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Case study

The challenge

A global biotechnology company needed a way to sift through extensive notes taken by its patient services division to improve customer care and patient outcomes. The company wanted to extract meaning from the call notes by analyzing the data to answer key questions on factors that influence patients to continue treatment. The company partnered with Cognizant to gain insights into what motivates patients to start, discontinue and switch their use of medications.

Our approach

Working with the company to understand its products, patients and business needs, we identified the words and phrases of greatest interest within its case notes and built the ontologies and taxonomies required to train an artificial intelligence application to recognize this content. Our life sciences technology experts and the company applied machine learning and natural language processing (NLP) to years of unstructured, free text notes. To more effectively share the findings with client stakeholders and senior leadership, we created a 40-page narrative that presented our results in an understandable and actionable format. 

Machine learning, NLP boosts patient outcomes and the business

The insights from machine learning and NLP improved patient support, increased the odds of patients properly taking medications and identified roadblocks that caused them to stop treatment. The company created new key performance indicators (KPIs) for business processes, workflow improvements and coaching for improved patient engagement. Next steps include more complete documentation of the insights, training in documentation techniques and exploring how this approach could improve other functions, such as sales and marketing.

Uncovered

30 meaningful insights and nine key recommendations

Partnered

with stakeholders to create taxonomies and ontologies

Developed

KPIs to monitor and encourage actions that maximize patient wellness and drug sales

Improved

training for patient services staff by creating better documentation and increasing the focus on patient interactions