Mukhtar Ahmed is Vice President, Product Strategy, Oracle Health Sciences.

Mukhtar Ahmed

	Life sciences organizations run a challenging gauntlet when it comes to clinical development-balancing rising clinical costs, relentless pressure to speed time to market, increasingly stringent regulatory requirements, and complex safety considerations. Ultimately, the answer to these challenges comes down to collecting, managing, and analyzing data-a process that is complicated by the rapidly increasing volume, velocity, variety, and value of data that is outstripping organizations’ capacity to make effective use of it.

	The result? Life sciences organizations continue to grapple with data in a number of ways, including:

		Obtaining meaningful insight and analysis from clinical data-when, increasingly, there’s just “too much data”

		Eliminating siloes-internal and external

		Incorporating and enabling the use of real-world data, including electronic medical records and electronic patient-reported outcomes

		Managing and assessing structured and unstructured data

		Effectively aggregating all relevant data-not just clinical but genomic, financial, project management, enterprise resource planning (ERP), and more

	The need for better and more universally adopted standards is another persistent issue. Companies continue to struggle with creating a standard data model that can accommodate the many types and sources of data that they require to drive discovery and innovation.
	 

	The promise and challenges of data-big and small-were the focus of a lively discussion at a recent Oracle customer advisory forum, where we posed this question to life sciences executives in the room:

If you could have perfection in clinical development, solving your operational and data challenges and providing ideal outcomes, what would it look like? What role would technology play?

	The group identified three core qualities of perfection within clinical development:

  Clinical development leaders look to reduce manual intervention with greater automation and machine intelligence/expert systems-increasing data quality, reducing trial risk and cost, and increasing time to market
		 

  Development processes are optimized and technology-enabled-for example, transforming a fragmented and inefficient study startup process to compress cycle-times by months or years; utilizing a data-driven approach to protocol design to drive greater efficiency, safety, and compliance; or implementing analytics to set, track, and assess key performance indicators and performance in real time to drive real operational improvement
		 

  As data grows in volume and complexity, life sciences organizations seek transparent and seamless data, with a goal of fully “liquid data.” What does that mean? It means data that is dynamic and standards-based in nature, composed of data from multiple sources, and giving people-from sponsors and contract research organizations to investigators and regulatory authorities-the ability to tap in from anywhere at any time to access, visualize, and analyze the data

	To achieve the objectives above requires an agile and dynamic technology infrastructure that is standards-based and can readily accommodate multiple types of data-structured and unstructured-from many different internal and external sources. Increasingly, this includes clinical, genomic, patient record, operational, financial, payer, enterprise resource planning, industry benchmark, outcome, social media data, and more.  

	The platform must also incorporate automated feedback loops to continuously update and refresh the data from all sources. In addition, it must be highly secure to protect from growing threats, scalable to easily handle massive amounts of data, and extensible to remain compliant with global and local regulatory guidelines and mandates.

	Life sciences organizations are optimistic about the future as clinical development processes and platforms continue to advance. They see several important benefits on the near horizon:

Better collaboration with strategic partners 

through the ability to process and analyze data in one place available for all partners. This will drive additional efficiency gains by enabling strategic development partners to use their internal systems and processes to capture both operational and clinical data.
		 

Transformation of the classic sequential and randomized trial model paradigm

 to better support adaptive approaches, implement fast and short pilot trials, conduct site-less trials, and more. With an integrated view of aggregated data, health sciences organizations would be ideally positioned to effectively explore therapeutic efficacy and safety and demonstrations in cohorts or sub-populations and make more informed and timely decisions to optimize study and program outcomes.
		 

that allows patients to access their own data to improve enrollment, retention, adherence, safety, and outcomes-and reduces operational costs.
		 

 through the integrated use and analysis of molecular data.
		 

 with centralized data, advanced analytics that enable better insight into trial progress, and key milestones across numerous dimensions, including the complete book of work or by therapeutic area. These capabilities support faster identification of promising trends and early warnings of underperforming trials that should be terminated early.
		 

Accelerated regulatory review and improved approval rates

 through pre-submission collaboration and data transparency. The ability to successfully manage data in a single platform would enable life sciences organizations to quickly and accurately acquire, clean, aggregate, and prepare clinical trial data for regulatory submissions.
		 

Rapid, accurate product defense and audit response

. Centralized data facilitates analysis, pooling, and reporting on trends that either support claims related to efficacy or safety or refute similar claims made by competitors-especially regarding post-marketing (Phase IV) trials. Clean data stored in one place also enables faster response to audit questions without having to spend resources and effort on programming and reproducing the situation that led to the audit
		 

 through process reuse and automation, including the reuse of objects, such as data models, transformations, and queries-freeing valuable resources to focus on high-value tasks rather than manual (custom) development activities

	While perfection in clinical development is certainly a lofty idea, the industry is uniquely poised to make significant and measureable gains in the year ahead when it comes to aggregating, managing, analyzing and, most important, using their data to advance discovery and drive innovation. Let the journey begin!

 is Global Vice President, Life Sciences Strategy, Oracle Health Sciences

Articles

Life sciences organizations run a challenging gauntlet when it comes to clinical development.

Clinical Development…In a Perfect World

	The health sciences industry received some important and encouraging news from the US Food and Drug Administration (FDA) in late June-an announcement that may, ultimately, help to transform the way data is collected in clinical trials and lead to safer studies and faster time to market. 

	The FDA issued new draft guidance that stated its intent to not actively regulate certain health IT devices. It also signaled that IT products that straddle the line between IT and devices would also be exempt. This is good news for mHealth solution developers and health sciences organizations engaged in clinical development, clearing the way for faster development and more confident adoption.

	mHealth technologies hold great potential to transform clinical development programs, including clinical trials. Health sciences organizations and their contract research organization (CRO) partners have been focused for the last two decades on improving the productivity of clinical trials by boosting data collection and management efficiency.  After many years, electronic data capture solutions have become the industry standard.

	In contrast, the industry has made limited progress on improving the fundamental quality of clinical trial data. This is where mHealth technologies have the opportunity to take center stage and drive much-needed transformation.

	Harnessing Big Data to Transform Clinical Trials

	Wireless technologies, including many types of remote sensors and wearable devices, have advanced at a breakneck pace in recent years, driving innovation across many industries as well as delivering new levels of consumer convenience and connectedness. With increased and clarified guidance from the FDA, the mHealth sector is now poised to more confidently capitalize on these advances and fuel new innovation of its own.

	Using mHealth and other machine-to-machine (M2M) solutions, trial sponsors and CROs can improve data quality and accuracy. They afford organizations the ability to collect higher volumes of information and an expanded array of data types from trial participants, which supports more thorough analysis and more informed insight. As important, automated data capture removes the risks of errors associated with manual data entry.

	mHealth and other M2M technologies enable continuous patient monitoring that can deliver important advantages to research teams because of the volume and variety of data collected. Sensors and devices, for example, can collect continuous data on physiologic vitals as well as activity levels and types, which can yield important insight into efficacy not possible with traditional patient journals and periodic exams.

	mHealth solutions can also enable immediate capture of data without human intervention, accelerating delivery of important data and indicators to researchers. This immediacy can help researchers to identify potential adverse events sooner to improve safety as well as accelerate the overall pace of data collection, helping to speed time to data lock and regulatory submission.

	Similarly, immediate access to data can provide greater visibility into a trial participant’s adherence to the study protocol. If a trial participant is not adhering, trial managers can quickly identify this and drop them from the study or explore other ways to help boost adherence across the participant pool as a whole.  This enhanced visibility yields earlier insights into how many subjects will be required to complete the trial and, ultimately, accelerates the clinical trial cycle.

	As important, access to real-time information can support adaptive trials, providing early indications of changes that might need to be made to protocol, sample size, or trial scope. Likewise, trial sponsors can have earlier insight into a therapy that is performing better than expected, which would accelerate the delivery of life-saving treatments to market.

	The advent of sophisticated wireless technologies that have the potential to collect unprecedented volumes and varieties of data accurately and in real time has the potential to transform the pace, safety and success of clinical trials. New and clear guidance from the FDA stands to give technologists and health sciences professionals the insight they need to proceed confidently in exploiting the full potential of mHealth solutions.

Mukhtar Ahmed, Vice President, Product Strategy, Oracle Health Sciences 

The health sciences industry received some important and encouraging news from the US Food and Drug Administration in late June...

mHealth Poised to Transform Clinical Trials

The advent of Accountable Care Organizations is causing providers to rethink their legacy financial models as they seek to effectively quantify and understand the clinical or financial experience of their patients—a fundamental requirement for managing patient care and financial risk effectively. In much the same way, clinical trial sponsors seek similar transformation when it comes to managing their clinical programs and optimizing research and development (R&D) spend. Many are looking to two proven project management methodologies—activity-based costing  and earned value management—as a path forward.

Activity-based costing is a cost accounting methodology that assigns costs to an organization’s activities (or cost drivers) related to its products and services to reflect the extent to which the activities (in this case R&D and clinical trial initiatives) consume the organization’s resources. It provides a higher degree of specificity and insight than can be achieved through traditional cost accounting methodologies. Earned value management aims to measure project performance in a more objective manner, looking at completion of authorized work against budget allocated for that work, finding the “earned value.”

Clinical studies present increasingly daunting project planning and tracking challenges, with hundreds and even thousands of complex and interdependent variables—such as site selection, patient enrollment and monitoring, data capture, regulatory approvals and more—that need to take place in the proper sequence. Slight changes from the operational plan during study execution can have a dramatic effect, presenting challenges for companies when designing a clinical plan to best achieve the goals of the study.

Historically, health sciences organizations tracked these variables in silos and often on spreadsheets, precluding the enterprise visibility required to implement activity-based costing and earned value management. Understanding the costs of various clinical trial components is a complex task, and many organizations cannot accurately assign costs to various activities associated with a study. Traditional methods using spreadsheets, generic planning software, and ad hoc planning processes have led to lengthy planning and contracting cycles, inaccurate budgets, and high variances between planned budgets and actual costs.

Health sciences organizations seek better visibility into the operational and financial plans for their studies as well as the ability to quickly model new scenarios as clinical assumptions, business requirements and outsourcing needs change. These capabilities drive more efficient and accurate clinical study planning, budgeting, outsourcing, and execution.

Creating an environment for activity-based costing and earned value management requires six primary components:  

Centralized repository for operational data that enables clinical plans to reside in a secure database, accessible via Web connection and user-driven privileges

Embedded global clinical intelligence, including algorithms for therapeutic indications and clinical development data, such as CRO labor rates by country 

Flexible “what-if” scenario planning and analysis that enables study managers to rapidly create and adjust clinical development variables and strategies to obtain the best scenario for the study’s objectives

Integrated project management capabilities that enable visibility into project status and analysis of trends, including time to complete and estimated costs to complete

Activity-based planning methodology that includes embedded study assumptions for activities such as study startup, monitoring, data management and more to build plans reflecting development goals

Accrual management capabilities and analytical tools that give study managers the ability to track budget accruals and compare them to the original operational plan

A large pharmaceutical company recently used this approach to support a move to an outsourced clinical development model to allow the company to focus on core capabilities. It looked to optimize clinical study planning and sourcing and rationalize deployment of R&D spend, while achieving more accurate clinical trial budgeting and planning. 

The organization adopted an activity-based costing approach and deployed a solution that gave it

the ability to accurately and rapidly model clinical trial costs and resource requirements and analyze contract resource organization (CRO) proposals for more precise budgeting and better insight into timeline and cost implications. It also created a consistent work breakdown structure across all proposals, and gained greater confidence in accuracy of CRO partner budgets. Further, replacing spreadsheet-based models and desktop information archiving with a central repository for all clinical trial budgeting information significantly improved historical data and collaboration, driving faster and better insight. For example, the outsourcing contracting team can now respond in as few as five minutes to internal client questions about study budgets.

Activity based-costing and earned value management can empower health sciences organization with new precision when it comes to managing their clinical programs. The key to success is creating a solid information foundation that aggregates key data and enables flexible and comprehensive analysis.

Mukhtar Ahmed is Global Vice President, Life Sciences Strategy, Oracle Health Sciences

The advent of Accountable Care Organizations is causing providers to rethink their legacy financial models as they seek to effectively quantify and understand the clinical or financial experience of their patients.

The New Science of Predicting and Managing Clinical Trial Cost and Performance

Risk-Based Monitoring – Beyond Theory In-Depth Review

The U.S. Food and Drug Administration opened the door to a new era of clinical trial management last summer when it released guidance for industry on clinical trial oversight and called for expanded use of risk-based monitoring. Specifically, it encouraged sponsors to take advantage of electronic systems and focus clinical trial oversight activity on preventing risks to data quality and critical processes to patient safety and trial integrity.  

Risk-based monitoring represents a smarter way to monitor clinical trials - a holistic, dynamic approach focusing on risk factors with the intent to conduct clinical trials more efficiently and cost-effectively, while ensuring patient safety, data integrity and compliance. 

Industry best practices have long dictated that monitors visit each investigator site regularly to evaluate data and processes that impact patient safety and protocol compliance, and to conduct 100% source data verification (SDV).  Risk-based monitoring turns that around through a focus on centralized and off-site monitoring instead of full on-site monitoring, and an emphasis on partial (or targeted) source data verification.

With risk-based monitoring, the role of the monitor doesn’t change – just as the reasons we monitor haven’t changed.  However, technology will play an expanded role in helping clinical research associates (CRAs) execute risk-based monitoring strategies. 

Core clinical systems should include activity-based study modeling, true end-to-end clinical data management, clinical trial management system, safety monitoring systems and operational analytics to inform study design and tie systems together in a single source of truth. In addition, innovative mobile apps are emerging that provide CRAs with visibility into real-time trial and site operational data that help them determine, based on critical data and process indicators such as screen failure rates, if they can skip a planned visit or make an unplanned visit – all while on the road.  CRAs can leverage this increased mobility to help them manage the on-site visits they do make to be more efficient and productive, and adapt more quickly to changes. 

Due to overarching concerns about patient safety, trial managers have been apprehensive to become early adopters of risk-based monitoring – despite the evident cost and efficiency benefits.  Now, with guidance from the FDA, European Medicines Agency (EMA), and Medicines and Healthcare Products Regulatory Agency (MHRA), as well as a widespread industry-group support and tools, such as TransCelerate’s Risk Assessment Categorization Tool, sponsors and contract research organizations (CROs) are starting to actively implement risk-based monitoring. 

Successful risk-based monitoring really comes down to three factors - the upfront risk assessment identifying the critical data and processes for that study; quality design and consistent application of that risk assessment across the organization (e.g., people, partners, processes, and investigator sites) and trial workflow (e.g., clinical systems, protocol design, and trial design); and the ability to quickly and easily track the critical data and processes identified in the risk assessment.

The benefits of risk-based monitoring are compelling. By reducing SDV – the most time-consuming activity for monitors on-site – CRAs can focus on more critical compliance activities, like patient-informed consent, completion of essential documents and site compliance with study protocol and Good Clinical Practice (GCP) principles. In addition, other studies suggest that data anomalies such as non-random data distributions and fabrication of data may be more easily detected by centralized monitoring versus frequent on-site monitoring. With regulatory guidance, industry standards and tools, and advances in technology, the path is clearer for adoption of effective risk-based monitoring to reduce trial time, cost and risk.  Most importantly of all, however, the increased adoption of risk-based monitoring will help enhance patient safety.

The U.S. Food and Drug Administration opened the door to a new era of clinical trial management last summer when it released guidance for industry on clinical trial oversight and called for expanded use of risk-based monitoring.

Time to Embrace Risk-Based Monitoring

This post first appeared on our sister publication's site, 

	Almost daily, we learn of stunning new advances in machine-to-machine (M2M) technologies in healthcare and life sciences, where sensors collect vast amounts of data that yield immediate and long-term insight, and that carry the promise of improved outcomes and quality of life.

	Just this summer, Vanderbilt University amazed us with the introduction of 

, which uses M2M technologies to integrate human and machine like never before. We are also seeing the advent of powerful M2M technologies in the surgical realm. For example, 

inertial microelectromechanical systems (MEMS) sensor technology

 can determine motion and location and significantly improve accuracy in aligning hip and knee implants with a patient’s anatomy.

	M2M technologies also have the potential to transform clinical development programs, including clinical trials. For the last 15 years, health sciences organizations and their contract research organization (CRO) partners have been focused on improving the productivity of clinical trials by boosting data collection and management efficiency. Electronic data capture solutions, after many years, have become the industry standard.

	Much less progress, however, has been made in improving the fundamental quality of clinical trial data. This is where M2M technologies have the opportunity to really shine.

More Data + Greater Accuracy = More Productive and Informed Studies

	M2M technologies, which enable continuous patient monitoring, capture dynamic data that deliver three important advantages to research teams.

		Variety and Volume – Researchers can collect more types and higher volumes of data than ever before. For example, M2M sensors can collect continuous data on physiologic vitals as well as activity levels and types, which can yield important insight into efficacy not possible with traditional patient journals and periodic exams.

		Immediacy – M2M enables immediate capture of data without human intervention, accelerating delivery of important data and indicators to researchers. This immediacy can help researchers to identify potential adverse events sooner to improve safety as well as accelerate the overall pace of data collection, helping to speed time to data lock and regulatory submission.

		Accuracy – Using M2M solutions, trial sponsors and CROs can improve data quality and accuracy in two important ways. First, the ability to collect higher volumes of information and an expanded array of data types from trial participants supports more thorough analysis and more informed insight. As important, automated data capture removes the risks of errors associated with manual data entry.

Improving Visibility into Protocol Adherence

	M2M technologies show important promise in validating and improving protocol adherence – an area that continues to challenge clinical trial sponsors and managers. A New England Journal of Medicine report cites that clinical trials report average adherence rates of only 43 percent to 78 percentamong patients receiving treatment for chronic conditions.

 These numbers can have a powerful impact on the economics and accuracy of clinical trials. For example, just a 20% decrease in medication adherence may result in the need for a greater than 50 percent increase in sample size in order to maintain equivalent power.

	Continuous monitoring, made possible through M2M technologies, can enable researchers to confirm treatment adherence with near absolute certainty, which we cannot do today. As a result, study sponsors and managers could more accurately determine efficacy as they can filter out non-adhering patients. In addition, continuous monitoring with M2M technology can facilitate subject recruitment and, ultimately, shorten the length of a trial. If a participant is not adhering, trial managers could drop them quickly or focus resources on boosting adherence, yielding earlier insight into how many subjects will be required to complete the trial.

	Further, M2M solutions can help to improve participant retention as recording critical data and adherence will be more convenient. In addition, the ability to automatically upload data to the clinical data management system would eliminate manual input into an electronic case report form – driving new levels of study efficiency.

	There is an important link between M2M-enabled continuous monitoring and improved safety. With vast and accurate information streaming in real time, researchers can more quickly identify potential adverse events or side effects, such as changes to heart rate, heart rhythm, blood pressure or sleeping patterns, after taking a medication or therapy, and take action to intercede. This capability also has promising application in post-market surveillance.

	As important, access to real-time information can support adaptive trials, providing early indications of changes that might need to be made to protocol, sample size or trial scope. Similarly, trial sponsors can have earlier insight into a therapy that is performing better than expected, which would accelerate the delivery of life-saving treatments to market.

	M2M technologies are beginning to show their powerful potential for improving adherence in clinical trials and for post-marketing surveillance. For example, Proteus Digital Health provides an FDA-approved ingestible sensor that works together with a wearable sensor to capture precise information about medication ingestion, dose timing, physiologic responses and other behaviors, sending the digital health information to a patient’s smart phone.

	The ingestible sensor sends a signal containing a unique identifier allowing recording of the time the patient took a pill. A wearable sensor worn on the skin captures continuous readings of the patient’s heart rate, temperature, activity and rest patterns. The solution can collect more than 5,000 data points per minute.

	Another use of M2M technology applicable to improving data quality in clinical trials is the emergence of surgical instruments equipped with sensors that support 4D visualization and simulation modeling. For therapies that have a surgical component, the ability of surgical instruments to capture rich data, which can be used to build a visual model of the surgery site as well as transmit critical physiologic data to researchers, can yield greater insight into outcomes and potential adverse events.

	The industry is just beginning its M2M journey, and as with any new technology, challenges arise. Security is an ongoing concern, as it must be when dealing with protected health information.

	Additionally, regulatory policies around the use of telemetry and M2M technologies in medicine are still taking shape and must strike a balance between promoting innovation and protecting patient safety.

	The influx of massive volumes of data emanating from M2M applications also presents a big data challenge for study sponsors and their CRO partners. To realize the true potential of M2M technology to transform clinical trials, organizations must be able to rapidly analyze and act on the vital information gathered in real time.

	These concerns, while valid, will not halt innovation. We are already seeing some progress on the regulatory front. In September 2013, the 

, “promising limited regulation for most health and wellness apps while applying risk-based standards to diagnostic and quasi-medical device apps.”

	Forward-looking health sciences organizations can position themselves to rapidly reap the benefits of M2M by building a foundation for adoption in the short term. Many of the core technologies - including data repositories, analytics and integration technologies - are already in place at health sciences organizations today, and we can learn a lot from other industries that are progressing rapidly using M2M technologies.

	M2M technologies are poised to transform the way that the industry captures clinical trial data. More important, they have the potential to accelerate the momentum of discovery by enabling new levels of insight, productivity and efficiency across the clinical development continuum.

	Mukhtar Ahmed is Vice President, Product Strategy, 

	[1] Osterberg L., Blaschke T., “Adherence to Medication,” New England Journal of Medicine, 2005; 353:487-97.

	[2] Pledger, G.W. “Compliance in Clinical Trials: Impact on Design, Analysis, and Interpretation,” Compliance in Epilepsy, Elsevier, 1988.