No abstract

BackgroundData capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.MethodsCase report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project.ResultsThe analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records.ConclusionsCommon data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0259-3) contains supplementary material, which is available to authorized users.

The model DMP SOP produced addresses most data management issues that are present in any phase clinical trial while providing a flexible framework.

With the focus of the COVID-19 pandemic, we wanted to reach all stakeholders representing communities concerned with good clinical data management practices. We wanted to represent not only data managers but bio-statisticians, clinical monitors, data scientists, informaticians, and all those who collect, organize, analyze, and report on clinical research data. In our paper we will discuss the history of clinical data management in the US and its evolution from the early days of FDA guidance. We will explore the role of biomedical research focusing on the similarities and differences in industry and academia clinical research data management and what we can learn from each other. We will talk about our goals for recruitment and training for the CDM community and what we propose for increasing the knowledge and understanding of good clinical data practice to all – particularly our front-line data collectors i.e., nurses, medical assistants, patients, other data collectors. Finally, we will explore the challenges and opportunities to see CDM as the hub for good clinical data research practices in all of our communities.We will also discuss our survey on how the COVID-19 pandemic has affected the work of CDM in clinical research.

Although much information is already available publically from information-sharing initiatives such as ClinicalTrials.gov, information about clinical programs is unstructured, inconsistent, and incomplete. Clinical research within bioscience companies, health care, academia, and governmental agencies could benefit from easier access to best practices, historical information, and improved information sharing. Facilitating information sharing requires a standardized information model. Information standards today focus on individual clinical trials and the representation of clinical trial data. Although work is ongoing to expand standards to cover the protocol, these are insufficient to capture the objectives, rationale, and design thinking behind clinical programs. An information model is proposed to cover the rationalization and decision-making aspects of designing a clinical program and its associated trials. This paper is the output of a newly formed multicompany working group that examines the merits of a clinical program-level information standard. An example information model is presented to explain the concept.