Electronic Health Record-Enabled Research in Children Using the Electronic Health Record for Clinical Discovery

Defining systolic blood pressure normative values in hospitalized pediatric patients: a single center experience

BACKGROUND

Normative blood pressure (BP) values and definition of hypertension (HTN) in children in outpatient setting cannot be reliably used for inpatient therapy initiation. No normative exists to describe HTN in hospitalized pediatric populations. We aimed to study the prevalence of hypertension and produce normative BP values in hospitalized children.

METHODS

Cross sectional observational study of all children hospitalized on acute care floors, ≥2 and <18 years age, at Stanford Children's Hospital, from Jan-01-2014 to Dec-31-2018. Cohort included 7468 hospital encounters with a total of 118,423 automated, oscillometric, BPs measured in the upper extremity during a hospitalization of >24 hours.

RESULTS

Overall prevalence of HTN, defined by outpatient guidelines, was 12-48% in boys and 6-39% in girls, stage 1 systolic HTN in 12-38% of boys and 6-31% of girls, stage 2 systolic HTN in 3-10% of boys and 1-8% of girls. Centile curves were derived demonstrating overall higher BP reading for hospitalized patients compared to the outpatient setting.

CONCLUSION

Higher blood pressures are anticipated during hospitalization. Thresholds provided by the centile curves generated in this study may provide the clinician with some guidance on how to manage hospitalized pediatric patients based on clinical circumstances.

IMPACT

Hospitalized children have higher blood pressures compared to patients in the ambulatory setting, hence outpatient normative blood pressure values cannot be reliably used for inpatient therapy initiation. No normative exists to describe hypertension in hospitalized pediatric populations. The thresholds provided by the centile curves generated in this study may provide the clinician with some guidance on how to manage hospitalized pediatric patients based on clinical circumstances.

Patterns in the Development of Pediatric Allergy

OBJECTIVES

Describe clinical and epidemiologic patterns of pediatric allergy using longitudinal electronic health records (EHRs) from a multistate consortium of US practices.

METHODS

Using the multistate Comparative Effectiveness Research through Collaborative Electronic Reporting EHR database, we defined a cohort of 218 485 children (0-18 years) who were observed for ≥5 years between 1999 and 2020. Children with atopic dermatitis (AD), immunoglobulin E-mediated food allergy (IgE-FA), asthma, allergic rhinitis (AR), and eosinophilic esophagitis (EoE) were identified using a combination of diagnosis codes and medication prescriptions. We determined age at diagnosis, cumulative incidence, and allergic comorbidity.

RESULTS

Allergic disease cumulative (and peak age of) incidence was 10.3% (4 months) for AD, 4.0% (13 months) for IgE-FA, 20.1% (13 months) for asthma, 19.7% (26 months) for AR, and 0.11% (35 months) for EoE. The most diagnosed IgE-FAs were peanut (1.9%), egg (0.8%), and shellfish (0.6%). A total of 13.4% of children had ≥2 allergic conditions, and respiratory allergies (ie, asthma, AR) were commonly comorbid with each other, and with other allergic conditions.

CONCLUSIONS

We detail pediatric allergy patterns using longitudinal, health care provider-based data from EHR systems across multiple US states and varied pediatric practice types. Our results support the population-level allergic march progression and indicate high rates of comorbidity among children with food and respiratory allergies.

Clinical Characterization of Pediatric Erythromelalgia: A Single-Center Case Series

Erythromelalgia is a descriptive term for severe burning pain and erythema in the distal extremities relieved by cold and exacerbated by heat. Pediatric case series to date are relatively small. We extracted and analyzed medical record data for 42 pediatric patients to describe clinical characteristics, associated conditions, and responses to treatments. Informed consent was obtained according to an IRB-approved protocol that included gene discovery. Three patients had confirmed Nav1.7 sodium channelopathies, with six additional patients under investigation with novel gene candidates. There was a female predominance (2.5:1), and the median onset age was 12 years (IQR = 3-14). Patients saw a median of three specialists (IQR = 2-3) for a diagnosis. The majority (90%) reported bilateral symptoms. Cooling methods usually provided partial relief, while heat and exercise exacerbated pain. No medication appeared to be consistently effective; commonly prescribed medications included sodium channel blockers (n = 37), topical analgesics (n = 26), gabapentin (n = 22), and aspirin (n = 15). Based on the currently published literature, we believe this cohort is the largest pediatric study of erythromelalgia to date. Many findings are consistent with those of previously published case series. Work is in progress to establish a prospective cohort and multi-center registry.

Diverse and unselected adults with clinically relevant ACADS variants lack evidence of metabolic disease

INTRODUCTION

The clinical significance of Short-chain acyl CoA dehydrogenase deficiency (SCADD), caused by biallelic variation in the ACADS gene, is contested. Clinically ascertained individuals have a range of reported metabolic and physical symptoms. Conversely, individuals identified through newborn screening remain overwhelmingly asymptomatic. Two common ACADS variants, c.511C > T (p.Arg171Trp) and c.625G > A (p.Gly209Ser) are known to reduce enzymatic activity with undetermined clinical correlate. We applied a genome-first approach to evaluate the prevalence and clinical consequences of ACADS variants in an ancestrally diverse and unselected patient population.

MATERIAL AND METHODS

We used exome sequence data linked to electronic health records (EHRs) to identify clinically relevant ACADS variants, and estimate their prevalence and clinical implications in 27,447 ancestrally diverse and unrelated adults from the BioMe Biobank in New York, NY. We extracted International Classification of Diseases, ninth (ICD-9) and tenth (ICD-10) revision codes corresponding to eight SCADD-associated phenotypes relevant to adults from participants' EHRs. Phenotypes included intellectual disability, behavioral disorders with onset in childhood, epilepsy or seizure disorders, hypoglycemia, muscle weakness, metabolic acidosis, fatty liver, and a diagnosis of SCADD or disorder of fatty acid oxidation. We performed manual chart reviews for individuals homozygous for rare pathogenic variants. Multivariate logistic regression was used to determine the association between clinically relevant ACADS variants and phenotypes of interest.

RESULTS

1 in 10,000 BioMe participants were homozygous for rare pathogenic variants (PVs) in ACADS, 1 in 20 were homozygous or presumed compound heterozygous for common variants (CVs), and 1 in 300 harbored both a PV and a CV. Of the 2035 variant positive individuals, none had a documented diagnosis of SCADD. We identified five PV/PV positive individuals, none of whom had evidence of symptomatic SCADD on manual chart review. CV/CV positive and CV/PV positive individuals did not have increased odds of any of the eight ACADS phenotypes evaluated compared to variant negative individuals (OR for CV/CV 0.99, 95% CI 0.86-1.1, p = .88; OR for CV/PV OR 1.49, 95% CI 0.87-2.6, p = .15).

CONCLUSIONS

The prevalence of clinically relevant ACADS variants in an unselected population was higher than previously reported SCADD prevalence of 1 in 35,000 in the United States. Clinically relevant variants in ACADS were not associated with evidence of metabolic disease in a large and ancestrally diverse adult population. These findings support the assertion that SCADD is more likely a biochemical entity without clinical correlate, in particular when caused by one or more common variants.

Nonspecific deidentification of date-like text in deidentified clinical notes enables reidentification of dates

To facilitate the secondary usage of electronic health record data for research, the University of California, San Francisco (UCSF) recently implemented a clinical data warehouse including, among other data, deidentified clinical notes and reports, which are available to UCSF researchers without Institutional Review Board approval. For deidentification of these notes, most of the Health Insurance Portability and Accountability Act identifiers are redacted, but dates are transformed by shifting all dates for a patient back by the same random number of days. We describe an issue in which nonspecific (ie, excess) transformation of nondate, date-like text by this deidentification process enables reidentification of all dates, including birthdates, for certain patients. This issue undercuts the common assumption that excess deidentification is a safe tradeoff to protect patient privacy. We present this issue as a caution to other institutions that may also be considering releasing deidentified notes for research.

Impact of pet dog or cat exposure during childhood on mental illness during adolescence: a cohort study

BACKGROUND

In our prior study of 643 children, ages 4-11 years, children with pet dogs had lower anxiety scores than children without pet dogs. This follow-up study examines whether exposure to pet dogs or cats during childhood reduces the risk of adolescent mental health (MH) disorders.

METHODS

Using a retrospective cohort study design, we merged our prior study database with electronic medical record (EMR) data to create an analytic database. Common MH diagnoses (anxiety, depression, ADHD) occurring from the time of prior study enrollment to 10/27/21 were identified using ICD-9 and ICD-10 codes. We used proportional hazards regression to compare time to MH diagnoses, between youths with and without pets. From 4/1/20 to 10/27/21, parents and youth in the prior study were interviewed about the amount of time the youth was exposed to a pet and how attached s/he was to the pet. Exposure included having a pet dog at baseline, cumulative exposure to a pet dog or cat during follow-up, and level of pet attachment. The main outcomes were anxiety diagnosis, any MH diagnosis, and MH diagnosis associated with a psychotropic prescription.

RESULTS

EMR review identified 571 youths with mean age of 14 years (range 11-19), 53% were male, 58% had a pet dog at baseline. During follow-up (mean of 7.8 years), 191 children received a MH diagnosis: 99 were diagnosed with anxiety (52%), 61 with ADHD (32%), 21 with depression (11%), 10 with combined MH diagnoses (5%). After adjusting for significant confounders, having a pet dog at baseline was associated with lower risk of any MH diagnosis (HR = 0.74, p = .04) but not for anxiety or MH diagnosis with a psychotropic prescription. Among the 241 (42%) youths contacted for follow-up, parent-reported cumulative exposure to pet dogs was borderline negatively associated with occurrence of any MH diagnosis (HR = 0.74, p = .06). Cumulative exposure to the most attached pet (dog or cat) was negatively associated with anxiety diagnosis (HR = 0.57, p = .006) and any MH diagnosis (HR = 0.64, p = .013).

CONCLUSION

Cumulative exposure to a highly attached pet dog or cat is associated with reduced risk of adolescent MH disorders.

Relative Incidence of Emergency Department Visits After Treatment for Prostate Cancer with Radiation Therapy or Radical Prostatectomy

PURPOSE

Side effect profiles play an important role in treatment decisions for localized prostate cancer. Emergency department (ED) visits, which may be due to side effects from treatment, can be measured in real-world, structured, electronic health record (EHR) data. The goal of this study was to determine whether treatments for localized prostate cancer are associated with ED visits, as a measure of side effects, using EHR data.

METHODS AND MATERIALS

We used a self-controlled case series study (SCCS) design, including patients treated at an urban academic medical center with radiation therapy (RT) or radical prostatectomy (RP) for prostate cancer between 2011 and 2020 who had visits documented for ≥ 6 months before and after treatment and ≥1 ED visit. We estimated relative incidences (RI) of ED visits, comparing incidence in the exposed and unexposed periods, with the exposed period being between start of treatment and 1 month after completion, and the unexposed period consisting of all other documented time.

RESULTS

Among men who had at least one ED visit and after adjusting for age, there were higher rates of ED visits after RP (RI 20.4, 95% confidence interval [CI] 15.4-27.0, p<0.001), RT overall (RI 2.4, CI 1.7-3.4, p<0.001), intensity modulated radiation therapy with high dose-rate brachytherapy (HDR) (RI 3.4, CI 1.7-6.8, p<0.001) or stereotactic body radiation therapy boost (RI 7.1, CI 3.4-14.8, p < 0.001), and HDR alone (RI 16.3, CI 7.2-36.9, p<0.001), compared to unexposed time. The number needed to harm to result in an ED visit was less for RP (17, CI 13-23) than RT overall (43, CI 25-126), but varied by RT modality.

CONCLUSIONS

In summary, relative rates of ED visits vary by treatment type, suggesting differing severities of side effects. These data may aid in selecting treatments and demonstrate the feasibility of using the SCCS study design on ED visits in real-world, structured EHR data to better understand side effects of treatment.

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster

The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children’s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.

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

OBJECTIVE

To describe and demonstrate use of pediatric data collected by the All of Us Research Program.

MATERIALS AND METHODS

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).

RESULTS

We identified 19 729 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.

DISCUSSION

We highlight the availability of retrospective pediatric EHR data for nearly 20 000 All of Us participants. These data are relevant to current issues such as the rise in pediatric obesity.

CONCLUSION

All of Us contains a rich resource of retrospective pediatric EHR data to accelerate pediatric research studies.

Pediatric Neurology Research in the Twenty-First Century: Status, Challenges, and Future Directions Post-COVID-19

BACKGROUND

The year 2020 marked a fundamental shift in the pediatric neurology field. An impressive positive trajectory of advances in patient care and research faced sudden global disruptions by the coronavirus disease 2019 pandemic and by an international movement protesting racial, socioeconomic, and health disparities. The disruptions revealed obstacles and fragility within the pediatric neurology research mission. However, renewed commitment offers unique opportunities for the pediatric neurology research community to enhance and prioritize research directions for the coming decades.

METHODS

The Research Committee of the Child Neurology Society evaluated the challenges and opportunities facing the pediatric neurology research field, including reviewing published literature, synthesizing publically available data, and conducting a survey of pediatric neurologists.

RESULTS

We identified three priority domains for the research mission: funding levels, active guidance, and reducing disparities. Funding levels: to increase funding to match the burden of pediatric neurological disease; to tailor funding mechanisms and strategies to support clinical trial efforts unique to pediatric neurology; and to support investigators across their career trajectory. Active guidance: to optimize infrastructure and strategies, to leverage novel therapeutics, enhance data collection, and improve inclusion of children in clinical trials. Reducing disparities: to reduce health disparities in children with neurological disease, to develop proactive measures to enhance workforce diversity and inclusion, and increase avenues to balance work-life obligations for investigators.

CONCLUSIONS

In this uniquely challenging epoch, the pediatric neurology research community has a timely and important mission to re-engage the public and government, advancing the health of children with neurological conditions.

Challenges Frequently Encountered in the Secondary Use of Electronic Medical Record Data for Research

The wide adoption of electronic medical records and subsequent availability of large amounts of clinical data provide a rich resource for researchers. However, the secondary use of clinical data for research purposes is not without limitations. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review to identify current issues related to secondary use of electronic medical record data via MEDLINE and CINAHL databases. All articles published until June 2018 were included. Sixty articles remained after title and abstract review, and four domains of potential limitations were identified: (1) data quality issues, present in 91.7% of the articles reviewed; (2) data preprocessing challenges (53.3%); (3) privacy concerns (18.3%); and (4) potential for limited generalizability (21.7%). Researchers must be aware of the limitations inherent to the use of electronic medical record data for research and consider the potential effects of these limitations throughout the entire study process, from initial conceptualization to the identification of adequate sources that can provide data appropriate for answering the research questions, analysis, and reporting study results. Consideration should also be given to using existing data quality assessment frameworks to facilitate use of standardized data quality definitions and further efforts of standard data quality reporting in publications.

Use of Machine Learning Techniques for Case-Detection of Varicella Zoster Using Routinely Collected Textual Ambulatory Records: Pilot Observational Study

Background

The detection of infectious diseases through the analysis of free text on electronic health reports (EHRs) can provide prompt and accurate background information for the implementation of preventative measures, such as advertising and monitoring the effectiveness of vaccination campaigns.

Objective

The purpose of this paper is to compare machine learning techniques in their application to EHR analysis for disease detection.

Methods

The Pedianet database was used as a data source for a real-world scenario on the identification of cases of varicella. The models’ training and test sets were based on two different Italian regions’ (Veneto and Sicilia) data sets of 7631 patients and 1,230,355 records, and 2347 patients and 569,926 records, respectively, for whom a gold standard of varicella diagnosis was available. Elastic-net regularized generalized linear model (GLMNet), maximum entropy (MAXENT), and LogitBoost (boosting) algorithms were implemented in a supervised environment and 5-fold cross-validated. The document-term matrix generated by the training set involves a dictionary of 1,871,532 tokens. The analysis was conducted on a subset of 29,096 tokens, corresponding to a matrix with no more than a 99% sparsity ratio.

Results

The highest predictive values were achieved through boosting (positive predicative value [PPV] 63.1, 95% CI 42.7-83.5 and negative predicative value [NPV] 98.8, 95% CI 98.3-99.3). GLMNet delivered superior predictive capability compared to MAXENT (PPV 24.5% and NPV 98.3% vs PPV 11.0% and NPV 98.0%). MAXENT and GLMNet predictions weakly agree with each other (agreement coefficient 1 [AC1]=0.60, 95% CI 0.58-0.62), as well as with LogitBoost (MAXENT: AC1=0.64, 95% CI 0.63-0.66 and GLMNet: AC1=0.53, 95% CI 0.51-0.55).

Conclusions

Boosting has demonstrated promising performance in large-scale EHR-based infectious disease identification.

Identifying the "Blip on the Radar Screen": Leveraging Big Data in Defining Drug Safety and Efficacy in Pediatric Practice

The immense amount of electronic health data (pharmacy and administrative claims, electronic health records, and clinical registries) that is being generated every day in the care of patients has the potential to be leveraged for improving clinical decisions at the point of care, uncovering or validating drug efficacy and drug safety. The potential use of big data for improving safe and effective use of medications is especially important in children because of their low drug exposure relative to adults. Electronic health data is collected primarily for clinical or billing purposes and not for research purposes. The major steps involved in data acquisition, extraction, aggregation, analysis, modeling, and interpretation are discussed. It is important to understand the limitation of big data and utilize appropriate study design and statistical methods. Possible applications are presented along with specific examples of how big data has been used in drug research to find that blip on the radar screen that may give an efficacy or safety signal that can lead to further investigation.

Precision medicine in adult and pediatric obesity: a clinical perspective

It remains largely unknown as to why some individuals experience substantial weight loss with obesity interventions, while others receiving these same interventions do not. Person-specific characteristics likely play a significant role in this heterogeneity in treatment response. The practice of precision medicine accounts for an individual's genes, environment, and lifestyle when deciding upon treatment type and intensity in order to optimize benefit and minimize risk. In this review, we first discuss biopsychosocial determinants of obesity, as understanding the complexity of this disease is necessary for appreciating how difficult it is to develop individualized treatment plans. Next, we present literature on person-specific characteristics associated with, and predictive of, weight loss response to various obesity treatments including lifestyle modification, pharmacotherapy, metabolic and bariatric surgery, and medical devices. Finally, we discuss important gaps in our understanding of the causes of obesity in relation to the suboptimal treatment outcomes in certain patients, and offer solutions that may lead to the development of more effective and targeted obesity therapies.

Creation of a Multicenter Pediatric Inpatient Data Repository Derived from Electronic Health Records

BACKGROUND

Integration of electronic health records (EHRs) data across sites and access to that data remain limited.

OBJECTIVE

We developed an EHR-based pediatric inpatient repository using nine U.S. centers from the National Institute of Child Health and Human Development Pediatric Trials Network.

METHODS

A data model encompassing 147 mandatory and 99 optional elements was developed to provide an EHR data extract of all inpatient encounters from patients <17 years of age discharged between January 6, 2013 and June 30, 2017. Sites received instructions on extractions, transformation, testing, and transmission to the coordinating center.

RESULTS

We generated 177 staging reports to process all nine sites' 147 mandatory and 99 optional data elements to the repository. Based on 520 prespecified criteria, all sites achieved 0% errors and <2% warnings. The repository includes 386,159 inpatient encounters from 264,709 children to support study design and conduct of future trials in children.

CONCLUSION

Our EHR-based data repository of pediatric inpatient encounters utilized a customized data model heavily influenced by the PCORnet format, site-based data mapping, a comprehensive set of data testing rules, and an iterative process of data submission. The common data model, site-based extraction, and technical expertise were key to our success. Data from this repository will be used in support of Pediatric Trials Network studies and the labeling of drugs and devices for children.

Personalized Medicine and the Power of Electronic Health Records

Personalized medicine has largely been enabled by the integration of genomic and other data with electronic health records (EHRs) in the United States and elsewhere. Increased EHR adoption across various clinical settings and the establishment of EHR-linked population-based biobanks provide unprecedented opportunities for the types of translational and implementation research that drive personalized medicine. We review advances in the digitization of health information and the proliferation of genomic research in health systems and provide insights into emerging paths for the widespread implementation of personalized medicine.

Data electronically extracted from the electronic health record require validation

OBJECTIVES

Determine sources of error in electronically extracted data from electronic health records.

STUDY DESIGN

Categorical and continuous variables related to early-onset neonatal hypoglycemia were preselected and electronically extracted from records of 100 randomly selected neonates within 3479 births with laboratory-proven early-onset hypoglycemia. Extraction language was written by an information technologist and data validated by blinded manual chart review. Kappa coefficient assessed categorical variables and percent validity continuous variables.

RESULTS

8/23 (35%) categorical variables had acceptable Κappa (1-0.81); 5/23 (22%) had fair-slight agreement, Κappa < 0.40. Notably, "hypoglycemia" had poor agreement, Κappa 0.16. In contrast, 6/8 continuous variables had validity ≥ 94%. After correcting extraction language, 6/9 variables were corrected and inter-rater validation improved. However, "hypoglycemia" was not corrected, remaining an issue.

CONCLUSIONS

Data extraction without validation procedures, especially categorical variables using International Classification of Diseases-9 (ICD-9) codes, often results in incorrect data identification. Electronically extracted data must incorporate built-in validating processes.

Big Data and Pediatric Acute Kidney Injury: The Promise of Electronic Health Record Systems

Over the last decade, our understanding of acute kidney injury (AKI) has evolved considerably. The development of a consensus definition standardized the approach to identifying and investigating AKI in children. As a result, pediatric AKI epidemiology has been refined and the consequences of renal injury are better established. Similarly, "big data" methodologies experienced a dramatic evolution and maturation, leading the critical care community to explore potential AKI/big data synergies. One such concept with tremendous potential is electronic health record (EHR) enabled informatics. Much of the promise surrounding these approaches is due to the unique position of the EHR which sits at the intersection of data accumulation and care delivery. EHR data is generated simply via the provision of routine clinical care and should be considered "big" from the standpoint of volume, variety, and velocity as a myriad of diverse elements accumulate rapidly in real time, spontaneously generating an immense dataset. This massive dataset interfaces directly with providers which creates tremendous opportunity. AKI can be diagnosed more accurately, AKI-related care can be optimized, and subsequent outcomes can be improved. Although applying big data concepts to the EHR has proven more challenging than originally thought, we have seen much success and continue to explore its potential. In this review article, we will discuss the EHR in the context of big data concepts, describe approaches applied to date, examine the challenges surrounding optimal application, and explore future directions.

Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation

Background

Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress.

Objective

The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas.

Methods

On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated.

Results

Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger.

Conclusions

The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

Designing health information technology tools for behavioral health clinicians integrated within a primary care team

Background

Electronic health records (EHRs) are a key tool for primary care practice. However, the EHR functionality is not keeping pace with the evolving informational and decision-support needs of behavioural health clinicians (BHCs) working on integrated teams.

Objective

Describe the workflows and tasks of integrated BHCs working with adult patients identify their health information technology (health IT) needs and develop EHR tools to address them.

Method

A mixed-methods, comparative case study of six community health centres (CHCs) in Oregon, each with at least one BHC integrated into their primary care team. We observed clinical work and conducted interviews to understand workflows and clinical tasks, aiming to identify how effectively current EHRs supported integrated care delivery, including transitions, documentation, information sharing and decision-making. We analysed these data and employed a user-centred design process to develop EHR tools addressing the identified needs.

Results

BHCs used the primary care EHR for documentation and communication with other team members, but the EHR lacked the functionality to fully support integrated care. Needs include the ability to: (1) automate and track paper-based screening; (2) document behavioural health history; (3) access patient social and medical history relevant to behavioural health issues and (4) rapidly document and track progress on goals. To meet these needs, we engaged users and developed a set of EHR tools called the Behavioural Health e-Suite (BH e-Suite).

Conclusion

US-based integrated primary care teams, and particularly BHCs working with adult populations, have unique information needs, workflows and tasks. These needs can be met and supported by the EHR with a moderate level of modification.

HiGHmed - An Open Platform Approach to Enhance Care and Research across Institutional Boundaries

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. HiGHmed brings together 24 partners from academia and industry, aiming at improvements in care provision, biomedical research and epidemiology. By establishing a shared information governance framework, data integration centers and an open platform architecture in cooperation with independent healthcare providers, the meaningful reuse of data will be facilitated. Complementary, HiGHmed integrates a total of seven Medical Informatics curricula to develop collaborative structures and processes to train medical informatics professionals, physicians and researchers in new forms of data analytics.

Governance and Policies: We describe governance structures and policies that have proven effective during the conceptual phase. These were further adapted to take into account the specific needs of the development and networking phase, such as roll-out, carerelated aspects and our focus on curricula development in Medical Inform atics.

Architectural Framework and Methodology: To address the challenges of organizational, technical and semantic interoperability, a concept for a scalable platform architecture, the HiGHmed Platform, was developed. We outline the basic principles and design goals of the open platform approach as well as the roles of standards and specifications such as IHE XDS, openEHR, SNOMED CT and HL7 FHIR. A shared governance framework provides the semantic artifacts which are needed to establish semantic interoperability.

Use Cases: Three use cases in the fields of oncology, cardiology and infection control will demonstrate the capabilities of the HiGHmed approach. Each of the use cases entails diverse challenges in terms of data protection, privacy and security, including clinical use of genome sequencing data (oncology), continuous longitudinal monitoring of physical activity (cardiology) and cross-site analysis of patient movement data (infection control).

Discussion: Besides the need for a shared governance framework and a technical infrastructure, backing from clinical leaders is a crucial factor. Moreover, firm and sustainable commitment by participating organizations to collaborate in further development of their information system architectures is needed. Other challenges including topics such as data quality, privacy regulations, and patient consent will be addressed throughout the project.

Leveraging Big Data and Electronic Health Records to Enhance Novel Approaches to Acute Kidney Injury Research and Care

While acute kidney injury (AKI) has been poorly defined historically, a decade of effort has culminated in a standardized, consensus definition. In parallel, electronic health records (EHRs) have been adopted with greater regularity, clinical informatics approaches have been refined, and the field of EHR-enabled care improvement and research has burgeoned. Although both fields have matured in isolation, uniting the 2 has the capacity to redefine AKI-related care and research. This article describes how the application of a consistent AKI definition to the EHR dataset can accurately and rapidly diagnose and identify AKI events. Furthermore, this electronic, automated diagnostic strategy creates the opportunity to develop predictive approaches, optimize AKI alerts, and trace AKI events across institutions, care platforms, and administrative datasets.

Diagnosis and Medication Treatment of Pediatric Hypertension: A Retrospective Cohort Study

BACKGROUND AND OBJECTIVES

Pediatric hypertension predisposes children to adult hypertension and early markers of cardiovascular disease. No large-scale studies have examined diagnosis and initial medication management of pediatric hypertension and prehypertension. The objective of this study was to evaluate diagnosis and initial medication management of pediatric hypertension and prehypertension in primary care.

METHODS

Retrospective cohort study aggregating electronic health record data on >1.2 million pediatric patients from 196 ambulatory clinics across 27 states. Demographic, diagnosis, blood pressure (BP), height, weight, and medication prescription data extracted. Main outcome measures include proportion of pediatric patients with ≥3 visits with abnormal BPs, documented hypertension and prehypertension diagnoses, and prescribed antihypertensive medications. Marginal standardization via logistic regression produced adjusted diagnosis rates.

RESULTS

Three hundred ninety-eight thousand seventy-nine patients, ages 3 to 18, had ≥3 visits with BP measurements (48.9% girls, 58.6% <10 years old). Of these, 3.3% met criteria for hypertension and 10.1% for prehypertension. Among practices with ≥50 eligible patients, 2813 of 12 138 patients with hypertension (23.2%; 95% confidence interval, 18.2%-28.2%) and 3990 of 38 874 prehypertensive patients (10.2%; 95% confidence interval, 8.2%-12.2%) were diagnosed. Age, weight, height, sex, and number and magnitude of abnormal BPs were associated with diagnosis rates. Of 2813 diagnosed, persistently hypertensive patients, 158 (5.6%) were prescribed antihypertensive medication within 12 months of diagnosis (angiotensin-converting enzyme inhibitors/angiotensin receptive blockers [35%], diuretics [22%], calcium channel blockers [17%], and β-blockers [10%]).

CONCLUSIONS

Hypertension and prehypertension were infrequently diagnosed among pediatric patients. Guidelines for diagnosis and initial medication management of abnormal BP in pediatric patients are not routinely followed.