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Home | Newsletter | UPDATE 2 | 2017 | Real World Data — An Innovation Boost for Life Sciences
July 19, 2017

Real World Data — An Innovation Boost for Life Sciences

Progress in health care depends on increasingly complex questions. Demands on efficiency and effectiveness, safety and quality of medication and treatments are increasing every year. At the same time, information systems in the health care industry are opening new points of access to massive amounts of data, the use of which was impossible just a few years ago. The potential for exploitation of these resources lies in the access, linking and analysis of the data.

Real World Data (RWD) are health data collected under real everyday conditions. They can include all the data that go beyond what is collected in phase 3 clinical trials. Through analyses, Real World Data become Real World Evidence (RWE).

 

Real World Data

 

What are the potentials of Real World Data?

RWD provide valuable additional information to the pharmaceutical industry and its associated players such as manufacturers of medical devices, authorities, health insurance companies, and clinics. The application fields range from the identification of behavioral patterns, the assessment of safety and effectiveness of treatments, the evaluation of the costs of certain diseases, and risk-benefit assessments to complex diagnoses and statements on the prevalence of certain diseases.

For pharmaceutical companies, RWD are useful for defining priorities for balanced clinical product development. The incorporation of RWD is also worthwhile in the planning of market launches. In addition to the randomized clinical trials (RCTs), pragmatic results from clinical practice can be considered and possibly reduce runtime and costs.

For diseases with no or only limited therapeutic possibilities, RWD can accelerate the earlier approval of medicinal products via adaptive development paths despite limited data.

 

Areas of application of RWD

The recruitment of study participants is simplified by RWD. Clinics can automatically search their collected patient data for suitable candidates. In addition to patient recruitment, RWD also help with the selection of suitable locations and scientists to carry out studies.

Smartphone apps support the patient in surveying, capturing, and transmitting data. Devices that continuously record health and activity data from individuals (so-called “wearables”), provide ample opportunities to better understand patients in a location-independent manner and to pose a variety of therapeutic questions.

The potential of the wearables for data collection is demonstrated by a statistic from the National Institutes of Health (NIH): By the end of 2015, around 300 clinical trials in which wearables were used had already been registered.

The new health care technologies also attract companies outside of the health care industry that provide tools for the use of wearables in studies. For example, with ResearchKit, Apple provides an open-source framework for creating mobile applications to help researchers gain robust and meaningful data. With 2net™, Qualcomm provides a platform for the secure storage and retrieval of continuously collected data from various sensors.

Testing questions, evaluating technologies, identifying business opportunities, and assessing their feasibility with proof of concept studies – the dynamic developments in the area of digital health require that pharmaceutical companies be agile and ready to use trial-and-error methods. Bayer, for example, has sponsored more than thirty projects over the past two years in the field of digital health with its Accelerator program Grants4Apps­®.

In addition to the data transmitted via smartphones and wearables, extensive data sets can also be obtained using sophisticated data extraction tools from social networks, internet forums and blogs, which are searched using the names of active substances. For example, Facebook and Twitter can help determine what patients are most worried about. Twitter has proven to be particularly beneficial for tracking unwanted drug side effects or identifying high-risk patients for HIV. Facebook is already communicating with health professionals and research institutes to develop common health care applications.

 

In some cases, the benefits of RWD are already undisputed

What are the opportunities and risks of using RWD for pharmaceutical companies and patients? In the benefit assessment of Phase 3 studies, expert opinion is still divided. In some cases, however, the benefits of RWD are already undisputed. Entirely new business models emerge based on RWD. The American Food and Drug Administration (FDA) is also quite positive about RWD and is taking steps with the “21st Century Cures Act” to involve RWD in regulatory approvals.

RWD are increasingly used by the FDA for post-market surveillance measures. They help validate the statements derived from clinical trials to justify prices to sponsors, and to uncover and correct deficits in the safety and effectiveness of medicines. Market tracking can reveal untapped business opportunities and growth potentials using the data, analytical methods and technologies that are now available. For example, integrated solutions (active substance and device) can be offered. RWD help pharmaceutical companies position themselves on the market. They enable precise targeting, the definition of target cohorts, and can improve market access.

 

Practical use of Real World Data

Innovative solutions are emerging for the challenges that arise in the collection and use of RWD.

Solid methodological knowledge is necessary for the preparation and evaluation of Real World Data so that data from different sources can be processed and evaluated. The conversion of RWD into vectors, which can be regarded as the fundamental raw material of RWD-based data science, is extremely time-consuming. Innovative approaches such as phenotype vectors promise dramatic progress in output, productivity, and findings that are based on machine learning.

A comparison of the results from traditional randomized controlled studies with the patient’s daily routine in “real life” requires the most complete routine data possible. Today, Philips already collects petabytes of data from a large variety of sources, directly at the point of care or even in the private patient environment, to offer new, user-friendly services on health platforms. In addition, visited web pages, search queries, social media activities, and geo data provided by mobile devices provide information for building complex personality profiles. Here, the issue of data protection must be handled very carefully.

 

Real World Data

 

Conclusion: Becoming active

RWD are becoming more and more essential in many fields of application. Even if data collection, linking, analysis and transformation into new solutions presents challenges, it is already becoming clear today that the multitude of benefits exceeds the costs. Reason enough to lay down the foundations and actively exploit these opportunities!

Dr. Jan Beckmann, Andreas Hock


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