By Jane Z. Reed, Ph.D., head of life science strategy, Linguamatics

The process of getting a new drug through development and out into the market for patient use is known to be slow and expensive. In fact, a March 2016 analysis by the Tufts Center for the Study of Drug Development found the average cost to develop a new drug was over $2.5 billion. Furthermore, market approval is not the end of the process for drug companies. In order to maintain market access for a product, companies must continue making investments to demonstrate the value of drugs to patients, health care providers, payers, and regulatory authorities.

Real World Data (RWD) is essential for understanding the benefits and risks of a drug product after regulatory approval. The FDA defines RWD as any data relating to patient outcomes gathered outside rigorous clinical trials, and Real World Evidence (RWE) as the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD.



RWE can shed light on real-world clinical effectiveness and on the safety profiles of products across a broad patient community, as well as provide better insights for disease epidemiology, assess patient-reported outcomes, understand product reputation management, and engage opinion leaders.

Pharmaceutical and biotech companies need RWE to understand the impact of their new products. RWE can be used to update labelling if necessary, to understand long-term effectiveness, to further demonstrate safety, tolerability or outcome superiority, or to identify subpopulations where products work better.

RWD and the free-text challenge
There are many sources of RWD, including EHRs, adverse event reports, social media, and customer call transcripts. Much of the information is hidden in unstructured text, which often provides a level of detail and granularity not available from the structured fields. Information in free text can be hugely valuable, but extracting the buried data and mapping these to standards is a challenge.

Artificial intelligence technologies such as natural language process (NLP)-based text mining provide a solution to transform unstructured data into actionable intelligence for decision-making.

NLP-based text mining: transforming RWD into actionable RWE
With NLP-based text analytics, users can extract key details from unstructured documents using relevant ontologies and focused queries. For example, queries can be written to extract information on treatment patterns to identify drug switching or discontinuation. Numeric-based queries can search for lab values and dosage information, as well as patient-specific details such as history of disease, problem lists, demographics, social factors, and lifestyle. Ideally the technology is flexible enough to apply different business rules based on particular data sets, such as sentiments from tweets, or outcomes and treatment patterns from EHRs.

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