Joanne Richardson spent over 20 years working in health insurance across multiple countries for AXA, including Mexico, France and the UK. She spent five years as a Health Director in the Group head office in Paris, where she developed group strategic initiatives such as Care Coordination. She then worked in Group Risk Management, leading in-depth reviews of the major AXA health entities, helping to ensure long term profitable growth through International best practice.
She spoke to us about the key challenges facing insurers today, and how Sprout.ai helps solve them.
Sprout.ai: What relationship do health insurers want to have with customers today?
Joanne Richardson: Health insurers today have a clear desire to be more customer-centric. They aim to help people be healthy and stay healthy.
They want seamless quick claims processing, and then they want to be able to concentrate on adding additional value services to customers. That means giving them access to the latest medical innovations, providing them with support to manage chronic illnesses, such as apps for diabetes management, understanding their needs and having the time to provide dedicated support for complex cases.
This enables customers to live healthy lives and to return to health as quickly as possible.
Sprout.ai: And what relationship do health insurers have with medical providers?
JR: Many insurance health insurers have a wide network of medical providers. These providers want the right money to be promptly paid for the work that they’ve done.
Insurers want to be able issue timely, accurate payments. They want to establish a high quality medical network at an optimal price point that is more a partnership than a transaction.
Shareholders need to be considered here too. Health insurance is a small margin product, so profitable growth has to be done through efficiency, innovation, and a good reputation in the market.
Sprout.ai: What challenges are health insurers facing today?
- Legacy Systems
JR: As mentioned, providers want to be paid accurately and quickly. Insurers want claims to happen seamlessly and without problems. However, because of the way that many health insurance products are designed to fit state provision, they are managed on old bespoke systems. These are slow to change. They’re incredibly expensive to replace, and have an awful lot of manual intervention.
Health claims are as prone to fraud, waste, abuse as all insurance claims. It’s estimated that 5-6% of all global claims payments are paid inaccurately.
Furthermore, it’s a slow process. It’s often not automated, and particularly complex claims take a long time to get through.
How does Sprout.ai help solve the problem of legacy systems?
JR: One of the things that attracted us to Sprout.ai in the first place was the fact that its claims system sits on top of your old bespoke systems, so you don’t need to replace them. As anybody who’s ever tried to replace the claim system in health will know, this is expensive, takes a long time, and often does not result in success.
- Unstructured data
JR: We then go forward to look at data that’s linked to claims. Old claims systems were written to capture the minimum amount of data required in order to pay claims, because data capture takes time and multiple attempts.
Lots of data is provided during the health claims process, but an awful lot of it is not structured. When it is on claims forms for medical information, it’s often not captured. This means that there isn’t enough structured data within the systems to be able to understand customers’ needs, to analyse fraud, waste and abuse, and assess quality of care.
How does Sprout.ai help solve the problem of unstructured data?
JR: One of the things we liked about Sprout.ai was its ability to capture much more data, capture much richer data, and then format it so it is structured. This then allows analysis into needs, leakage, and care quality to happen. And as Sprout.ai sits outside of the legacy systems, you don’t need to adjust all your data structures.
- Delays and inflation
JR: First, we’ve still got the backlog of treatments waiting to happen because of COVID within state systems, and also to an extent within private systems. This is an opportunity for health insurers. People are turning to private insurance as they realise how long they may have to wait to get treatment within the state. However, those treatments are also delayed in the private system. Delaying treatment often means that the final interventions are more complex, and consequently more expensive.
Second, there’s the challenge of inflation. We can see what’s happening with general inflation with the war in Ukraine, as well as the supply chain problems we have post COVID. We understand from historical analysis that medical inflation tends to be higher than general inflation. It isn’t clear yet exactly what the impact is going to be, but health insurers can’t wait a year to find out what the inflation results in. They have to price for it now.
That means that, unless they can find ways of reducing costs in the short term, they are going to end up putting their prices up to a point where for some people health insurance will become unaffordable. And let’s face it, the people who stop paying health insurance are not the ones having treatment. They’re the ones who aren’t. That means that the subsidy that comes from those people to the people who are claiming is reduced. Therefore, prices go up again. It’s a vicious circle.
So, it is important that insurers can react to both the opportunities of new health insurance customers, and also price health insurance in a way that they can retain their existing customer base.
Sprout.ai: How does Sprout.ai help solve the problems of delays and inflation?
Our AI powered technology delivers fast and accurate claims decisions, enabling you to better serve your customers.
It extracts all of the data in the claims documents from providers, customers, doctors. Then, the data is structured and put into the insurer’s system. Natural language processing (NLP) interprets the documents and unstructured data, then triages the claim, and checks for fraud, anomalies, and coverage. Then, using machine learning, our technology checks the claim against historical claims and recommends the next best step. The claim can either be settled right away, or, if it’s more complex, passed on to a claim handler.
Some answers have been lightly edited for clarity.
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