The insurance industry is automating their services and digitization offers opportunities to increase efficiency, offer new services, build better customer relations, and combat fraud. To undergo digitization you need the support of data entry companies that can convert all data into digital format and ensure accuracy. The insurance industry is in the initial stage of adopting Artificial Intelligence. Estimates state that 75 percent of insurance companies feel that AI will alter or transform the industry. However, the success of Machine Learning and Artificial Intelligence depends on accurate data. It helps to derive valuable insights that enables in effective decision making.
The insurance industry is one of the most competitive and less predictable industries instantly related to risk. Hence, it always depends on data and statistics. Today with big data and advanced technology, the insurance industry has various sources of information that can be used to predict risks and claims, monitor and analyze it, and develop effective strategies for customer attention.
Healthcare insurance refers to the coverage of medical costs incurred due to disease, accident, disability or death. The policies of insurance are supported by the governments in many countries. The global healthcare market is growing rapidly and the insurance industry has immense pressure to provide better customer care and reduce their costs. A wide range of data including insurance claims data, membership and provider data, medical records, customer and case data etc are collected, structured and processed and converted into valuable insights for healthcare insurance businesses. With proper data fraud detection, risk assessment and consumer engagement can be improved. Though a number of health plans are available, people often find it challenging to make the right choice. Consequently, many often end up choosing plans that land them with higher costs. Is it possible to develop a tool that would guide people to choose the plan just right for them?
Kate Bundorf, associate professor at Stanford School of Medicine, along with Maria Polyakova of the Stanford School of Medicine and Ming Tai-Seale of the University of California, San Diego developed a web-based tool with an algorithm that matched the medical records of Medicare Part D enrolees with the best health insurance options for prescription drugs.
The study participants were assigned to either a control group or one of two treatment groups. The control group was asked to make a choice from the online Medicare resources that had 22 prescription plans whereas the treatment groups got assistance from the algorithm which automatically collected information from their medical records and matched it against the prescription drug plans. Both groups were able to view a table online that showed individualized analysis of likely costs for each of the plans. Both treatment groups could view a table online that showed them a personalized analysis of possible costs for each of the plans. Besides, one of the treatment groups was also shown an “expert score” for each plan, i.e. a number from 0 to 100, which the algorithm generated to rank the plans with the three best choices ranked at the top of the table. The study found that while both treatments persuaded people to change to better insurance plans, the treatment that included the expert suggestions alongside cost estimates proved to be more effective. Compared to participants in the control group, those in this treatment group chose to switch plans 36% more often. It was clear that intervention affected people’s behavior especially when they were given expert advice. People who used the algorithm were more likely to change to a better plan. They were highly satisfied with the process of choosing health insurance, even though they had to spend more time on it.
The study showed that the changes resulted in $270,000 in savings for consumers, i.e. for 316 treatment subjects who were provided expert advice. If these effects could be extended to the 25 million people enrolled in Medicare Part D, savings could be in the range of $680 million provided there is an equivalent rate of participation as in the study. This is especially significant taking into consideration that the tool itself cost only less than $1.8 million to develop.
Can this finding be translated into policy? There are 2 main hitches.
- Only a small portion of eligible beneficiaries chose to enrol. Out of the nearly 30,000 invited for the study only 1,185 people took part in the study. Those who joined were more tech savvy than those who didn’t join. They were active consumers looking for information.
- The study’s demographics are not representative of the broader Medicare population. Those who took part in the study lived in one of the richest and most tech-savvy parts of the country. People with lower income resources and less exposure to tools could have a different attitude.
Just giving people information need not change their attitudes. For example, by merely telling people that data entry services are immensely beneficial, they may not get the full impact of what those solutions are all about. On the other hand, expert advice on the same could generate a different reaction or response. Information should be paired with professional advice to change people’s knowledge about a product or service, and how they actually value the features of that service or product. From the above study it can be surmised that people could prove responsive to intelligent algorithmic advice and make choices based on such advice and information. This would naturally entail considerations regarding how to protect consumers from interventions that could prove harmful.
