The implementation of a data learning series focused on clinical development teams in a contract research organization

Effective management of a clinical trialrequires having real time access to information that provides useful insightsinto trial progress and that lends itself to collaborative decisionmaking.  Data visualizations using datafrom multiple source systems employed during the conduct of a clinical trialhave become an essential tool in the recent past as support for collaborativedecision making by project teams. Having the ability to access, analyze, read,work with, and present data to support an argument are  important skills that ensure datavisualizations fulfill their purpose in clinical trial management. There is anexpectation that members of the clinical trial team either possess or developthe data literacy skill sets necessary to collaborate on the successfulexecution of a clinical drug development trial. Here we describe thedevelopment of a Data Learning Series program targeted to increase the data literacyskills within a Contract Research Organization in support of the digitalevolution of the drug development industry.

Clinical data management A Review of Current Practice in Australia

The practice of clinical data management (CDM) in Australia has seen and continues to experience tremendous growth. As such, this article reviews the current practice of CDM in Australia. The article addresses the history of the profession and provides insight into the difference between the sectors, the evolving role, ongoing requirements for training and education, and an overview of the regulations and how these impact the Australian CDM landscape. Current practice of CDM in Australia differs considerably between industry, academic, and non-profit sectors though the uniform regulatory requirements are provided nationwide. This has raised challenges for mostly academic, non-profit, and small-scale trials which are more likely to lack access to resources, facilities, management, and funding. Australian clinical data managers are required to have formal skills related to data, technology, security, and project management, though they are also expected to operate at the highest levels of excellence across all areas of their diverse roles. It is only in recent years that CDM has evolved to a stronger focus on data quality. Regardless of these challenges, clinical data managers have played, and continue to play, a key role in Australian biomedical research. They have provided guidance on data collection, processing, and management procedures to ensure that studies achieve high quality outcomes. However, more research is needed to develop specific CDM training courses to help Australian clinical data managers to meet a standard of knowledge, education, and experience to be officially recognised as a profession.

Practice of Clinical Data Management Worldwide: Introduction to the Special Issue

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Bring Your Own Device (BYOD): A practical framework to leverage in the electronic patient-reported outcomes(ePRO) data collection in clinical trials

Patient-reported outcomes (PROs) are used in pharmaceuticaltrials to obtain trends in health status. Companies commonly provision tabletand smartphone devices to collect PRO information. Alternatively, the BringYour Own Device (BYOD) model allows patients to leverage personal devices andis actively being explored as a solution.  This article investigates thepotential benefits of BYOD and outlines a framework of considerations. Theframework addresses current challenges and proposes potential solutions toMeasurement Equivalence, Technical, and Operational concerns. BYOD has not yet beenimplemented on any studies for regulatory submission. Nonetheless, there isreason to believe the model will gain traction in the coming years. With theprovided framework, sponsors can assess whether the BYOD model is right for theconsidered study.

The growing opportunity of clinical data management in Ukraine

Many specialized positions, even entry-level, in the pharmaceutical industry require training above and beyond standard University degree programs. A shortage of specialized clinical data managers in Ukraine means private sector companies are developing internal resource training programs to deepen their pool of available candidates. Given the strong medical education system and established IT outsourcing industry, we believed developing a pool of talented clinical data managers within Ukraine was a feasible goal.The IT outsourcing industry is the second largest export service industry in Ukraine, and one of the main sectors in the economy. More than 50% of Ukraine's IT services revenue came from the United States, the rest mostly from the EU.[1] Ukraine has built a workforce adapted to IT outsourcing, but the lack of local professionals in the fields of clinical data management and clinical data science hinders similar growth in the clinical research sector. Ukraine has a well-established medical education system that trains its healthcare professionals in accordance with EU regulations. Hospitals are predominantly state-owned; the private medical sector is almost nonexistent. The academic and non-profit clinical research sectors are small in comparison to Western European countries, and opportunities for careers within them limited. This leads to a 'brain drain' of medical professionals from Ukraine to other countries in search of higher wages and professional advancement. With its strong education system and highly educated medical workforce, Ukraine is an attractive but under-utilised location for clinical studies. [2] There are approximately 30 clinical research sites in Ukraine handling preclinical through Phase IV studies. In December 2020 on clinicaltrials.gov there were 557 active or recruiting clinical trials listed taking place in Ukraine. Regulatory hurdles and approval timelines have greatly improved in recent years.Currently, when CROs wish to hire data managers to assist with local clinical trials in Ukraine, they have to hire non-specialists who must teach themselves on the job. At present there are no university courses or formal training programs within the country for clinical data managers.Following the success of the Clinical Statistical Programming training program developed by our team and offered since 2013 in partnership V. N. Karazin Kharkiv National University,[3] we recently launched an in-house clinical data management training program in partnership between leading Biometrics CROs Cytel and Intego Group. Upon program completion, students have the opportunity to transition into full-time employment. Ours is the first centralized training program for clinical data managers in the country. We already started a conversation with some of the country's leading universities to help them develop a formal educational program in clinical data management. Our internal training program will serve as a pilot and a proof of concept. We expect that many elements, such as curriculum, admission requirements, quality control, internships, etc., will successfully scale up in an academic environment. Our paper will discuss opportunities for the clinical data management sector in Ukraine, the challenges of recruiting data managers from the existing healthcare workforce, the region’s unique strengths, laws and regulations. We also discuss specifics of the internal training program, development of a course syllabus, and transitioning students from coursework into hands-on data management training.Article length: 8 pages. Article reference count: 9 references.---------------[1] AVentures. Software Development in Ukraine, Poland, Belarus and Romania in 2019.[2] Sinichkina L,  Smolina A,  Svintsitskyi V. Positive Changes for Clinical Trials in Ukraine. Applied Clinical Trials. December 2017.[3] Pirbhai E, Glushakov S. Development of a Clinical SAS University Training Program in Eastern Europe. PharmaSUG. 2015.

Clinical research data management in the United States: Where we've been and where we're going

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.

eSource for Standardized Health Information Exchange in Clinical Research: A Systematic Review of Progress in the Last Year

To identify studies conductedusing the direct, electronic extraction of electronic health record (EHR) datato electronic data capture (EDC) systems, also known as eSource, and toidentify any gaps or limitations present for promoting standardized healthinformation exchange in clinical research.Materials and Methods:Articleswere included only if the solution described (1) utilized eSource to directlyexchange data electronically from EHR-to-EDC and (2) was relevant to aprospective clinical study use case.Results:Intotal, 20 relevant articles were identified, describing a total of 15 uniqueeSource interventions. Of the 15interventions, 12 were single-site, single-EHR (SS-SE) implementations and 3were multi-site, multi-EHR (MS-ME) implementations. All 15 implementationsmentioned the use of standards, but nearly all referenced older data exchangestandards.  Discussion:Following the trajectory of work towardsdirect EHR-to-EDC, eSource data collection, we appear to have arrived at thepoint where information systems leveraging data standards can offer efficiencyand increased quality in clinical research. However, these methods need to betested for effectiveness and acceptance in the context of real multicenterclinical trials. Several early studies using a single source of data forresearch and patient care appeared over a decade ago. Since that time,implementations and evaluations have been scarce and almost always confined tosingle-EHR, single-EDC, single-institution implementations.Conclusion:These results only further emphasize the observation thatthe clinical trial use case continues to be the most difficult and leastdemonstrated eSource-related initiative. Thus, additional work is criticallyneeded in this area to address the gaps identified from the literature.