. Those who experience rapid decline in early adulthood, compared to adolescence, typically exhibit less severe clinical disease but greater loss of lung function. The extent to which timing and degree of rapid decline are informed by social and environmental determinants of health (geomarkers) is unknown. Methods: A longitudinal cohort study was performed (24,228 patients, aged 6-21 years) using the U.S. CF Foundation Patient Registry. Geomarkers at the ZIP Code Tabulation Area level measured air pollution/respiratory hazards, greenspace, crime, and socioeconomic deprivation. A composite score quantifying social-environmental adversity was created and used in covariate-adjusted functional principal component analysis, which was applied to cluster longitudinal lung function trajectories. Results: Social-environmental phenotyping yielded three primary phenotypes that corresponded to early, middle, and late timing of peak decline in lung function over age. Geographic differences were related to distinct cultural and socioeconomic regions. Extent of peak decline, estimated as forced expiratory volume in 1 s of % predicted/year, ranged from 2.8 to 4.1 % predicted/year depending on social-environmental adversity. Middle decliners with increased social-environmental adversity experienced rapid decline 14.2 months earlier than their counterparts with lower social-environmental adversity, while timing was similar within other phenotypes. Early and middle decliners experienced mortality peaks during early adolescence and adulthood, respectively. Conclusion: While early decliners had the most severe CF lung disease, middle and late decliners lost more lung function. Higher social-environmental adversity associated with increased risk of rapid decline and mortality during young adulthood among middle decliners. This sub-phenotype may benefit from enhanced lung-function monitoring and personalized secondary environmental health interventions to mitigate chemical and non-chemical stressors.

Development and User Testing of a Dynamic Tool for Rheumatic Heart Disease Management.

OBJECTIVE: Most rheumatic heart disease (RHD) registries are static and centralized, collecting epidemiological and clinical data without providing tools to improve care. We developed a dynamic cloud-based RHD case management application with the goal of improving care for patients with RHD in Uganda. METHODS: The Active Community Case Management Tool (ACT) was designed to improve community-based case management for chronic disease, with RHD as the first test case. Global and local partner consultation informed selection of critical data fields and prioritization of application functionality. Multiple stages of review and revision culminated in user testing of the application at the Uganda Heart Institute. RESULTS: Global and local partners provided feedback of the application via survey and interview. The application was well received, and top considerations included avenues to import existing patient data, considering a minimum data entry form, and performing a situation assessment to tailor ACT to the health system setup for each new country. Test users completed a postuse survey. Responses were favorable regarding ease of use, desire to use the application in regular practice, and ability of the application to improve RHD care in Uganda. Concerns included appropriate technical skills and supports and potential disruption of workflow. CONCLUSION: Creating the ACT application was a dynamic process, incorporating iterative feedback from local and global partners. Results of the user testing will help refine and optimize the application. The ACT application showed potential for utility and integration into existing care models in Uganda.

Evaluating the implementation of a dynamic digital application to enable community-based decentralisation of rheumatic heart disease case management in Uganda: protocol for a hybrid type III effectiveness-implementation study.

INTRODUCTION: Rheumatic heart disease (RHD) affects over 39 million people worldwide, the majority in low-income and middle-income countries. Secondary antibiotic prophylaxis (SAP), given every 3-4 weeks can improve outcomes, provided more than 80% of doses are received. Poor adherence is strongly correlated with the distance travelled to receive prophylaxis. Decentralising RHD care has the potential to bridge these gaps and at least maintain or potentially increase RHD prophylaxis uptake. A package of implementation strategies was developed with the aim of reducing barriers to optimum SAP uptake. METHODS AND ANALYSIS: A hybrid implementation-effectiveness study type III was designed to evaluate the effectiveness of a package of implementation strategies including a digital, cloud-based application to support decentralised RHD care, integrated into the public healthcare system in Uganda. Our overarching hypothesis is that secondary prophylaxis adherence can be maintained or improved via a decentralisation strategy, compared with the centralised delivery strategy, by increasing retention in care. To evaluate this, eligible patients with RHD irrespective of their age enrolled at Lira and Gulu hospital registry sites will be consented for decentralised care at their nearest participating health centre. We estimated a sample size of 150-200 registrants. The primary outcome will be adherence to secondary prophylaxis while detailed implementation measures will be collected to understand barriers and facilitators to decentralisation, digital application tool adoption and ultimately its use and scale-up in the public healthcare system. ETHICS AND DISSEMINATION: This study was approved by the Institutional Review Board (IRB) at Cincinnati Children's Hospital Medical Center (IRB 2021-0160) and Makerere University School of Medicine Research Ethics Committee (Mak-SOMREC-2021-61). Participation will be voluntary and informed consent or assent (>8 but <18) will be obtained prior to participation. At completion, study findings will be communicated to the public, key stakeholders and submitted for publication.

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

OBJECTIVE: To determine whether automated, electronic alerts increased referrals for epilepsy surgery. METHODS: We conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system embedded in the electronic health record (EHR) at 14 pediatric neurology outpatient clinic sites. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit. Patients classified as a potential surgical candidate were randomized 2:1 for their provider to receive an alert or standard of care (no alert). The primary outcome was referral for a neurosurgical evaluation. The likelihood of referral was estimated using a Cox proportional hazards regression model. RESULTS: Between April 2017 and April 2019, at total of 4858 children were screened by the system, and 284 (5.8%) were identified as potential surgical candidates. Two hundred four patients received an alert, and 96 patients received standard care. Median follow-up time was 24 months (range: 12-36 months). Compared to the control group, patients whose provider received an alert were more likely to be referred for a presurgical evaluation (3.1% vs 9.8%; adjusted hazard ratio [HR] = 3.21, 95% confidence interval [CI]: 0.95-10.8; one-sided p = .03). Nine patients (4.4%) in the alert group underwent epilepsy surgery, compared to none (0%) in the control group (one-sided p = .03). SIGNIFICANCE: Machine learning-based automated alerts may improve the utilization of referrals for epilepsy surgery evaluations.

Built environment factors predictive of early rapid lung function decline in cystic fibrosis.

BACKGROUND: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. OBJECTIVE: To identify built environment characteristics predictive of rapid CF lung function decline. METHODS: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6-20 years, 2012-2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1 ) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. MEASUREMENTS AND MAIN RESULTS: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 µg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [-0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. CONCLUSION: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions.

Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.

BACKGROUND: Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing. OBJECTIVE: We aimed to describe current ventilator data in the electronic health record (EHR) and propose a framework for standardizing these data using a common data model (CDM) across multiple populations and sites. METHODS: We focused on a cohort of patients with LTMV dependence who were weaned from mechanical ventilation (MV). We extracted and described relevant EHR ventilation data. We identified the minimum necessary components, termed "Clinical Ideas," to describe MV from time of initiation to liberation. We then utilized existing resources and partnered with informatics collaborators to develop a framework for incorporating Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP). RESULTS: We identified 78 children with LTMV dependence who weaned from ventilator support. There were 25 unique device names and 28 unique ventilation mode names used in the cohort. We identified multiple Clinical Ideas necessary to describe ventilator support over time: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. We used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration. CONCLUSION: The proposed framework standardizes mechanical ventilation terminology and may facilitate efficient data exchange in a multisite network. Rapid data sharing is necessary to improve research and clinical care for children with LTMV dependence.

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.

BACKGROUND: Artificial intelligence (AI) technologies, such as machine learning and natural language processing, have the potential to provide new insights into complex health data. Although powerful, these algorithms rarely move from experimental studies to direct clinical care implementation. OBJECTIVE: We aimed to describe the key components for successful development and integration of two AI technology-based research pipelines for clinical practice. METHODS: We summarized the approach, results, and key learnings from the implementation of the following two systems implemented at a large, tertiary care children's hospital: (1) epilepsy surgical candidate identification (or epilepsy ID) in an ambulatory neurology clinic; and (2) an automated clinical trial eligibility screener (ACTES) for the real-time identification of patients for research studies in a pediatric emergency department. RESULTS: The epilepsy ID system performed as well as board-certified neurologists in identifying surgical candidates (with a sensitivity of 71% and positive predictive value of 77%). The ACTES system decreased coordinator screening time by 12.9%. The success of each project was largely dependent upon the collaboration between machine learning experts, research and operational information technology professionals, longitudinal support from clinical providers, and institutional leadership. CONCLUSIONS: These projects showcase novel interactions between machine learning recommendations and providers during clinical care. Our deployment provides seamless, real-time integration of AI technology to provide decision support and improve patient care.

Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.

OBJECTIVE: As the storage of clinical data has transitioned into electronic formats, medical informatics has become increasingly relevant in providing diagnostic aid. The purpose of this review is to evaluate machine learning models that use text data for diagnosis and to assess the diversity of the included study populations. METHODS: We conducted a systematic literature review on three public databases. Two authors reviewed every abstract for inclusion. Articles were included if they used or developed machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics were excluded. RESULTS: From 2,260 identified papers, we included 78. Of the machine learning models used, neural networks were relied upon most frequently (44.9%). Studies had a median population of 661.5 patients, and diseases and disorders of 10 different body systems were studied. Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1% were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included, 43.6% (N = 34) included the sex ratio of the patient population. DISCUSSION: With the power to build robust algorithms supported by massive quantities of clinical data, machine learning is shaping the future of diagnostics. Limitations of the underlying data create potential biases, especially if patient demographics are unknown or not included in the training. CONCLUSION: As the movement toward clinical reliance on machine learning accelerates, both recording demographic information and using diverse training sets should be emphasized. Extrapolating algorithms to demographics beyond the original study population leaves large gaps for potential biases.

The Risk of Coding Racism into Pediatric Sepsis Care: The Necessity of Antiracism in Machine Learning.

Machine learning holds the possibility of improving racial health inequalities by compensating for human bias and structural racism. However, unanticipated racial biases may enter during model design, training, or implementation and perpetuate or worsen racial inequalities if ignored. Pre-existing racial health inequalities could be codified into medical care by machine learning without clinicians being aware. To illustrate the importance of a commitment to antiracism at all stages of machine learning, we examine machine learning in predicting severe sepsis in Black children, focusing on the impacts of structural racism that may be perpetuated by machine learning and difficult to discover. To move toward antiracist machine learning, we recommend partnering with ethicists and experts in model development, enrolling representative samples for training, including socioeconomic inputs with proximate causal associations to racial inequalities, reporting outcomes by race, and committing to equitable models that narrow inequality gaps or at least have equal benefit.

Aligning Provider Prescribing With Guidelines for Soft Tissue Infections.

OBJECTIVE: Despite evidence-based guidelines, antibiotics prescribed for uncomplicated skin and soft tissue infections can involve inappropriate microbial coverage. Our aim was to evaluate the appropriateness of antibiotic prescribing practices for mild nonpurulent cellulitis in a pediatric tertiary academic medical center over a 1-year period. METHODS: Eligible patients treated in the emergency department or urgent care settings for mild nonpurulent cellulitis from January 2017 to December 2017 were identified by an International Classification of Diseases, Tenth Revision, code for cellulitis. The primary outcome was appropriateness of prescribed antibiotics as delineated by adherence with the Infectious Diseases Society of America guidelines. Secondary outcomes include reutilization rate as defined by revisit to the emergency department/urgent cares within 14 days of the initial encounter. RESULTS: A total of 967 encounters were evaluated with 60.0% overall having guideline-adherent care. Common reasons for nonadherence included inappropriate coverage of MRSA with clindamycin (n = 217, 56.1%) and single-agent coverage with sulfamethoxazole-trimethoprim (n = 129, 33.3%). There were 29 revisits within 14 days of initial patient encounters or a reutilization rate of 3.0%, which was not significantly associated with the Infectious Diseases Society of America adherence. CONCLUSIONS: Our data show antibiotic prescription for nonpurulent cellulitis as a potential area of standardization and optimization of care at our center.

An ontology-based review of transgender literature: Revealing a history of medicalization and pathologization.

OBJECTIVES: To evaluate the linguistic changes of transgender-related resources prior to 1999 to create a comprehensive dataset of resources using an ontology-derived search system, laying a framework for ontology-based reviews to be used in informatics. METHODS: We analyzed 77 bibliographies and 11 databases for transgender resources published prior to 31 December 1999. We used 858 variants of the term "transgender" to identify resources. Individual sources were tagged by subject matter and major conceptual terminology usage. We evaluated the accuracy of a Gender, Sex, and Sexual Orientation (GSSO) ontology-based mechanism on tagging relevant literature searches. RESULTS: We identified 3,058 sources in 19 languages. Primary subjects covered included surgery, psychology, psychiatry, endocrinology, and sexology. The GSSO-based tagging mechanism correctly tagged 97.7% of MEDLINE resources as transgender-related. DISCUSSION: The GSSO-based tagging mechanism was more effective than keyword-specific elucidations of terminologically complex literature and was just as effective at manual identification of subjects discussed within resources. Diverse language relating to transgender persons can be identified using the GSSO, which can also be used for structured literature review based on subject matter thus improving research in the area.

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

OBJECTIVES: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery. MATERIALS & METHODS: In this multicenter, retrospective, longitudinal cohort study, ML algorithms were trained on n-grams extracted from free-text neurology notes, EEG and MRI reports, visit codes, medications, procedures, laboratories, and demographic information. Site-specific algorithms were developed at two epilepsy centers: one pediatric and one adult. Cases were defined as patients who underwent resective epilepsy surgery, and controls were patients with epilepsy with no history of surgery. The output of the ML algorithms was the estimated likelihood of candidacy for resective epilepsy surgery. Model performance was assessed using 10-fold cross-validation. RESULTS: There were 5880 children (n = 137 had surgery [2.3%]) and 7604 adults with epilepsy (n = 56 had surgery [0.7%]) included in the study. Pediatric surgical patients could be identified 2.0 years (range: 0-8.6 years) before beginning their presurgical evaluation with AUC =0.76 (95% CI: 0.70-0.82) and PR-AUC =0.13 (95% CI: 0.07-0.18). Adult surgical patients could be identified 1.0 year (range: 0-5.4 years) before beginning their presurgical evaluation with AUC =0.85 (95% CI: 0.78-0.93) and PR-AUC =0.31 (95% CI: 0.14-0.48). By the time patients began their presurgical evaluation, the ML algorithms identified pediatric and adult surgical patients with AUC =0.93 and 0.95, respectively. The mean squared error of the predicted probability of surgical candidacy (Brier scores) was 0.018 in pediatrics and 0.006 in adults. CONCLUSIONS: Site-specific machine learning algorithms can identify candidates for epilepsy surgery early in the disease course in diverse practice settings.

Effectiveness of a Universally Offered Chlamydia and Gonorrhea Screening Intervention in the Pediatric Emergency Department.

PURPOSE: Adolescents represent more than half of the newly diagnosed sexually transmitted infections in the U.S. annually. Emergency departments (EDs) may serve as an effective, nontraditional setting to screen for chlamydia/gonorrhea (CT/GC). The objective was to evaluate the effectiveness of a universally offered CT/GC screening program in two pediatric ED settings. METHODS: This was a prospective, delayed start pragmatic study conducted over 18 months in two EDs within the same academic institution among ED adolescents aged 14-21 years with any chief complaint. Using a tablet device, adolescents were confidentially informed of CT/GC screening recommendations and were offered screening. If patients agreed to CT/GC testing, a clinical decision support tool was triggered to inform the provider and order testing. The main and key secondary outcomes were the proportion of CT/GC testing and positive CT/GC test results in each respective ED. RESULTS: Both EDs experienced modest but statistically significant increases in CT/GC testing post- versus pre-intervention (main: 11.5% vs. 7.9%; confidence interval [CI]: 2.9-4.2; p < .0001 and satellite: 3.8% vs. 2.6%; 95% CI: .7-1.7; p < .0001). Among those tested, the positivity rate at the main ED did not significantly change post- versus pre-intervention (24.1% vs. 23.2%; 95% CI: -1.9 to 3.8; p = .71) but significantly decreased at the satellite ED (7.6% vs. 14.8%; 95% CI: -12.2 to -2.2; p = .01). CONCLUSIONS: A universally offered screening intervention increased the proportion of adolescents who were tested at both EDs and the detection rates for CT/GC at the main ED, but patient acceptance of screening was low.

Development of the Gender, Sex, and Sexual Orientation ontology: Evaluation and workflow.

OBJECTIVE: The study sought to create an integrated vocabulary system that addresses the lack of standardized health terminology in gender and sexual orientation. MATERIALS AND METHODS: We evaluated computational efficiency, coverage, query-based term tagging, randomly selected term tagging, and mappings to existing terminology systems (including ICD (International Classification of Diseases), DSM (Diagnostic and Statistical Manual of Mental Disorders ), SNOMED (Systematized Nomenclature of Medicine), MeSH (Medical Subject Headings), and National Cancer Institute Thesaurus). RESULTS: We published version 2 of the Gender, Sex, and Sexual Orientation (GSSO) ontology with over 10 000 entries with definitions, a readable hierarchy system, and over 14 000 database mappings. Over 70% of terms had no mapping in any other available ontology. DISCUSSION: We created the GSSO and made it publicly available on the National Center for Biomedical Ontology BioPortal and on GitHub. It includes clarifications on over 200 slang terms, 190 pronouns with linked example usages, and over 200 nonbinary and culturally specific gender identities. CONCLUSIONS: Gender and sexual orientation continue to represent crucial areas of medical practice and research with evolving terminology. The GSSO helps address this gap by providing a centralized data resource.

Information Technology-Assisted Screening for Gonorrhea and Chlamydia in a Pediatric Emergency Department.

PURPOSE: The aim of the study was to design and implement a novel, universally offered, computerized clinical decision support (CDS) gonorrhea and chlamydia (GC/CT) screening tool embedded in the emergency department (ED) clinical workflow and triggered by patient-entered data. METHODS: The study consisted of the design and implementation of a tablet-based screening tool based on qualitative data of adolescent and parent/guardian acceptability of GC/CT screening in the ED and an advisory committee of ED leaders and end users. The tablet was offered to adolescents aged 14-21 years and informed patients of Centers for Disease Control and Prevention GC/CT screening recommendations, described the testing process, and assessed whether patients agreed to testing. The tool linked to CDS that streamlined the order entry process. The primary outcome was the patient capture rate (proportion of patients with tablet data recorded). The secondary outcomes included rates of patient agreement to GC/CT testing and provider acceptance of the CDS. RESULTS: Outcomes at the main and satellite EDs, respectively, were as follows: 1-year patient capture rates were 64.6% and 64.5%; 9.9% and 4.4% of patients agreed to GC/CT testing, and of those, the provider ordered testing for 73% and 72%. CONCLUSIONS: Implementation of this computerized screening tool embedded in the clinical workflow resulted in patient capture rates of almost two-thirds and clinician CDS acceptance rates >70% with limited patient agreement to testing. This screening tool is a promising method for confidential GC/CT screening among youth in an ED setting. Additional interventions are needed to increase adolescent agreement for GC/CT testing.

An interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real time.

OBJECTIVE: The study sought to create an online resource that informs the public of coronavirus disease 2019 (COVID-19) outbreaks in their area. MATERIALS AND METHODS: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. RESULTS: The Web resource, called the COVID-19 Watcher, can be accessed online (https://covid19watcher.research.cchmc.org/). It displays COVID-19 data from every county and 188 metropolitan areas in the United States. Features include rankings of the worst-affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths. DISCUSSION: The Centers for Disease Control and Prevention does not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak. CONCLUSIONS: The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.

Utilization of a Clinical Decision Support Tool to Reduce Child Tobacco Smoke Exposure in the Urgent Care Setting.

BACKGROUND: Clinical decision support systems (CDSS) may facilitate caregiver tobacco screening and counseling by pediatric urgent care (UC) nurses. OBJECTIVE: This study aimed to assess the feasibility of a CDSS to address caregivers' tobacco use and child tobacco smoke exposure (TSE). METHODS: We conducted a 3-month prospective study on caregivers screened using a CDSS. Nurses used the CDSS to advise, assess, and assist caregivers to quit. We assessed caregiver sociodemographics, smoking habits, and child TSE. RESULTS: We screened 185 caregivers whose children were exposed to TSE for study inclusion; 155 (84%) met the eligibility criteria, and 149 (80.5%) were included in the study. Study nurses advised 35.2% of the caregivers to quit, assessed 35.9% for readiness to quit, and assisted 32.4%. Of the 149 participants, 83.1% were female; 47.0% were white and 45.6% African American; 84.6% had public insurance or were self-pay; 71.1% were highly nicotine dependent; 50.0% and 50.7% allowed smoking in the home and car, respectively; and 81.3% of children were biochemically confirmed to be exposed to tobacco smoke. At follow-up (86.6% retention), 58.9% reported quit attempts at 3 months. There was a significant decrease in nicotine dependence and a significant increase in motivation to quit. Self-reported quit rate was 7.8% at 3 months. CONCLUSIONS: An electronic health record-embedded CDSS was feasible to incorporate into busy UC nurses' workloads and was associated with encouraging changes in the smoking behavior of caregivers. More research on the use of CDSS to screen and counsel caregivers who smoke in the UC and other acute care settings is warranted.

Feedback at the Point of Care to Decrease Medication Alert Rates in an Electronic Health Record.

Frequently overridden alerts in the electronic health record can highlight alerts that may need revision. This method is a way of fine-tuning clinical decision support. We evaluated the feasibility of a complementary, yet different method that directly involved pediatric emergency department (PED) providers in identifying additional medication alerts that were potentially incorrect or intrusive. We then evaluated the effect subsequent resulting modifications had on alert salience. METHODS: We performed a prospective, interventional study over 34 months (March 6, 2014, to December 31, 2016) in the PED. We implemented a passive alert feedback mechanism by enhancing the native electronic health record functionality on alert reviews. End-users flagged potentially incorrect/bothersome alerts for review by the study's team. The alerts were updated when clinically appropriate and trends of the impact were evaluated. RESULTS: More than 200 alerts were reported from both inside and outside the PED, suggesting an intuitive approach. On average, we processed 4 reviews per week from the PED, with attending physicians as major contributors. The general trend of the impact of these changes seems favorable. DISCUSSION: The implementation of the review mechanism for user-selected alerts was intuitive and sustainable and seems to be able to detect alerts that are bothersome to the end-users. The method should be run in parallel with the traditional data-driven approach to support capturing of inaccurate alerts. CONCLUSIONS: User-centered, context-specific alert feedback can be used for selecting suboptimal, interruptive medication alerts.