Dissecting the “Real Age” Phenomenon
Posted 23 August, 2007 in current trends, demos, problems, solutions
Recently a number of websites have been offering “real age” calculators which, upon asking a number of health/lifestyle questions, attempt to predict how long you will live. The difference between how long you are going to live and how long people live on average determines your “real age.” If, for example, you are a heavy smoker with a family history of heart disease, you might have been born 28 years ago, but your real age could be closer to 35. As a measure of its popularity, even Oprah and her ilk have been jumping on the real age bandwagon.
These real age calculators are not without their faults however.
- No (or little) research is offered to substantiate their healthcare calculations
- The numbers are frequently a little *too* clean (what are the chances that all bad things raise your real age by EXACTLY 1 year?)
- No distinction is made between elements you can and cannot control
- At the end of the survey, no action items are provided to allow the user to alter their Real Age. After all, unless you can glean some ACTIONABLE INTELLIGENCE from the results, these calculators are ultimately of little utility.
After seeing the calculator at http://www.poodwaddle.com/realage.htm, I spent a few hours reverse engineering it. healthtech’s real age calculator is an attempt to rectify the aforementioned deficits.
- Based on XML: see the real age XML now: download and modify the XML as new scientific studies are released. add your own questions, etc.
- Open Source: download the Real Age code and run it yourself
- Better health summary at the end (action checklist)
- Items are distinguished as controllable or not
RemedyMD’s tagline is “Better Data, Better Decisions, Better Outcomes,” and you might be tempted to think that better data leads automatically to better decisions, but that is not always the case. More often, it is the application of intelligent analytic algorithms (predictive informatics, if you will) which transforms the raw data into actionable information. A lot of EHR systems collect medical history, for example, but how many of them process that information to produce actionable knowledge?
