Real-Time Impact: The Value of Analytics in Healthcare and Insurance by Divya Chockalingam

In a data-driven world, as entities are being inundated with data, healthcare and insurance enterprises are increasingly turning to real-time data analytics for quicker decisions, improved outcomes, and enhanced efficiency.

Engaging with these transformations is Divya Chockalingam, an analyst whose work is helping organizations tap into the full potential of data analytics and AI-driven tools.

Through her work, we can see some examples of where enterprises are increasingly implementing data predictive analytic and AI tools to get the desired results.

Throughout the length of her entire career, Divya has been on both sides of the insurance-healthcare equation, guiding and executing analytics plans that enable more effective delivery of care as well as operational effectiveness. Her experience with predictive analytics initiatives in healthcare has led to the identification of patients at risk for chronic conditions-allowing for earlier interventions. In insurance, machine learning models have been applied to detect fraud and AI-based tools to expedite claims processing and accuracy, enhancing customer satisfaction and lowering costs. All this has increased efficiencies, service delivery, and cost-effectiveness in both areas.

"In one project," she recalls, "we developed a predictive platform that combined real-time data from electronic health records and other sources to flag patients likely to be readmitted." The result was a 20% drop in hospital readmissions within the first year of implementation, with the predictive model achieving 85% accuracy in identifying at-risk individuals, allowing for targeted interventions that improved patient outcomes and lowered costs.

Her experience in the insurance sector mirrors a similar commitment to efficiency and accuracy. By contributing to a project that deployed AI to automate claims processing, Divya helped reduce reimbursement turnaround time by 30% and cut operational costs by 15%. The shift not only improved internal efficiency but also raised customer satisfaction by 25%.

These insights came with their own considerations. One of the most pressing issues was data fragmentation, especially in health care. Patient data is often located in siloed systems, such as EHRs, labs, and even wearables. To make predictive analytics practical, Divya spearheaded efforts to standardize and bring these discrete sources together into one system. Combining them was a requirement to provide real-time data that could be used in real-time.

Equally important was navigating stakeholder scepticism. "There was understandable hesitation, especially from teams unfamiliar with analytics or AI," she explains. "People were concerned about disruptions to already established workflows." Her approach was to start small-with pilot programs that demonstrated concrete benefits. Once stakeholders saw the impact, adoption followed more readily.

The experiences in the field have given her a sense of the trends in the industry. She sees the growing adoption of AI and automation as inevitable but stresses that these tools must be used with human oversight.

She also points to broader trends that are likely to shape the next wave of innovation. These include blockchain for secure data sharing and real-time monitoring through wearable tech. Her advice to organizations? Invest in data governance, foster cross-industry collaboration, prioritize customer-centric design, and don't underestimate the value of change management.
Her work demonstrates how real-time analytics, when thoughtfully implemented, can be more than just a technical upgrade-it can be a step towards a more responsive and efficient system in healthcare and insurance alike.