August 8, 2022

Key Value Drivers for Life Sciences Analytics: Exciting Takeaways from the WhizAI Partner Summit 2022 - Part 2

Bijal Karande
Bijal Karande
Key Value Drivers for Life Sciences Analytics: Exciting Takeaways from the WhizAI Partner Summit 2022 - Part 2

In his presentation at the WhizAI Partner Summit 2022, Rahul Karkhanis, Commercial Specialist, WhizAI discussed interesting use cases of our solution while the technology-oriented presentation was led by Amitabh Patil, Co-founder & CTO, WhizAI and product management. They highlighted the key differentiators of WhizAI’s domain-specific augmented analytics solution for life sciences. 

WhizAI at Work in Other Life Sciences Analytics Use Cases 

Following the client presentation, Rahul Karkhanis, commercial analytics subject matter expert at WhizAI, shared details of two additional use cases in which WhizAI is providing value today:

Patient-level claims data

Rahul said there are numerous sources of patient-level claims data, from historical NBRx and total prescriptions to opportunities and barriers to patient treatments. He added companies can also consider geography, specialty groups, or customer tiers and segments. “The volumes of the data are huge. We are talking about a billion to five to 10 billion records,” he said. “The data is also complex because it comes from different touch points within the patient journey. So, you need to be able to analyze it using domain-specific information.”

“You also need to be able to slice and dice it to the most granular level. Maybe starting at the national level, going down to the region, district territory, all the way to the physician, as well as the anonymized patient or the claims level,” he added. 

Rahul said one global pharmaceutical company had purchased longitudinal patient-level claims data. The company utilized other analytics solutions vendors and created dashboards, but those solutions were not user-friendly. Additionally, dashboards required some manual work after they were created, so the organization couldn’t democratize data insights. 

With WhizAI, the company was able to compare its trend for claims to competitors to create a clearer picture of why NBRxs were on the decline. “But we didn’t stop at this, we went to the next level. We wanted to know why rejected claims were going up and the rejected claims for the competitor were coming down,” he said. 

“But think about this for a second. Just creating this simple chart may take you a few days or weeks to put together with a dashboard solution, trigger this question, and be able to answer it. WhizAI could do it in a matter of a few seconds with just a few clicks.”

Data Set Exploration for Data Scientists

Rahul said with the right insights, life science companies can uncover many business opportunities. They can estimate the market based on traditional TRx volume as well as diagnoses, procedures, and the volume of prescriptions for each and how they compare against specific disease states. 

The multiple data sources, while containing a wealth of information, also present a significant challenge. “In one particular case where we deployed WhizAI, they had 300 plus data sources, and they did not have any understanding of the underlying data within.”

“Once we deployed WhizAI on top of their data sources, they were able to answer many questions, such as the number of patients by diagnosis, by dataset,” he said. “You could also look at age or even race and ethnicity distributions in each data source. This helps the data scientist as well as the clinical researchers tremendously because they are able to do all these analyses using a simple question and answer method, as opposed to running those SQL queries, and then getting the answer for each of them.” 

With WhizAI, life sciences analytics teams saved time and worked more productively, and the company saw an increase in data analytics ROI. 

What Makes WhizAI Different from Other Life Science Analytics Solutions 

The event ended with Amitabh’s presentations at the Partner Summit focused on the technology that enables WhizAI to stand apart from its life science analytics competition.

First, the platform stack is built from the ground up with AI components, including a natural language processing (NLP) engine. It consists of natural language understanding, which allows users to ask questions in natural ways, and natural language generation, which enables the platform to summarize key insights for users. WhizAI is pre-trained for life sciences, Amitabh explained, “So you can leverage these pre-trained models to be able to answer questions directly without having to train anything from scratch.” 

Additionally, the NLP engine is a hybrid technology combining deep learning and linguistic techniques. Unlike other systems based on keyword or search mechanisms, WhizAI allows users to ask questions in natural language. It understands those questions and responds with a grammatically correct answer that users can understand. Moreover, WhizAI uses a relevancy model that chooses the right components from the data, so users receive the precise information needed to answer their questions. 

WhizAI is also designed to automatically select the right visualizations, whether bar charts, pie charts, trend lines, or other options, based on the question and the data sets. And, each time a life sciences team member asks a question, WhizAI learns. It also begins to anticipate the next question the user could ask and can provide that information proactively. 

The platform also includes an insights engine for anomaly and outlier detection for predictive analytics and root cause analysis. Furthermore, most life sciences companies have their own data science teams. WhizAI enables “bring your own model,” allowing teams to operationalize their own data models using WhizAI natural language and visualization interfaces. Companies can then make insights from those models available to users on the fly. 

“That’s how we are able to answer the questions in a better way, and also be able to generate more relevant and domain-specific insights”, he said. “If you combine all this, it's a fully integrated solution.” 

WhizAI’s Partner Summit was filled with the most recent information on life sciences analytics and AI’s position on the cutting edge. To learn more and get connected with WhizAI’s partner or user community, contact us

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