Big Health

Opportunity:

Healthy eating has come front and center in consumers’ minds. Half (49%) of global respondents believe they are overweight, and half (50%) are trying to lose weight1. And they’re willing to pay to achieve that goal with consumers paying an average 38% premium for healthy alternatives1. However, today’s consumers lack an accurate and easy way to determine how what they’re about to eat will impact their health, both in the short and long-term. The only out-dated technology that exists today (nutrition labels) is hard to translate to your individual portions, not easy to keep track of, and only available for pre-packaged foods (vs. food in restaurants, for example). So, although consumers know a cheeseburger is not the healthiest choice, since they don’t know exactly how unhealthy it is – they will choose to eat it anyway. And these instances add up over time.

Novel augmented intelligence solution:

We, at Big Health, are building the solution to all of these problems. Our hypothesis is that by delivering nutrition facts in an easy-to-consume (i.e. smart dashboards) and salient (i.e. clear impacts on health) way, consumers will be able to fight the urge for that cheeseburger and instead grab a salad. With our app, you can simply snap a picture of food and we will tell you:

• The nutritional facts
• Potential impact on health
• Healthier alternatives

We are able to achieve this through our advanced image recognition algorithm. This algorithm has been trained on photos of food pulled from Facebook, Instagram, and other social media sources and can can recognize the individual components of a dish (e.g. chicken, noodles, etc.). Using the size of the food to estimate portions, the app can now tell you the precise nutritional facts of each component. A separate algorithm will use health care data from millions of patients to show you your overall health picture with specific risks called out. Based on the underlying causes of your health situations, we will tell you whether or not a particular food is risky because of its ingredients. Then, using another model that can simulate the taste of a certain food based on its chemical make-up, we will serve you recommendations for similar dishes that hit the same taste metrics. Armed with all of this information, we believe consumers will start to make healthier choices.

Our plan is to sell this technology as a per user license to health insurance companies and to companies that want to support the health of their employees. Additionally, we will open up another revenue stream from restaurants that want to advertise their healthier alternatives on our platform.

Design of empirical demonstration:

We will conduct a study in which we track food consumption across two equally sized and similar sets of users with the test set utilizing the Big Health program and the control set eating as they normally do. The study will be run for a period of sixty days and at the conclusion of the study we will measure average daily calories consumed, macronutrients consumed (protein, carbohydrates, fat), and analyze the overall health quality of food consumed using metrics such as glycemic index and levels of healthy vs unhealthy fats. A successful outcome would be a materially improved health profile of the food consumed by the target group. We will also assess consumer happiness through the use of a variety of daily surveys surrounding areas such as energy levels and overall happiness surrounding food choices.

Pilot to reveal its plausibility, promise and appropriate value

The pilot will utilize the data obtained in the empirical demonstration in order to build a model to predict the long term positive health impact expected from improved food choices over a longer time period. In this manner, we can quantify the direct benefits to health insurance companies and larger corporations. On the health insurance side, we anticipate a significantly reduced percentage of obesity in patients which will reduce the likelihood of costly health issues such as heart disease and type two diabetes. On the corporate side, we will utilize the data to show the expected increase in worker productivity due to higher levels or energy and focus and a reduced amount of anticipated sick days.

Source

1. https://www.nielsen.com/content/dam/nielsenglobal/eu/nielseninsights/pdfs/Nielsen%20Global%20Health%20and%20Wellness%20Report%20-%20January%202015.pdf