Data standards in clinical research: gaps, overlaps, challenges and future directions

Establishment of a Dataset for the Traditional Korean Medicine Examination in Healthy Adults

We established a protocol for the traditional Korean medicine examination (KME) and methodically gathered data following this protocol. Potential indicators for KME were extracted through a literature review; the first KME protocol was developed based on three rounds of expert opinions. The first KME protocol's feasibility was confirmed, and data were collected over four years from traditional Korean medicine (KM) hospitals, focusing on healthy adults, using the final KME protocol. A literature review identified 175 potential core indicators, condensed into 73 indicators after three rounds of expert consultation. The first KME protocol, which was categorized under questionnaires and medical examinations, was developed after the third round of expert opinions. A pilot study using the first KME protocol was conducted to ensure its validity, leading to modifications resulting in the development of the final KME protocol. Over four years, data were collected from six KM hospitals, focusing on healthy adults; we obtained a dataset comprising 11,036 healthy adults. This is the first protocol incorporating core indicators of KME in a quantitative form and systematically collecting data. Our protocol holds potential merit in evaluating predisposition to diseases or predicting diseases.

Rise of the Allotrope Simple Model: Update from 2023 Fall Allotrope Connect

The Allotrope Foundation (AF) started as a group of pharmaceutical companies, instrument, and software vendors that set out to simplify the exchange of data in the laboratory. After a decade of work, they released products that have found adoption in various companies. Most recently, the Allotrope Simple Model (ASM) was developed to speed up and widen the adoption. As a result, the Foundation has recently added chemical companies and, importantly, is reworking its business model to lower the entry barrier for smaller companies. Here, we present the proceedings from the Allotrope Connect Fall 2023 conference and summarize the technical and organizational developments at the Foundation since 2020.

Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic

Introduction

The COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.

Methods

At the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output.

Results

Over the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020-2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19.

Discussion

Research is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.

Evaluation of OMOP CDM, i2b2 and ICGC ARGO for supporting data harmonization in a breast cancer use case of a multicentric European AI project

OBJECTIVE

Observational research in cancer poses great challenges regarding adequate data sharing and consolidation based on a homogeneous data semantic base. Common Data Models (CDMs) can help consolidate health data repositories from different institutions minimizing loss of meaning by organizing data into a standard structure. This study aims to evaluate the performance of the Observational Medical Outcomes Partnership (OMOP) CDM, Informatics for Integrating Biology & the Bedside (i2b2) and International Cancer Genome Consortium, Accelerating Research in Genomic Oncology (ICGC ARGO) for representing non-imaging data in a breast cancer use case of EuCanImage.

METHODS

We used ontologies to represent metamodels of OMOP, i2b2, and ICGC ARGO and variables used in a cancer use case of a European AI project. We selected four evaluation criteria for the CDMs adapted from previous research: content coverage, simplicity, integration, implementability.

RESULTS

i2b2 and OMOP exhibited higher element completeness (100% each) than ICGC ARGO (58.1%), while the three achieved 100% domain completeness. ICGC ARGO normalizes only one of our variables with a standard terminology, while i2b2 and OMOP use standardized vocabularies for all of them. In terms of simplicity, ICGC ARGO and i2b2 proved to be simpler both in terms of ontological model (276 and 175 elements, respectively) and in the queries (7 and 20 lines of code, respectively), while OMOP required a much more complex ontological model (615 elements) and queries similar to those of i2b2 (20 lines). Regarding implementability, OMOP had the highest number of mentions in articles in PubMed (130) and Google Scholar (1,810), ICGC ARGO had the highest number of updates to the CDM since 2020 (4), and i2b2 is the model with more tools specifically developed for the CDM (26).

CONCLUSION

ICGC ARGO proved to be rigid and very limited in the representation of oncologic concepts, while i2b2 and OMOP showed a very good performance. i2b2's lack of a common dictionary hinders its scalability, requiring sites that will share data to explicitly define a conceptual framework, and suggesting that OMOP and its Oncology extension could be the more suitable choice. Future research employing these CDMs with actual datasets is needed.

Data standards for atrial fibrillation/flutter and catheter ablation: the European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart)

AIMS

Standardized data definitions are essential for monitoring and assessment of care and outcomes in observational studies and randomized controlled trials (RCTs). The European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart) project of the European Society of Cardiology aimed to develop contemporary data standards for atrial fibrillation/flutter (AF/AFL) and catheter ablation.

METHODS AND RESULTS

We used the EuroHeart methodology for the development of data standards and formed a Working Group comprising 23 experts in AF/AFL and catheter ablation registries, as well as representatives from the European Heart Rhythm Association and EuroHeart. We conducted a systematic literature review of AF/AFL and catheter ablation registries and data standard documents to generate candidate variables. We used a modified Delphi method to reach a consensus on a final variable set. For each variable, the Working Group developed permissible values and definitions, and agreed as to whether the variable was mandatory (Level 1) or additional (Level 2). In total, 70 Level 1 and 92 Level 2 variables were selected and reviewed by a wider Reference Group of 42 experts from 24 countries. The Level 1 variables were implemented into the EuroHeart IT platform as the basis for continuous registration of individual patient data.

CONCLUSION

By means of a structured process and working with international stakeholders, harmonized data standards for AF/AFL and catheter ablation for AF/AFL were developed. In the context of the EuroHeart project, this will facilitate country-level quality of care improvement, international observational research, registry-based RCTs, and post-marketing surveillance of devices and pharmacotherapies.

Data Management 101 for drug developers: A peek behind the curtain

In drug development a frequently used phrase is "data-driven". Just as high-test gas fuels a car, so drug development "runs on" high-quality data; hence, good data management practices, which involve case report form design, data entry, data capture, data validation, medical coding, database closure, and database locking, are critically important. This review covers the essentials of clinical data management (CDM) for the United States. It is intended to demystify CDM, which means nothing more esoteric than the collection, organization, maintenance, and analysis of data for clinical trials. The review is written with those who are new to drug development in mind and assumes only a passing familiarity with the terms and concepts that are introduced. However, its relevance may also extend to experienced professionals that feel the need to brush up on the basics. For added color and context, the review includes real-world examples with RRx-001, a new molecular entity in phase III and with fast-track status in head and neck cancer, and AdAPT-001, an oncolytic adenovirus armed with a transforming growth factor-beta (TGF-β) trap in a phase I/II clinical trial with which the authors, as employees of the biopharmaceutical company, EpicentRx, are closely involved. An alphabetized glossary of key terms and acronyms used throughout this review is also included for easy reference.

Methodology for the development of international clinical data standards for common cardiovascular conditions: European Unified Registries for Heart Care Evaluation and Randomised Trials (EuroHeart)

AIMS

Data standards are consensual specifications for the representation of data arising from different sources. If provided with internationally harmonized variables, permissible values, and clinical definitions, they have the potential to enable reliable between- and within-country analysis of care and outcomes. The European Unified Registries for Heart Care Evaluation and Randomised Trials (EuroHeart) is a European Society of Cardiology project that allows participating countries to collect patient data to undertake quality improvement, observational studies, drug and device surveillance, and registry-based randomized controlled trials for cardiovascular conditions. This paper describes the methodology for development of harmonized data standards for EuroHeart.

METHODS AND RESULTS

We adopted a five-step process for the development of harmonized data standards. The process includes (i) identification of clinical domains for data standard development by evaluating specific cardiovascular conditions with high prevalence and opportunities for quality improvement; (ii) construction of data standard specifications by systematic review of the literature; (iii) selection of variables by a domain-specific Working Group using a modified Delphi method; (iv) validation of data standards by a domain-specific Reference Group; and (v) implementation of the developed data standards into an IT platform.

CONCLUSION

This paper describes the approach adopted by EuroHeart for the development of clinical data standards for cardiovascular disease. The methodology has been developed and is used by EuroHeart to create a suite of international data standards for cardiovascular diseases. The EuroHeart data standards may be used to systematically capture individual patient data about clinical care and for research.

Natural History and Real-World Data in Rare Diseases: Applications, Limitations, and Future Perspectives

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed.

TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse

BACKGROUND

 During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable.

OBJECTIVES

 This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization.

METHODS

 The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML.

RESULTS

 First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined.

CONCLUSIONS

 This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.

Clinical Data Interchange Standards in Clinical Trials on Alzheimer's Disease

OBJECTIVE

The Clinical Data Interchange Standards Consortium (CDISC) proposed outcome measures for clinical trials on Alzheimer's disease (AD) in the Therapeutic Area User Guide for AD (TAUG-AD). To investigate how well the clinical trials on AD registered in the ClinicalTrials.gov complied with the recommendations on outcome measures by the CDISC.

METHODS

We compared the outcome measures proposed in the TAUG-AD version 2.0.1 with those employed in the protocols of clinical trials on AD registered in ClinicalTrials.gov.

RESULTS

We analyzed 101 outcome measures from 305 protocols. The TAUG-AD listed ten scales for outcome measures of clinical trials on AD. The scales for cognition, activities of daily living, behavioral and psychological symptoms of dementia, and global severity listed in TAUG-AD were most frequently employed in the clinical trials on AD. However, TAUG-AD did not include any scale on quality of life. Also, several scales such as Montreal Cognitive Assessment, Alzheimer's Disease Cooperative Study-Activities of Daily Living, and Cohen- Mansfield Agitation Inventory not listed in the TAUG-AD were commonly employed in the clinical trials on AD and changed over time.

CONCLUSION

To properly standardize the data from clinical trials on AD, the gap between the TAUG-AD and the measures employed in real-world clinical trials should be filled.

Health Informatics: Engaging Modern Healthcare Units: A Brief Overview

In the current scenario, with a large amount of unstructured data, Health Informatics is gaining traction, allowing Healthcare Units to leverage and make meaningful insights for doctors and decision-makers with relevant information to scale operations and predict the future view of treatments via Information Systems Communication. Now, around the world, massive amounts of data are being collected and analyzed for better patient diagnosis and treatment, improving public health systems and assisting government agencies in designing and implementing public health policies, instilling confidence in future generations who want to use better public health systems. This article provides an overview of the HL7 FHIR Architecture, including the workflow state, linkages, and various informatics approaches used in healthcare units. The article discusses future trends and directions in Health Informatics for successful application to provide public health safety. With the advancement of technology, healthcare units face new issues that must be addressed with appropriate adoption policies and standards.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

BACKGROUND

Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks.

METHODS

The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology.

RESULTS

This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets.

CONCLUSIONS

The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Fast-Tracking Health Data Standards Development and Adoption in Real-World Settings: A Pilot Approach

BACKGROUND

Pilot-testing is important in standards development because it facilitates agile navigation of the gap between needs for and use of standards in real-world settings and can reveal the practicalities of implementation. As the implementation and use of health data standards are usually more complicated than anticipated, the Office of the National Coordinator for Health Information Technology (ONC) routinely oversees and organizes relevant pilot projects.

OBJECTIVES

This article provides an in-depth look into a sample of ONC's standards-focused pilot projects to (1) inform readers of the complexities of developing, implementing, and advancing standards and (2) guide those seeking to evaluate new standards through pilot projects.

METHODS

The ONC's approach to conducting pilot projects begins with identifying a clinical care need, research requirement, or policy outcome that is not well supported by existing standards through a landscape review. ONC then selects a testing approach based on the identified need and maturity of relevant standards. Next, ONC identifies use cases and sites to pilot-test the relevant standard. Once complete, ONC publishes a report that informs subsequent projects and standards development.

RESULTS

Pilot projects presented here are organized into three categories related to their demonstrated focus and related approach: (1) improving standards for presenting and sharing clinical genetic data, (2) accelerating the development and implementation of new standards, and (3) facilitating clinical data reuse. Each project illustrates the pilot approach from inception to next steps, capturing the role of collaboration among standards development organizations, stakeholders, and end-users to ensure standards are practical and fit for purpose.

CONCLUSION

The ONC approach identifies implementation difficulties prior to broader adoption and use of standards, and provides insight into the steps needed to scale use of standards. The ONC's organization of pilot projects serves as a natural accelerator for building communities of practice, often providing a well-connected beneficiary of lessons learned.

Coming of age of Allotrope: Proceedings from the Fall 2020 Allotrope Connect

The Allotrope Foundation (AF) is a group of pharmaceutical, device vendor, and software companies that develops and releases technologies [the Allotrope Data Format (ADF), the Allotrope Foundation Ontology (AFO), and the Allotrope Data Models (ADM)] to simplify the exchange of electronic data. We present here the first comprehensive history of the AF, its structure, a list of members and partners, and an introduction to the technologies. Finally, we provide current insights into the adoption and development of the technologies by summarizing the Fall 2020 Allotrope Connect virtual conference. This overview provides an easy access to the AF and highlights opportunities for collaboration.

Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models

BACKGROUND

COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization.

OBJECTIVE

This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time.

MATERIAL AND METHODS

The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020.

RESULTS

An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts.

CONCLUSION

This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs.

Core principles for the implementation of the neurodata without borders data standard

BACKGROUND

The Neurodata Without Borders data standard (NWB) unifies diverse modalities of neurophysiology data in a single format. Integrating NWB with a database unleashes its full potential to promote collaboration, standardize analyses, capitalize on historical data, and ensures data integrity by maintaining process transparency. NWB database technology is the bedrock of analytical systems used by academic leaders including the Allen Institute and the International Brain Laboratory. Here we present the benefits of incorporating NWB design principles in a big data analytics application.

NEW METHOD

Data standards and databases are the foundation of big data analytics. To demonstrate the benefits of using these systems together, we implemented NWB in Jupyter notebooks using DataJoint to streamline database operations.

RESULTS

We demonstrate the utility of combining the NWB with DataJoint in a Jupyter-based electronic lab journal. We convert open-field behavioral data (using X, Y coordinates) to NWB format and process it with a DataJoint pipeline. Additional notebooks demonstrate working NWB files, data sharing, combining data from diverse sources, and retrospective analyses with data query filtering techniques.

COMPARISON WITH EXISTING METHODS

NWB describes how to structure and store neurophysiology data and is streamlined for research settings. In contrast to other data standards, combining NWB with DataJoint's database interface can dramatically increase data analytical capabilities.

CONCLUSIONS

The joint use of NWB with DataJoint transforms traditional laboratory datasets and workflows. Our Jupyter notebooks showcase the analytical and collaborative advantages of adopting big data analytics and can be tailored to other modalities by researchers interested in evaluating NWB.

The inclusion of health data standards in the implementation of pharmacogenomics systems: a scoping review

Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffuse widely. In this study, we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods: We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation.

Composite CDE: modeling composite relationships between common data elements for representing complex clinical data

BACKGROUND

Semantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical data elements (DEs). However, the standard model has both structural and semantic limitations, and the number of DEs continues to increase due to poor term reusability. Semantic types and constraints are lacking for comprehensively describing and evaluating DEs on real-world clinical documents.

METHODS

We addressed these limitations by defining three new types of semantic relationship (dependency, composite, and variable) in our previous studies. The present study created new and further extended existing semantic types (hybrid atomic and repeated and dictionary composite common data elements [CDEs]) with four constraints: ordered, operated, required, and dependent. For evaluation, we extracted all atomic and composite CDEs from five major clinical documents from five teaching hospitals in Korea, 14 Fast Healthcare Interoperability Resources (FHIR) resources from FHIR bulk sample data, and MIMIC-III (Medical Information Mart for Intensive Care) demo dataset. Metadata reusability and semantic interoperability in real clinical settings were comprehensively evaluated by applying the CDEs with our extended semantic types and constraints.

RESULTS

All of the CDEs (n = 1142) extracted from the 25 clinical documents were successfully integrated with a very high CDE reuse ratio (46.9%) into 586 CDEs (259 atomic and 20 unique composite CDEs), and all of CDEs (n = 238) extracted from the 14 FHIR resources of FHIR bulk sample data were successfully integrated with high CDE reuse ration (59.7%) into 96 CDEs (21 atomic and 28 unique composite CDEs), which improved the semantic integrity and interoperability without any semantic loss. Moreover, the most complex data structures from two CDE projects were successfully encoded with rich semantics and semantic integrity.

CONCLUSION

MDR-based extended semantic types and constraints can facilitate comprehensive representation of clinical documents with rich semantics, and improved semantic interoperability without semantic loss.

An Interactive Visualization Tool for HL7 FHIR Specification Browsing and Profiling

The rich semantic representation and sophisticated structure definition of the HL7 Fast Healthcare Interoperability Resources (FHIR) specification requires relatively great efforts to understand and utilize. The objective of our study is to design, develop and evaluate an open-source and user-friendly visualization interface for exploring the FHIR specification. We prototyped an interactive visualization tool for navigating and manipulating the FHIR core resources, profiles and extensions. The utility of the tool was evaluated using evaluation metrics mainly focusing on its interactive mechanism and content expressiveness. We demonstrated that the visualization techniques are helpful for navigating the HL7 FHIR specification and aiding its profiling.

Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics

Background

The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents.

Methods

CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.g., synonyms, preferred terms, and definitions) for each CMDO concept, and (5) evaluating the utility of CMDO.

Results

We created CMDO comprising 189 concepts under the 4 first-level classes of Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice.

Conclusions

CMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0877-x) contains supplementary material, which is available to authorized users.

Reducing defects in the datasets of clinical research studies: conformance with data quality metrics

Background

A dataset is indispensable to answer the research questions of clinical research studies. Inaccurate data lead to ambiguous results, and the removal of errors results in increased cost. The aim of this Quality Improvement Project (QIP) was to improve the Data Quality (DQ) by enhancing conformance and minimizing data entry errors.

Methods

This is a QIP which was conducted in the Department of Biostatistics using historical datasets submitted for statistical data analysis from the department’s knowledge base system. Forty-five datasets received for statistical data analysis, were included at baseline. A 12-item checklist based on six DQ domains (i) completeness (ii) uniqueness (iii) timeliness (iv) accuracy (v) validity and (vi) consistency was developed to assess the DQ. The checklist was comprised of 12 items; missing values, un-coded values, miscoded values, embedded values, implausible values, unformatted values, missing codebook, inconsistencies with the codebook, inaccurate format, unanalyzable data structure, missing outcome variables, and missing analytic variables. The outcome was the number of defects per dataset. Quality improvement DMAIC (Define, Measure, Analyze, Improve, Control) framework and sigma improvement tools were used. Pre-Post design was implemented using mode of interventions. Pre-Post change in defects (zero, one, two or more defects) was compared by using chi-square test.

Results

At baseline, out of forty-five datasets; six (13.3%) datasets had zero defects, eight (17.8%) had one defect, and 31(69%) had ≥2 defects. The association between the nature of data capture (single vs. multiple data points) and defective data was statistically significant (p = 0.008). Twenty-one datasets were received during post-intervention for statistical data analysis. Seventeen (81%) had zero defects, two (9.5%) had one defect, and two (9.5%) had two or more defects. The proportion of datasets with zero defects had increased from 13.3 to 81%, whereas the proportion of datasets with two or more defects had decreased from 69 to 9.5% (p = < 0.001).

Conclusion

Clinical research study teams often have limited knowledge of data structuring. Given the need for good quality data, we recommend training programs, consultation with data experts prior to data structuring and use of electronic data capturing methods.

Development of Technologic Solutions to Address Complex Local Requirements of an International Prostate Cancer Clinical Quality Registry

PURPOSE

To detail the process for importing a defined data set into a centralized global registry via a secure file transfer platform and to understand the barriers to the establishment of a centralized global registry.

RESULTS

A bespoke solution was developed to allow transmission of data from international local data centers to a centralized repository. Data elements included in the import template were drawn from existing International Consortium for Health Outcome Measurement variables and refined to ensure accurate benchmarking as well as feasibility in data completeness. The data set was organized in accordance with the prostate cancer care trajectory. Key considerations in developing the data transfer platform included import file format, process of input validation, and technical provisions. Given the diversity in the legislation and ethical requirements with respect to consent, data handling, and cross-border data transfer across geographic locations, we encouraged each local data center to consult with its legal advisors and research ethics committee early on in the process.

DISCUSSION

A global collaboration, although highly valuable, posed many challenges because of inconsistent methods of data collection. User acceptance of a system is paramount to the success of establishing a metaregistry. Local information technology support and regular regression testing ensures quality and maintenance of the database.

CONCLUSION

We developed a Web-based system to facilitate the collection and secure storage of common data, which is scalable and secure. It is anticipated that through systematic recording of data, global standards of clinical practice and outcomes of care will see vast improvements.

CDEGenerator: an online platform to learn from existing data models to build model registries

OBJECTIVE

Best-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases.

METHODS

Based on prior work, a European information infrastructure with a large collection of medical data models was established. A newly developed analysis module called CDEGenerator provides systematic comparison of selected data models and user-tailored creation of minimum data sets or harmonized item catalogs. Usability was assessed by eight external medical documentation experts in a workshop by the umbrella organization for networked medical research in Germany with the System Usability Scale.

RESULTS

The analysis and item-tailoring module provides multilingual comparisons of semantically complex eligibility criteria of clinical trials. The System Usability Scale yielded "good usability" (mean 75.0, range 65.0-92.5). User-tailored models can be exported to several data formats, such as XLS, REDCap or Operational Data Model by the Clinical Data Interchange Standards Consortium, which is supported by the US Food and Drug Administration and European Medicines Agency for metadata exchange of clinical studies.

CONCLUSION

The online tool provides user-friendly methods to reuse, compare, and thus learn from data items of standardized or published models to design a blueprint for a harmonized research database.

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the “Shared Nationwide Interoperability Roadmap” from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study.

A standard-driven approach for electronic submission to pharmaceutical regulatory authorities

OBJECTIVE

Using standards is not only useful for data interchange during the process of a clinical trial, but also useful for analyzing data in a review process. Any step, which speeds up approval of new drugs, may benefit patients. As a result, adopting standards for regulatory submission becomes mandatory in some countries. However, preparing standard-compliant documents, such as annotated case report form (aCRF), needs a great deal of knowledge and experience. The process is complex and labor-intensive. Therefore, there is a need to use information technology to facilitate this process.

MATERIALS AND METHODS

Instead of standardizing data after the completion of a clinical trial, this study proposed a standard-driven approach. This approach was achieved by implementing a computer-assisted "standard-driven pipeline (SDP)" in an existing clinical data management system. SDP used CDISC standards to drive all processes of a clinical trial, such as the design, data acquisition, tabulation, etc. RESULTS: A completed phase I/II trial was used to prove the concept and to evaluate the effects of this approach. By using the CDISC-compliant question library, aCRFs were generated automatically when the eCRFs were completed. For comparison purpose, the data collection process was simulated and the collected data was transformed by the SDP. This new approach reduced the missing data fields from sixty-two to eight and the controlled term mismatch field reduced from eight to zero during data tabulation.

CONCLUSION

This standard-driven approach accelerated CRF annotation and assured data tabulation integrity. The benefits of this approach include an improvement in the use of standards during the clinical trial and a reduction in missing and unexpected data during tabulation. The standard-driven approach is an advanced design idea that can be used for future clinical information system development.

Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions

BACKGROUND

A new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels.

OBJECTIVE

The objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice.

METHODS

We conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content.

RESULTS

We present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation.

CONCLUSIONS

Efforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics.

A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer

INTRODUCTION

The introduction of omics data and advances in technologies involved in clinical treatment has led to a broad range of approaches to represent clinical information. Within this context, patient stratification across health institutions due to omic profiling presents a complex scenario to carry out multi-center clinical trials.

METHODS

This paper presents a standards-based approach to ensure semantic integration required to facilitate the analysis of clinico-genomic clinical trials. To ensure interoperability across different institutions, we have developed a Semantic Interoperability Layer (SIL) to facilitate homogeneous access to clinical and genetic information, based on different well-established biomedical standards and following International Health (IHE) recommendations.

RESULTS

The SIL has shown suitability for integrating biomedical knowledge and technologies to match the latest clinical advances in healthcare and the use of genomic information. This genomic data integration in the SIL has been tested with a diagnostic classifier tool that takes advantage of harmonized multi-center clinico-genomic data for training statistical predictive models.

CONCLUSIONS

The SIL has been adopted in national and international research initiatives, such as the EURECA-EU research project and the CIMED collaborative Spanish project, where the proposed solution has been applied and evaluated by clinical experts focused on clinico-genomic studies.

Shiny FHIR: An Integrated Framework Leveraging Shiny R and HL7 FHIR to Empower Standards-Based Clinical Data Applications

In this study, we describe our efforts in building a clinical statistics and analysis application platform using an emerging clinical data standard, HL7 FHIR, and an open source web application framework, Shiny. We designed two primary workflows that integrate a series of R packages to enable both patient-centered and cohort-based interactive analyses. We leveraged Shiny with R to develop interactive interfaces on FHIR-based data and used ovarian cancer study datasets as a use case to implement a prototype. Specifically, we implemented patient index, patient-centered data report and analysis, and cohort analysis. The evaluation of our study was performed by testing the adaptability of the framework on two public FHIR servers. We identify common research requirements and current outstanding issues, and discuss future enhancement work of the current studies. Overall, our study demonstrated that it is feasible to use Shiny for implementing interactive analysis on FHIR-based standardized clinical data.

Developing a modular architecture for creation of rule-based clinical diagnostic criteria

BACKGROUND

With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified architecture to support the need for diagnostic criteria computerization. In this study, we present a modular architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies.

METHODS AND RESULTS

The architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation.

CONCLUSION

Our efforts in developing and prototyping a modular architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization.

Care coordination gaps due to lack of interoperability in the United States: a qualitative study and literature review

Background

Health information technology (HIT) could improve care coordination by providing clinicians remote access to information, improving legibility, and allowing asynchronous communication, among other mechanisms. We sought to determine, from a clinician perspective, how care is coordinated and to what extent HIT is involved when transitioning patients between emergency departments, acute care hospitals, skilled nursing facilities, and home health agencies in settings across the United States.

Methods

We performed a qualitative study with clinicians and information technology professionals from six regions of the U.S. which were chosen as national leaders in HIT. We analyzed data through a two person consensus approach, assigning responses to each of nine care coordination activities. We also conducted a literature review of MEDLINE®, CINAHL®, and Embase, analyzing results of studies that examined interventions to improve information transfer during transitions of care.

Results

We enrolled 29 respondents from 17 organizations and conducted six focus groups. Respondents reported how HIT is currently used for care coordination activities. HIT is currently used to monitor patients and to align systems-level resources with population needs. However, we identified multiple areas where the lack of interoperability leads to inefficient processes and missing data. Additionally, the literature review identified ten intervention studies that address information transfer, seven of which employed HIT and three of which utilized other communication methods such as telephone calls, faxed records, and nurse case management.

Conclusions

Significant care coordination gaps exist due to the lack of interoperability across the United States. We must design, evaluate, and incentivize the use of HIT for care coordination. We should focus on the domains where we found the largest gaps: information transfer, systems to monitor patients, tools to support patients’ self-management goals, and tools to link patients and their caregivers with community resources.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1373-y) contains supplementary material, which is available to authorized users.

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

Big Data: the challenge for small research groups in the era of cancer genomics

In the past decade, cancer research has seen an increasing trend towards high-throughput techniques and translational approaches. The increasing availability of assays that utilise smaller quantities of source material and produce higher volumes of data output have resulted in the necessity for data storage solutions beyond those previously used. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective and secure manner. This requires technical solutions and administrative support not normally financially accounted for in small- to moderate-sized research groups. In this review, we highlight the Big Data challenges faced by translational research groups in the precision medicine era; an era in which the genomes of over 75 000 patients will be sequenced by the National Health Service over the next 3 years to advance healthcare. In particular, we have looked at three main themes of data management in relation to cancer research, namely (1) cancer ontology management, (2) IT infrastructures that have been developed to support data management and (3) the unique ethical challenges introduced by utilising Big Data in research.

Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM)

Efficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM's initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements. Using a case study approach, we evaluated ODM's ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard.

Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7's Virtual Medical Record standard

OBJECTIVE

We show how the HL7 Virtual Medical Record (vMR) standard can be used to design and implement a data integrator (DI) component that collects patient information from heterogeneous sources and stores it into a personal health record, from which it can then retrieve data. Our working hypothesis is that the HL7 vMR standard in its release 1 version can properly capture the semantics needed to drive evidence-based clinical decision support systems.

MATERIALS AND METHODS

To achieve seamless communication between the personal health record and heterogeneous data consumers, we used a three-pronged approach. First, the choice of the HL7 vMR as a message model for all components accompanied by the use of medical vocabularies eases their semantic interoperability. Second, the DI follows a service-oriented approach to provide access to system components. Third, an XML database provides the data layer.Results The DI supports requirements of a guideline-based clinical decision support system implemented in two clinical domains and settings, ensuring reliable and secure access, high performance, and simplicity of integration, while complying with standards for the storage and processing of patient information needed for decision support and analytics. This was tested within the framework of a multinational project (www.mobiguide-project.eu) aimed at developing a ubiquitous patient guidance system (PGS).

DISCUSSION

The vMR model with its extension mechanism is demonstrated to be effective for data integration and communication within a distributed PGS implemented for two clinical domains across different healthcare settings in two nations.

A domain analysis model for eIRB systems: addressing the weak link in clinical research informatics

Institutional Review Boards (IRBs) are a critical component of clinical research and can become a significant bottleneck due to the dramatic increase, in both volume and complexity of clinical research. Despite the interest in developing clinical research informatics (CRI) systems and supporting data standards to increase clinical research efficiency and interoperability, informatics research in the IRB domain has not attracted much attention in the scientific community. The lack of standardized and structured application forms across different IRBs causes inefficient and inconsistent proposal reviews and cumbersome workflows. These issues are even more prominent in multi-institutional clinical research that is rapidly becoming the norm. This paper proposes and evaluates a domain analysis model for electronic IRB (eIRB) systems, paving the way for streamlined clinical research workflow via integration with other CRI systems and improved IRB application throughput via computer-assisted decision support.

Harmonization of detailed clinical models with clinical study data standards

INTRODUCTION

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems".

BACKGROUND

Data sharing and integration between the clinical research data management system and the electronic health record system remains a challenging issue. To approach the issue, there is emerging interest in utilizing the Detailed Clinical Model (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator awarded Strategic Health IT Advanced Research Projects for normalization (SHARPn) project for normalizing patient data from the electronic health records (EHR).

OBJECTIVE

The objective of the present study is to describe our preliminary efforts toward harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards.

METHODS

We were focused on three generic domains: demographics, lab tests, and medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs.

RESULTS

We have identified a set of data elements that are common to the context of both clinical study and broad secondary use of EHR data and discussed outstanding harmonization issues.

CONCLUSIONS

We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoperability between systems for both clinical study and broad secondary use domains.

Development of a Methodological Approach for Data Quality Ontology in Diabetes Management

The role of ontologies in chronic disease management and associated challenges such as defining data quality (DQ) and its specification is a current topic of interest. In domains such as Diabetes Management, a robust Data Quality Ontology (DQO) is required to support the automation of data extraction semantically from Electronic Health Record (EHR) and access and manage DQ, so that the data set is fit for purpose. A five steps strategy is proposed in this paper to create the DQO which captures the semantics of clinical data. It consists of: (1) Knowledge acquisition; (2) Conceptualization; (3) Semantic modeling; (4) Knowledge representation; and (5) Validation. The DQO was applied to the identification of patients with Type 2 Diabetes Mellitus (T2DM) in EHRs, which included an assessment of the DQ of the EHR. The five steps methodology is generalizable and reusable in other domains.

Using a content analysis to identify study eligibility criteria concepts in cancer nursing research

The aims of this study were to (1) identify and categorize study eligibility criteria concepts used in cancer nursing randomized controlled trials and (2) determine the extent to which a previously identified set of study eligibility criteria, based primarily on medical randomized controlled trials, were represented in cancer nursing randomized controlled trials. A total of 145 articles of cancer nursing randomized controlled trials indexed in PubMed or Cumulative Index to Nursing and Allied Health Literature and published in English from 1986 to 2010 were screened, and 114 were eligible. Directed content analysis was conducted until data saturation was achieved. Forty-three concepts categorized into eight domains were extracted from 49 articles published in 27 different journals. Most of the concepts identified were related to health status, treatment, and demographics domains. Although many concepts matched to the previously identified study eligibility concepts based on medical research, new concepts may need to be added to fully represent cancer nursing research. This study provides a solid foundation for future study of mapping the concepts to existing standardized terminologies to identify which systems can be adopted. Nursing researchers can use these eligibility criteria concepts as a guideline in structuring the eligibility criteria for their studies.

Automated Tools for Clinical Research Data Quality Control using NCI Common Data Elements

Clinical research data generated by a federation of collection mechanisms and systems often produces highly dissimilar data with varying quality. Poor data quality can result in the inefficient use of research data or can even require the repetition of the performed studies, a costly process. This work presents two tools for improving data quality of clinical research data relying on the National Cancer Institute's Common Data Elements as a standard representation of possible questions and data elements to A: automatically suggest CDE annotations for already collected data based on semantic and syntactic analysis utilizing the Unified Medical Language System (UMLS) Terminology Services' Metathesaurus and B: annotate and constrain new clinical research questions though a simple-to-use "CDE Browser." In this work, these tools are built and tested on the open-source LimeSurvey software and research data analyzed and identified to contain various data quality issues captured by the Comprehensive Research Informatics Suite (CRIS) at the University of Arkansas for Medical Sciences.

Simplifying complex clinical element models to encourage adoption

Clinical Element Models (CEMs) were developed to provide a normalized form for the exchange of clinical data. The CEM specification is quite complex and specialized knowledge is required to understand and implement the models, which presents a significant barrier to investigators and study designers. To encourage the adoption of CEMs at the time of data collection and reduce the need for retrospective normalization efforts, we developed an approach that provides a simplified view of CEMs for non-experts while retaining the full semantic detail of the underlying logical models. This allows investigators to approach CEMs through generalized representations that are intended to be more intuitive than the native models, and it permits them to think conceptually about their data elements without worrying about details related to the CEM logical models and syntax. We demonstrate our approach using data elements from the Pharmacogenomics Research Network (PGRN).

Building a common pediatric research terminology for accelerating child health research

Longitudinal observational clinical data on pediatric patients in electronic format is becoming widely available. A new era of multi-institutional data networks that study pediatric diseases and outcomes across disparate health delivery models and care settings are also enabling an innovative collaborative rapid improvement paradigm called the Learning Health System. However, the potential alignment of routine clinical care, observational clinical research, pragmatic clinical trials, and health systems improvement requires a data infrastructure capable of combining information from systems and workflows that historically have been isolated from each other. Removing barriers to integrating and reusing data collected in different settings will permit new opportunities to develop a more complete picture of a patient's care and to leverage data from related research studies. One key barrier is the lack of a common terminology that provides uniform definitions and descriptions of clinical observations and data. A well-characterized terminology ensures a common meaning and supports data reuse and integration. A common terminology allows studies to build upon previous findings and to reuse data collection tools and data management processes. We present the current state of terminology harmonization and describe a governance structure and mechanism for coordinating the development of a common pediatric research terminology that links to clinical terminologies and can be used to align existing terminologies. By reducing the barriers between clinical care and clinical research, a Learning Health System can leverage and reuse not only its own data resources but also broader extant data resources.

Implementing unique device identification in electronic health record systems: organizational, workflow, and technological challenges

BACKGROUND

The United States Food and Drug Administration (FDA) has proposed creating a unique device identification (UDI) system for medical devices to facilitate postmarket surveillance, quality improvement, and other applications. Although a small number of health care institutions have implemented initiatives comparable with the proposed UDI system by capturing data in electronic health record (EHR) systems, it is unknown whether institutions with fewer resources will be able to similarly implement UDI.

OBJECTIVE AND METHODS

This paper calls attention to organizational, workflow, and technological challenges in UDI system implementation by drawing from the literature on EHR and clinical research systems implementation.

FINDINGS

Organizational challenges for UDI system implementation include coordinating multiple stakeholders to define UDI attributes and characteristics for use in EHRs, guiding organizational change within individual institutions for integrating UDI with EHRs, and guiding organizational change for reusing UDI data captured in EHRs. Workflow challenges include capturing UDI data in EHRs using keyboard entry and barcode scanning. Technological challenges involve interfacing UDI data between EHRs and surgical information systems, transforming UDI and related patient data from EHRs for research, and applying data standards to UDI within and beyond EHRs.

DISCUSSION AND CONCLUSIONS

We provide recommendations for regulations, organizational sharing, and professional society engagement to raise awareness of and overcome UDI system implementation challenges. Implementation of the UDI system will require integration of people, process, and technology to achieve benefits envisioned by FDA, including improved postmarket device surveillance and quality of care.

Implementing health reform: improved data collection and the monitoring of health disparities

The relative lack of standards for collecting data on population subgroups has not only limited our understanding of health disparities, but also impaired our ability to develop policies to eliminate them. This article provides background about past challenges to collecting data by race/ethnicity, primary language, sex, and disability status. It then discusses how passage of the Affordable Care Act has provided new opportunities to improve data-collection standards for the demographic variables of interest and, as such, a better understanding of the characteristics of populations served by the U.S. Department of Health and Human Services (HHS). The new standards have been formally adopted by the Secretary of HHS for application in all HHS-sponsored population health surveys involving self-reporting. The new data-collection standards will not only promote the uniform collection and utilization of demographic data, but also help the country shape future programs and policies to advance public health and to reduce disparities.

A semantic framework for intelligent matchmaking for clinical trial eligibility criteria

An integral step in the discovery of new treatments for medical conditions is the matching of potential subjects with appropriate clinical trials. Eligibility criteria for clinical trials are typically specified as inclusion and exclusion criteria for each study in freetext form. While this is sufficient for a human to guide a recruitment interview, it cannot be reliably and computationally construed to identify potential subjects. Standardization of the representation of eligibility criteria can enhance the efficiency and accuracy of this process. This article presents a semantic framework that facilitates intelligent matchmaking by identifying a minimal set of eligibility criteria with maximal coverage of clinical trials. In contrast to existing top-down manual standardization efforts, a bottom-up data driven approach is presented to find a canonical nonredundant representation of an arbitrary collection of clinical trial criteria. The methodology has been validated with a corpus of 709 clinical trials related to Generalized Anxiety Disorder containing 2,760 inclusion and 4,871 exclusion eligibility criteria. This corpus is well represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which corresponds to a semantically distinct criterion. Internal and external validation measures provide an objective evaluation of the method. An eligibility criteria ontology has been constructed based on the clustering. The resulting model has been incorporated into the development of the MindTrial clinical trial recruiting system. The prototype for clinical trial recruitment illustrates the effectiveness of the methodology in characterizing clinical trials and subjects and accurate matching between them.