Writing for Publication in Informatics and Data Science in Clinical Research

BACKGROUND Real World Data (RWD) and Real World Evidence (RWE) have an increasingly important role in clinical research and health care decision making in many countries. In order to leverage RWD and generate reliable RWE, a framework must be in place to ensure that the data is well-defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for clinical medicine and therapeutics development. CDISC data standards are mature, globally recognized and heavily utilized by the pharmaceutical industry for regulatory submission in the US and Japan and are recommended in Europe and China. Against this backdrop, the CDISC RWD Connect Initiative was initiated to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement CDISC standards for this purpose. We believe that bridging the gap between RWD and clinical trial generated data will benefit all stakeholders. OBJECTIVE The aim of this project was to understand the barriers to implementing CDISC standards for Real World Data (RWD) and to identify what tools and guidance may be needed to more easily implement CDISC standards for this purpose. METHODS We conducted a qualitative Delphi survey involving an Expert Advisory Board (EAB) with multiple key stakeholders, with three rounds of input and review. RESULTS In total, 66 experts participated in round 1, 56 participated in round 2 and 49 participated in round 3 of the Delphi Survey. Their input was collected and analyzed culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data-sharing and the quality of RWE. The priorities for RWD standardization include electronic health records, such as data shared using HL7 FHIR, and data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify areas where synergies and efficiencies are possible and then collaborate with stakeholders to create, or extend existing, mappings between CDISC and other standards, controlled terminologies and models to represent data originating across different sources. CONCLUSIONS There are many ongoing data standardization efforts that span the spectrum of human health data related activities including, but not limited to, those related to healthcare, public health, product or disease registries and clinical research, each with different definitions, levels of granularity and purpose. Amongst these standardization efforts, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards, and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to using a new standard, insufficient training and implementation support are significant barriers for setting up the use of CDISC standards for RWD. The collection and dissemination of use cases showing in detail how to effectively implement CDISC standards for RWD, developing tools and support systems specifically for the RWD community, and collaboration with other standards development organizations and initiatives are potential steps towards connecting RWD to research. The integrity of RWE is dependent on the quality of the RWD and the data standards utilized in its collection, integration, processing, exchange and reporting. Using CDISC as part of the database schema will help to link clinical trial data and RWD and promote innovation in health data science. The authors believe that CDISC standards, if adapted carefully and presented appropriately to the RWD community, can provide “FAIR” structure and semantics for common clinical concepts and domains and help to bridge the gap between RWD and clinical trial generated data. CLINICALTRIAL Not Applicable

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Clinical Research - A Big Data Science Approach

International Journal of Trend in Scientific Research and Development ◽

10.31142/ijtsrd9547 ◽

2018 ◽

Vol Volume-2 (Issue-2) ◽

pp. 1075-1078

Author(s):

Dr. Elton Mathias ◽

Dr. Roveena Goveas ◽

Manish Rajak ◽

Keyword(s):

Big Data ◽

Clinical Research ◽

Data Science ◽

Science Approach

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Good Clinical Research Practice: What is it and is it Possible in the Intensive Care Unit?

Anaesthesia and Intensive Care ◽

10.1177/0310057x9802600515 ◽

1998 ◽

Vol 26 (5) ◽

pp. 568-574 ◽

Cited By ~ 1

Author(s):

J. Scribante ◽

J. Lipman ◽

R. Saadia

Keyword(s):

Intensive Care ◽

Clinical Research ◽

Data Science ◽

Research Practice ◽

Research Projects ◽

Management Skills ◽

Basic Principles ◽

Patient Protection ◽

Data Control ◽

Care Situation

With the growing quest for answers to vexing dilemmas in critically ill patients, more intensive care units are embarking on clinical research. This places increasing importance on Good Clinical Research Practice (GCRP), a set of guidelines drawn up by the Pharmaceutical Industry to assist investigators in conducting ethical, reliable scientific studies. GCRP is a combination of good basic management skills allied to ethical principles for scientific research. Based on the principles of the Declaration of Helsinki, it consists of three tenets: patient protection (ethics), credible data (science) and data control. This article describes GCRP specifically relating it to the intensive care situation, illustrating some of the concepts with practical examples. With a minimum of extra time and effort these basic-principles can be integrated as routine into all research projects.

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Key Contributions in Clinical Research Informatics

Yearbook of Medical Informatics ◽

10.1055/s-0041-1726514 ◽

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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