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Vol 19, No. 10, October 2015   |   Issue PDF view/purchase
Big Data in Clinical Research Sector
Mr Naz Haji holds a dual role as the Head of India, and SVP and CIO Asia. He joined Quintiles in 2006 as Vice President, Global IT in Research Triangle Park, North Carolina, leading Global Infrastructure and Operations. During this time, he led the consolidation and globalization of the Global Information Technology function instituting key processes, functions and delivery capability resulting in world-class performance, quality and stability.

In 2007, he was promoted to Senior Vice President. Bringing more than 25 years of experience in international business systems, technology, process re-engineering and consulting, Mr Naz Haji has been a key leader within multiple industries, including pharmaceutical, manufacturing, utilities, oil and gas, engineering and aerospace. His experience also spans the globe having worked and lived in North America, Asia, Africa, Europe and the Middle East. Born in Tanzania and raised in Canada, Naz earned his degree in business from York University in Toronto.

  1. Can you tell us something about big data in the clinical research sector?

    Drug development is dependent upon the availability of high quality data with which to collaborate and make informed decisions at every step of the way during the evolution of a product or treatment.

    Quintiles has access to 61 million de-identified health records representing patient lives. We deal with petabytes of de-identified patient data aggregated into illustrative datasets in different therapeutic areas of varied complexities that include clinical research operations, laboratory, ECG and imaging data to genome sequencing data.

    Given the challenges that today’s biopharma industry faces, the patent cliff looming large and the need to bring better and more cost effective medicines to market faster than ever before, technology and Big Data are playing a major role in transforming the industry.

  2. Data is everywhere, and including the Cloud services, if it were to remain in the storage area and not analyzed, there is no good reasoning for data collection. What are the approaches we could do to utilize and translate data analytical work for healthcare and drug development?

    When developing a new compound or a device, one is inundated with data from a wide range of sources — patient, industry, operations, labs and investigators. One of the key challenges is how to use data to deliver insightful information that will lead to faster decision -making, and ultimately more accessible and affordable healthcare. For example, at Quintiles, we integrate information from diverse data sources and combine that capability with our therapeutic expertise and our analytical thinking to deliver what we call the Data Driven Difference.

    Two instances of how Quintiles analyses data to deliver value in clinical trials are Quintiles Infosario® and our Data-driven Trial Execution (DTE) offering.

    With the Quintiles Infosario platform, Quintiles has developed the technology to take data from multiple source systems and integrate, synchronize and normalize and present this data in near real-time. The Infosario platform creates a seamless integration of data, therapeutic expertise and clinical trial processes in a system that provides data transparency, optimized workflows, and real time insights into patient, study/site and programme activities. Powering this is the Infosario Data Factory which is the clearinghouse and data hub for clinical and commercial data for the industry, supporting clinical development and integrated health analytics. The platform is designed to deliver accurate and consistent data insights, to enhance enterprise knowledge management, improve business insights and decision-making, and increase operational efficiencies.

    With our Data-driven Trial Execution, we are using data-driven processes and insights to drive faster, more informed decisions that can improve a customer’s probability of success. By harnessing the power of integrated, holistic data, we’re able to respond to signals and trends that could affect patient safety and operational performance and help make smarter decisions about which sites to select and how to deploy monitoring resources. Combining risk-based thinking, data analytics and years of process refinement has enabled this evolution to deliver greater efficiencies and more predictable trial outcomes.

  3. Big Data may be over-generalized as a big fishnet, thrown into the ocean and to capture different types of ‘fishes’, and with almost no data filtering process. Where are the data sources catered to addressing healthcare needs and demands? How much of an emphasis should a good analyst place on data quality?

    We can only speak with reference to the work that Quintiles does. The visual below depicts the various sources of data that we deal with. In clinical research, the quality of data is integral to our work and we are committed to making research more informative and ensuring valid results. Whether it is Infosario or our Data-driven Trial Execution programmes, they are all driven towards delivering insights and information that will improve patient safety and data quality.

  4. What do you see as the next big data and analytics led technological innovations in clinical trials?

    If I had to highlight one, it would be Predictive Analytics. Quintiles recently announced two enhancements to its Data-driven Trial Execution solution. These enhancements enable study teams to identify the right signals and predict clinical trial site performance and potential patient safety issues before they occur. Called Predictive and Advanced Analytics, the capabilities combine advanced statistical monitoring and predictive analytics to improve precision in risk identification by better understanding underlying “white noise” from safety trigger processes. Predictive and Advanced Analytics are the first such model-based capabilities fully integrated into a DTE solution in the market today.

    Additionally, mobile and wearable technologies will be significant going forward. The evolution of wearable technology goes beyond just the gadget itself and encompasses the data it collects, the significance of this data, and how the data is interpreted. Organizations that are able to integrate all of this seamlessly will be able to harness the power of data across the drug development continuum to improve a customer’s probability of success.

  5. How is data being used in patient recruitment?

    According to a Quintiles White Paper, “Data-Driven Patient Recruitment to Deliver Qualified Patients, Faster”, almost 80% of clinical trials fail to meet their patient enrolment quotas on time, causing delays in bringing new drugs to market and costing the biopharmaceutical industry large sums in revenue for each day a drug is delayed. Traditional patient recruitment methods are inadequate and do not scale to reach the entire populace. And although web-based methods may hold promise for the future, today’s competitive market for clinical trial participants necessitates a new approach. A small percentage of the general public has ever participated in a clinical trial. The vast majority of eligible candidates are either unaware of available trials or have a poor perception of clinical research. Fortunately, the growth of electronic health data is paving the way for a more efficient, scalable method to recruit patients for clinical trials. Data-driven patient recruitment can enable greater reach and efficiency, reduce trial delays and save valuable resources. Innovative companies are already dialling-in data to increase the speed, number and quality of patients recruited for clinical trials. With the continued growth of electronic health data, and plans for interconnected health records systems on the horizon, possibilities could dramatically increase for clinical research.

  6. There appears to be two extreme possibilities of a good-valued data: good analytical work and, better policies & sales regulations which later translates to better economic opportunities; the data is likely to meet hackers-prone scenarios and it is uncertain what may happen when it falls in the ‘black hole’. Population and clinical data are the few vulnerable biometric datasets. What are the improvements to be done for better data protection?

    As far as Quintiles is concerned, given the nature of our work, the protection of personal data, particularly patients’ personal health information, and customer confidential data, is critical for our company and our customers. With regard to personal data and customer confidential data, we have been widely acknowledged for our long-established and robust global data protection programme that follows the “Privacy by Design” concept.

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