Pediatric Electronic Health Record Research Articles

Multisource representation learning for pediatric knowledge extraction from electronic health records.

Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts. MUGS integrates graphical data from both pediatric and general EHR systems, along with hierarchical medical ontologies, to create embeddings that adaptively capture both the homogeneity and heterogeneity between hospital systems. These embeddings enable refined EHR feature engineering and nuanced patient profiling, proving particularly effective in identifying pediatric patients similar to specific profiles, with a focus on pulmonary hypertension (PH). MUGS embeddings, resistant to negative transfer, outperform other benchmark methods in multiple applications, advancing evidence-based pediatric research.

A Clinical Outcomes Data Archive for a Comprehensive Fetal Diagnosis and Treatment Center

Introduction: Data on near- and long-term clinical outcomes are critical for the care of all maternal-fetal patients presenting to a fetal center. This is especially important since physiologic and neurodevelopmental attributes do not manifest until later childhood when multilevel (e.g., individual, family, policy) factors have a direct influence on health outcomes. Electronic health records (EHRs) create opportunity for efficient data collection. However, documentation structures are not designed for acquisition of key attributes, and changes over time and between-clinician differences can affect resultant output. Therefore, EHR derived datasets have limited ability to accurately characterize the clinical presentation and care trajectory of patients with congenital anomalies. In addition, in most systems, the fetus lacks a digital identity and requires relinking fetal attributes documented in the maternal chart to those from the pediatric EHR. This conundrum amplifies in the setting of multiple gestation, returning maternal patients, and pregnancies with fetal demise. Moreover, current data capture systems result in incomplete abstraction of variables that may confound, mediate, or moderate critical associations. Our objective was to develop and implement a prospective data capture platform to transform EHR data into an analytic-grade database for multipurpose use. Methods: A unified platform for longitudinal follow-up of maternal-child dyads cared for at our fetal center, named the Clinical Outcomes Data Archive (CODA), was constructed. CODA was designed using a data dictionary based on multidisciplinary and interprofessional expert input, a relational identity for each patient, fetus, and pregnancy, and a process by which EHR-sourced and chart-abstracted data are validated by a well-trained team. Descriptive analyses were performed for data acquired between July 2022 and July 2023, and a comparison of studies before and after implementation of CODA is presented. Conclusion: 5,394,106 data points were validated for 7,662 patients across 12 conditions. 2% of data points were found to be unreliable or undocumented. 91% of data points were sourced from the EHR. Eighty-five percent of condition-specific variables required manual chart abstraction. The study conducted with CODA was able to contribute to 18 other studies. CODA successfully merges EHR-sourced and manually abstracted documentation for longitudinal study of the maternal-child dyad.

Improving electronic health record documentation and use to promote evidence-based pediatric care.

Electronic health records (EHRs) often lack the necessary functionalities to support the full implementation of national clinical guidelines for pediatric care outlined in the American Academy of Pediatrics Bright Futures Guidelines. Using HealthySteps (HS), an evidence-based pediatric primary care program, as an exemplar, this study aimed to enhance pediatric EHRs, identify facilitators and barriers to EHR enhancements, and improve data quality for delivering clinical care as part of HS implementation and evidence building. Three HS sites-each differing in location, setting, number of children served, and mix of child insurance coverage-participated in the study. Sites received technical assistance to support data collection and EHR updates. A comprehensive evaluation, including a process evaluation and outcomes monitoring, was conducted to gauge progress toward implementing study data requirements over time. Data sources included administrative records, surveys, and interviews. All sites enhanced their EHRs yet relied on supplemental data systems to track care coordination. Sites improved documentation of required data, demonstrating reductions in missing data and increases in extractable data between baseline and follow-up assessments. For example, the percentage of missing social-emotional screening results ranged from 0% to 8.0% at study conclusion. Facilitators and barriers to EHR enhancements included organizational supports, leadership, and capacity building. With significant investment of time and resources, practices modified their EHRs to better capture services aligned with HS and Bright Futures. However, more scalable digital solutions are necessary to support EHR updates to help drive improvements in clinical care and outcomes for children and families.

Clinician Note Documentation of Parental Justice Involvement: Preliminary Evidence from Pediatric Electronic Health Records

Abstract: Little is known about clinical documentation for youth exposed to parental justiceinvolvement (e.g., parole, probation, jail, prison). We reviewed the electronic health records of 100 youth with at least one mention of parental incarceration between 2011–2019 from a large Midwestern pediatric hospital-based institution to describe clinical documentation and health characteristics. Within the sample, youth more commonly experienced incarceration of a father-identified figure (68%) as opposed to a mother-identified figure (32%). Seventeen percent (17%) of the youth were between zero and four years of age when clinicians documented exposure to a parent's incarceration. Nearly one-third of youth charts had no documentation regarding service referrals or follow-up from providers upon disclosure of parental incarceration. Few clinician documentation details were present related to the context of parental justice involvement (timing, type, and duration). Future research is needed to better understand the intersection of parental justice involvement and child health and service connection.

Cardiovascular health trajectories from age 2–12: a pediatric electronic health record study

PurposeMany children have non-ideal cardiovascular health (CVH), but little is known about the course of CVH in early childhood. We identified CVH trajectories in children and assess the generalizability of these trajectories in an external sample. MethodsWe used data spanning 2010–2018 from children aged 2–12 years within the Chicago Area Patient-Centered Outcomes Research Network—an electronic health record network. Four clinical systems comprised the derivation sample and a fifth the validation sample. Body mass index, blood pressure, cholesterol, and blood glucose were categorized as ideal, intermediate, and poor using clinical measurements, laboratory readings, and International Classification of Diseases diagnosis codes and summed for an overall CVH score. Group-based trajectory modeling was used to create CVH score trajectories which were assessed for classification accuracy in the validation sample. ResultsUsing data from 122,363 children (47% female, 47% non-Hispanic White) three trajectories were identified: 59.5% maintained high levels of clinical CVH, 23.4% had high levels of CVH that declined, and 17.1% had intermediate levels of CVH that further declined with age. A similar classification emerged when the trajectories were fitted in the validation sample. ConclusionsStratification of CVH was present by age 2, implicating the need for early life and preconception prevention strategies.

Views, Use, and Experiences of Web-Based Access to Pediatric Electronic Health Records for Children, Adolescents, and Parents: Scoping Review.

Ongoing efforts worldwide to provide patients with patient-accessible electronic health records (PAEHRs) have led to variability in adolescent and parental access across providers, regions, and countries. There is no compilation of evidence to guide policy decisions in matters such as access age and the extent of parent proxy access. In this paper, we outline our scoping review of different stakeholders' (including but not limited to end users) views, use, and experiences pertaining to web-based access to electronic health records (EHRs) by children, adolescents, and parents. The aim of this study was to identify, categorize, and summarize knowledge about different stakeholders' (eg, children and adolescents, parents, health care professionals [HCPs], policy makers, and designers of patient portals or PAEHRs) views, use, and experiences of EHR access for children, adolescents, and parents. A scoping review was conducted according to the Arksey and O'Malley framework. A literature search identified eligible papers that focused on EHR access for children, adolescents, and parents that were published between 2007 and 2021. A number of databases were used to search for literature (PubMed, CINAHL, and PsycINFO). The approach resulted in 4817 identified articles and 74 (1.54%) included articles. The papers were predominantly viewpoints based in the United States, and the number of studies on parents was larger than that on adolescents and HCPs combined. First, adolescents and parents without access anticipated low literacy and confidentiality issues; however, adolescents and parents who had accessed their records did not report such concerns. Second, the main issue for HCPs was maintaining adolescent confidentiality. This remained an issue after using PAEHRs for parents, HCPs, and other stakeholders but was not an experienced issue for adolescents. Third, the viewpoints of other stakeholders provided a number of suggestions to mitigate issues. Finally, education is needed for adolescents, parents, and HCPs. There is limited research on pediatric PAEHRs, particularly outside the United States, and on adolescents' experiences with web-based access to their records. These findings could inform the design and implementation of future regulations regarding access to PAEHRs. Further examination is warranted on the experiences of adolescents, parents, and HCPs to improve usability and utility, inform universal principles reducing the current arbitrariness in the child's age for own and parental access to EHRs among providers worldwide, and ensure that portals are equipped to safely and appropriately manage a wide variety of patient circumstances. RR2-10.2196/36158.

Mining Severe Drug Hypersensitivity Reaction Cases in Pediatric Electronic Health Records: Methodology Development and Applications.

BackgroundSevere drug hypersensitivity reactions (DHRs) refer to allergic reactions caused by drugs and usually present with severe skin rashes and internal damage as the main symptoms. Reporting of severe DHRs in hospitals now solely occurs through spontaneous reporting systems (SRSs), which clinicians in charge operate. An automatic identification system scrutinizes clinical notes and reports potential severe DHR cases.ObjectiveThe goal of the research was to develop an automatic identification system for mining severe DHR cases and discover more DHR cases for further study. The proposed method was applied to 9 years of data in pediatrics electronic health records (EHRs) of Beijing Children’s Hospital.MethodsThe phenotyping task was approached as a document classification problem. A DHR dataset containing tagged documents for training was prepared. Each document contains all the clinical notes generated during 1 inpatient visit in this data set. Document-level tags correspond to DHR types and a negative category. Strategies were evaluated for long document classification on the openly available National NLP Clinical Challenges 2016 smoking task. Four strategies were evaluated in this work: document truncation, hierarchy representation, efficient self-attention, and key sentence selection. In-domain and open-domain pretrained embeddings were evaluated on the DHR dataset. An automatic grid search was performed to tune statistical classifiers for the best performance over the transformed data. Inference efficiency and memory requirements of the best performing models were analyzed. The most efficient model for mining DHR cases from millions of documents in the EHR system was run.ResultsFor long document classification, key sentence selection with guideline keywords achieved the best performance and was 9 times faster than hierarchy representation models for inference. The best model discovered 1155 DHR cases in Beijing Children’s Hospital EHR system. After double-checking by clinician experts, 357 cases of severe DHRs were finally identified. For the smoking challenge, our model reached the record of state-of-the-art performance (94.1% vs 94.2%).ConclusionsThe proposed method discovered 357 positive DHR cases from a large archive of EHR records, about 90% of which were missed by SRSs. SRSs reported only 36 cases during the same period. The case analysis also found more suspected drugs associated with severe DHRs in pediatrics.

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients.

Respiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record (EHR) data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.

Intimate Partner Violence and the Pediatric Electronic Health Record: A Qualitative Study.

To explore expert perspectives on risks associated with the pediatric electronic health record (EHR) for intimate partner violence (IPV) survivors and their children and to identify strategies that may mitigate these risks. We conducted semistructured interviews with multidisciplinary pediatric IPV experts (nursing, physicians, social workers, hospital security, IPV advocates) recruited via snowball sampling. We coded interview transcripts using thematic analysis, then consolidated codes into themes. Twenty-eight participants completed interviews. Participants identified the primary source of risk as an abuser's potential access to a child's EHR by legal and illegal means. They noted that abuser's access to multiple pediatric EHR components (eg, online health portals, clinical notes, contact information) may result in escalated violence, stalking, and manipulation of IPV survivors. Suggested risk mitigation strategies included limited and coded documentation, limiting EHR access, and discussing documentation with the IPV survivor. Challenges to using these strategies included healthcare providers' usual practice of detailed documentation and that information documented may confer both risk and benefit concurrently. Reported potential benefits of the pediatric EHR for IPV survivors included ensuring continuity of care, decreasing need to repeatedly talk about trauma histories, and communication of safety plans. Our findings suggest the pediatric EHR may confer both risks and benefits for IPV survivors and their children. Further work is needed to develop best practices to address IPV risks related to the pediatric EHR, to ensure consistent use of these practices, and to include these practices as standard functionalities of the pediatric EHR.

Learning Models for Traumatic Brain Injury Mortality Prediction on Pediatric Electronic Health Records.

BackgroundTraumatic Brain Injury (TBI) is one of the leading causes of injury related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences, complicating medical interpretation and prognosis. Gathering clinical, demographic, and laboratory data to perform a prognosis requires time and skill in several clinical specialties. Machine learning (ML) methods can take advantage of the data and guide physicians toward a better prognosis and, consequently, better healthcare. The objective of this study was to develop and test a wide range of machine learning models and evaluate their capability of predicting mortality of TBI, at hospital discharge, while assessing the similarity between the predictive value of the data and clinical significance.MethodsThe used dataset is the Hackathon Pediatric Traumatic Brain Injury (HPTBI) dataset, composed of electronic health records containing clinical annotations and demographic data of 300 patients. Four different classification models were tested, either with or without feature selection. For each combination of the classification model and feature selection method, the area under the receiver operator curve (ROC-AUC), balanced accuracy, precision, and recall were calculated.ResultsMethods based on decision trees perform better when using all features (Random Forest, AUC = 0.86 and XGBoost, AUC = 0.91) but other models require prior feature selection to obtain the best results (k-Nearest Neighbors, AUC = 0.90 and Artificial Neural Networks, AUC = 0.84). Additionally, Random Forest and XGBoost allow assessing the feature's importance, which could give insights for future strategies on the clinical routine.ConclusionPredictive capability depends greatly on the combination of model and feature selection methods used but, overall, ML models showed a very good performance in mortality prediction for TBI. The feature importance results indicate that predictive value is not directly related to clinical significance.

Novel Pediatric Height Outlier Detection Methodology for Electronic Health Records via Machine Learning With Monotonic Bayesian Additive Regression Trees.

To create a new methodology that has a single simple rule to identify height outliers in the electronic health records (EHR) of children. We constructed 2 independent cohorts of children 2 to 8 years old to train and validate a model predicting heights from age, gender, race and weight with monotonic Bayesian additive regression trees. The training cohort consisted of 1376 children where outliers were unknown. The testing cohort consisted of 318 patients that were manually reviewed retrospectively to identify height outliers. The amount of variation explained in height values by our model, R2 , was 82.2% and 75.3% in the training and testing cohorts, respectively. The discriminatory ability to assess height outliers in the testing cohort as assessed by the area under the receiver operating characteristic curve was excellent, 0.841. Based on a relatively aggressive cutoff of 0.075, the outlier sensitivity is 0.713, the specificity 0.793; the positive predictive value 0.615 and the negative predictive value is 0.856. We have developed a new reliable, largely automated, outlier detection method which is applicable to the identification of height outliers in the pediatric EHR. This methodology can be applied to assess the veracity of height measurements ensuring reliable indices of body proportionality such as body mass index.

The patient record and the rise of the pediatric EHR

Medical documentation arose as individual case reports written for teaching purposes. Documentation for patient care later occurred in physicians' personal daybooks and only evolved into the individual patient record in the early 20th century. Dr. Lawrence Weed improved the utility of the patient record by introducing a problem-oriented/subject-object-assessment-plan structure and he and other innovators transformed the patient record into electronic form. Pediatricians built on these innovations to create a child health electronic health record (EHR) for primary care. An American Academy of Pediatrics task force formally specified the child-specific needs of the EHR, but much work remains to integrate the EHR into the pediatric primary care of the future.

Innovation in the pediatric electronic health record to realize a more effective platform

Commercial electronic health records (EHRs) were first developed to automate business processes. As EHRs developed, design principles focused on transferring existing paper-based documentation to comparable electronic forms. In addition, a strong industry focus on adult healthcare settings and quality measures has limited attention and resources for high priority EHR functionality needed for the unique health care of children. The objective of this paper is to provide a review of innovation in the EHR, that includes a variety of established and emerging technologies that may help realize a more effective EHR in child health settings. A more effective EHR would serve as an electronic hub. Existing EHR infrastructure could provide the foundation upon which new technologies and approaches branch and extend, enabling more rapid and customizable innovation to better meet shifting stakeholder and end-user needs. Among many areas for improvement, key goals of innovation could include technology that relieves ambulatory primary care clinician documentation burden, identifies needs, and supports improved care coordination and outcomes, focused on the following key areas: identification of child and family care needs, decision support, documentation, care coordination, and family communication.

Pediatric data from the All of Us research program: demonstration of pediatric obesity over time.

To describe and demonstrate use of pediatric data collected by the All of Us Research Program. All of Us participant physical measurements and electronic health record (EHR) data were analyzed including investigation of trends in childhood obesity and correlation with adult body mass index (BMI). We identified 19729 participants with legacy pediatric EHR data including diagnoses, prescriptions, visits, procedures, and measurements gathered since 1980. We found an increase in pediatric obesity diagnosis over time that correlates with BMI measurements recorded in participants' adult EHRs and those physical measurements taken at enrollment in the research program. We highlight the availability of retrospective pediatric EHR data for nearly 20000 All of Us participants. These data are relevant to current issues such as the rise in pediatric obesity. All of Us contains a rich resource of retrospective pediatric EHR data to accelerate pediatric research studies.

Catalyzing Pediatric Electronic Health Record Usability and Safety Improvements.

* Abbreviation: EHR — : electronic health record Nearly every office-based pediatrician now uses an electronic health record (EHR).1 There is now evidence that using EHRs can improve guideline adherence, reduce medication errors, and improve other measures of health care quality,2 but the rapid adoption of EHRs has also precipitated unintended and undesired consequences, including the poor usability of EHRs.3 Usability is the extent to which a technology can be used efficiently, effectively, and satisfactorily.4 Poor EHR usability results in clinician frustration, contributes to clinician burnout, and has direct patient safety consequences.5–7 To improve EHR usability, we need rigorous and robust measures of usability to identify specific problem areas, quantify the effect of improvement efforts, and facilitate product comparisons so that consumers can purchase products that meet their needs. In this issue of Pediatrics , Overhage and Johnson8 report on a large-scale analysis of Cerner Millennium EHR use in >20 million pediatric ambulatory encounters by almost 30 000 physicians. Their approach provides a broad assessment of the time spent on tasks in the EHR, and time on task is a usability measure commonly used to … Address correspondence to Raj Ratwani, PhD, National Center for Human Factors in Healthcare, Medstar Health Research Institute, MedStar Health, 3007 Tilden St NW, Suite 6N, Washington, DC 20008. E-mail: raj.m.ratwani{at}medstar.net

Not so implausible: impact of longitudinal assessment of implausible anthropometric measures on obesity prevalence and weight change in children and adolescents

PurposeImplausible anthropometric measures are typically identified using population outlier definitions, conflating implausible and extreme measures. We determined the impact of a longitudinal outlier approach on prevalence of body mass index (BMI) categories and mean change in anthropometric measures in pediatric electronic health record data. MethodsWe examined 996,131 observations from 147,375 children (10–18 years) in the ADVANCE Clinical Data Research Network, a national network of community health centers. Sex-stratified, mixed effects, linear spline regression modeled weight, height, and BMI as a function of age. Longitudinal outliers were defined as observations with studentized residual greater than |6|; population outliers were defined by Centers for Disease Control-defined z-score thresholds. ResultsAt least 99.7% of anthropometric measures were not extreme by longitudinal or population definitions (agreement ≥ 0.995). BMI category prevalence after excluding longitudinal or population outliers differed by less than 0.1%. Among children greater than 85th percentile at baseline, annual mean changes in anthropometric measures were larger in data that excluded longitudinal (girls: 1.24 inches, 12.39 pounds, 1.53 kg/m2; boys: 2.34, 14.08, 1.07) versus population outliers (girls: 0.61 inches, 8.22 pounds, 0.75 kg/m2; boys: 1.53, 11.61, 0.48). ConclusionsLongitudinal outlier methods may reduce underestimation of anthropometric change in children with elevated baseline values.

171. Impact of Limiting Antimicrobial Indication Options in a Pediatric Electronic Health Record

BackgroundIn April 2005, our Antimicrobial Stewardship Program (ASP) started to require prescribers to select an indication as part of an antimicrobial (AM) order. The ASP developed a list of approved indications for each AM with an unlimited number of options including “other.” In 2015–2016, we modified the indication lists to decrease the number of options. The goal of this project is to compare the frequency of indication “other” and the appropriateness of provider-selected indications before and after the intervention.MethodsWe performed a retrospective cohort study of cefepime, ceftriaxone, piperacillin/tazobactam, and ciprofloxacin (IV) orders for all children in our facility excluding orders placed in ambulatory locations and the emergency department. AM orders and provider-selected indications from January to March 2014 (preintervention) and 2017 (postintervention) were compared. Chart review was performed on a sample of pre- and postmodification orders to assess the appropriateness of provider-selected indications. An indication was considered appropriate if the provider-selected indication matched the clinical indication documented.ResultsA total of 747 orders were included in the data analysis, 350 and 397 orders from pre- and postintervention period, respectively. Ceftriaxone was the most commonly prescribed AM:13.7 and 17.2 orders per 1,000 inpatients-days during pre- and postintervention periods. The percent of indication “other” orders increased in the postintervention period for ceftriaxone while it decreased for ciprofloxacin and remained about the same for cefepime and piperacillin/tazobactam. Most prescribers who selected indication “other” for ceftriaxone during the postintervention period did not provide a reason (29.8%). The agreement between clinical and provider-selected indications was consistent in pre- and postintervention period except piperacillin/tazobactam (RR = 0.56).ConclusionRequiring selection of an indication encourages prescribers to evaluate their rationale for initiating an AM. Decreasing the number of indication options for some AMs was associated with increased use of indication “other,” suggesting that the prescriber could not find an indication that matched their needs.Disclosures All authors: No reported disclosures.

Improving Pediatric Electronic Health Record Usability and Safety Through Certification

Improving Pediatric Electronic Health Record Usability and Safety Through Certification

Automated identification of implausible values in growth data from pediatric electronic health records.

Large electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data. Using deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors. Of 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors. This automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.

Is It Feasible to Use Electronic Health Records for Quality Measurement of Adolescent Care?

To determine the extent to which it is feasible to implement quality measures on electronic health records (EHRs) as currently implemented in pediatric health centers. A survey of information technology professionals at 10 institutions that provide primary care services to adolescents. The survey asked whether data about care was being captured electronically across the nine domains relevant to adolescent well care: Screening, Health Risks, Sexual Health, Diagnosis and History, Laboratory Results, Prescriptions, Referrals, Forms Management, and Patient Demographics. For each domain, we developed a scale of the extent to which the EHR makes quality measurement feasible. Overall feasibility scores varied across centers from 34% to 85% and from 53% to 80% across care domains. One centre reported 100% feasibility for 8 of 10 care domains. Electronic health records can facilitate quality improvement, but the feasibility of such use depends on the presence, validity, and accessibility of the quality data in the EHR. Even among the largest and most sophisticated pediatric EHR systems, quality of care measurement is not possible yet for all aspects of adolescent well care without manual effort to review and code data. Nevertheless, almost all quality measures were reported to be feasible in some systems.