health insurance plans

Health Insurance Decision Support Tools

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This blogpost is about the significance of automated decision support tools for purchasing health insurance plans. After the approval of Patient Protection and Affordable Care Act (PPACA) commonly known as Affordable Care Act (ACA) or Obamacare, in 2010, the health insurance companies in the United States have emerged as one of the important stakeholders of the healthcare ecosystem. The purpose of the act is to ensure the availability of affordable health insurance to more people. To help people and businesses find affordable plans, the concept of health insurance marketplaces was introduced. The online portal provided by the marketplaces aid people in making comparisons among the health insurance plans. However, an important question here is that to what level the presented information is realistic or contextually true from the perspective of the users or consumers.

In the presence of a large number of health insurance and dental plans, it becomes difficult for the consumers to identify the plans that best fit their needs and criteria. Although there are certain Web based tools available to help people find health insurance plans, however, these tools lack the capabilities to help people find personalized insurance plans. By personalized insurance plans, we mean the plans that the users or consumers select by specifying their own requirements in terms of the coverage and premiums or costs. Without such tools, finding appropriate health insurance plans that cater the diversified needs of a variety of consumers from the huge collection of plans is difficult. The reason is that sometimes due to the unawareness of the details of the plans and uncertainty about their requirements, people might select the plans that do not suit to what they are looking for actually. Also, the coverage and premiums for the health insurance plans being offered by different providers is not uniform that eventually makes the decision regarding the selection of health insurance plans difficult. Hence, this calls for the need to develop decision support tools that could provide personalized recommendations about the plans by considering the relevant context of the consumers.

The concept of recommendation systems to help identify the services or products based on the contextual information and criteria has widely been in use. For example, recommendation of products based on the users’ past search or purchase history, related products, and other context specific information like gender and location have already successfully been developed. The aforementioned solutions besides facilitating users or consumers in finding their desired product or service, are successfully helping the businesses. However, such types of health insurance tools or decision support systems that offer personalized recommendations to the users based on their specific requirements are not available. Therefore, it is the time to make best use of technology to develop the consumer-centred systems for health insurance plan recommendation and identification.

Decision support tools that allow the consumers to compare different health insurance plans based on multiple coverage and cost-based criteria, for example co-pay, co-insurance, deductibles, maximum benefits offered by the plan, and premium etc. can help identify the most suitable plans for individuals and families. Although the marketplace allows comparing plans, but the available tools are not flexible enough to help users make comparisons based on their own specified criteria. Since it is expected that in near future more and more plans will be available therefore, the need for such tools will even be increased. Off course these tools can be made to offer greater deal of flexibility for their intended users, for example if a certain disease is prevalent in a particular area or locality, then the personalized recommendations generated by the insurance tools should consider the coverage for that specific disease in the plan. Moreover, the information about the network of hospitals or healthcare providers that a particular plan covers can also help users make appropriate decisions about purchasing the plans.

Similarly, the tools should be flexible enough so that the people can easily make estimates of their monthly and yearly premiums for certain specific types of diseases or preconditions. Employing artificial intelligence and big data techniques to compute the premiums for the people interested in purchasing the health insurance can also help the insurance companies make better estimates of the premiums or costs, off course without losing anything at their end. Big data analytics can make certain decisions much easier for both the companies as well as the users. For example, assessing the probabilities of turning a user to be a high-risk purchaser based on demographics and other pre-conditions makes it fairly easy for the companies to decide about the premium and coverage which otherwise is done through some hard coded data. Thus, the data driven computations and decisions about the health insurance plans can be effective both from the perspectives of the users as well as the health insurance providers.

In fact, purchasing the health insurance for self and family is a bit complicated task due to non-uniformity and complexity of the offered plans by multiple providers and also the users possess little or no knowledge and awareness about the health insurance plans. A consequence of lack of such knowledge might be the wrong or inappropriate decisions made by the people. Moreover, there are not sufficient decision support and recommendation tools that can help users identify the insurance plans according to the prescribed criteria. Therefore, this is the high time that seeks the attention of the authorities for the development of user-friendly decision support tools for purchasing the health insurance.

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