A Dynamic Platform for Data Integration, Standardization and Management Brooks Fowler and Nareen Katta AbbVie Brooks Fowler is the global head of data sciences at AbbVie. Brooks is specifically accountable for data management operations, clinical informatics and clinical sample management operations. Brooks began his career in pharma with G.D. Searle in 2000. He joined AbbVie in 2003 as a section manager of clinical data management. Over the course of the last nine years, Brooks and the AbbVie team have designed and implemented enterprise solutions for EDC, epro and IRT. Nareen Katta is the senior manager, data sciences at AbbVie. Nareen is specifically accountable for managing the company s EDCsystem and clinical databases, including design and definition, data integrations, standardization and ETL operations. To effectively compete in the current economic climate and, in the face of changing trends in commerce, the life sciences industry has had to evolve and become more cost-effective, efficient and responsive. There is increased emphasis on optimizing the clinical trial process and enabling maximum use of data, the industry s key assest. To this effect, pharmaceutical companies such as AbbVie are continually searching for ways to maximize value from data. Better analyses of clinical trial data and optimization of operational aspects (e.g. administrative and financial) of each trial can improve both cycle time and efficiency. It is about cultivating previously unused data whether it s clinical or operational, and putting it to good use. In addition, AbbVie recognizes the need for better data management, achieved through better IT solutions. Indeed, although IT is not a core competency of the life sciences industry, there is a high demand for up-to-date IT infrastructure and solutions. Thus, an IT provider or specialist company builds and implements the IT infrastructure while the pharmaceutical company uses this infrastructure to consolidate, mine, and explore data, thereby informing clinical and operational decisions with reduced need for specialist skills internally. First published March 2013
Addressing industry needs and business drivers AbbVie identified a number of unmet needs that led to a clinical data warehouse as a solution. Key unmet needs included the: 1) Absence of an archiving solution in the company s current and legacy clinical data management systems (CDMSs) there was no functional system from which archived clinical data could be accessed on demand, as there was no archival facility in the previous CDMS for clinical data from the company s own trials, or data inherited from mergers and acquisitions 2) Use of numerous and varied data entry systems, thus data were disparate rather than standardized making analysis challenging 3) Use of data management systems with a fixed structure restricted data integration as data had to be in a certain format 4) Inability to perform cross-study analyses the company s vision was to create a system that allowed all users, and not just specialists like statisticians, to conduct ad hoc analyses and be able to visualize data, thereby maximizing the value of data. AbbVie needed to address key business drivers, including minimizing the number of manual steps required to access data. It was imperative to identify the right IT tools for the right functions thereby allowing near real-time data access in a consolidated, accessible and effortless manner. In addition, AbbVie required a solution that was dynamic and allowed upgrading and switching of systems as new versions or peripheral applications became available. Thus, a solution that could readily evolve with minimal disruptions was needed. A flexible clinical data warehouse presented the most suitable solution based on the fact that it does not have a pre-defined data structure. As a result, it was possible to integrate data from any data structure, for example other clinical and operational systems, and subsequently make these data conform to AbbVie s structure templates, that are source system agnostic within the warehouse, with minimal disruption. Furthermore, with a clinical data warehouse it is possible to build integrated data access layers. These enable non-specialist users to readily access data and perform cross-trial data analysis. 2
A phased approach to clinical data warehouse implementation Once the unmet needs and business drivers had been defined, the company scored and ranked business issues via internal interviews to highlight possible approaches for finding a solution. The approaches were categorized as process, application/requirements or infrastructure changes. Through this screening, application emerged as the most commonly requested change. The company responded to this by replacing its existing data management system with a clinical data warehouse. AbbVie wanted its solution to serve as an end-to-end clinical data management system with both data warehousing and data management capabilities; the warehousing aspect for data aggregation, standardization and reporting and the clinical data management system for data cleaning, blinding and medical coding requirements. We [AbbVie] were looking for, not only a clinical data warehouse and repository, but a full blown clinical data management system. The clinical data warehouse was deployed over two phases. Phase 1 involved assessment and implementation of core functionality, as determined by a cross-functional group in workshop settings, while Phase 2 involved the addition of tools and further refinement. Prior to Phase 1, AbbVie performed a proof-of-concept test to assess the core functionality, process change and use cases, thereby reconfirming the suitability of a clinical data warehouse solution. To define how processes and the business, as a whole, were likely to change as a result of a clinical data warehouse, the company extrapolated and mapped the final project outcomes to the base requirements. This exercise defined the end-user interaction with the new framework and highlighted areas that would need further development in order to optimize functionality. There were various integration processes during Phase 1, for example with electronic data capture (EDC) and Laboratory Information Management (LIMS) systems, making it possible to amalgamate and consolidate data with the core system. To ensure continued accessibility of data to the statistics teams, the extraction methodology for pulling clinical data from the clinical database to the analysis database was redesigned to fit the clinical data warehouse. For example, a previous storage facility of metadata was repurposed for the clinical data warehouse and enabled the company to begin processing some studies through the warehouse. In addition, a metadata driven study setup utility, a parameterdriven edit check engine to enable discrepancy management and integration with coding solutions (e.g. Oracle Thesaurus Management System) used to standardize medical encoding terminology across studies, were developed. With core functionality achieved, the company was able to deploy its clinical data warehouse at the end of Phase 1. In Phase 2, there was additional integration of tools to enable the users to more extensively use the system. For example, using metadata, AbbVie created a tool to allow users to identify a new study and search for similar studies from legacy 3
data. In addition, there was integration with additional data sources like AbbVie s Phase 1 management system, as well as bi-directional integration with the electronic data capture (EDC) system to enable discrepancy management with sites, thereby enabling end-to-end data flow. Reporting and data browsing tools were added to further simplify user interaction and access of the clinical data warehouse. Finally, Phase 2 also involved process automation. The clinical data warehouse is currently in the early stage of rollout and is therefore restricted to use by the global data management and statistics divisions; however, the company envisages further roll-out and expansion of the user community in the future as reporting and visualization tools are added to the platform. We [AbbVie] are utilizing the tool across our global data management and statistics sites. We are making sure that the use of the system and the implementation is geographically dispersed rather than focusing it here [Chicago] at our single headquarter office. AbbVie expects to process all of its studies through the clinical data warehouse once it is fully scaled up. Thus, the overall intention is that every global site will utilize the new system, increasing operational efficiencies and cost-effectiveness. Key challenges and data standardization AbbVie s main challenge with implementing a clinical data warehouse has been the fact that the new clinical data warehouse framework is a complete paradigm shift. A clinical data warehouse is an entirely novel undertaking and completely different to the company s previous experience. It was challenging to fully comprehend the capabilities and select appropriate tools to be integrated onto the technology platform. However, the IT provider was instrumental in this endeavor and provided the guidance and expertise needed to manage the process change. To be able to translate this [a clinical data warehouse] into a future vision and be able to execute it was a challenge. It is a technology framework, not just a business process. Implementing a clinical data warehouse was a major IT initiative and to ensure its success it was important to improve both the IT and business infrastructures, including but not limited to process and resource development, which were likely to impact the project. The volume of technology and process integrations required also presented a challenge. One of the drivers for implementing a clinical data warehouse was to reduce the amount of manual activities. Automation of manual tasks required an assessment of both present systems/processes and future clinical data warehouse environment capabilities/processes. Based on these, tasks for automation were identified. 4
Stakeholder buy-in was less of a challenge. Through specific use cases and business cases there was clear demonstration of improved efficiencies, drug safety implications of data integration (in terms of having aggregated data for regulatory queries and analyses), and the cost justification which will become increasingly evident over time as more clinical trial data are collected and reduction in manual effort manifests more broadly. Due to these elements, unanimous support from project sponsors was won. The clinical data warehouse has enabled AbbVie to implement the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) standards. The company required flexibility to allow incorporation of data from various sources, but to also have industry recognized standards. To do this, a number of tools were added to the platform to convert native standards to CDISC STDM formats, which were then accessible to users. In addition, a data governance team was built to manage these processes. Benefits to users AbbVie has realized a number of benefits from its clinical data warehouse solution including the ability to extract value from metadata and legacy data. For example, using legacy data, programmers are able to more efficiently design future trials and processes. In addition, a single information hub has allowed the use of one or two key reporting visualization systems which provide data in readily usable formats to end users. Previously there had been numerous, different reporting and visualization tools providing data in diverse formats. Sample management logistics is another potential benefit and may allow sample tracking from origination and collection to process end within a robust warehousing environment. A clinical data warehouse also provides a central repository for storing data inherited from mergers and acquisitions. It provides an open but secure framework onto which acquired data can be archived, mined and standardized, as required. Clinical data from three legacy CDMS applications inherited from mergers and acquisitions have been archived to date. An additional benefit, though not specifically identified by the company when defining the use cases, has been the ability to lock clinical trial databases more quickly. The ability to manage blinding and un-blinding of sensitive clinical data in the clinical data warehouse contributes to further reduction in the cycle time. For example, in previous systems, blinding data were added once the database had been locked and this was time consuming. With a clinical data warehouse, sensitive data can be uploaded and stored in a secure/non-accessible area long before database lock. Thus, on database lock un-blinding can potentially be performed instantaneously as all data are already on the system. With regard to being able to lock databases quicker and being able to change and increase the frequency of data refreshers into our [AbbVie] system we have realized efficiencies but I think it will be a while before we completely realize the efficiencies that are associated with the new system. 5
Furthermore, the system allows direct linkage to AbbVie s electronic data capture tool, thereby making it easier to access data with less administrative burden. The overall response from both stakeholders and users has been positive thus far. However, further time is required to fully appreciate the efficiencies and benefits of the new system, particularly with regard to future enhancements. Some of the expected benefits include system portability, reduced manual effort related to data cleaning and data loading, and for aggregating data for cross-study analysis. Looking towards the future As the company looks towards the future, the intention is to replace current processes (e.g. various reporting tools) with the new system. Improved data archiving will allow storing of original data in a well-controlled environment with subsequent standardization and reporting in a readily usable format. This will provide an ad hoc analysis capability within the drug development process making it possible to assess, for example, whether anything was missed in the initial analysis, whether there were any safety implications of note, and whether a drug mechanism currently under investigation had been previously tested; all of which will be used to drive future decisions. AbbVie is also looking to capture large-volume data, particularly from its post-marketing registry trials which typically involve a large number of patients, into the clinical data warehouse. Future plans include partnering with health outcomes organizations and utilizing electronic medical record data and claims data from these organizations to maneuver the structure of these data into a more usable format for both internal personnel and health outcomes teams. In summary, AbbVie s implementation of a new clinical data warehouse (integrated with clinical data management capabilities) has provided a platform that enables data integration, standardization and management. The company has focused on automating its data flow from collection to analysis to minimize manual steps, thereby decreasing sources of error and increasing operational efficiency. Clinical data warehouse implementation has been successful using a two-phased approach. The future user community is predicted to increase as data stored in the clinical data warehouse becomes more integrated and accessible. The clinical data warehouse is able to store both production and legacy data, allowing standardization, exploration, mining and analyses of these data to inform future decisions. The company views its clinical data warehouse as a dynamic, evolving platform that will eventually replace most of its current technologies and systems. 6 Copyright 2013, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners