scholarly journals Key Contributions in Clinical Research Informatics

2021 ◽  
Vol 30 (01) ◽  
pp. 233-238
Author(s):  
Christel Daniel ◽  
Ali Bellamine ◽  
Dipak Kalra ◽  
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Data Science ◽  
Adverse Drug Events ◽  
Quality Data ◽  
Generative Adversarial Networks ◽  
Editorial Team ◽  
Free Text ◽  
Clinical Research Informatics ◽  
Electronic Health

Summary Objectives: 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.

scholarly journals Clinical Research Informatics

2020 ◽  
Vol 29 (01) ◽  
pp. 203-207
Author(s):  
Christel Daniel ◽  
Dipak Kalra ◽  
Keyword(s):  
Clinical Research ◽  
Real World ◽  
Reporting System ◽  
Quality Data ◽  
Editorial Team ◽  
Common Data Model ◽  
Free Text ◽  
Real World Data ◽  
World Data ◽  
Clinical Research Informatics

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.

scholarly journals Clinical Research Informatics Contributions from 2015

2016 ◽  
Vol 25 (01) ◽  
pp. 219-223
Author(s):  
R. Choquet ◽  
C. Daniel ◽  
Keyword(s):  
Clinical Research ◽  
Data Privacy ◽  
Design Stage ◽  
Editorial Team ◽  
Legal Requirements ◽  
Double Blind ◽  
Blind Review ◽  
Healthcare Data ◽  
Clinical Research Informatics ◽  
Electronic Health

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015. Method: 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. Results: 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. Conclusions: 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.

scholarly journals Clinical Research Informatics: Contributions from 2017

2018 ◽  
Vol 27 (01) ◽  
pp. 177-183 ◽  
Author(s):  
Christel Daniel ◽  
Dipak Kalra ◽  
Keyword(s):  
Electronic Health Records ◽  
Clinical Research ◽  
Association Studies ◽  
Bias Reduction ◽  
Lessons Learned ◽  
Editorial Team ◽  
Private Industry ◽  
Health Records ◽  
Clinical Research Informatics ◽  
Electronic Health

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.

scholarly journals Clinical Research Informatics: Contributions from 2018

2019 ◽  
Vol 28 (01) ◽  
pp. 203-205 ◽  
Author(s):  
Christel Daniel ◽  
Dipak Kalra ◽  
Keyword(s):  
Clinical Research ◽  
Data Quality ◽  
Quality Assessment ◽  
Clinical Data ◽  
Hospital Admissions ◽  
Assessment Tool ◽  
Editorial Team ◽  
Free Text ◽  
Data Quality Assessment ◽  
Clinical Research Informatics

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.

scholarly journals Clinical Research Informatics: Supporting the Research Study Lifecycle

2017 ◽  
Vol 26 (01) ◽  
pp. 193-200 ◽  
Author(s):  
S. B. Johnson
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Search Strategy ◽  
Research Study ◽  
Computational Statistics ◽  
Medical Subject Headings ◽  
Pubmed Database ◽  
Subject Headings ◽  
Clinical Research Informatics ◽  
New Applications

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.

scholarly journals Clinical Research Informatics: Supporting the Research Study Lifecycle

2017 ◽  
Vol 26 (01) ◽  
pp. 193-200
Author(s):  
S. B. Johnson
Keyword(s):  
Clinical Research ◽  
Large Scale ◽  
Search Strategy ◽  
Research Study ◽  
Computational Statistics ◽  
Medical Subject Headings ◽  
Pubmed Database ◽  
Subject Headings ◽  
Clinical Research Informatics ◽  
New Applications

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.

scholarly journals Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Rishi J. Desai ◽  
Michael E. Matheny ◽  
Kevin Johnson ◽  
Keith Marsolo ◽  
Lesley H. Curtis ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Science ◽  
Claims Data ◽  
The United States ◽  
Medical Product ◽  
Product Safety ◽  
Electronic Health Record Data ◽  
Data Infrastructure ◽  
Safety Surveillance ◽  
Electronic Health

AbstractThe Sentinel System is a major component of the United States Food and Drug Administration’s (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center’s initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.

scholarly journals Clinical Research Informatics and Electronic Health Record Data

2014 ◽  
Vol 23 (01) ◽  
pp. 215-223 ◽  
Author(s):  
M. M. Horvath ◽  
S. A. Rusincovitch ◽  
R. L. Richesson
Keyword(s):  
Clinical Research ◽  
Electronic Health Record ◽  
Clinical Data ◽  
Health Record ◽  
Healthcare Operations ◽  
Electronic Health Record Data ◽  
Pragmatic Clinical Trials ◽  
Clinical Research Informatics ◽  
Electronic Health ◽  
The Impact

Summary Objectives: 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. Results: 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. Conclusions: 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.

scholarly journals Clinical Research Informatics: Contributions from 2016

2017 ◽  
Vol 26 (01) ◽  
pp. 209-211
Author(s):  
C. Daniel ◽  
R. Choquet
Keyword(s):  
Machine Learning ◽  
Clinical Research ◽  
Scientific Data ◽  
Machine Learning Techniques ◽  
Editorial Team ◽  
Real World Data ◽  
Double Blind ◽  
Patient Reported ◽  
Clinical Research Informatics ◽  
Privacy Breaches

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.

scholarly journals ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0187121 ◽  
Author(s):  
Ehtesham Iqbal ◽  
Robbie Mallah ◽  
Daniel Rhodes ◽  
Honghan Wu ◽  
Alvin Romero ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Adverse Drug Events ◽  
Free Text ◽  
Health Records ◽  
Electronic Health
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