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 Exploring completeness in clinical data research networks with DQe-c

2017 ◽  
Vol 25 (1) ◽  
pp. 17-24 ◽  
Author(s):  
Hossein Estiri ◽  
Kari A Stephens ◽  
Jeffrey G Klann ◽  
Shawn N Murphy
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Clinical Data ◽  
Data Science ◽  
Ad Hoc ◽  
Assessment Tool ◽  
Data Repository ◽  
Process Data ◽  
Data Repositories ◽  
Data Quality Assessment

Abstract Objective To provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories. Materials and Methods This article describes the tool’s design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix. Results DQe-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators. Discussion Open science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQe-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup. Conclusion EHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQe-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.

QUANTIS: Data quality assessment tool by clustering analysis

2019 ◽  
Vol 51 (11) ◽  
pp. 872-885
Author(s):  
Steffen H. Symoens ◽  
Syam Ukkandath Aravindakshan ◽  
Florence H. Vermeire ◽  
Kevin De Ras ◽  
Marko R. Djokic ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Clustering Analysis ◽  
Assessment Tool ◽  
Quality Assessment Tool ◽  
Data Quality Assessment

scholarly journals A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research

Medical Care ◽  
2012 ◽  
Vol 50 ◽  
pp. S21-S29 ◽  
Author(s):  
Michael G. Kahn ◽  
Marsha A. Raebel ◽  
Jason M. Glanz ◽  
Karen Riedlinger ◽  
John F. Steiner
Keyword(s):  
Clinical Research ◽  
Electronic Health Record ◽  
Data Quality ◽  
Quality Assessment ◽  
Single Site ◽  
Health Record ◽  
Data Quality Assessment ◽  
Electronic Health

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 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.

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