Data

The Evolution of Clinical Data Management

Data

12:00, January 29 2018

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Diego Herrera, Head of Global Data Management, Almirall, SA, explores the trends in clinical data management and how its role has evolved

It must be said that clinical development and clinical trials don’t always operate like clockwork. Clinical data management (CDM) organizations need to evolve continuously to properly respond to an environment that changes rapidly and constantly. Data management departments are facing two forces of change, both internal and external.

The internal changes are originally generated because of the company’s strategies, reorganizations, and differing outsourcing models. The external forces for the change come mainly from market, regulatory and health authorities, from rapid clinical technological development, as well as the associated cost of clinical trial services.

Contract research organizations (CROs) and sponsors have been pushing data management departments to collect clinical trial data more efficiently in terms of quality and time. As a result, CDM organizations have evolved meaningfully, from data deliverers working with closed systems, to data leadership working within a digital environment with more strategic and extended functions.

Data Collection Remains a Lengthy Process

For years, the “entropy” of clinical trial data has been continuously growing. Today, patient data are not only collected in a case report form (CRF), huge volumes of data are also processed electronically by multiple data collection instruments, such as IVRS and electronic patient reported outcomes (ePRO) systems. Such systems could be integrated where coding and the reconciliation process is dynamically done during the study.

Additionally, investigators and patients are also becoming key data contributors in the data management process. Together they collect and transfer significant data by means of electronic systems, such as the electronic case report form (eCRF), ePRO, or wearables devices.

The above scenario is challenging for data managers as they have been forced to be adapt new skills with a more strategic view about what is relevant and what is not to avoid failures in data integrity. Study data collection are a lengthy process, but data managers are key players to find new, shortened procedures to get systems, vendors and study data ready for sharing. Because the data manager knows better than anyone else to how study data should be processed, how systems should be configured and validated to get true data that can fit the protocol endpoints.

CDM Departments are broadening their scope

In order to find out new alternatives to evolve CDM accordingly with the times, one forum of CDM leadership representatives from pharma companies in Europe meet regularly to share and discuss non-competitive information.

The CDM forum is defined by the following statement: European leadership representatives of Clinical Data Management groups of pharmaceutical companies meet annually to engage in an informal, open and low-threshold discussion on strategic and operational aspects of clinical data management, in relation to technological and regulatory developments.

The evolution of the role and function of CDM departments is one of the repeated topics discussed during these meetings. With the aim of providing practical insights, the forum representatives have recently provided information about department resources, functions and roles through a short survey.

The survey was responded by senior representatives from nine out 12 pharma companies during the week of Dec. 18, 2017. After looking into the answers given, we can see that diversity is a constant within the pharma industry.

There is not one model or mission that can define current CDM departments – even CDM (33 percent) still appears as one of the two most frequent names together with Data Science (33 percent). However, alternative CDM department names reveal how departments have extended their area of responsibilities according to their own evolution. This includes company strategies and resources, like Global Data Management and Project Information (10 percent) or Clinical Data Science and Clinical Systems (10 percent). These department names may suggest that clinical data management does not involve just managing study data.

A Continuing Evolution

An important revelation from the survey is the Data Lead (88 percent) can be considered as the trending role title within CDM departments, exceeding the historic role of the Data Manager (44 percent). Therefore, it is shown that pharma companies have taken seriously the necessary role evolution, and the extension of functions or responsibilities. The fact is found in a very high variety of different role titles reported (> 50).

The role titles enable the classification by groups into five principal areas of responsibilities, as follows:

  • Data Management, including data coding, capture and cleaning
  • Data Standards, including governance
  • Clinical Systems, software, management and programming systems, such as eCRF, ePRO and Wearables
  • Central Data Review/Analytics as data ready for interactive visualization, data oversight or risk-based monitoring strategies
  • Innovation

The above indicates how classical CDM departments have evolved, by revisiting skills, functions to be ready for rapid adoption of new processes, standards, technologies, and regulatory demands. The ancient CRF paper process when study data was in-house, entered using on-premise closed systems with limited data re-use capabilities, is a thing of the past. A different reality around CDM is managing non-CRF data, electronically captured in cloud systems with full or functional outsourced services.

CDM is currently performed by professionals with the analytical and management skills able to lead, not only the internal, but the external resources contracted out. This is to strategically cover the entire project development data lifecycle.

Lastly, this highlights how important it is to implement organizational changes in order to create new habits and capacities with limited resources within a strong competitive environment.

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