Jim Streeter11.06.09
EDC Focus: Data Transparency
The time is now
By Jim Streeter
Increased concerns over drug safety and demands by regulatory authorities for higher quality data have led to more complex, costly clinical trials that are taking longer to complete. For this reason, there has been an increase in the use of electronic data capture (EDC) systems to collect and manage clinical trial data. EDC technology and systems are evolving beyond data collection and piecing together simple applications and reports. The need for these systems to provide transparent, integrated and intelligently presented data has become critical for the industry.
What is Data Transparency?
Data transparency is realtime access to data that allows pharmaceutical, biotech and/or device companies to monitor their studies, view data trends, and determine how a particular study or program is performing similarly as if their data were being managed in house rather than with a CRO. Data transparency allows clients to analyze realtime data from multiple sources throughout the entire clinical development process to make intelligent business decisions about study execution.
Why Data Transparency?
Historically, many clients have made significant financial investments on in-house systems to track and manage their studies to ensure their compounds are meeting timeline expectations for submission to a regulatory agency.
Regardless of whether a client is fully outsourcing a study or a program to one CRO, or it is using multiple CROs as functional providers, pharmaceutical companies want the same level of access to their data as if they were collecting and managing the data. Ultimately, the client and CRO want to build trusted, collaborative relationship based on timely, efficient and high quality execution. Data transparency is the bridge that allows clients to gain comfort that their investment is being well-managed. As this occurs clients are able to reduce internal management of these efforts and ultimately reduce their infrastructure investments by relying on the CRO’s data transparency.
The complexity of data collection has also increased. Adaptive study designs, increased number of sites around the world and increasingly varied types of data from many vendor sources/ systems are all indications that realtime data decisions can no longer be managed on a simple spreadsheet or in a quick review meeting. Simple, static data reports and listing of metric data no longer meet the need of CROs or clients.Therefore, dynamic data transparency is required to make data from complex trials more manageable and interpretable.
At a Crossroads
The industry now uses EDC technologies (RDC, CTMS, IVRS, safety, e-diary, etc.) as the primary data collection systems for clinical studies. It has become apparent that each of these systems was developed to address a specific business need within the clinical trial process. Systems were created independent of one another, and data collected from various systems have not been shared at the database or reporting level.
The shift toward CRO outsourcing has also driven a new requirement from clients to have day-to-day access to data collected and reported from these systems.This access includes both focus on the collection of clinical data and the metrics around the data collected. In addition, as access is given to this data in order to clearly see the status of a clinical study, there needs to be an integration of data among all systems for data transparency to be successful.
Where is the Industry?
Many systems were developed for single studies and client-specific visions, not to share data collection among systems or to support multiple clients and CROs needing to access the same study data at the same time. Many systems collect data without the ability to share it or intelligently report it among systems or in conjunction with other system data.
To stay current on study status, quickly intervene for better contingency planning, and continue to forecast progress like enrollment forecasting, clinical data managers at pharmaceutical companies and CROs need to review the same data simultaneously. Effective use of leading indicators of data issues and study performance allow rapid escalation and decision making, thus setting the foundation for data transparency.
Changes are Needed
With data collected in separate systems, the challenge of integrating and providing realtime access to meaningful data and metrics to support data transparency is upon the industry. Solving this challenge sounds simple, but there are many obstacles to conquer.
Security – At present, most systems/applications and company infrastructure security have not been designed to support data transparency. Opening systems to other clients and CROs requires careful management of infrastructure security. VPNs and remote access are key tools to ensure security. The biggest issue is applications security, because many systems were designed for company-specific access, not for providing the same access to multiple CROs and clients. These systems must be modified or integrated with a centralized security management system to achieve data transparency.
Data Viewing – The reporting function of systems is static and not integrated and can be very system- or function-specific. Portals and business intelligent (BI) tools are the new weapons of our clinical trial arsenal. Not only do BI tools allow for quick views into systems and data, but they can also be quickly customized to determine who can view select data. BI tools make static reports and excel files obsolete as they allow users to slice, add and build their own reports based upon available data. Combining data warehousing and BI tools brings data together from multiple systems and various vendors to create a more integrated, cohesive view of data.
Data Reporting – Many reporting and analysis systems have been built based on end-of-study reporting requirements, not ad hoc reporting requirements. Developing access to system data that allows multiple vendors or client access will be important to achieving data transparency. In addition, realtime access to data analysis without a decline in the system’s ability to collect data will be another key factor for data reporting. Systems/processes need to be designed or built to handle both of these areas for increased data transparency.
System Integrations – Collection and integration of data between systems is not widely implemented. Multiple collection points have caused confusion in differing results with clinical data and metric data. Use of enterprise service buses (ESBs) to allow sharing of data among systems is needed to collect data once and allow systems to share data and achieve data transparency. This technology will solve the use of multiple data collection systems depending upon the client’s needs. ESBs also allow for integration between multiple vendor systems to core management or reporting systems, client or CRO (i.e. many EDC systems to a single CTMS system or CRO CTMS system to client CTMS system).
System Study Design/ Master Data Planning – Setup within systems to collect data has not been managed across systems at a data modeling level. Data conventions across systems are important to warehousing, business intelligence and the success of system integration. For example, a simple naming convention in the setup of three systems demonstrates this issue. An EDC system is set up to store a visit collected as “baseline,” an IVRS systems is set up to store the same visit collected as “Visit 0,” and the finance payment system is set up to store the same visit collected as “Unit 1.” This example shows how data standardization is needed for data transparency to work, as it is important to sharing and viewing data among systems and data warehousing.
Standards like HL7 and CDISC are the base starting point for collecting and reporting standards for data, but there is still a need to integrate and model all metric and study data between systems. In addition, data modeling has to be implemented in all systems a company uses, not just systems used for study data collection. Data from human resource, finance, facilities and other company systems add benefits in companies implementing data transparency for clinical trials, both internally and externally.
While the need for data transparency provides a huge benefit to the pharmaceutical industry, CROs and clients must overcome challenges from the past and adjust to what is needed in the future.
Achieving data transparency will take time as system providers work to identify and implement solutions. Some clients and CROs have already successfully implemented pieces of data transparency within their internal systems and processes, while others have moved toward the use of external hosting/ASP services as a solution.
Data transparency will force open communication, collaboration and new ways of thinking for clients and CROs. Providers are now in a position to identify and report study issues immediately to clients to provide them with real-time awareness of an issue. However, clients should avoid over-managing an issue and have confidence their CROs will respond in a timely manner or escalate the situation appropriately.
Data transparency will be the bridge that allows clients to be comfortable that their investment is being well-managed by their CROs. As this occurs, clients will be able to reduce their internal management and infrastructure investments by relying on the CROs’ data transparency.
Data transparency will change industry operations for clinical trials and meet the management needs of client and CRO as the future of clinical trials is defined.