Accelerating Innovation And Efficiency Through Analytics In The Life Sciences Industry

By Neil de Crescenzo

The healthcare and life sciences (or “health sciences”) industry stands at a critical juncture. The last 25 years have brought rapid expansion in our understanding of disease as well as a wave of innovation in therapy and prevention.

However, against this backdrop of remarkable progress, global healthcare systems today face unprecedented challenges as they struggle with skyrocketing costs, inconsistent quality, and increasingly, inaccessibility to timely, efficient care for many citizens — a reality that has prompted calls for reform in the United States and abroad. At the same time, there are growing concerns over the safety of complex and expensive new therapeutics, medical devices, and other treatments — leading to new legislation and expanded regulation.

In this environment framed by increased complexity and cost pressures, health sciences companies seek new and more efficient models for bringing innovation to market, beginning with R&D and extending across the enterprise. A critical priority for many of the leading companies is the use of business intelligence and analytics.

As we continue to search for ways to improve people’s health while moderating the increases in costs in our healthcare delivery system, the larger health sciences ecosystem also has prioritized analytics as a method for helping achieve broader healthcare goals. The use of analytics to turn information into actionable insights is fundamental to advancing personalized medicine, which holds the potential to help address the cost/quality challenge, as well as sustain innovation.

The Potential Value And Impact Of Analytics
In the business bestseller Competing On Analytics, authors Tom Davenport and Jeanne Harris make important distinctions between capabilities that enable “access and reporting” and those that provide true analytical insights. Much of the “business intelligence” industry has focused on capabilities that enable reporting, structured or ad hoc queries, and alerts that make surfacing information to decision makers more efficient. However, Davenport and Harris point out that the next level of value will be through more predictive and optimization-oriented solutions and practices. Their careful analysis of high-performing companies across multiple industries found a high correlation between the extensive use of analytics among high performers vs. low performers.

Pharmaceutical companies contain massive amounts of data spanning from discovery to distribution. Just as important, they have access to a growing pool of external data from sources as diverse as healthcare providers, payers, drug distributors, and retailers, all of which have the potential to reveal important business and clinical insights. Modern business intelligence and analytics solutions provide a methodology and the tools for bringing this disparate, but vital, data together in a meaningful and cost-effective way.

Continuing To Advance R&D
R&D is one of pharma’s largest cost centers, making it a popular target for initiatives designed to improve efficiency and productivity. In particular, the industry is taking a hard look at clinical trials, the cost of which has increased by 40% over the last decade, according to analysts at Frost & Sullivan.

As clinical trials become more complex and global, pharma manufacturers and their CRO partners are challenged to manage many more trial sites, as well as exponential growth in data volume. At the same time, pharmas are working to improve R&D productivity and accelerate the clinical trial process. Analytics can drive insight and efficiency across the clinical trial process in the following areas, often cited as critical pain points:

Study Enrollment
Nearly 90% of clinical trials are delayed due to difficulties with patient enrollment, costing pharmaceutical companies between $600,000 and $8 million per day in lost sales. Furthermore, there is evidence that enrollment during the first two months of a trial is strongly correlated with subsequent accrual and a strong predictor of attaining a statistically significant result. Early insights into study enrollment through analytics, therefore, offer a powerful, early window into the future success of a trial, enabling life sciences companies to make informed go/no-go decisions as early as possible across their entire R&D pipeline.

Analytics can also help pharmaceutical manufacturers gain tighter control over enrollment processes by flagging struggling trial sites at an early stage and enabling trial sponsors to apply additional resources to them or further invest in the best performing sites. Looking ahead, analytics offer potential in helping trial sponsors to identify candidates for highly specialized therapies — a need that will become increasingly acute with the transition to personalized medicine. Life sciences companies and their CRO partners can interface with health networks and leverage analytics to quickly screen large numbers of anonymized electronic records for potential trial candidates, helping to accelerate and drive down the costs of trial participant recruitment.

Clinical Monitor And Site Productivity
As the number of clinical sites increases, so does the management burden. Analytics tools can enable managers and clinical monitors to better balance workloads and reduce unnecessary travel expenses. Monitors can gain rapid insight into how their sites are performing against defined milestones and targets. Analytics can offer insights into both subject enrollment progress and data quality at a site and can be used to optimize monitoring visit frequency. The ability to overlay key performance indicators, by site, on a geographic image can enable monitors to visually cluster by region and tune their travel plans in accordance with trial demand.

Clinical Effectiveness
To improve productivity and accelerate the delivery of safe products to market, pharmas require analytics applications that provide a comprehensive view of clinical programs across all levels of the organization. To achieve greater clinical effectiveness, clinical data managers, for example, require insight into the completeness and “cleanliness” of study data, while executives need to measure progress across all clinical programs.

Industry costs to resolve discrepancies range from $50 to $100 per discrepancy, depending upon EDC (electronic data capture) or paper-based processes. It is not uncommon for a typical Phase 3 trial to have more than 50,000 discrepancies, leading to multimillion-dollar discrepancy management costs. Early identification of the root cause of discrepancies using analytics and associated process adaption can not only yield direct financial benefits but can also help accelerate database lock through fewer discrepancies.

Safety, Pharmacovigilance, Postmarketing Surveillance
In recent years, more parties (e.g. sponsors, CROs, trial sites, regulatory agencies, medical institutions) are managing research and clinical development and generating data. New and observational safety data sources are emerging, including insurance claims, diagnostic tests, prescriptions, and registries. Increasing amounts of data from multiple sources have made the collection and analysis of safety data more complex. Analytics play a critical role in safety programs, integrating information from clinical and adverse event systems, as well as external sources, to rapidly identify patterns that may remain obscured in siloed environments.

As pharma companies broaden their focus from transactional case processing to proactive life cycle risk management, adverse event signal detection becomes critical. However, signal detection by itself does not promote adequate risk management. Instead, a structured signal management process will help lead to improved risk evaluation, management, and action. Such a process requires a structured methodology for postsignal management. Industry participants will focus on risk management and prevention in future years, encouraged by regulatory bodies and legislation, and move well beyond reporting capabilities. Investments will increase significantly across the health sciences ecosystem for more extensive and timely pharmacovigilance and postmarketing surveillance.

Therapeutic Candidate Effectiveness
The failure rate for therapy candidates entering the clinical trial stage hovers at around 80% — a costly proposition. As such, it is not surprising that the industry is increasingly embracing adaptive design for clinical trials, an approach that offers greater flexibility but one that also requires near real-time visibility into, and an understanding of, study results. Analytics are essential to providing direction for adaptive design and/or accelerating notification of when a trial should be halted due to apparent failure or better than expected results.

Beyond Clinical Development And Safety
The benefits of analytics in the pharmaceutical enterprise extend far beyond clinical trials. It can play a vital role in discovery, as organizations work to optimize the use of scientific knowledge within their enterprise, as well as leverage data from partners or in the public domain.

Analytics are critical in manufacturing, where pharma companies and their contract partners are increasingly focused on improving quality and reducing waste through initiatives such as lean Six-Sigma. In sales and marketing, analytics can help managers to optimize the impact of their spend in an area increasingly restricted by legislation.

Similarly, enterprisewide analytics can yield insight that guides strategic direction. Executives can make more informed decisions about therapeutic area priorities by analyzing internal data, such as sales and R&D productivity information, as well as data from external sources, information on population trends, reimbursement schedules, and comparative effectiveness results from external sources.

A Path Forward
Many pharmaceutical organizations continue to rely on first generation, generic business intelligence tools to gain actionable insights, but leading organizations are increasingly realizing they need a comprehensive approach to analytics based on robust, proven technology that is tailored to their objectives. Many current approaches are resource-intensive and do not yield the near real-time insights required to achieve new levels of agility and effectiveness. Point-to-point integrations offer some level of visibility but are costly and difficult to maintain. Instead, companies and CROs require a flexible layer that enables the integration of data from various sources. As organizations consider their approach to adopting analytics, several best practices can guide them to a successful initiative.

Accommodate Diverse Data
All data is not the same. For comprehensive analytics capabilities, pharmaceutical manufacturers require tools that enable retrospective analytics (reporting and alerts), empowering them to identify patterns and trends based on recent and historic events, as well as prospective analytics, which support modeling, forecasting, and optimization.
Analytics solutions must be able to accommodate data from internal and external sources, both of which hold enormous potential for use in the discovery, clinical trials, and even postcommercialization surveillance.

Enable Rapid Payback
Today’s health sciences organizations cannot afford a slow payback on their IT investments. When considering an analytics solution, organizations should seek features that enable rapid rollout and use. Prebuilt graphical dashboards and adapters enable lower total cost of ownership and faster time to value.

Something for Everyone
Analytics tools should also provide benefits to multiple audiences — from the site monitor to the sales executive to the C-suite — with flexibility and configurability that enables various groups to get the information they need. Platform flexibility is also important, enabling users to access data on PCs as well as from their mobile devices to support both office and field personnel.

The dramatically changing health sciences landscape has put an end to business as usual in the health sciences industry, requiring organizations to rethink and adapt their formula for innovation and success. Analytics are essential to this transformation. With careful planning and adherence to best practices, health sciences organizations are poised to gain timely access to business-critical information that is personalized, relevant, and actionable — enabling them to elevate performance and innovation across their enterprises.

About The Author
Neil de Crescenzo is the senior vice president and general manager for Oracle Health Sciences, where he supports Oracle's business unit dedicated to delivering integrated end-to-end solutions that help health sciences organizations to accelerate discovery and delivery of safe and effective products to the market. Previously, he directed the healthcare units at IBM Corp. and Perot Systems.