What’s Next – Predictive “Scores” for Health?

In the United States health information privacy is protected by the Health Information Portability and Accountability (HIPAA) act.  However, new gene sequencing technologies are now available making it feasible to read an individual’s DNA for as little as $1,000 USD.  If there is predictive value in reading a person’s gene sequence, what are implications of this advancement? And will healthcare data privacy laws be enough to protect employees from discrimination?

The Financial Times reports a breakthrough in technology for gene sequencing, where a person’s chemical building blocks can be catalogued—according to one website—for scientific purposes such as exploration of human biology and other complex phenomena. And whereas DNA sequencing was formerly a costly endeavor, the price has dropped from $100 million to just under $1,000 per genome.

These advances are built on the back of Moore’s Law where computation power doubles every 12-18 months paired with plummeting data storage costs and very sophisticated software for data analysis.  And from a predictive analytics perspective, there is quite a bit of power in discovering which medications might work best for a certain patient’s condition based on their genetic profile.

However, as Stan Lee’s Spiderman reminds us, with great power comes great responsibility.

The Financial Times article mentions; “Some fear scientific enthusiasm for mass coding of personal genomes could lead to an ethical minefield, raising problems such as access to DNA data by insurers.”  After all, if indeed there is predictive value via analyzing a patient’s genome, it might be possible to either offer or deny that patient health insurance—or employment—based  on potential risks of developing a debilitating disease.

In fact, it may become possible in the near future to assign a certain patient or group of patients something akin to a credit score based on their propensity to develop a particular disease.

And something like a predictive “score” for diseases isn’t too outlandish a thought, especially when futurists such as Aaron Saenz forecast; “One day soon we should have an understanding of our genomes such that getting everyone sequenced will make medical sense.”

Perhaps in the near future, getting everyone sequenced may make medical sense (for both patient and societal benefit) but there will likely need to be newer and more stringent laws—and associated penalties for misuse) to ensure such information is protected and not used for unethical purposes.

Question:

  • With costs for DNA sequencing now around $1000 per patient, it’s conceivable universities, research firms and other companies will pursue genetic information and analysis. Are we opening Pandora’s Box in terms of harvesting this data?

Are Data Scientists the Next Masters of the Universe?

Back in the late 1970s, traders buying and selling mortgages were pushed aside for new masters of the universe—“quants” or individuals that used mathematics to slice and dice mortgages into debt tranches. And in the same way, today’s traditional Business Intelligence (BI) professionals must be looking over their collective shoulders as business and IT publications tout the emerging role of “data scientist”.

Before Lew Ranieri came on the scene, mortgages were a very staid business. Banks would loan money and keep assets on the books for up to thirty years (depending on how quickly the loan was paid back). Except for underwriting skills, there wasn’t much complexity to the mortgage business.

As a trader for Salomon Brothers, Lew Ranieri changed all that.  Ranieri’s insight was that mortgages could be bundled together and then sliced into different tranches of varied risk.  This slicing exercise was quite complex because of a buyer’s ability to prepay their loans early or refinance.  Michael Lewis, of Liar’s Poker fame writes; “Mortgages were acknowledged to be the most mathematically complex securities in the marketplace. The complexity arose entirely out of the option the homeowner has to prepay his loan…mortgages were about math.” 

Suddenly the very boring business of home loans became a very complex business challenge in how to slice the pie based on risk profiles and cash flows from interest and principal. Lewis writes; “Different investors place different prices on risk. Risk could be canned and sold like tomatoes.” And this mathematical complexity demanded a new skill set—quantitative analysis—to perform the necessary mathematical modeling to ensure investment banks remained profitable in this new business.

Pushed out by a new breed of mathematical whizz-kids, many former investment bankers and traders either retired or left for smaller financial firms. And the rise of the quants—or the new masters of the universe—was complete by the mid-1980s.

Is a similar shift happening in the field of Business Intelligence with the emerging “data scientist” role? The skill set of today’s data scientist is much more robust than one who solely performs BI or ETL application development.  With new sources and types of data (i.e. multi-structured), the data scientist must be able to develop new data driven products such as churn models, create recommendation algorithms, assist marketers with behavioral segmentation and targeting and more.

But that’s not all. Fellow SmartDataCollective contributor Daniel Tunkelang says the data scientist; “Also needs to possess creativity and strong communication skills. Creativity drives the process of hypothesis generation, i.e., picking the right problems to solve that will create value for users and drive business decisions.”  Tall order to find all these skill sets in one person, much less build an internal competency center with such talent.

Perhaps for the foreseeable future, there’s room for both traditional BI professionals and the new breed of data scientists, as today both are valuable contributors in the field of analytics. However, with data growth on a fast paced exponential curve, much less the complexity and velocity of multi-structured data, it’s easy to see how the mix of skill sets to succeed in the future will tilt more in favor of the data scientist role.

The mortgage bankers never saw Lew Ranieri coming. Regarding the rise of data scientists—should traditional BI professionals be worried?

Data Tracking for Asthma Sufferers?

Despite the recent privacy row with smartphones and other GPS enabled devices, a Wisconsin doctor is proposing use of an inhaler with built in global positioning system to track where and when asthma sufferers use their medication. By capturing data on inhaler usage, the doctor proposes that asthma sufferers can learn more about what triggers an attack and the medical community can learn more about this chronic condition. However, the use of such a device has privacy implications that need serious consideration.

For millions of people on a worldwide basis, asthma is no joke. An April 9, 2011 Economist article mentions that asthma affects more than 300 million people, almost 5% of the world’s population.

Scientists and the medical community have long pondered the question; ‘What triggers an asthma attack?’ Is it pollen, dust in the air, mold spores or other environmental factors? The key to learning the answer to this question is not only relevant for asthma sufferers themselves, but also society (and healthcare costs) as there are more than 500,000 asthma related hospital admissions every year.

In an effort to better understand factors behind asthma attacks, Dr. David Van Sickle, co-founded a company that makes an inhaler with GPS to track usage. Van Sickle once worked for the Centers for Disease Control (CDC), and he believes that with better data society can understand asthma in a deeper manner.  By capturing data on asthma inhaler usage and then plotting the results with visualization tools, Van Sickle hopes that this information can be sent back to primary care physicians to help patients understand asthma triggers.

A better understanding of asthma makes sense for patients, health insurers and society at large. The Economist article notes that pilot studies of device usage thus far have resulted in basic understandings of asthma coming into question. However, there are surely privacy implications in the capture, management and use of this data, despite reassurances from the medical community that data will be anonymized and secured.

Should societal and patient benefits outweigh privacy concerns when it comes to tracking asthma patients? What do you think?  I’d love to hear from you.

The Next Wave in Recommendation Systems?

While some internet privacy experts fret over use of cookies and web profiles for targeted advertising, the quest for personalization is about to go much deeper as web companies create new profiling techniques based on the science of influence.

Behavioral targeting on the web using cookies, http referrer data, registered user accounts and more is about to be significantly enhanced says columnist Eli Pariser.  In the May 2011 issue of Wired Magazine, in an article titled “Mind Reading”, Pariser discusses how website recommendation and targeting algorithms; “analyze our consumption patterns and use that information to figure out (what to pitch us next).”   However, Parser notes that the next chapter for recommendation systems is to discern the best approach in influencing online shoppers to buy.

In the article, Pariser cites an experiment by a doctoral student at Stanford where online shopping sites attempted to not only track clicks and items of interest, but also determine the best way to pitch a product. For example, pitches would alternate between an “Appeals to Authority”; as in someone you respect says you’ll like this product to “Social Proof”—everyone’s buying this product, so should you!

Taking a cue from the work completed by Dr. Robert Cialdini it appears that the next wave in recommendation algorithms is to learn our “decision triggers”, or the best way to persuade us to act. In his book “Influence: Science and Practice”, Cialdini documented six decision triggers of consistency, reciprocation, social proof, liking, authority and scarcity as mental shortcuts that help humans deal with the “richness and intricacy of the outside environment.”

Getting back to the Wired Magazine article, Eli Pariser says this means that websites will hone in on the best pitch for a particular online consumer and –if effective—continue to use it.  To illustrate this concept, Pariser says; “If you respond a few times to a 50% off in the next ten minutes deal, you could find yourself surfing a web filled with blaring red headlines and countdown clocks.”

Of course, shoppers buy in various ways and not always in the same manner. However, the work of Robert Cialdini shows that in the messy and complicated lives of most consumers that mental shortcuts help with the daily deluge of information. Therefore, this new approach of recommendation systems using principles of psychology in tailoring the right way to “pitch” online shoppers, might just work.

There’s no doubt that recommendation systems already take into account principles of social proof and liking, but there’s a lot more room for improvement, especially other areas that Cialdini has researched. The answer to ‘why we buy’ is about to be taken to a whole new level.

Questions:

  • What’s your take on this next development in recommendation systems? Benefit or too much “Big Brother”?
  • Are you moved by “act now” exhortations? What persuasion technique/s work best on you?