Today, opportunities for innovation in life sciences often lie in the analysis of data beyond its primary use. New technologies and policies are beginning to improve access to, and the analysis of, this data while ensuring protection of individual privacy.
It is a common mistake to assume that value of big data lies in the data itself, for example; the volume, accuracy and accessibility of the data. In reality the ‘bigger’ the data, the less this holds true. Even with high-quality data, it is not possible to leap straight to business value.
Key client issues
Life science companies typically understand what interventions work for which patients at what cost. Internal and externally generated data must now support more complex applications.
There are a number of challenges that make it difficult to fuse vastly diverse data sets together with the aim of improving patient outcomes. Healthcare data typically resides in silos (see box). By making use of all of these incomparable datasets, the industry can greatly improve patient outcome analysis.
Another challenge is using data for secondary and tertiary analysis. For instance, administrative data is collated primarily to account for services rendered and collect payment. Electronic Health Record (EHR) data helps track patient progress, treatment and clinical status. When this data is used to measure quality, outcomes, and used for real-world evidence (comparative effectiveness, cost reimbursements, behavioural analysis and so on), then the original use of the data must be acknowledged. This is because it may be a potential limitation and may compromise the reliability and validity of any resulting conclusions.
This secondary and tertiary analysis is often performed in a data warehouse where all contextual analysis is removed. This means the results are degraded and links are highly suspect. Unstructured content is neither stored nor searchable against the structured information, creating an inability to link and correlate information.
However, the most significant challenge of collecting and analysing healthcare data is that much of it is unstructured, poorly stored, retrieved, queried and viewed. The content is spread across multiple data models, systems and data sets.
Accelerating awareness and understanding of big data is critical to increase public and private investment in the short-term. The pharmaceutical industry has a wealth of experience and insight that will be key to realising its potential. Initiatives to grow awareness across the ecosystem will help different organisations find ways to engage around big data for mutual benefit.
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