Field Of Clinical Research Informatics Research Articles

Key Contributions in Clinical Research Informatics.

SummaryObjectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020. Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers. Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records. Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.

Clinical Research Informatics.

Summary Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019. Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers. Results : Among the 517 papers, published in 2019, returned by the search, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance. Conclusions : The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.

Clinical Research Informatics: Contributions from 2018.

Summary Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018. Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers. Results : Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher. Conclusions : Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

Clinical Research Informatics: Contributions from 2017.

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2017. Method: A bibliographic search using a combination of MeSH descriptors and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Results: Among the 741 returned papers published in 2017 in the various areas of CRI, the full review process selected five best papers. The first best paper reports on the implementation of consent management considering patient preferences for the use of de-identified data of electronic health records for research. The second best paper describes an approach using natural language processing to extract symptoms of severe mental illness from clinical text. The authors of the third best paper describe the challenges and lessons learned when leveraging the EHR4CR platform to support patient inclusion in academic studies in the context of an important collaboration between private industry and public health institutions. The fourth best paper describes a method and an interactive tool for case-crossover analyses of electronic medical records for patient safety. The last best paper proposes a new method for bias reduction in association studies using electronic health records data. Conclusions: Research in the CRI field continues to accelerate and to mature, leading to tools and platforms deployed at national or international scales with encouraging results. Beyond securing these new platforms for exploiting large-scale health data, another major challenge is the limitation of biases related to the use of “real-world” data. Controlling these biases is a prerequisite for the development of learning health systems.

Clinical Research Informatics: Contributions from 2016

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2016. Methods: A bibliographic search using a combination of MeSH and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Results: Among the 452 papers published in 2016 in the various areas of CRI and returned by the query, the full review process selected four best papers. The authors of the first paper utilized a comprehensive representation of the patient medical record and semi-automatically labeled training sets to create phenotype models via a machine learning process. The second selected paper describes an open source tool chain securely connecting ResearchKit compatible applications (Apps) to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). The third selected paper describes the FAIR Guiding Principles for scientific data management and stewardship. The fourth selected paper focuses on the evaluation of the risk of privacy breaches in releasing genomics datasets. Conclusions: A major trend in the 2016 publications is the variety of research on “real-world data” - healthcare-generated data, person health data, and patient-reported outcomes -highlighting the opportunities provided by new machine learning techniques as well as new potential risks of privacy breaches.

Clinical Research Informatics: Supporting the Research Study Lifecycle

Summary Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the “research study lifecycle”, with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

Clinical Research Informatics: Contributions from 2016.

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2016. Methods: A bibliographic search using a combination of MeSH and free terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers. Results: Among the 452 papers published in 2016 in the various areas of CRI and returned by the query, the full review process selected four best papers. The authors of the first paper utilized a comprehensive representation of the patient medical record and semi-automatically labeled training sets to create phenotype models via a machine learning process. The second selected paper describes an open source tool chain securely connecting ResearchKit compatible applications (Apps) to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). The third selected paper describes the FAIR Guiding Principles for scientific data management and stewardship. The fourth selected paper focuses on the evaluation of the risk of privacy breaches in releasing genomics datasets. Conclusions: A major trend in the 2016 publications is the variety of research on "real-world data" - healthcare-generated data, person health data, and patient-reported outcomes -highlighting the opportunities provided by new machine learning techniques as well as new potential risks of privacy breaches.

Clinical Research Informatics: Supporting the Research Study Lifecycle.

Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

Clinical Research Informatics Contributions from 2015.

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015. A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations. A major trend in the 2015 publications is the analysis of observational, "nonexperimental" information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.

Information technology for clinical, translational and comparative effectiveness research. Findings from the section clinical research informatics.

To select and summarize key contributions to current research in the field of Clinical Research Informatics (CRI). A bibliographic search using a combination of MeSH and free terms search over PubMed was performed followed by a blinded review. The review process resulted in the selection of four papers illustrating various aspects of current research efforts in the area of CRI. The first paper tackles the challenge of extracting accurate phenotypes from Electronic Healthcare Records (EHRs). Privacy protection within shared de-identified, patient-level research databases is the focus of the second selected paper. Two other papers exemplify the growing role of formal representation of clinical data - in metadata repositories - and knowledge - in ontologies - for supporting the process of reusing data for clinical research. The selected articles demonstrate how concrete platforms are currently achieving interoperability across clinical research and care domains and have reached the evaluation phase. When EHRs linked to genetic data have the potential to shift the research focus from research driven patient recruitment to phenotyping in large population, a key issue is to lower patient re-identification risks for biomedical research databases. Current research illustrates the potential of knowledge engineering to support, in the coming years, the scientific lifecycle of clinical research.

Clinical research informatics and electronic health record data.

The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI's key role in the infrastructure of a learning healthcare system.

Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project.

Background:The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems.Objective:Investigators from Washington State’s Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure.Methods:SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm’s Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill ‘go-live’ at each site.Progress to Date:A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15–18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted—25% from structured data; 25% from text mining.Conclusion:In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.

Information Technology for Clinical, Translational and Comparative Effectiveness Research

Summary Objectives: To summarize advances of excellent current research in the new emerging field of Clinical Research Informatics. Method: Synopsis of four key articles selected for the IMIA Yearbook 2013. The selection was performed by querying PubMed and Web of Science with predefined keywords. From the original set of 590 papers, a first subset of 461 articles which was in the scope of Clinical Research Informatics was refined into a second subset of 79 relevant articles from which 15 articles were retained for peer-review. Results: The four selected articles exemplify current research efforts conducted in the areas of data representation and management in clinical trials, secondary use of EHR data for clinical research, information technology platforms for translational and comparative effectiveness research and implementation of privacy control. Conclusions: The selected articles not only illustrate how innovative information technology supports classically organized randomized controlled trials but also demonstrate that the long promised benefits of electronic health care data for research are becoming a reality through concrete platforms and projects.

Clinical Research Informatics: Survey of Recent Advances and Trends in a Maturing Field

To provide a survey of the field of clinical research informatics (CRI), focusing in particular on significant developments over the past 3 years and the insights they provide about the progress and state of the field. An iterative "scoping review" of the literature published in scientific journals and conference proceedings that are relevant to CRI, from late 2009 to early 2013. 212 articles were identified, and 64 were selected to illustrate recent advances in the field. Based on those, six categories of CRI activity were identified: Data and Knowledge Management, Discovery and Standards; Clinical Data Re-Use for Research; Researcher Support and Resources; Participant Recruitment; Patients/Consumers and CRI; Policy, Regulatory and Fiscal Matters. This survey demonstrates that the field of CRI has matured and is now well established. The ongoing work is essential to overcome many of the challenges the clinical research enterprise is facing and more work is needed. Even as work continues to establish necessary infrastructure, methods, and best practices, CRI researchers should strive for more rigorous study designs to evaluate the impacts of the work in the field. There is little doubt that the field is poised for rapid growth, and that the CRI literature will continue to reflect that growth in years to come.