The driving force behind modern-day innovation – from medical discoveries to technological breakthroughs – is the data behind the decisions. While most data is collected in a strictly regulated clinical environment, many companies, organizations, and research institutions have begun to integrate real world data, or real world evidence, into their processes.
What is Real World Data?
Real world data generally refers to data collected in the medical or pharmaceutical field. Per Eye for Pharma, “Equivital’s definition of RWD states that ‘real world human data refers to subject orientated data captured without interference or bias (effectively noise) from the environment and methodology of the data collection itself.'” While this is the simplest definition, real world data is much more expansive, referring to their unique design of integrating multiple sources.
Per the Network for Excellence in Health Innovation, real world data “is seen as a way to tailor health care decision-making more closely to the characteristics of individual patients, and thus as a step towards making health care more personalized and effective.”
While the use of real world data is mostly kept to the medical sector, the theory can be carried over into any organization using big data. Yet, with any new process comes challenges.
Real world data provides a unique way to conduct research and perform analysis from differing points of view. Simply put, it provides an “out of the lab” perspective that most curated data lacks.
Per NEHI, real world data has the ability to “weave together different sources of data, such as clinical data, genomic data and socioeconomic data, to yield a better picture of individual patient characteristics and improve medicine’s ability to treat individual patient needs.”
- Data Quality
Curated, enriched, or appended data is extremely useful on its own. Unfortunately, this is not the case with real world data. Real world data acts much like raw data and requires absolutely no clinical regulations and therefore must be paired with a set of controlled data for an accurate analysis.
If real world data is not subset with curated data, scientists or IT analysts must rake through and clean the data for inconsistencies. In the end, the validity of research based solely upon real world data is extremely lacking.
The cost of real world data presents a double-edged sword. While the cost of collecting real world data is oftentimes cheaper due to the lack of curating, in order to use real world data you must create or purchase a second set of controlled data. Therefore, depending on how the second set of data is acquired, using real world data at this point is oftentimes more expensive.
In the end, real world data is truly an innovative idea in the stages of maturity. While there are various advantages in using this type of informative palate, there are still challenges that must be overcome.
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