Health Data: Little White Lie Detector

Written by: Mandi Bishop

As we bring 2012 to a close and ponder the new year ahead, many of us make resolutions to change something in our lives, and frequently, that something is our health. According to the University of Scranton Journal of Psychology, 47% of Americans make New Years Resolutions. Of those, the #1 New Years Resolution for 2012 is to lose weight. Staying fit and healthy and quitting smoking also appear in the top 10. Each of these health-related resolutions translates into quantifiable healthcare data that is, or can be, captured and measured to assist the resolution-makers in achieving their goals. Our calorie consumption and burn can be calculated, our blood oxygen level monitored, our ratio of fat:lean muscle mass tracked over time. If only we were all a bit more like George Washington, and couldn’t tell a lie, the success rate for annual resolutions would be higher than 8%.

The inclination to tell little white lies to protect ourselves from inconvenient, uncomfortable truths exists in all of us. “Do these jeans make my butt look fat,” meets, “Of course not,” rather than, “Yes, your butt DOES look fat in those jeans – but it’s not the jeans’ fault.” “Can Timmy come play,” warrants, “We already have plans – let’s rain check,” in lieu of, “Your child is a brat who cannot enter my home because I prefer to keep all my hair rooted in my scalp.”

Many, if not most, of us extend these white lies to ourselves. The dress that fit last month but doesn’t today “shrunk at the dry cleaner”. Cigarettes only smoked during cocktail hour don’t really count as “smoking”. You count the time you spend standing to give office presentations as “exercise”. You “usually” eat healthy, except for the tell-tale McDonald’s bags in your garbage showing a once-a-day burger and fries habit.

What if there were a way to identify and hold you accountable for these self-delusions – a health data lie detector? Would you change your behavior? Could you achieve your healthy resolution? And might it have a quantifiable impact on healthcare cost if you did?

I had a partial thyroidectomy a few years ago. A year after my surgery, I found I had gained 7 pounds in 11 days, was feeling lethargic and was having difficulty sleeping. As a very active adult who meticulously maintained body weight for a decade, I was disturbed, and convinced that my symptoms were a result of my remaining thyroid tissue failing. I went to my primary care physician to request a hormone test.

The nurse and doctor both agreed that, in 90% of cases, the root cause of weight gain is diet, and they asked myriad questions, capturing all my answers in the clinical notes of their EMR: had I been eating differently, had I altered my exercise routine, had I been traveling. I was adamant that nothing had drastically changed. Given my fitness and history, they agreed to order the hormone test, and a blood vitamin test, as well.

All lab work came back normal. BETTER than normal. So I retraced every detail of my routine over those 11 days. And I discovered the culprit: office candy.

A bad meeting one day led to grabbing a handful of chocolates from one co-workers bowl, which became grabbing a handful of chocolates from each bowl I encountered on my department’s floor…several times a day. Did you know there are 35 calories in a single Hershey’s kiss? 220 calories in a handful of peanut M&Ms? 96 calories in a mini-Butterfinger bar? Turns out, I was eating between 500-700 calories a day in office candy. And that wasn’t all.

Along with the chocolate snacks, I’d fallen into some poor nutrition habits at meals. I started to consume other starchy carbs regularly: the pre-dinner bread basket at restaurants, pizza, pasta, sandwich bread. I didn’t feel I ate to excess, but I also didn’t take into account the difference in nutrient density between the mass quantities of fruits and vegetables I had been eating for years, and the smaller (yet still plentiful) quantities of processed starches I was currently eating.

The changes in diet likely disturbed my sleeping pattern and led to my lethargy, which in turn made my daily workouts less intense and effective at calorie-burning.

In short, my weight gain was legit, and the two doctor visits and the lab tests could have been avoided had I been completely honest with myself. I cost each actor in the healthcare system money with my self-deluding little white lie: the office administrative staff, the LRNP, the doctor, the medical coder, the lab, the insurance company, myself. There is also a per-transaction cost associated with each HIPAA-covered request that the doctors’ office EMR and lab information system generated. Given that I have only been to the doctor three times this year, and twice was for this weight gain concern, one could accurately conclude that 66% of my annual medical costs could have been avoided in 2012.

The health data exists within Meaningful Use-certified EMR systems to capture and communicate both the absolute data (height, weight, lab results, etc.) and the unstructured notes data (patient comments, doctor notes, responses to questionnaires, etc.). The capability to automatically compare the absolute with the unstructured data already exists. It wouldn’t take an inordinate amount of effort to program a lie detector to call out many of the most common little white lies.

What would happen to medical cost if we stopped lying to ourselves, and to our healthcare providers? And how high a percentage of the nation’s total healthcare bill could be avoided by this type of analysis? Better still, how much would the healthcare industry change if patients not only took responsibility for their own action/inaction, but modified their behaviors accordingly?

I’ll tell you what happened to me. I dropped the candy and starchy carbs, and I lost those 7 pounds. Keeping them off will be 2013′s New Years Resolution.