Associations. Correlations. Inferences. Signals. Yes, That's Big Data

The Population Health Blog’s recent travels recently included a speaking gig at the just concluded National Association of Corporate Directors (“NACD”) annual conference meeting.  It was part of a panel discussion focusing on health care innovation that was ably moderated by tech guru John Hotta.

The PHB’s educational mission was to enable the persons who serve on Boards of Directors understand how “big data” is going to change health care.  After giving its standard definition (the use of large, disparate and unrelated data sets to find correlations and draw inferences that are actionable at the individual level), it turned to the following example:

“Imagine standing at the top of the Empire State Building and analyzing the noise from below to find out what’s most likely happening down on Fifth Avenue.”

In other words, its the use of computational analytics to separate the noise from the signals, and using those signals to ascertain a probability.

An informed guess.  Or, a probabilistic choice.

Folks in the audience seemed to get it, especially when the PHB noted that insurance (ICD-9 250), electronic record (“diabetes”) pharmacy (insulin), public health (obesity prevalence data by zip code), survey (“have you ever been told you have diabetes?”), government (car registration; overweight persons prefer minivans), web-usage (recent interest in low calorie foods?) and purchasing (grocery purchases) data could be marshalled to assign a risk that diabetes is present, and if it’s present, the risk of complications, and if there is a high risk, whether it’s actionable.

The value proposition? 

By understanding the risk and being able to array it from high to low, precious health care resources can be scaled to the burden of illness in the population.  So, instead of “carpet bombing” all persons with a diagnosis of diabetes with one-size-fits-all reminders to see their doctor along with mass mailings of educational materials, personalized outreach can be targeted on those persons most likely to be hospitalized (and there are big data signals that can predict it) in the next year.

Bottom line: it can save money by rationalizing health care.

The PHB wanted to point out some other need-to-knows, which it did with variable success:

1. Quantum jumps in processing power and server capacity have put this within reach of desk-top personal computers.  As an added bonus, you don’t need an army of mathematicians.

2. “Actionable” also means that the information is meaningfully available at the point of care, i.e. in the doctor’s office where 80% of the decisions that drive health care spending occur.

3. Big data can also point to way toward more accurate diagnoses (imagine if all the risk factors for an Ebola infection had been rolled up into a single score in that Texas ER) as well as treatment (deciding on the “best” cancer treatment program after knowing the relative influences of genetics, lifestyle and past medical history).