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.

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.

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.

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.

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.

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

OBJECTIVE: Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores. METHODS: The application was trained on notes from (1) patients with a diagnosis of epilepsy and a history of resective epilepsy surgery and (2) patients who were seizure-free without surgery. The testing set included all patients with unknown surgical candidacy status and an upcoming neurology visit. Training and testing sets were updated weekly for 1 year. One- to three-word phrases contained in patients' notes were used as features. Patients prospectively identified by the application as candidates for surgery were manually reviewed by two epileptologists. Performance metrics were defined by comparing NLP-derived surgical candidacy scores with surgical candidacy status from expert chart review. RESULTS: The training set was updated weekly and included notes from a mean of 519 ± 67 patients. The area under the receiver operating characteristic curve (AUC) from 10-fold cross-validation was 0.90 ± 0.04 (range = 0.83-0.96) and improved by 0.002 per week (P < .001) as new patients were added to the training set. Of the 6395 patients who visited the neurology clinic, 4211 (67%) were evaluated by the model. The prospective AUC on this test set was 0.79 (95% confidence interval [CI] = 0.62-0.96). Using the optimal surgical candidacy score threshold, sensitivity was 0.80 (95% CI = 0.29-0.99), specificity was 0.77 (95% CI = 0.64-0.88), positive predictive value was 0.25 (95% CI = 0.07-0.52), and negative predictive value was 0.98 (95% CI = 0.87-1.00). The number needed to screen was 5.6. SIGNIFICANCE: An electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting.

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients). The model was tested on 8340 notes from 3776 patients with epilepsy whose surgical candidacy status was unknown (2029 male, 1747 female, median age = 9 years; age range = 0-60 years). Multiple linear regression using demographic variables as covariates was used to test for correlations between patient race and surgical candidacy scores. After accounting for other demographic and socioeconomic variables, patient race, gender, and primary language did not influence surgical candidacy scores (P > .35 for all). Higher scores were given to patients >18 years old who traveled farther to receive care, and those who had a higher family income and public insurance (P < .001, .001, .001, and .01, respectively). Demographic effects on surgical candidacy scores appeared to reflect patterns in patient referrals.

The Reliability of Computerized Physician Order Entry Data for Research Studies.

OBJECTIVES: In the United States, adolescents account for nearly half of the newly diagnosed sexually transmitted infections annually, and many of these infections are asymptomatic. Adolescents often seek care in pediatric emergency departments; thus, the emergency department is an important setting to implement adolescent sexually transmitted infection screening. Before implementation, baseline data reflecting current screening rates of symptomatic and asymptomatic patients were needed. This study aimed to evaluate the accuracy of provider-reported rates of symptomatic and asymptomatic chlamydia (CT) and gonorrhea (GC) testing in adolescents overall and pre-electronic health record (EHR) and post-EHR order modification in preparation for a research intervention. METHODS: This was a 1-year prospective, observational study. Provider reason for CT/GC testing was added to the existing EHR order. Chart reviews were performed to ensure the accuracy of clinician CT/GC testing choices (symptomatic vs asymptomatic). Frequencies of testing choices were obtained. Order modifications were made to further clarify the definitions. A Student t test was used to compare data preorder and postorder modification. RESULTS: When relying on providers to report reasons for CT/GC testing (symptomatic vs asymptomatic), many patients were misclassified based on a priori defined testing reasons. After order modification, rates of provider-reported symptomatic testing remained unchanged (P = 0.16). Provider-reported asymptomatic testing significantly declined (P = 0.004); however, 23.2% of those tested continued to be misclassified. CONCLUSIONS: Provider-entered EHR data are increasingly being used in research studies; thus, it is important to ensure its accuracy and reliability before study implementation.

A Time-and-Motion Study of Clinical Trial Eligibility Screening in a Pediatric Emergency Department.

OBJECTIVE: Challenges with efficient patient recruitment including sociotechnical barriers for clinical trials are major barriers to the timely and efficacious conduct of translational studies. We conducted a time-and-motion study to investigate the workflow of clinical trial enrollment in a pediatric emergency department. METHODS: We observed clinical research coordinators during 3 clinically staffed shifts. One clinical research coordinator was shadowed at a time. Tasks were marked in 30-second intervals and annotated to include patient screening, patient contact, performing procedures, and physician contact. Statistical analysis was conducted on the patient enrollment activities. RESULTS: We conducted fifteen 120-minute observations from December 12, 2013, to January 3, 2014 and shadowed 8 clinical research coordinators. Patient screening took 31.62% of their time, patient contact took 18.67%, performing procedures took 17.6%, physician contact was 1%, and other activities took 31.0%. CONCLUSIONS: Screening patients for eligibility constituted the most time. Automated screening methods could help reduce this time. The findings suggest improvement areas in recruitment planning to increase the efficiency of clinical trial enrollment.

A Comparison of Existing Methods to Detect Weight Data Errors in a Pediatric Academic Medical Center.

Dosing errors due to erroneous body weight entry can be mitigated through algorithms designed to detect anomalies in weight patterns. To prepare for the development of a new algorithm for weight-entry error detection, we compared methods for detecting weight anomalies to human annotation, including a regression-based method employed in a real-time web service. Using a random sample of 4,000 growth charts, annotators identified clinically important anomalies with good inter-rater reliability. Performance of the three detection algorithms was variable, with the best performance from the algorithm that takes into account weights collected after the anomaly was recorded. All methods were highly specific, but positive predictive value ranged from < 5% to over 82%. There were 203 records of missed errors, but all of these were either due to no prior data points or errors too small to be clinically significant. This analysis illustrates the need for better weight-entry error detection algorithms.

An Electronic Health Record-Based Strategy to Address Child Tobacco Smoke Exposure.

INTRODUCTION: A high proportion of children presenting to pediatric urgent cares are exposed to tobacco smoke. An electronic health record-based clinical decision support system for nurses to facilitate guideline-based tobacco smoke exposure screening and counseling for caregivers who smoke was designed and evaluated. DESIGN: A mixed-methods, 3-month, prospective study that began in November 2015, data were analyzed in June 2016. SETTING/PARTICIPANTS: Five urgent cares that were part of a large children's hospital in Cincinnati, OH. Participants were urgent care nurses. INTERVENTION: The clinical decision support system prompted nurses to Ask, Advise, Assess, and Assist caregivers to quit smoking. Monthly feedback reports were also provided. MAIN OUTCOME MEASURE: Clinical decision support system use rates, nurses' attitudes towards tobacco smoke exposure intervention, and percentage of children screened and caregivers counseled. RESULTS: All nurses used the clinical decision support system. Compared with Month 1, nurses were twice as likely to advise and assess during Months 2 and 3. There was significant improvement in nurses feeling prepared to assist caregivers in quitting. Nurses reported that feedback reports motivated them to use the clinical decision support system, and that it was easy to use. Almost 65% of children were screened for tobacco smoke exposure; 19.5% screened positive. Of caregivers identified as smokers, 26% were advised to quit and 29% were assessed for readiness to quit. Of those assessed, 67% were interested in quitting, and of those, 100% were assisted. CONCLUSIONS: A clinical decision support system increased rates of tobacco smoke exposure screening and intervention in pediatric urgent cares. Rates might further improve by incorporating all components of the clinical decision support system into the electronic health record. TRIAL REGISTRATION: This study is registered at www.clinicaltrials.gov NCT02489708.

Laboratory Screening for Children Entering Foster Care.

OBJECTIVES: To determine the prevalence of medical illness detected by laboratory screening in children entering foster care in a single, urban county. METHODS: All children entering foster care in a single county in Ohio were seen at a consultation foster care clinic and had laboratory screening, including testing for infectious diseases such as HIV, hepatitis B, hepatitis C, syphilis, and tuberculosis as well as for hemoglobin and lead levels. RESULTS: Over a 3-year period (2012-2015), laboratory screening was performed on 1977 subjects entering foster care in a consultative foster care clinic. The prevalence of hepatitis B, hepatitis C, syphilis, and tuberculosis were all found to be <1%. There were no cases of HIV. Seven percent of teenagers entering foster care tested positive for Chlamydia. A secondary finding was that 54% of subjects were hepatitis B surface antibody-negative, indicating an absence of detected immunity to the hepatitis B virus. CONCLUSIONS: Routine laboratory screening for children entering foster care resulted in a low yield. Targeted, rather than routine, laboratory screening may be a more clinically meaningful approach for children entering foster care.

Development of a Tobacco Cessation Clinical Decision Support System for Pediatric Emergency Nurses.

Almost 50% of children who visit the pediatric emergency department are exposed to tobacco smoke. However, pediatric emergency nurses do not routinely address this issue. The incorporation of a clinical decision support system into the electronic health record may improve the rates of tobacco exposure screening and interventions. We used a mixed-methods design to develop, refine, and implement an evidence-based clinical decision support system to help nurses screen, educate, and assist caregivers to quit smoking. We included an advisory panel of emergency department experts and leaders and focus and user groups of nurses. The prompts include the following: (1) "Ask" about child smoke exposure and caregiver smoking; (2) "Advise" caregivers to reduce their child's smoke exposure by quitting smoking; (3) "Assess" interest; and (4) "Assist" caregivers to quit. The clinical decision support system was created to reflect nurses' suggestions and was implemented in five busy urgent care settings with 38 nurses. The nurses reported that the system was easy to use and helped them to address caregiver smoking. The use of this innovative tool may create a sustainable and disseminable model for prompting nurses to provide evidence-based tobacco cessation treatment.

Development and Testing of a Computerized Decision Support System to Facilitate Brief Tobacco Cessation Treatment in the Pediatric Emergency Department: Proposal and Protocol.

BACKGROUND: Tobacco smoke exposure (TSE) is unequivocally harmful to children's health, yet up to 48% of children who visit the pediatric emergency department (PED) and urgent care setting are exposed to tobacco smoke. The incorporation of clinical decision support systems (CDSS) into the electronic health records (EHR) of PED patients may improve the rates of screening and brief TSE intervention of caregivers and result in decreased TSE in children. OBJECTIVE: We propose a study that will be the first to develop and evaluate the integration of a CDSS for Registered Nurses (RNs) into the EHR of pediatric patients to facilitate the identification of caregivers who smoke and the delivery of TSE interventions to caregivers in the urgent care setting. METHODS: We will conduct a two-phase project to develop, refine, and integrate an evidence-based CDSS into the pediatric urgent care setting. RNs will provide input on program content, function, and design. In Phase I, we will develop a CDSS with prompts to: (1) ASK about child TSE and caregiver smoking, (2) use a software program, Research Electronic Data Capture (REDCap), to ADVISE caregivers to reduce their child's TSE via total smoking home and car bans and quitting smoking, and (3) ASSESS their interest in quitting and ASSIST caregivers to quit by directly connecting them to their choice of free cessation resources (eg, Quitline, SmokefreeTXT, or SmokefreeGOV) during the urgent care visit. We will create reports to provide feedback to RNs on their TSE counseling behaviors. In Phase II, we will conduct a 3-month feasibility trial to test the results of implementing our CDSS on changes in RNs' TSE-related behaviors, and child and caregiver outcomes. RESULTS: This trial is currently underway with funding support from the National Institutes of Health/National Cancer Institute. We have completed Phase I. The CDSS has been developed with input from our advisory panel and RNs, and pilot tested. We are nearing completion of Phase II, in which we are conducting the feasibility trial, analyzing data, and disseminating results. CONCLUSIONS: This project will develop, iteratively refine, integrate, and pilot test the use of an innovative CDSS to prompt RNs to provide TSE reduction and smoking cessation counseling to caregivers who smoke. If successful, this approach will create a sustainable and disseminable model for prompting pediatric practitioners to apply tobacco-related guideline recommendations. This systems-based approach has the potential to reach at least 12 million smokers a year and significantly reduce TSE-related pediatric illnesses and related costs.

Prompting clinicians about preventive care measures: a systematic review of randomized controlled trials.

Preventive care measures remain underutilized despite recommendations to increase their use. The objective of this review was to examine the characteristics, types, and effects of paper- and computer-based interventions for preventive care measures. The study provides an update to a previous systematic review. We included randomized controlled trials that implemented a physician reminder and measured the effects on the frequency of providing preventive care. Of the 1,535 articles identified, 28 met inclusion criteria and were combined with the 33 studies from the previous review. The studies involved 264 preventive care interventions, 4,638 clinicians and 144,605 patients. Implementation strategies included combined paper-based with computer generated reminders in 34 studies (56%), paper-based reminders in 19 studies (31%), and fully computerized reminders in 8 studies (13%). The average increase for the three strategies in delivering preventive care measures ranged between 12% and 14%. Cardiac care and smoking cessation reminders were most effective. Computer-generated prompts were the most commonly implemented reminders. Clinician reminders are a successful approach for increasing the rates of delivering preventive care; however, their effectiveness remains modest. Despite increased implementation of electronic health records, randomized controlled trials evaluating computerized reminder systems are infrequent.