Personalized Decision Support System Takes the Guesswork Out of Health Plan Selection

We have to make a lot of decisions based on best guesses. Choosing a health plan shouldn’t be one of them. 

Health insurance is one of the biggest financial and medical decisions consumers make every year. But often, people choose a plan based on what they’ve had in the past or what their colleagues choose, not what’s in their own best interest. It’s a guessing game that can cost consumers up to $1,700 a year.

A personalized decision support system can change that. Our decision support system helps employees choose a health plan that fits their medical and financial needs. Personalization is important because analyses are based on each individual’s expected medical usage. This is far more specific than estimates based on people who are like you (e.g., are in the same income bracket or family size).

“If we couldn’t capture individual data on expected medical use, then it would make sense to base decisions on people in similar life circumstances. It’s far better than guessing,” says healthcare policy expert Dr. Elizabeth Coté, MD, MPH. “But if there’s a way to personalize that analysis… to factor in information specific to you, your health, your life circumstances and your medical use, the analysis becomes not only much more relevant to you, but also a more dependable guide for your personal decision.”

This is what MyHealthMath does. Through individual phone calls and an online intake platform, MyHealthMath collects detailed information on consumer’s expected medical usage, from specifics about their health to the brand and dose of their medication. This individual data is combined with data on employer contributions and plan design—over 8,000 data points in total— to determine each individual’s most cost-effective health plan.

This personalized approach yields uniquely accurate estimates. Here’s why:

You should choose a health plan based on what you need: your specific health care, your finances and spending plans, and your personal level of risk aversion. These individual factors can get lost when you’re lumped in with others who generally fit your profile.

Consider these two scenarios:

1. Out-of-state medical bills: If you have two kids in college, it often makes sense to choose a PPO, so that you don’t have to worry about out-of-state healthcare costs. However, if your kids attend school in state, out-of-state medical bills are less of a concern. In this case, paying for the more expensive PPO will likely mean that you’re overspending on your health insurance.
2. Upfront costs: Consumer-driven health plans (CDHPs) have higher deductibles and don’t include a copay—you pay for prescriptions upfront. For some, these upfront costs are too risky, even if the long-term cost is cheaper than a typical PPO. On the other hand, if you have no prescriptions and a solid savings account, those upfront costs are much more manageable. It all depends on your specific situation.

Like a doctor, MyHealthMath bases the entire decision support system on evidence collected from large groups of consumers and then applies that knowledge to the individual’s case. This personalization leads to more accurate analyses.

“Your doctor wouldn’t prescribe a blood pressure medicine without talking to you first, and learning about you, your drug allergies, your diet, the medicines you’ve tried before and how they’ve worked for you,” says Coté. “Just like in medicine, your knowledge and experience need to be part of the health insurance equation. Remember, you know more about you than artificial intelligence or big data.”

MyHealthMath collects predicted medical utilization data through a series of personalized questions. We’ve made these questions as clear as possible. Even so, health insurance is overwhelming, and people need support. That’s why we have live analysts ready to help as an integral part of our decision support system. They can walk employees through the questions, helping them provide accurate, relevant responses that will lead to precise predictions. Employee can also follow up with a live person if they have any questions about their results.

This personalized approach not only leads to more accurate analyses; it also gives people more support than a bot, video, or email can provide. That support empowers people to make a change and choose a better plan.

MyHealthMath’s results—over $1,300 in annual savings per employee—are powered by behavioral science. We’ve improved the decision support system by tapping a few key levers that drive better decisions. One of these levers is trust: if people trust a result, they’re more likely to act on it.

MyHealthMath’s approach fosters trust. Since our analysis is based on individualized data and we build individual relationships, people are more likely to trust us and our findings. By building trust, we drive better consumer decisions.