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Q&A: How a hyper-automated AI ecosystem can help life sciences organisations effortlessly crack the data code

17 Oct 2023
The life sciences industry is in the middle of a digital transformation, with companies of all sizes racing to seek innovative solutions that will help them to stay ahead of the curve. One of the most promising solutions on the horizon is the concept of a hyper-automated AI ecosystem.
In an exclusive Q&A session with PMLiVE, Indegene’s Nitin Raizada and Vikas Mahajan delved into concept of hyper-automated AI, discussing the transformative power of this technology in life sciences, the many use cases it helps solve, and how automation on such a large scale can accelerate research, enhance precision and ultimately shape the future of healthcare. Here’s a summary of that discussion.
Can you break down what hyper-automated AI means and its practical applications in the dynamic world of life sciences?
NR: At its core, hyper-automated AI eliminates the laborious manual processes involved in extracting insights from data. It harnesses the power of AI/ML algorithms, leverages the efficiency of Robotic Process Automation (RPA) and taps into Natural Language Query’s (NLQ) intuitive language processing techniques, thereby enabling organisations to scale up their data processing capabilities and access actionable insights in a fraction of the time it would typically take.
Would you say this capability is good to have, or a must-have, in the AI toolkit?
VM: Hyper-automated AI is undeniably indispensable for the life sciences industry. Companies face significant challenges in collecting and analysing vast amounts of data from various sources to provide personalised experiences for physicians and patients. The rate at which data is being generated is staggering, with the healthcare industry alone contributing to approximately 30% of the world’s data volume. By 2025, it is projected to grow at a compound annual growth rate of 36%. Coping with this data deluge, without a framework in place, is like drinking water from a firehose! And that’s where hyper-automated AI steps in.
Could you walk us through how a hyper-automated AI ecosystem works to extract patient insights?
VM: Firstly, by using prebuilt connectors, hyper-automated AI enables the automatic extraction, transformation and ingestion of data from various sources such as electronic health records, claims data, patient surveys, social media and sales databases. This unified approach creates a comprehensive picture of your patient and prescriber data estate.
The collected data then undergoes automatic preparation and cleansing. Duplicates are removed, formats are standardised and missing or erroneous values are addressed.
Pre-configured algorithms and AI models are subsequently deployed to uncover hidden patterns, correlations and trends within the data. This automated analytical process swiftly analyses vast volumes of information, revealing crucial insights previously hidden within the noise.
The system also generates curated analytics-ready data sets tailored for specific purposes, providing easily accessible information aligned with the team’s objectives. During this time, continuous integration and deployment of new data and insights are carried out, ensuring the team stays at the forefront of knowledge and can respond swiftly to emerging trends.
RPA-powered bots, or auto FTEs (automated full-time equivalents), automate repetitive, routine and rule-based tasks, freeing up human experts to focus on more complex endeavours. These tireless assistants excel at gathering and organising data, generating reports and even performing basic analysis. Teams also have user-friendly access to insights through NLQ interfaces. Users can ask questions using natural language, much like conversing with a knowledgeable assistant. The AI-powered system understands intent and retrieves relevant information promptly, transforming complex data into easily digestible insights.
This end-to-end fully integrated framework is governed through an MLOps workflow, which integrates machine learning models into the company’s infrastructure to always ensure smooth functioning, monitoring and maintenance of the models.
How would patients and prescribers be positively impacted by a fully automated and streamlined workflow of this magnitude?
NR: By monitoring and analysing patient data, the system can identify non-adherent patients within a specific cohort. Once non-adherent patients are identified, the AI system takes action by instantly triggering automated reminders and personalised messages to patients through their preferred communication channels, such as mobile apps, text messages or emails. It can also notify healthcare providers (HCPs) about patients who require special attention, enabling proactive outreach and additional support as needed.
Life sciences companies can also stand to benefit an equally greater deal with an intelligent hyper-automation-driven environment. They can achieve high levels of efficiency and cost savings. It helps them streamline the entire process from data ingestion to data processing to insights generation, potentially reducing the associated time and costs by up to 80% and delivering faster results, sometimes up to ten times quicker – all while the system continually retrains itself to perform even better, ensuring ongoing improvement and optimisation.
Vikas Mahajan is Senior Director of Data and Analytics and Nitin Raizada is Vice President, Enterprise Commercial Solutions, both at Indegene

Author

Vikas Mahajan

Senior Director, Data and Analytics

Indegene

Vikas Mahajan
Nitin Raizada

Vice President, Enterprise Commercial Solutions

Indegene

Nitin Raizada