An i2b2-based, generalizable, open source, self-scaling chronic disease registry

Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using the DataGauge Process

The learning health systems aim to support the needs of patients with chronic diseases, which require methods that account for electronic health recorded (EHR) data limitations. EHR data is often used to calculate cardiovascular risk scores. However, it is unclear whether EHR data presents high enough quality to provide accurate estimates. Still, there is currently no open standard available to assess data quality for such applications. We applied the DataGauge process to develop a data quality standard based on expert clinical, analytical and informatics knowledge by conducting four interviews and one focus group that produced 61 individual data quality requirements. These requirements covered all standard data quality dimensions and uncovered 705 quality issues in EHR data for 456 patients. These requirements will be expanded and further validated in future work. Our work initiates the development of open and explicit data quality standards for specific secondary uses of clinical data.

Exploring the Hazards of Scaling Up Clinical Data Analyses: A Drug Side Effect Discovery Case Report

We assessed the scalability of pharmacological signal detection use case from a single-site CDW to a large aggregated clinical data warehouse (single-site database with 754,214 distinct patient IDs vs. multisite database with 49.8M). We aimed to explore whether a larger clinical dataset would provide clearer signals for secondary analyses such as detecting the known relationship between prednisone and weight. We found significant weight gain rate using the single-site data but not from using aggregated data (0.0104 kg/day, p<0.0001 vs. -0.050 kg/day, p<.0001). This rate was also found more consistently across 30 age and gender subgroups using the single-site data than in the aggregated data (26 vs. 18 significant weight gain findings). Contrary to our expectations, analyses of much larger aggregated clinical datasets did not yield stronger signals. Researchers must check the underlying model assumptions and account for greater heterogeneity when analyzing aggregated multisite data to ensure reliable findings.

Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR

Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter DX, problem list, etc.). We provide initial evidence corroborating these findings in EHRs from patients with diabetes. We hypothesized that the odds of recording an "uncontrolled diabetes" DX increased after a hemoglobin A1c result above 9% and that this rate would vary across EHR segments. Our statistical models revealed that each DX indicating uncontrolled diabetes was 2.6% more likely to occur post-A1c>9% overall (adj-p=.0005) and 3.9% after controlling for EHR segment (adj-p<.0001). However, odds ratios varied across segments (1.021 Collapse

Key Words Collapse

MESH Headings

• Chronic Disease
• Datasets as Topic
• Diabetes Mellitus/diagnosis
• Electronic Health Records
• Glycated Hemoglobin/analysis
• Humans
• Machine Learning
• Medical Informatics/methods
• Models, Statistical
• Odds Ratio

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Grants

• UL1 TR001420 NCATS NIH HHS

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Affiliation(s)

Franck Diaz-Garelli University of North Carolina at Charlotte. Charlotte, NC
Kristin M Lenoir Wake Forest School of Medicine, Winston Salem, NC
Brian J Wells Wake Forest School of Medicine, Winston Salem, NC

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Scale-up of the Accrual to Clinical Trials (ACT) network across the Clinical and Translational Science Award Consortium: a mixed-methods evaluation of the first 18 months

Introduction

The Clinical and Translational Science Award (CTSA) Program is a Consortium of nearly 60 academic medical research centers across the USA and a natural network for evaluating the spread and uptake of translational research innovation across the Consortium.

Methods

Dissemination of the Accrual to Clinical Trials (ACT) Network, a federated clinical informatics data network for population-based cohort discovery, began January 2018 across the Consortium. Diffusion of innovation theory guided dissemination design and evaluation. Mixed-methods assessed the spread and uptake across the Consortium through July 1, 2019 (n = 48 CTSAs). Methods included prospective time activity tracking (Kaplan-Meier curves), and survey and qualitative interviews.

Results

Within 18 months, nearly 80% of CTSAs had joined the data network and two-thirds of CTSAs achieving technical readiness had initiated launch to local clinical investigators. Over 10,000 ACT Network queries are projected for 2019; and by 2020, nearly all CTSAs will have joined the network. Median time-from-technical-readiness-to-local-launch was 154 days (interquartile range: 87-225 days]. Quality improvement processes reduced time-to-launch by 35.2% (64 days, p = 0.0036). Lessons learned include: (1) conceptualize dissemination as two-stage adoption demonstrating value for both CTSA hub service providers and clinical investigators; (2) include institutional trial into dissemination strategies so CTSA hubs can refine internal workflows and gather local user feedback endorsement; (3) embrace designing-for-dissemination during technology development; and (4) sustain adaptive dissemination and customer relationship management to keep CTSA hubs and users engaged.

Conclusions

Scale-up and spread of the ACT Network provides lessons learned for others disseminating innovation across the CTSA Consortium. The Network is primed for embedded implementation research.

Pain, functional disability, and their Association in Juvenile Fibromyalgia Compared to other pediatric rheumatic diseases

BACKGROUND

Severe pain and impairments in functioning are commonly reported for youth with juvenile fibromyalgia. The prevalence and impact of pain in other diseases commonly managed in pediatric rheumatology comparatively have been rarely systematically studied. The objective of the current study was to determine the extent to which high levels of pain and functional limitations, and the strength of their association, are unique to youth with juvenile primary fibromyalgia syndrome/JPFS) relative to other pediatric rheumatic diseases.

METHODS

Using data from 7753 patients enrolled in the multinational Childhood Arthritis and Rheumatology Research Alliance (CARRA) Legacy Registry, we compared the levels and association of pain and functional limitations between youth with JPFS and those with other rheumatic diseases.

RESULTS

Pain levels were rated highest among youth with JPFS (M = 6.4/10, SD = 2.4) and lowest for juvenile dermatomyositis (M = 1.7/10, SD = 2.2), with pain significantly higher in the JPFS group than any other pediatric rheumatic disease (effect sizes = .22 to 1.05). Ratings on measures of functioning and well-being also were significantly worse for patients with JPFS than patients with any other rheumatic disease (effect sizes = .62 to 1.06). The magnitude of association between pain intensity and functional disability, however, generally was higher in other rheumatic diseases than in JPFS. Pain was most strongly associated with functional limitations in juvenile dermatomyositis, juvenile idiopathic arthritis, and mixed connective tissue disease.

CONCLUSIONS

JPFS is unique among conditions seen in pediatric rheumatology with regard to ratings of pain and disability. However, pain appears to be comparably or more highly associated with level of functional impairment in other pediatric rheumatic diseases. Pain in childhood rheumatic disease thus would benefit from increased prioritization for research and treatment.

A method for the graphical modeling of relative temporal constraints

Searching for patient cohorts in electronic patient data often requires the definition of temporal constraints between the selection criteria. However, beyond a certain degree of temporal complexity, the non-graphical, form-based approaches implemented in current translational research platforms may be limited when modeling such constraints. In our opinion, there is a need for an easily accessible and implementable, fully graphical method for creating temporal queries. We aim to respond to this challenge with a new graphical notation. Based on Allen's time interval algebra, it allows for modeling temporal queries by arranging simple horizontal bars depicting symbolic time intervals. To make our approach applicable to complex temporal patterns, we apply two extensions: with duration intervals, we enable the inference about relative temporal distances between patient events, and with time interval modifiers, we support counting and excluding patient events, as well as constraining numeric values. We describe how to generate database queries from this notation. We provide a prototypical implementation, consisting of a temporal query modeling frontend and an experimental backend that connects to an i2b2 system. We evaluate our modeling approach on the MIMIC-III database to demonstrate that it can be used for modeling typical temporal phenotyping queries.

Demographic, clinical, and treatment characteristics of the juvenile primary fibromyalgia syndrome cohort enrolled in the Childhood Arthritis and Rheumatology Research Alliance Legacy Registry

BACKGROUND

To describe the demographic, clinical, and treatment characteristics of youth diagnosed with juvenile primary fibromyalgia syndrome (JPFS) who are seen in pediatric rheumatology clinics.

METHODS

Information on demographics, symptoms, functioning, and treatments recommended and tried were obtained on patients with JPFS as part of a multi-site patient registry (the Childhood Arthritis and Rheumatology Research Alliance Legacy Registry). Data were summarized using descriptive statistics. In a subset of patients completing registry follow-up visits, changes in symptoms, pain, and functioning were evaluated using growth modeling.

RESULTS

Of the 201 patients with JPFS enrolled in the registry, most were Caucasian/White (85%), non-Hispanic (83%), and female (84%). Ages ranged from 9 to 20 years (M = 15.4 + 2.2). The most common symptoms reported were widespread musculoskeletal pain (91%), fatigue (84%), disordered sleep (82%), and headaches (68%). Pain intensity was rated as moderate to severe (M = 6.3 + 2.4/10). Scores on measures of functioning indicated mild to moderate impairment, with males observed to report significantly greater impairments. For the 37% of the initial cohort having follow-up data available, indicators of function and well-being were found to either worsen over time or remain relatively unchanged.

CONCLUSIONS

The symptoms of JPFS remained persistent and disabling for many patients treated by pediatric rheumatologists. Further study appears warranted to elucidate gender differences in the impact of JPFS symptoms. Work also is needed to identify accessible and effective outpatient treatment options for JPFS that can be routinely recommended or implemented by pediatric rheumatology providers.

The REDCap consortium: Building an international community of software platform partners

The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.

The Association of Black Cardiologists (ABC) Cardiovascular Implementation Study (CVIS): A Research Registry Integrating Social Determinants to Support Care for Underserved Patients

African Americans, other minorities and underserved populations are consistently under- represented in clinical trials. Such underrepresentation results in a gap in the evidence base, and health disparities. The ABC Cardiovascular Implementation Study (CVIS) is a comprehensive prospective cohort registry that integrates social determinants of health. ABC CVIS uses real world clinical practice data to address critical gaps in care by facilitating robust participation of African Americans and other minorities in clinical trials. ABC CVIS will include diverse patients from collaborating ABC member private practices, as well as patients from academic health centers and Federally Qualified Health Centers (FQHCs). This paper describes the rationale and design of the ABC CVIS Registry. The registry will: (1) prospectively collect socio-demographic, clinical and biospecimen data from enrolled adults, adolescents and children with prioritized cardiovascular diseases; (2) Evaluate the safety and clinical outcomes of new therapeutic agents, including post marketing surveillance and pharmacovigilance; (3) Support National Institutes of Health (NIH) and industry sponsored research; (4) Support Quality Measures standards from the Center for Medicare and Medicaid Services (CMS) and Commercial Health Plans. The registry will utilize novel data and technology tools to facilitate mobile health technology application programming interface (API) to health system or practice electronic health records (EHR). Long term, CVIS will become the most comprehensive patient registry for underserved diverse patients with cardiovascular disease (CVD) and co morbid conditions, providing real world data to address health disparities. At least 10,000 patients will be enrolled from 50 sites across the United States.

The REDCap consortium: Building an international community of software platform partners

The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.

Lost in Translation: Diagnosis Records Show More Inaccuracies After Biopsy in Oncology Care EHRs

The use of diagnosis (DX) data is crucial to secondary use of electronic health record (EHR) data, yet accessible structured DX data often lack in accuracy. DX descriptions associated with structured DX codes vary even after recording biopsy results; this may indicate poor data quality. We hypothesized that biopsy reports in cancer care charts do not improve intrinsic DX data quality. We analyzed DX data for a manually well-annotated cohort of patients with brain neoplasms. We built statistical models to predict the number of fully-accurate (i.e., correct neoplasm type and anatomical location) and inaccurate DX (i.e. type or location contradicts cohort data) descriptions. We found some evidence of statistically larger numbers of fully-accurate (RR=3.07, p=0.030) but stronger evidence of much larger numbers of inaccurate DX (RR=12.3, p=0.001 and RR=19.6, p<0.0001) after biopsy result recording. Still, 65.9% of all DX records were neither fully-accurate nor fully-inaccurate. These results suggest EHRs must be modified to support more reliable DX data recording and secondary use of EHR data.

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium

Background

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations.

Results

The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers.

Conclusions

In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2748-y) contains supplementary material, which is available to authorized users.

How Returning Aggregate Research Results Impacts Interest in Research Engagement and Planned Actions Relevant to Health Care Decision Making: Cohort Study

Background

Collection of patient-reported outcomes measures (PROs) may augment clinical data and inform health research, improving care, yet approaches to sustaining interest among patient cohorts in research participation are needed. One approach may involve returning aggregate research results (ARRs), which may help patients contextualize personal experiences, prompt conversations with providers or family, and encourage information seeking. This model has been demonstrated for Web-based patient-centered registries. Studies with clinical cohorts may further elucidate the model, its impacts on interest in research participation and planned actions, and potential for participants to experience this as helpful or harmful—gap areas.

Objective

We sought to investigate the impacts of returning ARRs comprising summaries of PROs and clinical metrics to parents of children with rheumatic disease, assessing interest in future research participation among parents who viewed ARRs and plans for acting on returned information. Further, we sought to investigate reactions to viewing ARRs and how these reactions impacted planned actions.

Methods

Clinical and PRO data were obtained about children in a national clinical disease registry, summarized, and processed into annotated infographics, comprising ARRs for children’s parents. Parents who viewed ARRs (n=111) were surveyed about the information’s perceived value and their reactions. Reaction patterns were summarized using principal components analysis (PCA), and associations among reaction patterns and interest in research participation and planned actions were estimated using multivariate logistic regression.

Results

Parental endorsement of the value of ARRs for understanding their child’s condition and making care decisions was high (across 10 topics for which ARRs were shared, 42.2%-77.3% of the parents reported information was “very valuable”). Most (58/111, 52.3%) parents reported being more interested in participating in research after viewing ARRs, with the remainder reporting that their interest levels were unchanged. Reactions to viewing ARRs reflected experiencing validation/affirmation and information burden based on PCA. Reactions were not associated with child demographic or clinical characteristics and PROs, except that parents from households with less education reported greater information burden than those from more educated households (P=.007). In adjusted models, parents with higher validation/affirmation scores had increased odds of reporting heightened interest in research participation (adjusted odds ratio [AOR] 1.97, 95% CI 1.18-3.30), while higher information burden scores were associated with decreased odds of planned discussions with their child (AOR 0.59, 95% CI 0.36-0.95) and increased odds of planned discussions with providers (AOR 1.75, 95% CI 1.02-3.00).

Conclusions

Returning ARRs may foster a “virtuous cycle” of research engagement, especially where ARRs are experienced favorably and affect plans to share and discuss ARRs in support of a child’s chronic disease care and treatment. Reactions to ARRs vary with education level, underscoring the need for attention to equity for this model.

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.

Integrating Multimodal Radiation Therapy Data into i2b2

Background  Clinical data warehouses are now widely used to foster clinical and translational research and the Informatics for Integrating Biology and the Bedside (i2b2) platform has become a de facto standard for storing clinical data in many projects. However, to design predictive models and assist in personalized treatment planning in cancer or radiation oncology, all available patient data need to be integrated into i2b2, including radiation therapy data that are currently not addressed in many existing i2b2 sites.

Objective  To use radiation therapy data in projects related to rectal cancer patients, we assessed the feasibility of integrating radiation oncology data into the i2b2 platform.

Methods  The Georges Pompidou European Hospital, a hospital from the Assistance Publique – Hôpitaux de Paris group, has developed an i2b2-based clinical data warehouse of various structured and unstructured clinical data for research since 2008. To store and reuse various radiation therapy data—dose details, activities scheduling, and dose-volume histogram (DVH) curves—in this repository, we first extracted raw data by using some reverse engineering techniques and a vendor's application programming interface. Then, we implemented a hybrid storage approach by combining the standard i2b2 “Entity-Attribute-Value” storage mechanism with a “JavaScript Object Notation (JSON) document-based” storage mechanism without modifying the i2b2 core tables. Validation was performed using (1) the Business Objects framework for replicating vendor's application screens showing dose details and activities scheduling data and (2) the R software for displaying the DVH curves.

Results  We developed a pipeline to integrate the radiation therapy data into the Georges Pompidou European Hospital i2b2 instance and evaluated it on a cohort of 262 patients. We were able to use the radiation therapy data on a preliminary use case by fetching the DVH curve data from the clinical data warehouse and displaying them in a R chart.

Conclusion  By adding radiation therapy data into the clinical data warehouse, we were able to analyze radiation therapy response in cancer patients and we have leveraged the i2b2 platform to store radiation therapy data, including detailed information such as the DVH to create new ontology-based modules that provides research investigators with a wider spectrum of clinical data.

Biopsy Records Do Not Reduce Diagnosis Variability in Cancer Patient EHRs: Are We More Uncertain After Knowing?

Diagnostic codes are crucial for analyses of electronic health record (EHR) data but their accuracy and precision are often lacking. Although providers enter precise diagnoses into progress notes, billing standards may limit the particularity of a diagnostic code. Variability also arises from the creation of multiple descriptions for a particular diagnostic code. We hypothesized that the variability of diagnostic codes would be greater before surgical pathology results were recorded in the medical record. A well annotated cohort of patients with brain neoplasms was studied. After diagnostic pathology reporting, the odds of more distinct diagnostic descriptions were 2.30 times higher (p=0.00358), entropy in diagnostic sequences was 2.26 times higher (p=0.0259) and entropy in diagnostic precision scores was 15.5 times higher (p=0.0324). Although diagnostic codes became more distinct on average after diagnostic pathology reporting, there was a paradoxical increase in the variability of the codes selected. Researchers must be aware of the inconsistencies and variability in particularity in structured diagnostic coding despite the presence of a definitive diagnosis.

Identifying Unmet Treatment Needs for Patients With Osteoporotic Fracture: Feasibility Study for an Electronic Clinical Surveillance System

BACKGROUND

Traditional clinical surveillance relied on the results from clinical trials and observational studies of administrative databases. However, these studies not only required many valuable resources but also faced a very long time lag.

OBJECTIVE

This study aimed to illustrate a practical application of the National Taiwan University Hospital Clinical Surveillance System (NCSS) in the identification of patients with an osteoporotic fracture and to provide a high reusability infrastructure for longitudinal clinical data.

METHODS

The NCSS integrates electronic medical records in the National Taiwan University Hospital (NTUH) with a data warehouse and is equipped with a user-friendly interface. The NCSS was developed using professional insight from multidisciplinary experts, including clinical practitioners, epidemiologists, and biomedical engineers. The practical example identifying the unmet treatment needs for patients encountering major osteoporotic fractures described herein was mainly achieved by adopting the computerized workflow in the NCSS.

RESULTS

We developed the infrastructure of the NCSS, including an integrated data warehouse and an automatic surveillance workflow. By applying the NCSS, we efficiently identified 2193 patients who were newly diagnosed with a hip or vertebral fracture between 2010 and 2014 at NTUH. By adopting the filter function, we identified 1808 (1808/2193, 82.44%) patients who continued their follow-up at NTUH, and 464 (464/2193, 21.16%) patients who were prescribed anti-osteoporosis medications, within 3 and 12 months post the index date of their fracture, respectively.

CONCLUSIONS

The NCSS systems can integrate the workflow of cohort identification to accelerate the survey process of clinically relevant problems and provide decision support in the daily practice of clinical physicians, thereby making the benefit of evidence-based medicine a reality.

Exploiting Distributed, Heterogeneous and Sensitive Data Stocks while Maintaining the Owner’s Data Sovereignty

Background: To achieve statistical significance in medical research, biological or data samples from several bio- or databanks often need to be complemented by those of other institutions. For that purpose, IT-based search services have been established to locate datasets matching a given set of criteria in databases distributed across several institutions. However, previous approaches require data owners to disclose information about their samples, raising a barrier for their participation in the network.

Objective: To devise a method to search distributed databases for datasets matching a given set of criteria while fully maintaining their owner’s data sovereignty.

Methods: As a modification to traditional federated search services, we propose the decentral search, which allows the data owner a high degree of control. Relevant data are loaded into local bridgeheads, each under their owner’s sovereignty. Researchers can formulate criteria sets along with a project proposal using a central search broker, which then notifies the bridgeheads. The criteria are, however, treated as an inquiry rather than a query: Instead of responding with results, bridgeheads notify their owner and wait for his/her decision regarding whether and what to answer based on the criteria set, the matching datasets and the specific project proposal. Without the owner’s explicit consent, no data leaves his/ her institution.

Results: The decentral search has been deployed in one of the six German Centers for Health Research, comprised of eleven university hospitals. In the process, compliance with German data protection regulations has been confirmed. The decentral search also marks the centerpiece of an open source registry software toolbox aiming to build a national registry of rare diseases in Germany.

Conclusions: While the sacrifice of real-time answers impairs some use-cases, it leads to several beneficial side effects: improved data protection due to data parsimony, tolerance for incomplete data schema mappings and flexibility with regard to patient consent. Most importantly, as no datasets ever leave their institution, owners can reject projects without facing potential peer pressure. By its lower barrier for participation, a decentral search service is likely to attract a larger number of partners and to bring a researcher into contact with the right potential partners.

A Comprehensive Assessment Tool for Patient Registry Software Systems: The CIPROS Checklist

Background: Patient registries are an important instrument in medical research. Often their structure is complex and their implementation uses composite software systems to meet the wide spectrum of challenges.

Objectives: For the implementation of a registry, there is a wide range of commercial, open source, and self-developed systems available and a minimal standard for the critical appraisal of their architecture is needed.

Methods: We performed a systematic review of the literature to define a catalogue of relevant criteria to construct a minimal appraisal standard.

Results: The CIPROS list is developed based on 64 papers which were found by our systematic review. The list covers twelve sections and contains 72 items.

Conclusions: The CIPROS list supports developers to assess requirements on existing systems and strengthens the reporting of patient registry software system descriptions. It can be a first step to create standards for patient registry software system assessments.

Integrated Data Repository Toolkit (IDRT)

Background: In recent years, research data warehouses moved increasingly into the focus of interest of medical research. Nevertheless, there are only a few center-independent infrastructure solutions available. They aim to provide a consolidated view on medical data from various sources such as clinical trials, electronic health records, epidemiological registries or longitudinal cohorts. The i2b2 framework is a well-established solution for such repositories, but it lacks support for importing and integrating clinical data and metadata.

Objectives: The goal of this project was to develop a platform for easy integration and administration of data from heterogeneous sources, to provide capabilities for linking them to medical terminologies and to allow for transforming and mapping of data streams for user-specific views.

Methods: A suite of three tools has been developed: the i2b2 Wizard for simplifying administration of i2b2, the IDRT Import and Mapping Tool for loading clinical data from various formats like CSV, SQL, CDISC ODM or biobanks and the IDRT i2b2 Web Client Plugin for advanced export options. The Import and Mapping Tool also includes an ontology editor for rearranging and mapping patient data and structures as well as annotating clinical data with medical terminologies, primarily those used in Germany (ICD-10-GM, OPS, ICD-O, etc.).

Results: With the three tools functional, new i2b2-based research projects can be created, populated and customized to researcher’s needs in a few hours. Amalgamating data and metadata from different databases can be managed easily. With regards to data privacy a pseudonymization service can be plugged in. Using common ontologies and reference terminologies rather than project-specific ones leads to a consistent understanding of the data semantics.

Conclusions: i2b2’s promise is to enable clinical researchers to devise and test new hypothesis even without a deep knowledge in statistical programing. The approach pre -sented here has been tested in a number of scenarios with millions of observations and tens of thousands of patients. Initially mostly observant, trained researchers were able to construct new analyses on their own. Early feedback indicates that timely and extensive access to their “own” data is appreciated most, but it is also lowering the barrier for other tasks, for instance checking data quality and completeness (missing data, wrong coding).

Registries in Clinical Epidemiology: the European Surveillance System on Contact Allergies (ESSCA)

Background: Disease registries rely on consistent electronic data capturing (EDC) pertinent to their objectives; either by using existing electronic data as far as available, or by implementing specific software solutions.

Objectives: To describe the current practice of an international disease registry (European Surveillance System on Contact Allergies, ESSCA, www.essca-dc.org) against different state of the art approaches for EDC.

Methods: Since 2002, ESSCA is collecting data, currently from 53 departments in 12 countries. Departmental EDC software ranges from spreadsheets to comprehensive “patch test software” based on a relational database. In the Erlangen data centre, such diverse data is imported, converted to a common format, quality checked and pooled for scientific analyses.

Results: Feed-back to participating departments for quality control is provided by standardised reports. Varying author teams publish scientific analyses addressing the objective of contact allergy surveillance.

Conclusions: Although ESSCA represents a historically grown, heterogeneous network and not one unified approach to EDC, some of its features have contributed to its viability in the last 12 years and may be useful to consider for similar investigator-initiated networks.

Adding patient-reported outcomes to a multisite registry to quantify quality of life and experiences of disease and treatment for youth with juvenile idiopathic arthritis

BACKGROUND

Children with Juvenile Idiopathic Arthritis (JIA) often have poor health-related quality of life (HRQOL) despite advances in treatment. Patient-centered research may shed light on how patient experiences of treatment and disease contribute to HRQOL, pinpointing directions for improving care and enhancing outcomes.

METHODS

Parent proxies of youth enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry shared patient-reported outcomes about their child's HRQOL and experiences of disease and treatment burden (pain interference, morning stiffness, history of medication side effects and methotrexate intolerance). Contributions of these measures to HRQOL were estimated using generalized estimating equations accounting for site and patient demographics.

RESULTS

Patients (N = 180) were 81.1% white non-Hispanic and 76.7% female. Mean age was 11.8 (SD = 3.6) years, mean disease duration was 7.7 years (SD = 3.5). Mean Total Pediatric Quality of Life was 76.7 (SD = 18.2). Mean pain interference score was 50.1 (SD = 11.1). Nearly one-in-five (17.8%) youth experienced >15 min of morning stiffness on a typical day, more than one quarter (26.7%) reported ≥1 serious medication side effect and among 90 methotrexate users, 42.2% met criteria for methotrexate intolerance. Measures of disease and treatment burden were independently negatively associated with HRQOL (all p-values <0.01). Negative associations among measures of treatment burden and HRQOL were attenuated after controlling for disease burden and clinical characteristics but remained significant.

CONCLUSIONS

For youth with JIA, HRQOL is multidimensional, reflecting disease as well as treatment factors. Adverse treatment experiences undermine HRQOL even after accounting for disease symptoms and disease activity and should be assessed routinely to improve wellbeing.

Models of Care for addressing chronic musculoskeletal pain and health in children and adolescents

Chronic musculoskeletal pain among children and adolescents is common and can negatively affect quality of life. It also represents a high burden on the health system. Effective models of care for addressing the prevention and management of pediatric musculoskeletal pain are imperative. This chapter will address the following key questions: (1) Why are pediatric-specific models of pain care needed? (2) What is the burden of chronic musculoskeletal pain among children and adolescents? (3) What are the best practice approaches for early identification and prevention of chronic musculoskeletal pain in children and adolescents? (4) What are the recommended strategies for clinical management of chronic pain, including pharmacological, physical, psychological and complementary, and alternative approaches? (5) What are the most effective strategies for implementing models of pain care across different care settings? (6) What are the research priorities to improve models of care for children and adolescents with chronic musculoskeletal pain?

What incentives increase data sharing in health and medical research? A systematic review

BACKGROUND

The foundation of health and medical research is data. Data sharing facilitates the progress of research and strengthens science. Data sharing in research is widely discussed in the literature; however, there are seemingly no evidence-based incentives that promote data sharing.

METHODS

A systematic review (registration: 10.17605/OSF.IO/6PZ5E) of the health and medical research literature was used to uncover any evidence-based incentives, with pre- and post-empirical data that examined data sharing rates. We were also interested in quantifying and classifying the number of opinion pieces on the importance of incentives, the number observational studies that analysed data sharing rates and practices, and strategies aimed at increasing data sharing rates.

RESULTS

Only one incentive (using open data badges) has been tested in health and medical research that examined data sharing rates. The number of opinion pieces (n = 85) out-weighed the number of article-testing strategies (n = 76), and the number of observational studies exceeded them both (n = 106).

CONCLUSIONS

Given that data is the foundation of evidence-based health and medical research, it is paradoxical that there is only one evidence-based incentive to promote data sharing. More well-designed studies are needed in order to increase the currently low rates of data sharing.

The new Childhood Arthritis and Rheumatology Research Alliance (CARRA) registry: design, rationale, and characteristics of patients enrolled in the first 12 months

BACKGROUND

Herein we describe the history, design, and rationale of the new Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry and present the characteristics of patients with juvenile idiopathic arthritis (JIA) enrolled in the first 12 months of operation.

METHODS

The CARRA Registry began prospectively collecting data in the United States and Canada in July 2015 to evaluate the safety of therapeutic agents in persons with childhood-onset rheumatic disease, initially restricted to JIA. Secondary objectives include the evaluation of disease outcomes and their associations with medication use and other factors. Data are collected every 6 months and include clinical assessments, detailed medication use, patient-reported outcomes, and safety events. Follow-up is planned for at least 10 years for each participant and is facilitated by a telephone call center.

RESULTS

As of July 2016, 1192 patients with JIA were enrolled in the CARRA Registry at 49 clinical sites. At enrollment, their median age was 12.4 years old and median disease duration was 2.6 years. Owing to preferential enrollment, patients with systemic JIA (13%) and with a polyarticular course (75%) were over-represented compared to patients in typical clinical practice. Approximately 49% were currently using biologic agents and ever use of oral glucocorticoids was common (47%). The CARRA Registry provides safety surveillance data to pharmaceutical companies to satisfy their regulatory requirements, and several independently-funded sub-studies that use the Registry infrastructure are underway.

CONCLUSION

The new CARRA Registry successfully enrolled nearly 1200 participants with JIA in the first 12 months of its operation. Sustainable funding has been secured from multiple sources. The CARRA Registry may serve as a model for the study of other uncommon diseases.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

The Systemic Juvenile Idiopathic Arthritis Cohort of the Childhood Arthritis and Rheumatology Research Alliance Registry: 2010–2013

Objective.

We aimed to identify the (1) demographic/clinical characteristics, (2) medication usage trends, (3) variables associated with worse disease activity, and (4) characteristics of patients with persistent chronic arthritis in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Legacy Registry’s systemic juvenile idiopathic arthritis (sJIA) cohort.

Methods.

Demographics, disease activity measures, and medications at enrollment of patients with sJIA in the CARRA Registry were analyzed using descriptive statistics. Multivariate analyses were conducted to identify associations with increased disease activity. Medication usage frequencies were calculated by year.

Results.

There were 528 patients with sJIA enrolled in the registry (2010–2013). There were 435 patients who had a complete dataset; of these, 372 met the International League of Associations for Rheumatology criteria and were included in the analysis. At enrollment, median disease duration and joint count were 3.7 years and 0, respectively; 16.4% had a rash and 6.7% had a fever. Twenty-six percent were taking interleukin 1 (IL-1) inhibitors and 29% glucocorticoids. Disease-modifying antirheumatic drugs and tumor necrosis factor inhibitors use decreased, while IL-6 inhibitor use increased between 2010 and 2013. African American patients had worse joint counts (p = 0.003), functional status (p = 0.01), and physician’s global assessment (p = 0.008). Of the 255 subjects with > 2 years of disease duration, 56% had no arthritis or systemic symptoms, while 32% had persistent arthritis only.

Conclusion.

Most patients in the largest sJIA cohort reported to date had low disease activity. Practice patterns for choice of biologic agents appeared to change over the study period. Nearly one-third had persistent arthritis without systemic symptoms > 2 years after onset. African Americans were associated with worse disease activity. Strategies are needed to improve outcomes in subgroups with poor prognosis.

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories

Objective To assess the variability in data distributions among data sources and over time through a case study of a large multisite repository as a systematic approach to data quality (DQ).

Materials and Methods Novel probabilistic DQ control methods based on information theory and geometry are applied to the Public Health Mortality Registry of the Region of Valencia, Spain, with 512 143 entries from 2000 to 2012, disaggregated into 24 health departments. The methods provide DQ metrics and exploratory visualizations for (1) assessing the variability among multiple sources and (2) monitoring and exploring changes with time. The methods are suited to big data and multitype, multivariate, and multimodal data.

Results The repository was partitioned into 2 probabilistically separated temporal subgroups following a change in the Spanish National Death Certificate in 2009. Punctual temporal anomalies were noticed due to a punctual increment in the missing data, along with outlying and clustered health departments due to differences in populations or in practices.

Discussion Changes in protocols, differences in populations, biased practices, or other systematic DQ problems affected data variability. Even if semantic and integration aspects are addressed in data sharing infrastructures, probabilistic variability may still be present. Solutions include fixing or excluding data and analyzing different sites or time periods separately. A systematic approach to assessing temporal and multisite variability is proposed.

Conclusion Multisite and temporal variability in data distributions affects DQ, hindering data reuse, and an assessment of such variability should be a part of systematic DQ procedures.

Federalist principles for healthcare data networks

Applying federalist principles to networked health record data could facilitate realization of the potential of shared health data.

Trends in biomedical informatics: automated topic analysis of JAMIA articles

Biomedical Informatics is a growing interdisciplinary field in which research topics and citation trends have been evolving rapidly in recent years. To analyze these data in a fast, reproducible manner, automation of certain processes is needed. JAMIA is a "generalist" journal for biomedical informatics. Its articles reflect the wide range of topics in informatics. In this study, we retrieved Medical Subject Headings (MeSH) terms and citations of JAMIA articles published between 2009 and 2014. We use tensors (i.e., multidimensional arrays) to represent the interaction among topics, time and citations, and applied tensor decomposition to automate the analysis. The trends represented by tensors were then carefully interpreted and the results were compared with previous findings based on manual topic analysis. A list of most cited JAMIA articles, their topics, and publication trends over recent years is presented. The analyses confirmed previous studies and showed that, from 2012 to 2014, the number of articles related to MeSH terms Methods, Organization & Administration, and Algorithms increased significantly both in number of publications and citations. Citation trends varied widely by topic, with Natural Language Processing having a large number of citations in particular years, and Medical Record Systems, Computerized remaining a very popular topic in all years.

Design and implementation of a privacy preserving electronic health record linkage tool in Chicago

OBJECTIVE

To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research.

METHODS

The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches.

RESULTS

The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%.

CONCLUSIONS

Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.

A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research

INTRODUCTION

We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research.

DESCRIPTION OF ARCHITECTURE

We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests.

SUGGESTIONS FOR FUTURE USE

The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however.

CONCLUSIONS

We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

Integrated image data and medical record management for rare disease registries. A general framework and its instantiation to theGerman Calciphylaxis Registry

Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany. With respect to the study management tool that is already successfully operating at the Clinical Trial Center Aachen, the Google Web Toolkit was chosen with Hibernate and Gilead connecting a MySQL database management system. Image and signal data integration and processing is supported by Apache Commons FileUpload-Library and ImageJ-based Java code, respectively. As a proof of concept, the framework is instantiated to the German Calciphylaxis Registry. The framework is composed of five mandatory core modules: (1) Data Core, (2) EDC, (3) Access Control, (4) Audit Trail, and (5) Terminology as well as six optional modules: (6) Binary Large Object (BLOB), (7) BLOB Analysis, (8) Standard Operation Procedure, (9) Communication, (10) Pseudonymization, and (11) Biorepository. Modules 1-7 are implemented in the German Calciphylaxis Registry. The proposed RDR framework is easily instantiated and directly integrates image management and analysis. As open source software, it may assist improved data collection and analysis of rare diseases in near future.

Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2

Nationwide Children's Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semistructured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.

OBJECTIVE

We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.

METHODS

We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.

RESULTS

26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.

CONCLUSION

Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.

A systematic approach for using DICOM structured reports in clinical processes: focus on breast cancer

This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.

Taking advantage of continuity of care documents to populate a research repository

OBJECTIVE

Clinical data warehouses have accelerated clinical research, but even with available open source tools, there is a high barrier to entry due to the complexity of normalizing and importing data. The Office of the National Coordinator for Health Information Technology's Meaningful Use Incentive Program now requires that electronic health record systems produce standardized consolidated clinical document architecture (C-CDA) documents. Here, we leverage this data source to create a low volume standards based import pipeline for the Informatics for Integrating Biology and the Bedside (i2b2) clinical research platform. We validate this approach by creating a small repository at Partners Healthcare automatically from C-CDA documents.

MATERIALS AND METHODS

We designed an i2b2 extension to import C-CDAs into i2b2. It is extensible to other sites with variances in C-CDA format without requiring custom code. We also designed new ontology structures for querying the imported data.

RESULTS

We implemented our methodology at Partners Healthcare, where we developed an adapter to retrieve C-CDAs from Enterprise Services. Our current implementation supports demographics, encounters, problems, and medications. We imported approximately 17 000 clinical observations on 145 patients into i2b2 in about 24 min. We were able to perform i2b2 cohort finding queries and view patient information through SMART apps on the imported data.

DISCUSSION

This low volume import approach can serve small practices with local access to C-CDAs and will allow patient registries to import patient supplied C-CDAs. These components will soon be available open source on the i2b2 wiki.

CONCLUSIONS

Our approach will lower barriers to entry in implementing i2b2 where informatics expertise or data access are limited.

Mission and Sustainability of Informatics for Integrating Biology and the Bedside (i2b2)

INTRODUCTION

A visible example of a successfully disseminated research project in the healthcare space is Informatics for Integrating Biology and the Bedside, or i2b2. The project serves to provide the software that can allow a researcher to do direct, self-serve queries against the electronic healthcare data form a hospital. The goals of these queries are to find cohorts of patients that fit specific profiles, while providing for patient privacy and discretion. Sustaining this resource and keeping its direction has always been a challenge, but ever more so as the ten year National Centers for Biomedical Computing (NCBCs) sunset their funding.

FINDINGS

Building on the i2b2 structures has helped the dissemination plans for grants leveraging it because it is a disseminated national resource. While this has not directly increased the support of i2b2 internally, it has increased the ability of institutions to leverage the resource and generally leads to increased institutional support.

DISCUSSION

The successful development, use, and dissemination i2b2 has been significant in clinical research and informatics. Its evolution has been from a local research data infrastructure to one disseminated more broadly than any other product of the National Centers for Biomedical Computing, and an infrastructure spawning larger investments than were originally used to create it. Throughout this, there were two main lessons about the benefits of dissemination: that people have great creativity in utilizing a resource in different ways and that broader system use can make the system more robust. One option for long-term sustainability of the central authority would be to translate the function to an industry partner. Another option currently being pursued is to create a foundation that would be a central authority for the project.

CONCLUSION

Over the past 10 years, i2b2 has risen to be an important staple in the toolkit of health care researchers. There are now over 110 hospitals that use i2b2 for research. This open-source platform has a community of developers that are continuously enhancing the analytic capacities of the platform and inventing new functionality. By understanding how i2b2 has been sustained, we hope that other research infrastructure projects may better navigate options in making those initiatives sustainable over time.

Developing a provisional, international minimal dataset for Juvenile Dermatomyositis: for use in clinical practice to inform research

BACKGROUND

Juvenile dermatomyositis (JDM) is a rare but severe autoimmune inflammatory myositis of childhood. International collaboration is essential in order to undertake clinical trials, understand the disease and improve long-term outcome. The aim of this study was to propose from existing collaborative initiatives a preliminary minimal dataset for JDM. This will form the basis of the future development of an international consensus-approved minimum core dataset to be used both in clinical care and inform research, allowing integration of data between centres.

METHODS

A working group of internationally-representative JDM experts was formed to develop a provisional minimal dataset. Clinical and laboratory variables contained within current national and international collaborative databases of patients with idiopathic inflammatory myopathies were scrutinised. Judgements were informed by published literature and a more detailed analysis of the Juvenile Dermatomyositis Cohort Biomarker Study and Repository, UK and Ireland.

RESULTS

A provisional minimal JDM dataset has been produced, with an associated glossary of definitions. The provisional minimal dataset will request information at time of patient diagnosis and during on-going prospective follow up. At time of patient diagnosis, information will be requested on patient demographics, diagnostic criteria and treatments given prior to diagnosis. During on-going prospective follow-up, variables will include the presence of active muscle or skin disease, major organ involvement or constitutional symptoms, investigations, treatment, physician global assessments and patient reported outcome measures.

CONCLUSIONS

An internationally agreed minimal dataset has the potential to significantly enhance collaboration, allow effective communication between groups, provide a minimal standard of care and enable analysis of the largest possible number of JDM patients to provide a greater understanding of this disease. This preliminary dataset can now be developed into a consensus-approved minimum core dataset and tested in a wider setting with the aim of achieving international agreement.

Outcomes research in childhood autoimmune diseases

This article provides an introduction to key aspects of outcomes research in pediatric rheumatology, focusing on arthritis. Patient-centered outcomes research addresses questions of interest to multiple stakeholders in order to guide the best health care decisions suited to a particular patient's circumstances and preferences. Discussion includes the importance of maintaining high-quality longitudinal patient registries and use of valid clinical and patient-reported outcome measures. Rapid, reliable translation of research on best practices into clinical care, as facilitated by quality improvement learning networks, leads to timely and meaningful improvement in patient outcomes.

BigMouth: a multi-institutional dental data repository

Few oral health databases are available for research and the advancement of evidence-based dentistry. In this work we developed a centralized data repository derived from electronic health records (EHRs) at four dental schools participating in the Consortium of Oral Health Research and Informatics. A multi-stakeholder committee developed a data governance framework that encouraged data sharing while allowing control of contributed data. We adopted the i2b2 data warehousing platform and mapped data from each institution to a common reference terminology. We realized that dental EHRs urgently need to adopt common terminologies. While all used the same treatment code set, only three of the four sites used a common diagnostic terminology, and there were wide discrepancies in how medical and dental histories were documented. BigMouth was successfully launched in August 2012 with data on 1.1 million patients, and made available to users at the contributing institutions.

Query Health: standards-based, cross-platform population health surveillance

OBJECTIVE

Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects.

MATERIALS AND METHODS

Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language.

RESULTS

We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed.

DISCUSSIONS

This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative.

CONCLUSIONS

Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites.

Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative 'apps' to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

Traits and types of health data repositories

We review traits of reusable clinical data and offer a typology of clinical repositories with a range of known examples. Sources of clinical data suitable for research can be classified into types reflecting the data's institutional origin, original purpose, level of integration and governance. Primary data nearly always come from research studies and electronic medical records. Registries collect data on focused populations primarily to track outcomes, often using observational research methods. Warehouses are institutional information utilities repackaging clinical care data. Collections organize data from more organizations than a data warehouse, and more original data sources than a registry. Therefore even if they are heavily curated, their level of internal integration, and thus ease of use, can be less than other types. Federations are like collections except that physical control over data is distributed among donor organizations. Federations sometimes federate, giving a second level of organization. While the size, in number of patients, varies widely within each type of data source, populations over 10 K are relatively numerous, and much larger populations can be seen in warehouses and federations. One imagined ideal structure for research progress has been called an "Information Commons". It would have longitudinal, multi-leveled (environmental through molecular) data on a large population of identified, consenting individuals. These are qualities whose achievement would require long term commitment on the part of many data donors, including a willingness to make their data public.

From FUS to Pandora syndrome

New concepts:

Ideas about the causes of lower urinary tract signs (LUTS) in cats have changed significantly in the past 40 years. Recent research is challenging the conventional view that the bladder is always the perpetrator of LUTS, and suggests that the bladder can also be one victim of a systemic process associated with a sensitized central stress response system.

Aim:

In this article the authors provide their perspective on the implications of these findings for the diagnosis and treatment of cats with LUTS, provide some historical context, and suggest ways that the veterinary profession might work together to better understand the disorders underlying these signs, and possibly reduce their prevalence.

Translational informatics in personalized medicine: an update for 2014

Many things have changed but much has remained the same as we have seen a dramatic increase in the generation of genetics, genomics and a variety of clinical data leading to increased data density and continued challenges in organizing and managing that data in pursuit of personalized medicine. Simultaneously, we have seen an increase in commercial and open-source solutions, and marked movement toward open sharing of tools and data in public–private partnerships, yet still few examples of traditional companion diagnostics for personalized medicine products. Most encouraging are examples of focused public and private efforts that have resulted in knowledge leading to critical assessment of existing therapies and the development of new therapies. These examples lay highly emulatable informatics foundations for rapid advances in personalized medicine.