Biotech companies are being approached with technological advancements that can make drastic improvements in efficiency, speed, and accuracy of clinical trials. But experts see a disconnect between the future of clinical trials and the technology being adopted to support them.
Some biotechs continue to purchase new tech in the hopes it will position them for the future without truly understanding the time and monetary investment needed to address the gap between their current needs and future requirements.
Behavioral Health, Interoperability and eConsent: Meeting the Demands of CMS Final Rule Compliance
In a webinar on April 16 at 1pm ET, Aneesh Chopra will moderate a discussion with executives from DocuSign, Velatura, and behavioral health providers on eConsent, health information exchange and compliance with the CMS Final Rule on interoperability.
Innovation in clinical trials is being encouraged and in some cases required by international regulators. Efforts by Transcelerate and the FDA to define and pave the way for a risk-based approach to research are becoming expectations, rather than ideals. International regulatory organizations are influencing the clinical research model by requiring risk-based approaches to clinical trial design and execution as well as more transparency throughout the study. European requirements for data transparency while maintaining privacy cannot be ignored.
The traditional model of phased studies and “waterfall” clinical trials is rapidly breaking down in favor of more collaboration with patients, healthcare providers, and within the biotech organization itself. Insurance companies and payers want to see comparative cost and efficacy with available clinical options prior to the product reaching the market. Rapid exploration of compounds to meet multiple indications is becoming commonplace. These stakeholders are asking biotechs to address these requirements by developing processes while also creating technology roadmaps to meet their business and research requirements.
The future of clinical trials will see a shift in resources to support real-time analysis of clinical data from new sources and the increased leveraging of analytics to more quickly identify performance issues. This adjustment is required to meet regulatory expectations and to remain competitive in product development by identifying success or failure of products across multiple indications. The heavy concentration of resources sent out to monitor sites will be shifted to analyzing data and determining the correct application of monitoring resources based on site activity and issues identified from data analytics.
The technology required for supporting this future model is not consistent with the traditional solutions used in biotech for the past few decades. To support collaborative and real-time models, solution providers are enhancing their offerings with mobile technology and analytics. Mobile technology is required to reach patients and sites, and user interfaces must be updated to be as intuitive as commercial apps. Analytics and reporting must be developed and deployed to meet the requirements of a broad range of stakeholders — from executives to scientifically-savvy data analysts.
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A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
Technology is being made available at a frantic pace, with major players such as Apple, Google, Amazon, and mobile providers, making public efforts to apply their capabilities to clinical research and healthcare.
While many biotechs and research organizations have begun to see the future state model and position themselves for it, most have not advanced quickly into this new model. This is due to the level of change management required and the enduring legacy of the conservative nature in the biotech industry.
With the level of change required, most biotechs are best served by creating a “2.0” version of their organization and processes, coupled with technology to support this new research approach. The new version begins with study planning and design that incorporates metrics, data capture, and analysis. Execution of the study includes revisiting roles within the organization to ensure alignment based on data analysis. Issue identification requires a model and tools for collaboration, tracking, investigation, and resolution. Review of the study requires the functional stakeholders – from management to scientists to the safety team – to be equipped with common data, reported to them in perspectives and formats that are relevant to their roles.
Once process and organizational maps are defined, technology can be reviewed to ensure it meets the requirements of the new model. In some cases, existing solutions can be used or adapted, but in many cases, gaps will need to be addressed. The technical ecosystem must support the new research model to encourage adoption of risk-based, transparent clinical trials or stakeholders will fall back into existing strategies and processes.
Adopting new or faster technology to speed existing processes and enable the existing processes is unlikely to position organizations to compete in the new research model. A new approach is required to bridge the gaps.
Photos: DragonImages, Getty Images
