Expert view

edge:
What are you working on right now that will have an impact on our customers in the near future?

Teresa:
It is an exciting time to be working in the technology field and especially with a company like Sedgwick that is on the cutting edge of innovation. In the not too distant past, the industry was abuzz with talk about predictive analytics – which provides a way to identify key factors or triggers that historically indicate a claim will take a certain path – giving claims professionals and other experts an opportunity to intervene early if needed to ultimately ensure a better outcome.

We wrapped our advocacy approach into our predictive analytics model and we have seen dramatic results for consumers and customers alike. We believe we will continue to have great success in this area.

Although predictive analytics is still important, and will continue to be refined, we are also seeing a shift (and perhaps pushing for a shift) into prescriptive analytics.

edge:
That’s an interesting distinction. Can you define the difference between predictive and prescriptive analytics as they relate to data and customer service solutions?

Teresa:
Predictive analytics is based on data and triggers that provide insight into patterns that can be addressed early in the claims process to achieve a better outcome. Prescriptive analytics is taking that data and then prescribing the next steps to be taken on a claim in a more automated fashion, all while meeting the needs of the individual.

We can no longer take a one-size-fits-all approach to triggers and flags, but really must work to use prescriptive analytics to build a more personalized experience for the employee. This involves going beyond their immediate medical needs, looking at specific characteristics of the claim, and then addressing concerns early to help guide them toward the best possible outcome. To do that effectively, for the millions of people Sedgwick serves, requires smart technology and automation embedded within our advocacy approach.

edge:
Can you give an example of how this could work?

Teresa:
An example of prescriptive analytics in another industry that may bring this to light is the invention and deployment of driverless cars. The technology behind this automation allows cars to predict a set of possible scenarios and be able to make decisions effectively and safely. However, when a car is actually in use, it needs more than a set of predictions and triggers; it will need to appropriately react to each situation it encounters, including unpredictable human drivers in other vehicles. In essence, it is learning and prescribing the right approach to create the right outcome instantaneously. It isn’t retrospectively learning and adjusting for future scenarios; it is learning and adjusting in the moment to have a positive outcome right then and there.    

Because Sedgwick has the technology to aggregate millions of transactions in seconds and has the depth of millions of claims in our databases, we believe driving towards a prescriptive analytic approach is a near-term innovation and the right thing to do for our customers, consumers and the industry.

edge:
Why is Sedgwick investing in this next level of innovation?

Teresa:
It is all part of thinking differently and using data to help in terms of the customer experience. We are using technology to make the consumer the focus. The more information we have about the injured employee, the better we can serve them. I believe we can use analytics to determine the right path to help the person get well, return to work and have the best possible experience. 

With our advocacy approach, our goal is to center the process on the consumer and connect and speak with them in the way they prefer. We are building a different way of using data to drive the interactions; taking the data and really understanding the pattern of the consumer and how they want to engage. For example, people in different generations may have different needs for communication, interaction and information.

edge:
Have you done this already in specific verticals and what has the data shown?

Teresa:
The prescriptive model is on the horizon, but today we deploy predictive analytics very effectively. We have been working with customers to benchmark data on their employee population against industry data to help identify insights about performance. For example, we worked with the airline industry to gain a robust view of workers’ compensation statistics from the pilot’s view, the flight attendant’s view, the technician’s view and more. Customers can compare against the industry by state, cost, injury type, etc., and the tool helps us to easily visualize performance and results. These insights are simplifying the message, giving employers a clear picture of what they want to see and how they want to see it in a timely manner. The analytics are helping set expectations and, most importantly, we can use the data to take action in our processes. If there is a propensity for a certain kind of injury in a specific age group, we can use that data to tailor care for the employees. To be clear, we are mindful of privacy issues and always respect the restrictions on data that our customers request.

edge:
What else is on the horizon for prescriptive analytics and data solutions?

Teresa:
In addition to using our own data integration, we are looking at pre-loss criteria and applying the prescriptive analysis to a claim to avoid complexity and ultimately achieve a better outcome. We want to look specifically at the injured employees to see what we can do to prevent incidents that might impede recovery and enhance our medical programs to meet their individual needs. This includes developing better practices around resource management. We are also applying this approach to guide the claims escalation process. By using analytics to flag specific claims and address potential issues early, we are seeing marked improvements for our customers and consumers.