Advanced Technology Moves Health Plan Decision-Making from Confusing to Confident

jay-headshot copyConsumer selection of the “right plan” is key to health and financial success

There is a national movement occurring. This upcoming open enrollment period, tens of millions of people will be making health insurance purchasing decisions on their own – a majority of them for the very first time.

According to the Health Research Institute, organizations are shifting from a business-to-business health plan selection model to a business-to-consumer model. Employers are moving away from offering group health plans to providing stipends for purchasing health plans independently. Additionally, the first wave of Baby Boomers are beginning to claim Social Security benefits, and according to the National Association of Insurance Commissioners (NAIC), many are confused about their post-retirement health insurance options, including Medicare eligibility. All the while, the Congressional Budget Office is estimating 13 million new people will enroll in the Affordable Care Act in 2015.

The selection of a health plan is one of the biggest cost decisions a consumer will make year to year. This means that consumers need to be able to make educated and confident health plan selections. Unfortunately, the majority does a poor job of valuing factors in their choice appropriately. Often times individuals do not understand potential changes in medical treatment, place a greater weight on premiums than expected out-of-pocket costs, don’t value risk reduction, and overvalue certain plan characteristics. The wrong choice could end up costing hundreds or even thousands of dollars more at the end of the year.

In addition to the lack of understanding, the sheer complexity of making a decision, with hundreds of thousands of variables to consider, is beyond what any one person can process. It is impossible for consumers to understand every nuance of health care and insurance. Delivery, changes in medical treatment, and the ability to predict the next year’s utilization of health care services, are just a few factors that are unforeseeable for consumers.

Over ten billion dollars is wasted every year to consumer choice errors in Medicare Part D, alone. Furthermore, according to the Value of Choice Architecture, the average consumer stands to lose an average $611, roughly half a week’s salary for a family making $42,000 per year on poor plan selection.

How is a consumer supposed to know where to start? How does one determine what is the right plan to choose? Enter technology.

Sorting engines are the most basic kind of “support tool.” If you have ever ventured to a city for the first time, you’ve probably used a travel website (think Travelocity and Priceline.com), to make recommendations, for example, to chose a hotel brand you’re familiar with and trust. While a great resource for consumers planning a vacation, this type of model is much too simple for choosing the best health plan. The sorting tool is overly reliant on consumers knowing and using appropriate sorting logic (we already covered that they don’t know which factors to value).  The data is simply too shallow, and only organizes a select number of plans with too little information to truly provide an accurate recommendation.

Another tool currently being utilized is the “Predictive 1.0” model (the “TurboTax” approach). This type utilizes information from the individual’s previous year. However, just as you wouldn’t buy stock using data from the previous year, using last year’s health data is a poor predictor for the coming year. Additionally, this model often requires lengthy questionnaires resulting in a high consumer dropout rate. Other versions have the individual hypothesize which health services they may need next year. However no one plans for cancer or knee surgery, and if they did, it is no longer   “insurance”.  Unfortunately, these predictors lack the sophistication to provide consumers with an estimate of what their true out of pocket costs will look like. Without the use of Big Data, these models are also not able to contemplate changes in medical practice or treatment.

The final, most advanced, and the most beneficial to consumers, leverages the power of all the latest in Big Data, predictive analytics and machine learning.  The capabilities that are driving many of our leading consumer facing technologies could now be brought to health plan decision-making. This model would leverage public and proprietary data on tens of millions of Americans, apply advanced analytics –including prediction algorithms, and ranks health plans, using personalized scores, leading to better and faster decision-making, and a reduced cost to consumers and society.  

The model would have the capability to sort through the complexity of the variables, process instantaneously, and provide simple digestible solutions making an educated health plan selection possible. Predictive analytics married with Big Data and machine learning provides an in-depth, easy to use and understandable system, putting the consumer in control. By providing the most potential savings and aligning individuals with the best possible plan, which may not always appear to be the cheapest, prospective enrollees no longer have to be the expert on health and health insurance. For example, if a person selects a health plan because it covers their oral diabetes medication, they may not realize that 50 percent of those who are prescribed that medication will also need insulin within six months, which might not be covered. This could mean hundreds or thousands of dollars more out of pocket for the patient. The predictive analytics/Big Data model has this information within its vast sandbox of data and is able to take that into consideration, suggesting a plan that covers both the oral and injectable medication.

The future is now, and needs to be.  These capabilities have become an integral part of our everyday lives; there are even decision support tools for almost every aspect of it. Why not harness it to turn what is one of the most expensive and confusing purchases consumers will make each year, into an easier and more confident purchase? With the power of advanced decision support tools, this is now possible.

About Jay Silverstein, CEO of Picwell
Jay is a thirty year veteran of the US health industry, and has developed a reputation as one of the industry’s leading thinkers both creatively and strategically. He has played an instrumental role in the development of some of the most significant and impactful paradigm shifts in the health sector, including the development of the Point-of-Service category, the integration of complementary medicine into mainstream insurance, the development of database driven preventive medicine, the launch of physician report cards, and the elimination of medical necessity review.

Jay has served as Chief Marketing Officer and a member of the Executive Council at UnitedHealthcare, and was a founding principal and Chief Imagineer at Oxford Health Plans, where he designed many of the marketing, product, and service programs that helped grow the company from its inception to over $6 billion in revenues. He has also served as SVP of Marketing and a member of the Executive Operating Committee at HealthNet, Chief Operating Officer at Revolution Health, and most recently, Chief Branding Officer at Medco Health Solutions.

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