Build a Global Evidence Generation Strategy

In the field of life sciences and healthcare, one of the paramount objectives is enhancing patient outcomes through evidence-based practices. The key to achieving this goal lies in the intelligent utilization of data and many organizations have embraced the concept of creating a global evidence generation strategy to enable the best results.

Use of evidence-based methods has been part of research and innovation in healthcare for some time. By leveraging data-driven insights to shape treatment recommendations and facilitate drug approvals, stakeholders have the tools to make well-informed and confident decisions. Moreover, the strategy’s implementation has led to accelerated research outcomes.

However, as with any approach, certain challenges persist. While the methodology has excelled in generating results, it does not always cater to the intricacies of the global population it intends to serve.

Consequently, we are left with an intriguing question: How can the industry effectively harness this powerful methodology in the face of real-world data gaps for underrepresented global populations?

What Exactly Is an Evidence Generation Strategy?

An evidence-generation strategy is an approach used by life sciences and healthcare institutions to build a unified body of evidence, which collects and synthesizes data from multiple evidence sources, to enable more informed decision making. For the strategy to produce the desired outcomes, it most likely needs to incorporate data from beyond what’s readily available in the organization, unifying real-world data (RWD) with data collected from randomized clinical trials (RCTs), patient-generated data, population research, and other data sources.

The Importance of a “Global” Evidence Generation Strategy

For life sciences stakeholders, including medical researchers and drug developers, using an evidence generation strategy requires tapping into rich, diverse data. In order for research outcomes to apply to population health at large, the data must also include global representation.

Unfortunately, RWD that fully represents global populations is often in short supply. This lack of diversity in available RWD has been a concerning gap for some time ,as documented in a research paper published in the BMC Medical Research Methodology journal describes the following key challenges regarding real-world data accessibility and why they limit evidence generation strategies:

Data quality: RWD can lack standardization, be missing critical endpoints, and be too homogeneous.
Explainability and interpretability: Researchers sometimes use machine learning algorithms in RWD analysis, often delivering only trial-and-error results. There is often little to no clarification regarding the relationship between inputs and outputs or causal effects alongside these results, which can limit the effectiveness of evidence generation.
Reproducibility and replicability: Scientific research depends on these two principles. Failures here often occur because RWDis not robust. Incomplete and unstructured data are the primary issues in achieving reproducibility and replicability.
Diversity, equity, algorithmic fairness, and transparency (DEAT): The primary challenge that affects the ability for evidence generation to be global is DEAT. Most real-world data contains bias and imbalances due partially to lack of diversity.

These challenges keep evidence generation from being global in nature. However, the life sciences ecosystem is exploring new ways to address these gaps to drive progress with real-world evidence.

With more global real-world data, researchers can produce a more comprehensive understanding of how a product would affect the lives of patients, including its safety and efficacy. Organizations would also have more insight into a product’s economic value to patient subpopulations.

Organizations looking to implement an evidence generation strategy must consider these data limitations throughout the process.

Key Elements in Implementing the Strategy

Successfully deploying an evidence generation strategy requires following a series of steps:

Identify and evaluate the available evidence related to the initiative, including where gaps exist.
Define the target patient outcome that accounts for unmet needs and demonstrates how the treatment will address those needs.
Prioritize evidence required to support the target patient outcomes by defining the resources desired. These become requirements for your project.
Analyze databases containing real-world data and evidence for populating diversity and completeness to supplement your existing sources. Post-analysis, seek out sources beyond what’s currently available to your research facility, such as clinical and genomic data from untapped global sources.
Outline the communication plan regarding how your team will deploy the strategy to ensure transparency and that the right evidence reaches the proper audience at the right time.

When implementing the strategy and going through these steps, you may very well conclude that you lack global RWD. No matter the patient outcome you seek to address, it’s possible that the most complete data has yet to be discovered.

Identifying Complete Sources of Data

Is it possible that there are clinical data sources yet to be accessible and available? After all, life sciences organizations have been leveraging data for decades to drive better population outcomes. Yet, researchers know they are barely scratching the surface regarding data sources. So, where are these untapped sources of data?

Global data is a critical pillar in building a proper evidence generation strategy. The connection between these two is significant. If data remains homogeneous, you must accept the risk of bias, and, as a result, outcomes may only apply to a subset of the population. In a research article, Evidence generation, decision making, and consequent growth in health disparities, the authors provide an example related to diabetes, one of the most common chronic diseases across populations.

The research revealed that programs to diagnose the disease early and promote lifestyle changes worked to decrease incidences for non-Hispanic whites. In this case, the evidence and data were partial to that group. While this group saw declines, non-Hispanic blacks and Hispanics saw the incidences continue to rise. The authors noted that the evidence-based methodology approach may have misapplied information from RCTs and needed more diversity.

This example is one of many that highlights the need for global data to fuel evidence-based strategies. The key to addressing information gaps to inform your approach is utilizing a data platform that expands your data sets to be more representative of all populations. Ideally, this data platform provides secure, centralized access to global real-world data sets.

Syntium, the Syndesis data platform, aggregates de-identified data from numerous healthcare organizations in many countries beyond the US and Europe. It augments currently available data sources with clinical data from representing additional global populations.

Life sciences and healthcare professionals can access this data by joining the Syndesis Health Network. Our member community promotes research collaboration, information sharing, and access to common tools and applications.

Broadening the availability of data in this way enables evidence generation strategies which inform better decision-making.

The Role of Evidence Generation Strategy in Decision-Making

Data, and the evidence generated from it, are the most objective guides in decision-making. Life sciences organizations can improve their decision-making process with an evidence generation strategy aimed at delivering targeted outcomes. Looking at the information flow in clinical evidence generation and practice, the connection of strategy to decision-making is clear.

Researchers initially make decisions (informed hypotheses) regarding treatment and interventions based on precedents and data sets. They then evaluate these decisions through clinical trials to generate real-world evidence of their performance. These results may include anomalies for underrepresented populations like those in the diabetes treatment example above.

Once identified, organizations often follow the steps of developing an evidence generation strategy, seeking more diverse data for inclusion. Based on this full range of analysis, decision-making regarding treatment selection in the real world is more accurate and beneficial to the entire population.

Without using an evidence generation strategy, the outcomes will likely only address the demographics contained in the data sets. This creates a disparity in recommended interventions that may not work for other groups as seen in our diabetes example.

Typically when an organization is trying to bring a new treatment to market in the US, they will present their decision-making and clinical trial evidence to the FDA (Food and Drug Administration). However, since 2017 there has been a trend of fewer clinical trials being required to support drug approval. In 2021, only 36% of approved drugs had two clinical trials to support them. Two decades ago, that number was 81%.

Several factors have created this trend, including the increase in drugs fast-tracked through the Accelerated Approval Program, which allows for earlier approval of drugs that treat serious conditions and fill an unmet medical need based upon a surrogate endpoint.

Another driver is that precision medicine has faster approvals with fewer and smaller trials than other medications, according to research that examined data from the FDA. The study found that 48% of precision medicines qualified for the FDA’s Breakthrough Therapy designation. This process expedites the development and review of drugs intended to treat severe conditions, and preliminary clinical evidence demonstrates that it is a significant improvement over current treatments.

Fewer clinical trials may also result from cost-cutting, as the average cost to bring a drug to market is $1-2 billion. The FDA approvals alone have a median expense of $19 million.

Does the future of drug development hinge on more real-world evidence? In some areas of medicine, the demand for evidence will be instrumental. One example is oncology drug development. Life sciences research invests billions into this yearly, even though the success rate is only 3%. These funds also don’t alleviate the challenges of clinical trials in cancer drugs.

Those working on these drugs cannot answer critical questions about what treatment is best for certain populations and how it will perform in the real world. Hence, the need for more evidence. With it could come a higher success rate and more context about the efficacy of the treatment.

Without evidence, enabling access, continued usage, and demonstration of patient benefit on a global scale will be impossible. If not addressed, the research community and the FDA may have skewed decision-making.

Your Evidence Generation Strategy Must Center Around Real-World Clinical Data

You have much to gain by adopting an evidence generation strategy. It focuses on specific outcomes and designing initiatives with a foundation of unified evidence.

Achieving an evidence generation strategy depends on real-world clinical data access that is international in nature. As discussed, these sources can be hard to find, even from leading global organizations.

You can overcome these challenges with Syntium. Adding these data sources to your evidence generation strategy gives you high-quality data from largely untapped global resources, including hospital networks. With Syntium, you can propel research and innovation further and address global needs and outcomes.

Learn more about Syntium and how it can support your evidence generation approach by contacting our team.