The Longitudinal Reciprocal Relationships of Tooth Brushing Behaviors Between Underserved Children and Their Caregivers.

BACKGROUND: Tooth brushing is effective in preventing early childhood caries. However, it is unclear how children's and caregiver's tooth brushing are reciprocally related. PURPOSE: The current study investigated whether the longitudinal relationships between children and caregiver tooth brushing are moderated by a caregiver-targeted child oral health intervention and caregiver depression. METHODS: Secondary analysis of a randomized clinical trial that tested whether caregiver-targeted oral health text messages (OHT) outperformed child wellness text messages (CWT) on pediatric dental caries and oral health behaviors (n = 754, mean child age = 2.9 years, 56.2% Black, 68.3%

Tooth brushing is effective in preventing dental cavities in children, but we do not know if or how children and caregiver brushing frequencies are related. This is important because interventions targeting children's oral health may also have the potential to benefit their caregiver's behaviors. Our study examined whether caregiver brushing of their own teeth and caregiver brushing of their young child's teeth positively influenced each other over time. We also explored whether this relationship was less likely if caregivers experienced depressive symptoms and more likely if caregivers participated in a text message program focused on improving their child's oral health. Results showed that caregiver and child tooth brushing behaviors positively influenced each other over time, but this relationship was observed only in caregivers who received the child oral health program (as opposed to the control group) and who reported low depressive symptoms (in contrast to caregivers with high depression symptoms). Our findings suggest that while caregivers and children positively influence each other's tooth-brushing behaviors over time, additional support is essential for caregivers experiencing depression to fully realize these reciprocal benefits.

A GPT-based EHR modeling system for unsupervised novel disease detection.

OBJECTIVE: To develop an Artificial Intelligence (AI)-based anomaly detection model as a complement of an "astute physician" in detecting novel disease cases in a hospital and preventing emerging outbreaks. METHODS: Data included hospitalized patients (n = 120,714) at a safety-net hospital in Massachusetts. A novel Generative Pre-trained Transformer (GPT)-based clinical anomaly detection system was designed and further trained using Empirical Risk Minimization (ERM), which can model a hospitalized patient's Electronic Health Records (EHR) and detect atypical patients. Methods and performance metrics, similar to the ones behind the recent Large Language Models (LLMs), were leveraged to capture the dynamic evolution of the patient's clinical variables and compute an Out-Of-Distribution (OOD) anomaly score. RESULTS: In a completely unsupervised setting, hospitalizations for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection could have been predicted by our GPT model at the beginning of the COVID-19 pandemic, with an Area Under the Receiver Operating Characteristic Curve (AUC) of 92.2 %, using 31 extracted clinical variables and a 3-day detection window. Our GPT achieves individual patient-level anomaly detection and mortality prediction AUC of 78.3 % and 94.7 %, outperforming traditional linear models by 6.6 % and 9 %, respectively. Different types of clinical trajectories of a SARS-CoV-2 infection are captured by our model to make interpretable detections, while a trend of over-pessimistic outcome prediction yields a more effective detection pathway. Furthermore, our comprehensive GPT model can potentially assist clinicians with forecasting patient clinical variables and developing personalized treatment plans. CONCLUSION: This study demonstrates that an emerging outbreak can be accurately detected within a hospital, by using a GPT to model patient EHR time sequences and labeling them as anomalous when actual outcomes are not supported by the model. Such a GPT is also a comprehensive model with the functionality of generating future patient clinical variables, which can potentially assist clinicians in developing personalized treatment plans.

Maternal pre-pregnancy BMI, offspring epigenome-wide DNA methylation, and childhood obesity: findings from the Boston Birth Cohort.

BACKGROUND: Maternal pre-pregnancy obesity is an established risk factor for childhood obesity. Investigating epigenetic alterations induced by maternal obesity during fetal development could gain mechanistic insight into the developmental origins of childhood obesity. While obesity disproportionately affects underrepresented racial and ethnic mothers and children in the USA, few studies investigated the role of prenatal epigenetic programming in intergenerational obesity of these high-risk populations. METHODS: This study included 903 mother-child pairs from the Boston Birth Cohort, a predominantly urban, low-income minority birth cohort. Mother-infant dyads were enrolled at birth and the children were followed prospectively to age 18 years. Infinium Methylation EPIC BeadChip was used to measure epigenome-wide methylation level of cord blood. We performed an epigenome-wide association study of maternal pre-pregnancy body mass index (BMI) and cord blood DNA methylation (DNAm). To quantify the degree to which cord blood DNAm mediates the maternal BMI-childhood obesity, we further investigated whether maternal BMI-associated DNAm sites impact birthweight or childhood overweight or obesity (OWO) from age 1 to age 18 and performed corresponding mediation analyses. RESULTS: The study sample contained 52.8% maternal pre-pregnancy OWO and 63.2% offspring OWO at age 1-18 years. Maternal BMI was associated with cord blood DNAm at 8 CpG sites (genome-wide false discovery rate [FDR] < 0.05). After accounting for the possible interplay of maternal BMI and smoking, 481 CpG sites were discovered for association with maternal BMI. Among them 123 CpGs were associated with childhood OWO, ranging from 42% decrease to 87% increase in OWO risk for each SD increase in DNAm. A total of 14 identified CpG sites showed a significant mediation effect on the maternal BMI-child OWO association (FDR < 0.05), with mediating proportion ranging from 3.99% to 25.21%. Several of these 14 CpGs were mapped to genes in association with energy balance and metabolism (AKAP7) and adulthood metabolic syndrome (CAMK2B). CONCLUSIONS: This prospective birth cohort study in a high-risk yet understudied US population found that maternal pre-pregnancy OWO significantly altered DNAm in newborn cord blood and provided suggestive evidence of epigenetic involvement in the intergenerational risk of obesity.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses.

BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records.

BACKGROUND: Extensive data available in electronic health records (EHRs) have the potential to improve asthma care and understanding of factors influencing asthma outcomes. However, this work can be accomplished only when the EHR data allow for accurate measures of severity, which at present are complex and inconsistent. OBJECTIVE: Our aims were to create and evaluate a standardized pediatric asthma severity phenotype based in clinical asthma guidelines for use in EHR-based health initiatives and studies and also to examine the presence and absence of these data in relation to patient characteristics. METHODS: We developed an asthma severity computable phenotype and compared the concordance of different severity components contributing to the phenotype to trends in the literature. We used multivariable logistic regression to assess the presence of EHR data relevant to asthma severity. RESULTS: The asthma severity computable phenotype performs as expected in comparison with national statistics and the literature. Severity classification for a child is maximized when based on the long-term medication regimen component and minimized when based only on the symptom data component. Use of the severity phenotype results in better, clinically grounded classification. Children for whom severity could be ascertained from these EHR data were more likely to be seen for asthma in the outpatient setting and less likely to be older or Hispanic. Black children were less likely to have lung function testing data present. CONCLUSION: We developed a pragmatic computable phenotype for pediatric asthma severity that is transportable to other EHRs.

Asking the Right Questions About Secondhand Smoke.

INTRODUCTION: Despite knowledge about major health effects of secondhand tobacco smoke (SHS) exposure, systematic incorporation of SHS screening and counseling in clinical settings has not occurred. METHODS: A three-round modified Delphi Panel of tobacco control experts was convened to build consensus on the screening questions that should be asked and identify opportunities and barriers to SHS exposure screening and counseling. The panel considered four questions: (1) what questions should be asked about SHS exposure; (2) what are the top priorities to advance the goal of ensuring that these questions are asked; (3) what are the barriers to achieving these goals; and (4) how might these barriers be overcome. Each panel member submitted answers to the questions. Responses were summarized and successive rounds were reviewed by panel members for consolidation and prioritization. RESULTS: Panelists agreed that both adults and children should be screened during clinical encounters by asking if they are exposed or have ever been exposed to smoke from any tobacco products in their usual environment. The panel found that consistent clinician training, quality measurement or other accountability, and policy and electronic health records interventions were needed to successfully implement consistent screening. CONCLUSIONS: The panel successfully generated screening questions and identified priorities to improve SHS exposure screening. Policy interventions and stakeholder engagement are needed to overcome barriers to implementing effective SHS screening. IMPLICATIONS: In a modified Delphi panel, tobacco control and clinical prevention experts agreed that all adults and children should be screened during clinical encounters by asking if they are exposed or have ever been exposed to smoke from tobacco products. Consistent training, accountability, and policy and electronic health records interventions are needed to implement consistent screening. Increasing SHS screening will have a significant impact on public health and costs.

Exploring Opportunities for Household-Level Chronic Care Management Using Linked Electronic Health Records of Adults and Children: A Retrospective Cohort Study.

OBJECTIVE: Linking electronic health records (EHR) of pediatric and adult patients living in the same household has the potential to improve chronic care management efficiencies by facilitating the delivery of services to multiple household members at once. However, little is known about relationship between the chronic medical (CM) and behavioral health (CBH) of adults and children living in common households. METHODS: EHR data for children were linked to the EHR data of adults living at the same address during the same time in a retrospective cohort study from 2006 to 2014 to evaluate associations between adult and child CM and CBH conditions within a Boston safety-net primary care patient sample. RESULTS: Of the 13,845 included children, 61.6% lived with at least one adult with ≥ 1 CM or CBH condition. Compared to children living with an adult(s) without a chronic condition, children living with an adult with a CM or CBH condition had a respective 16.2% and 18.1% increased likelihood of having a chronic condition themselves, with multiple adult chronic conditions in adults increasing children's likelihood. CONCLUSIONS FOR PRACTICE: We found a positive association between the chronic diseases of adult and child household members. Given the clustering of child and adult chronic disease within households, using EHR data to support the care management needs of multiple members of households may be a promising approach to improving child and adult health in safety-net settings.

Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach.

Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large datasets from the Boston Medical Center, the largest safety-net hospital system in New England.

Electronic health record (EHR) based postmarketing surveillance of adverse events associated with pediatric off-label medication use: A case study of short-acting beta-2 agonists and arrhythmias.

PURPOSE: Use electronic health record (EHR) data to (1) estimate the risk of arrhythmia associated with inhaled short-acting beta-2 agonists (SABA) in pediatric patients and (2) determine whether risk varied by on-label versus off-label prescribing. METHODS: Retrospective cohort study of 335 041 children ≤18 years using EHR primary care data from 2 pediatric health systems (2011-2013). A series of monthly pseudotrials were created, using propensity score methodology to balance baseline characteristics between SABA-exposed (identified by prescription) and SABA-unexposed children. Association between SABA and subsequent arrhythmia for each health system was estimated through pooled logistic regression with separate estimates for children initiating under and over 4 years old (off-label and on-label, respectively). RESULTS: Eleven percent of the cohort received a SABA prescription, 57% occurred under the age of 4 years (off-label). During the follow-up period, there were 283 first arrhythmia events, most commonly atrial tachyarrhythmias and premature ventricular/atrial contractions. In 1 health system, adjusted risk for arrhythmia was increased among exposed children (OR 1.89, 95% CI 1.31-2.73) without evidence of interaction between label status and risk. The absolute adjusted rate difference was 3.6/10 000 person-years of SABA exposure. The association between SABA exposure and arrhythmias was less strong in the second system (OR 1.26, 95% CI 0.30-5.33). CONCLUSION: Using EHR data, we could estimate the risk of a rare event associated with medication use and determine difference in risk related to on-label versus off-label status. These findings support the value of EHR-based data for postmarketing drug studies in the pediatric population.

Clinical Operations Variables are Associated With Blood Pressure Outcomes.

BACKGROUND: Uncontrolled blood pressure (BP), among patients diagnosed and treated for the condition, remains an important clinical challenge; aspects of clinical operations could potentially be adjusted if they were associated with better outcomes. OBJECTIVES: To assess clinical operations factors' effects on normalization of uncontrolled BP. RESEARCH DESIGN: Observational cohort study. SUBJECTS: Patients diagnosed with hypertension from a large urban clinical practice (2005-2009). MEASURES: We obtained clinical data on BP, organized by person-month, and administrative data on primary care provider (PCP) staffing. We assessed the resolution of an episode of uncontrolled BP as a function of time-varying covariates including practice-level appointment volume, individual clinicians' appointment volume, overall practice-level PCP staffing, and number of unique PCPs. RESULTS: Among the 7409 unique patients representing 50,403 person-months, normalization was less likely for the patients in whom the episode starts during months when the number of unique PCPs were high [the top quintile of unique PCPs was associated with a 9 percentage point lower probability of normalization (P<0.01) than the lowest quintile]. Practice appointment volume negatively affected the likelihood of normalization [episodes starting in months with the most appointments were associated with a 6 percentage point reduction in the probability of normalization (P=0.01)]. Neither clinician appointment volume nor practice clinician staffing levels were significantly associated with the probability of normalization. CONCLUSIONS: Findings suggest that clinical operations factors can affect clinical outcomes like BP normalization, and point to the importance of considering outcome effects when organizing clinical care.

Improvement in influenza vaccination rates in a pediatric sickle cell disease clinic.

BACKGROUND: Children with sickle cell disease (SCD) are at increased risk of complications from influenza. However, despite widespread recommendations that these patients receive an annual influenza immunization, reported vaccination rates remain very low at under 50%. PROCEDURE: Our aim was to increase the influenza vaccination rate among our pediatric patients with SCD aged 6 months to 21 years over two influenza seasons, 2012-2013 and 2013-2014, to 80%, consistent with the Health People 2020 goal. We used multiple quality improvement methods, based on the literature and our previous experience in other aspects of SCD care, including parent and provider education, enhancement of our EHR, use of a SCD patient registry and reminder and recall done by a patient navigator. RESULTS: We vaccinated 80% of our pediatric patients with SCD for influenza during the 2012-2013 season and 90% of patients in 2013-2014. Our early season vaccination rates were nearly double that of those for the general population. CONCLUSIONS: Use of quality improvement methods can increase rates of influenza vaccination for this high-risk population, suggesting that less health care utilization and lower cost might result.

Evaluation of a web-based asthma self-management system: a randomised controlled pilot trial.

BACKGROUND: Asthma is the most common chronic condition of childhood and disproportionately affects inner-city minority children. Low rates of asthma preventer medication adherence is a major contributor to poor asthma control in these patients. Web-based methods have potential to improve patient knowledge and medication adherence by providing interactive patient education, monitoring of symptoms and medication use, and by facilitation of communication and teamwork among patients and health care providers. Few studies have evaluated web-based asthma support environments using all of these potentially beneficial interventions. The multidimensional website created for this study, BostonBreathes, was designed to intervene on multiple levels, and was evaluated in a pilot trial. METHODS: An interactive, engaging website for children with asthma was developed to promote adherence to asthma medications, provide a platform for teamwork between caregivers and patients, and to provide primary care providers with up-to-date symptom information and data on medication use. Fifty-eight (58) children primarily from inner city Boston with persistent-level asthma were randomised to either usual care or use of BostonBreathes. Subjects completed asthma education activities, and reported their symptoms and medication use. Primary care providers used a separate interface to monitor their patients' website use, their reported symptoms and medication use, and were able to communicate online via a discussion board with their patients and with an asthma specialist. RESULTS: After 6-months, reported wheezing improved significantly in both intervention and control groups, and there were significant improvements in the intervention group only in night-time awakening and parental loss of sleep, but there were no significant differences between intervention and control groups in these measures. Emergency room or acute visits to a physician for asthma did not significantly change in either group. Among the subgroup of subjects with low controller medication adherence at baseline, adherence improved significantly only in the intervention group. Knowledge of the purpose of controller medicine increased significantly in the intervention group, a statistically significant improvement over the control group. CONCLUSIONS: This pilot study suggests that a multidimensional web-based educational, monitoring, and communication platform may have positive influences on pediatric patients' asthma-related knowledge and use of asthma preventer medications.

Assessing parental self-efficacy for obesity prevention related behaviors.

BACKGROUND: Reliable, valid and theoretically consistent measures that assess a parent's self-efficacy for helping a child with obesity prevention behaviors are lacking. OBJECTIVES: To develop measures of parental self-efficacy for four behaviors: 1) helping their child get at least 60 minutes of moderate intensity physical activity every day, 2) helping one's child consume five servings of fruits and vegetables each day, 3) limiting sugary drinks to once a week, and 4) limiting consumption of fruit juice to 6 ounces every day. METHODS: Sequential methods of scale development were used. An item pool was generated based on theory and qualitative interviews, and reviewed by content experts. Scales were administered to parents or legal guardians of children 4-10 years old. The item pool was reduced using principal component analysis. Confirmatory factor analysis tested the resulting models in a separate sample. SUBJECTS: 304 parents, majority were women (88%), low-income (61%) and single parents (61%). Ethnic distribution was 40% Black and 37% white. RESULTS: All scales had excellent fit indices: Comparative fit index> .98 and chi-squares (Pediatrics 120 Suppl 4:S229-253, 2007) = .85 - 7.82. Alphas and one-week test-retest ICC's were ≥.80. Significant correlations between self-efficacy scale scores and their corresponding behaviors ranged from .13-.29 (all p < 0.03). CONCLUSIONS: We developed four, four-item self-efficacy scales with excellent psychometric properties and construct validity using diverse samples of parents. CLINICAL TRIAL REGISTRATION: NCT01768533.

The Health Equity Explorer: An open-source resource for distributed health equity visualization and research across common data models.

Introduction: There is an urgent need to address pervasive inequities in health and healthcare in the USA. Many areas of health inequity are well known, but there remain important unexplored areas, and for many populations in the USA, accessing data to visualize and monitor health equity is difficult. Methods: We describe the development and evaluation of an open-source, R-Shiny application, the "Health Equity Explorer (H2E)," designed to enable users to explore health equity data in a way that can be easily shared within and across common data models (CDMs). Results: We have developed a novel, scalable informatics tool to explore a wide variety of drivers of health, including patient-reported Social Determinants of Health (SDoH), using data in an OMOP CDM research data repository in a way that can be easily shared. We describe our development process, data schema, potential use cases, and pilot data for 705,686 people who attended our health system at least once since 2016. For this group, 996,382 unique observations for questions related to food and housing security were available for 324,630 patients (at least one answer for all 46% of patients) with 65,152 (20.1% of patients with at least one visit and answer) reporting food or housing insecurity at least once. Conclusions: H2E can be used to support dynamic and interactive explorations that include rich social and environmental data. The tool can support multiple CDMs and has the potential to support distributed health equity research and intervention on a national scale.

Food and housing insecurity, COVID-19 pandemic effects on health-related activities, and care plans for children with obesity.

OBJECTIVE: To understand the association between food insecurity (FI) and housing insecurity (HI) risk, the effects of the COVID-19 pandemic on health-related activities among children with overweight or obesity, and caregivers' and clinicians' challenges and priorities related to pediatric weight management. METHODS: We conducted surveys with caregivers of children with overweight and obesity and pediatric clinicians at two academic medical centers in the Greater Boston area. We used multivariable logistic regression models to examine associations between FI and HI risk and the effects of the COVID-19 pandemic on health-related activities and descriptive statistics to summarize caregivers' and clinicians' challenges and priorities related to pediatric weight management. RESULTS: We analyzed data from surveys with 344 caregivers and 100 pediatric clinicians. Overall, 37% of caregivers endorsed both FI+HI, 18% FI alone, 10% HI alone, and 35% neither FI/HI. In the adjusted logistic regression models, combined FI+HI (reference: neither FI/HI) was significantly associated with higher odds of sleeping less (aOR 2.96 [95% confidence interval (CI): 1.46, 6.01]) and higher odds of spending less time outside (aOR 2.10 [95% CI: 1.06, 4.16]). Top priorities for pediatric weight management identified by both caregivers and clinicians were related to physical activity and availability of outdoor spaces. CONCLUSIONS: Endorsement of both FI+HI was associated with children getting less sleep and spending less time outside during the COVID-19 pandemic. Future innovations in care plans for children with overweight and obesity should be adapted to a family's social context and should incorporate caregivers' and clinicians' challenges and priorities.

Racial Disparities in Diabetes Care and Outcomes for Patients with Visual Impairment: A Descriptive Analysis of the TriNetX Research Network.

Background: This research delves into the confluence of racial disparities and health inequities among individuals with disabilities, with a focus on those contending with both diabetes and visual impairment. Methods: Utilizing data from the TriNetX Research Network, which includes electronic medical records of roughly 115 million patients from 83 anonymous healthcare organizations, this study employs a directed acyclic graph (DAG) to pinpoint confounders and augment interpretation. We identified patients with visual impairments using ICD-10 codes, deliberately excluding diabetes-related ophthalmology complications. Our approach involved multiple race-stratified analyses, comparing co-morbidities like chronic pulmonary disease in visually impaired patients against their counterparts. We assessed healthcare access disparities by examining the frequency of annual visits, instances of two or more A1c measurements, and glomerular filtration rate (GFR) measurements. Additionally, we evaluated diabetes outcomes by comparing the risk ratio of uncontrolled diabetes (A1c > 9.0) and chronic kidney disease in patients with and without visual impairments. Results: The incidence of diabetes was substantially higher (nearly double) in individuals with visual impairments across White, Asian, and African American populations. Higher rates of chronic kidney disease were observed in visually impaired individuals, with a risk ratio of 1.79 for African American, 2.27 for White, and non-significant for the Asian group. A statistically significant difference in the risk ratio for uncontrolled diabetes was found only in the White cohort (0.843). White individuals without visual impairments were more likely to receive two A1c tests, a trend not significant in other racial groups. African Americans with visual impairments had a higher rate of glomerular filtration rate testing. However, White individuals with visual impairments were less likely to undergo GFR testing, indicating a disparity in kidney health monitoring. This pattern of disparity was not observed in the Asian cohort. Conclusions: This study uncovers pronounced disparities in diabetes incidence and management among individuals with visual impairments, particularly among White, Asian, and African American groups. Our DAG analysis illuminates the intricate interplay between SDoH, healthcare access, and frequency of crucial diabetes monitoring practices, highlighting visual impairment as both a medical and social issue.

Better Guidelines and Policies: AAP's Partnership for Policy Implementation.

OBJECTIVES: In 2005, the American Academy of Pediatrics founded the Partnership for Policy Implementation (PPI). The PPI has collaborated with authors to improve the quality of clinical guidelines, technical reports, and policies that standardize care delivery, improve care quality and patient outcomes, and reduce variation and costs. METHODS: In this article, we describe how the PPI trained informaticians apply a variety of tools and techniques to these guidance documents, eliminating ambiguity in clinical recommendations and allowing guideline recommendations to be implemented by practicing clinicians and electronic health record (EHR) developers more easily. RESULTS: Since its inception, the PPI has participated in the development of 45 published and 27 in-progress clinical practice guidelines, policy statements, technical and clinical reports, and other projects endorsed by the American Academy of Pediatrics. The partnership has trained informaticians to apply a variety of tools and techniques to eliminate ambiguity or lack of decidability and can be implemented by practicing clinicians and EHR developers. CONCLUSIONS: With the increasing use of EHRs in pediatrics, the need for medical societies to improve the clarity, decidability, and actionability of their guidelines has become more important than ever.

Validation of an ICD-code-based case definition for psychotic illness across three health systems.

Background and Hypothesis: Early detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. Study Design: Using EHRs at three health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into five higher-order groups. 1,133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. Study Results: PPVs across all diagnostic groups and hospital systems exceeded 70%: Massachusetts General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). Conclusions: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the development of risk prediction models designed to predict or detect undiagnosed psychosis.

Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems.

BACKGROUND AND HYPOTHESIS: Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. STUDY DESIGN: Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. STUDY RESULTS: PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). CONCLUSIONS: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis.