Extracting Organizational Value from Your Data with NLP 

By Hywel Evans, Director, NLP, IQVIA

Life science companies gather and generate large amounts of data; however, this data often remains widely untouched, ungoverned, unstructured, and stored in disparate silos. In 2022 and beyond, life sciences organizations that uncover how to make use of their expansive data will be positioned to distinguish themselves competitively. To drive success, it will be essential to mine and connect data across their businesses making critical use of natural language processing (NLP) technology.

Hywel Evans

NLP converts unstructured data to structured formats from diverse sources such as field interaction notes, call transcripts, and internal reports as well as helping life sciences companies gain holistic intelligence about their business. This can be done as NLP leverages text mining to instantly search substantial amounts of data. By leveraging NLP to capture and connect information housed across siloed systems, organizations can automatically feed that intelligence into their workflows in real time and tap into their historically untouched data to better manage their organizations, gain deeper insights.

Addressing legacy data processes

There are several obstacles that organizations will need to address in their data collection processes to realize the full potential of their data. The amount of data collected is constantly growing and being added to siloed internal databases, often remaining widely untouched despite its critical role in driving clinical and commercial outcomes. This can be due to a variety of reasons.

In some cases where organizations have an excess of siloed and idle data, there can be thousands of individual data streams that are unstructured and thus unusable by typical analysis and reporting systems. For example, patient electronic medical records (EMRs) not only combine documentation from across the healthcare ecosystem, but data types from each visit can differ greatly. Some elements of EMRs may be in structured data formats such as lab results, while others like physician’s and nurses’ notes taken during patient visits are not, making them tremendously difficult to extract insights.

Often, EMRs will also contain a variety of complex medical vocabulary and synonyms that must be reviewed, consolidated, and manually entered into a database. This could skew the data and impact the appropriate care management, analytics, research, and reporting. Using NLP technology opens the opportunity to address all these challenges by embedding data at-scale to drive insight generation, decision-making, and innovation.

Extracting and understanding data insights

With exponential data growth, organizations are finding manual abstraction to be an unsustainable and infeasible strategy to integrate information across multiple business areas, leaving potential insights untapped, locked away in unstructured and semi structured formats. For life science and healthcare companies, gaining the full understanding of their data is a vital step to drive positive business as well as clinical outcomes.

These companies can create organizational value through leveraging the capability of NLP workflows to automatically process and normalize data from millions of documents and disparate sources, such as EMR extracts, call transcripts, interaction notes, internal reports — even news, congress information and social media. This helps deliver prepared data that will close gaps in existing knowledge, surface important context, and support better informed decision making. Additional benefits include freeing up time spent gathering and sorting information so organizations can focus on other key strategies and initiatives.

Enabling more holistic data use

Finally, with life science companies collecting vast amounts of real world data about patient experience every day, there is a critical call to action to tap into this unfiltered data to ultimately drive more patient centric care and improve patient outcomes. In unlocking the newfound ability to process and structure hard-to-abstract textual data, NLP presents the opportunity to conduct more holistic data analysis that is needed to support business-critical decisions. The organizations that consider the weight of these capabilities and implement a strategic approach to connecting their data assets ultimately position themselves to deliver better products that differentiate them in the competitive landscape, as well as improve the quality of life of the patients they serve.