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An “how to” primer for life sciences companies on applying end-to-end evidence strategies in demonstrating product value.
As the shift to value-based, personalized healthcare continues, life sciences companies will need to move product conversations away from marketing-centric messages to those that highlight evidence of efficacy as well as extending the value proposition to include services “beyond the pill.” To thrive in this “facts-beat-marketing-hype” future, companies must embrace a new operating model based on end-to-end (E2E) evidence management across the entire product life cycle.
The shift to outcomes- and value-based healthcare, along with more personalized therapies, is increasing the importance of end-to-end (E2E) evidence management capabilities in product development, marketing, and distribution. In the past, a value proposition was driven by marketing campaigns, claims-based messaging, and scientific sound bites. While clearly based on fact, the value proposition did not highlight the evidence dimension or, if it did, make it central to the measurement of outcomes in the real world. The new market reality is to require empirical evidence to be positioned at the heart of the value conversation along with patient services “beyond the pill,” rather than on marketing profiles and unit cost and rebate pricing strategies.
Given this shift, how should companies manage evidence differently throughout the product life cycle and across the various channels of the healthcare ecosystem? This strategy analysis examines the imperative for E2E evidence management for a “facts-beat-marketing-hype” future. Specifically, this article:
E2E evidence management: Making the case
Reimbursement in the healthcare market is shifting toward rewarding value instead of volume; this is also true for pharmaceutical interventions, as seen in an increasing number of outcomes-based contracts in the sector. As a result, life sciences organizations are adapting their product value conversations with payers and other commercial stakeholders to concentrate on evidence around product efficacy, safety, and economic value as well as offering beyond-the-pill services to deliver a more robust value proposition to their customers.
Additionally, researchers are targeting more specific disease states based on a growing understanding that patients comprise more than a mass of generic diseases and can be broken down into smaller, defined groups based on
biomarkers, comorbidities, or treatment pathways. To succeed, these new approaches to drug development require a greater volume and variety of evidence.
To mark an effective response to these changes, pharma companies should consider an E2E evidence management strategy (see figure above) as one of the key ingredients to identify targeted breakthrough innovations, rapidly get the new products through development, and successfully launch and market them in a value-based care environment.
An E2E strategy will assist organizations in becoming more insights-driven so that decision-makers can act on the basis of rigorous, data-based insights rather than general strategy. Implementing processes to capture and analyze data and to leverage and apply that data for meaningful and sustainable results is a necessity, for a number of reasons: these processes result in better data
transparency and utilization; provide increased availability of insights to a wider group of people; facilitate less expensive, targeted clinical trials; accelerate product approvals; enable on-target market access strategies; create comprehensive, real-world effectiveness analyses; and help to support new customer engagement models. Ultimately, in the new health ecosystem, evidence is at the core of decision-making, and a strong evidence base will produce more productive conversations with customers and other healthcare stakeholders.
In Figure 1, we divide the E2E evidence value chain into three components: discovering value, generating value, and optimizing value. During the drug development process, companies should first make portfolio decisions by “discovering value” through clinical trials, then move to “generating value” through product approvals and compliance and, ultimately, “optimizing value” through commercialization and market access.
During early-stage research, evidence management can help an organization discover value by enhancing pre-clinical and clinical research productivity through precise target and patient cohort identification. As concepts mature into products, evidence management may enable an organization to generate value and maximize a product’s potential for clinical and commercial success by leveraging traditional clinical trial data and RWE to segment patient populations for optimal therapeutic response and safety. Finally, when therapeutic interventions are approved and launched, companies can optimize their value by assessing market dynamics to support pricing and market
access strategies, and by applying RWE to develop, support, and sustain a compelling value story as the products are utilized in a healthcare environment.
A strong evidence strategy is cyclical, so the evidence used to optimize product value should also be filtered back to inform new opportunities for therapeutic discovery and development. Conversely, the pace and disruption of scientific innovation also means that data and insights from the early part of the evidence life cycle should be rapidly leveraged to modify and anticipate shifts in market behavior.
E2E evidence strategies and capabilities are no longer a “nice to have;” they are a necessary business component to develop, launch, and sustain successful products. Therefore, the imperative for new E2E evidence strategies and capabilities has implications for technology investments, organizational constructs, and new business models, including external partnerships.
What are the essential elements of an E2E evidence management strategy?
Developing and implementing an E2E evidence management strategy requires a level of organizational maturity and insight to move beyond the traditional view of product life cycle management, break down the resulting silos, and incorporate important functions such as strategy, governance, external collaborations, new informatics approaches, and talent management. Evidence management also calls for reducing or eliminating the walls around patient data throughout the R&D process by establishing new platforms that enable end-to-end views of data and analytics centers of excellence to support the new paradigm.
Effective evidence management can help to shave off cycle time and dollars from the product development process by informing clinical trial protocol development, thus helping to justify a company’s investment in a certain asset that could impact a new disease area or, conversely, supporting the decision to forego development of a particular product because the evidence indicates low potential for economic success. In addition, an E2E strategy can help increase on-patent revenue generation and keep organizations competitive by being “first to market” and thus contribute to return on investment.
The essential components of an E2E strategy can vary by organization-and are likely to involve and impact people, processes, and technology across multiple internal and external touch points. Determining specific needs calls for an assessment of core capabilities such as infrastructure, governance, talent, R&D, and commercialization.
A few essential elements are necessary across all organizations for an effective E2E evidence strategy:
How can an E2E evidence management strategy support an insights-driven organization?
Often, R&D and market strategies are driven by a new technology innovation (e.g., cognitive computing) or a promising data type (e.g., genomics, social media). However, the journey to becoming an insights-driven organization and achieving the promise of E2E evidence management requires alignment and execution across the key components of strategy, people, process, data, and technology (see figure below).
Some of the questions organizations need to answer about these components include:
Strategy: For the patient populations we serve, how will changing science, reimbursement models, and patient behavior impact the need for generating evidence to inform product design, patient care, and overall business decisions?
People: Do we have the right talent to make sense of the new types of healthcare information available to us? Who is the accountable business owner for this information? What organizational structure do we need to put in place to support our analytical strategy? Who do we need to engage in other departments and what are their roles? What other talents do we need and what is the plan for acquiring them? What competencies should we source from external partners?
Process: How do we ensure that insights are shared across traditional organizational silos? What are the steps we need to take to make these projects a success? How will we comply with relevant regulations? How do we fund and measure the impact of these investments and new operating models?
Data: What data do we need to answer today’s questions? How do we source data through novel collaborations that our competitors can’t access easily? How do we bring data together and what are the challenges in transforming, linking, and publishing it? How can we improve data quality and accuracy?
Technology: What technology architecture supports the new needs of expanding sets of data? What tools do we need to process the evidence? What security measures do we need to consider in federated models of data collaboration? How do we scale up the technology when we need to roll out our solution to the rest of the business?
The process of implementing an E2E evidence management strategy may be complex, and time- and resource-intensive, but the innovation, efficiency, and commercial benefits may help to justify the investment. Moreover, with the right insights, E2E evidence can become a major source of competitive differentiation.
Using evidence across the development life cycle: Five hurdles
There are five dimensions of complexity that may make it difficult for life sciences companies to successfully execute an E2E evidence management strategy: disease state complexity, domain or functional complexities, geography, regulations/reimbursement, and operations (see figure below). Each category has implications for a company’s business strategy, operating model, and requisite IT and informatics platforms.
Disease state complexity: Understanding and mapping the patient journey for a chronic disease such as diabetes requires different data sources, data sets, informatics approaches, and external collaborations than the patient journey for a rare cancer. As the scientific understanding around a disease evolves, changing definitions, dependencies, or new classifications can result in data and informatics challenges as well as talent and training implications.
Domain complexity: Exacerbating disease state complexity is the fact that an E2E strategy touches every functional domain group in the product life cycle. This means that the design and implementation of every process -from the way partnerships are established, to regulatory compliance, to how third-party data sets are procured-will need to consider the specific needs of the functional groups that will use the data sets.
Geography: Evidence gathering, management, and measurement requirements can vary dramatically by geography. For example, the health technology assessment (HTA) process in many European countries has different evidence requirements than the process for US health plans and pharmacy benefits managers (PBMs). Additionally, data analytics have uncovered major differences in care patterns across and even within countries.
Regulatory: Health information is highly regulated by local governing agencies. Researchers cannot always use health information for secondary research, transport data outside local borders, or approve a product without local research samples. Even when it can be transported and aggregated for analytical purposes, the integration and semantic challenges inherent in healthcare information can present regulatory complications around its use and analysis.
Operational: Operational considerations include the ability to integrate data sets and understand the appropriate resources for the necessary analytics as well as tactical issues around data quality and data access. If the necessary problem-solving capabilities do not reside within the organization, an external partner may provide valuable support. This can impact technology infrastructure and external partnerships.
The combination of these five dimensions of complexity and inherent IT and informatics challenges negates a one-size-fits-all approach to developing and successfully executing E2E evidence management. Rather, the process is multi-dimensional and requires an enterprise-wide strategy, operating plan, informatics and data platforms, and cultural shift.
The slow pace of fast disruption
Some life sciences companies may choose to opt out of an aggressive, E2E evidence management strategy, citing that similar assertions about disruption have been overstated in the past. By doing so, they may face risks, including poor decision-making around capital and asset allocations, a less efficient product life cycle, and failure to realize a product’s full market potential and, possibly, the opportunity to develop the next life-saving medical treatment. This is true because of the perfect storm of reimbursement changes, scientific advancements, significant cost and quality pressures on healthcare, the proliferation of healthcare information, and the IT advancements (security, cloud computing, big data, analytics) that make pursuing such a strategy feasible. As a result, E2E evidence management is becoming a core business capability-companies that are unable or unwilling to embrace this new paradigm may experience significant disruption and bottom-line impacts.
Leveraging data to demonstrate value: Questions to guide your strategy
An E2E evidence management strategy provides a cohesive approach to leveraging data in new ways to rapidly translate breakthroughs into clinical practice as well as demonstrate and understand the value of those innovations in real-world settings. The following questions may be important for an organization to ask in order to start the journey to an E2E evidence strategy:
Staying the course
The life sciences industry and larger healthcare ecosystem are constantly adapting to better meet the needs of patient health and well-being. This article presents a moment-in-time snapshot to illustrate a “good” place to be in the evidence management space; but, like the industry itself, the concept of “good” will evolve.
As technology, regulatory compliance, and market demands change, so will a company’s evidence strategy; therefore, that strategy should always include elements of continuous learning and continuous improvement.
Terry Hisey is a Principal, Life Sciences and Health Care, at Deloitte Consulting LLP. He can be reached at firstname.lastname@example.org
Brett J. Davis is a Principal, Life Sciences and Health Care, at Deloitte Consulting LLP. He can be reached at email@example.com