Your Health Plan Will See You Now

My father is 92 years old and lives in a very nice retirement community.  He received a call from his health insurance plan, Tufts Medicare Advantage, inviting him to have a free in-home doctor visit.  He was told that the doctor would do a complete evaluation and make recommendations to improve his care.  He was a bit puzzled and flattered.  He remembered having a doctor visit many decades ago and was nostalgic about doing so again.  He was urged to accept the invitation quickly as “doctors are in the area now” and “this is a limited time offer.”  The doctor spent an hour with him and told him he was in great health but should consider taking testosterone for his fatigue.

All of this seemed rather suspicious to me.  I called Tufts and they referred me to CenseoHealth, a firm that contracts with Tufts to provide doctor visits.  I got the same script about doctors in the neighborhood, how it would improve his care, and act now.  I asked if the information gathered would be used for any other purpose but to improve his care and was assured that it would not.  I asked my father to request a copy of the report.  He has not received it.

 

After some research, I now understand that the purpose of the visit was to gather information on his “risk score” that could lead to the insurer getting much higher payments from Medicare.  According to a recent investigation by the Center for Public Integrity, Medicare made nearly $70 billion in “improper” payments to Medicare Advantage plans from 2008 through 2013, mostly due to over-billings based on inflated risk scores.  But, my personal concern is not that health plans are gaming the system to increase revenues.  After all, they have been the target of gaming for a long time from providers “upcoding” billing records to get better payments.   It just seems to be part of the culture of health insurance.

My concern is about trust.  My father did not derive any benefit from the visit.  He was deceived about the purpose.  The purpose was to extract information from him so that Tufts could increase their revenues while pretending to do the doctor-thing to improve his health.  He was preyed upon as an elderly person.  This type of deception has no place in health care and especially not from the #1 health plan in the US, as Tufts promotes itself. 

Health insurers need to work on trust.  Let’s face it, it took an act of Congress to force them not to discriminate against the sick by denying coverage for pre-existing conditions.  Health plans come in dead last among major industries when it comes to customer engagement according to Forrester Research.  And a recent Gallop poll found that only 26% of Americans place a lot of trust in health insurance companies to keep their personal information secure. 

Indeed, the use and abuse of personal data is at the cutting edge of gauging the trust factor of companies today.  Health insurers harbor a vast amount of data about us.  They know our diagnoses and medications.  In addition, many buy personal data on what we buy, who we voted for, and where we travel and use it to drive algorithms about whether we are worthy of health management programs, deserve good customer service, and offer a high lifetime value as members worth keeping on.  For example, one company that services health insurers, Predilytics, touts that its use of advanced analytics results in “more accurate identification of risk adjustment opportunities” and that these “high opportunity members generated 25% more coding value than prior models.”

I want my father to live a long and healthy life.  That should be job #1 for those he pays to look after his health.   Decisions about his health should respect his point of view and his privacy and abide by the saying “nothing about me, without me”.  Tufts says on its website, “We strive every day to be a health plan you can brag about to your friends and family.  Do you have ideas on how we may improve your experience?”  Here’s an idea:  Use all the data you have collected on my father to identify ways to make him healthier.  Coordinate with him and his health care providers to make sure it happens.  And, use your precious information resources wisely to make a difference in people’s lives rather than to scrounge for more revenues.  

Protecting the Wealth of Your Health

How much is your life worth?  Of course, it is priceless.  But economists actually monetize it at more than $70 thousand per year of life.  At birth, we are given the gift of life which, for a person born in 2012, amounts to 79 years and a lifetime value of $5.5 million.  For 99.9% of us, it is the most important asset we will ever have. 

Unfortunately, the American way of producing a long and healthy life is failing.  Abundant research indicates that the U.S. ranks 28th out of 34 OECD countries in producing a long life as measured by years of life lost due to premature mortality.  When compared to countries with the lowest premature mortality rates, Americans lose 36 million years of life every year.  The years of life lost have a value of $2.6 trillion which is nearly equivalent to annual health care expenditures of $2.8 trillion.  The fact is that producing a long and healthy life and capitalizing on our lifetime worth is not on any organization’s mission statement but our own.

The health care system is focused on sickness, not health; on services, not outcomes; on medicine, not on prevention or social determinants of health.  Public health program budgets have been slashed and programs tend to focus on the emergent, e.g. one ebola death in the U.S, but not on the important, e.g. over a million deaths attributed to lifestyle behaviors.  Government attempts to improve health through social programs are beaten down with socialist rhetoric and contempt for redistributing wealth.  And, food, alcohol, tobacco and marketing companies seduce us with tasty but very harmful foods, play to our hopes through advertising, and keep us coming back for more by getting us addicted.

It’s up to us.  Research shows that our own behaviors are far more consequential in determining our healthy longevity than the actions taken by others on our behalf.  Indeed five behaviors of everyday life account for almost two-thirds of the loss of healthy years of life.  These behaviors include eating poorly, smoking tobacco, drinking alcohol, exercising too little, and not taking medications.   

Doctors, governments and a burgeoning self-help industry exhort people to change these behaviors and have achieved a modest degree of success, but there is still a yawning gap as evidenced by the numbers above.   The missing piece is that people have not invested in their health asset for a variety of very understandable reasons.

But, this is changing.  People are breaking free of the medical paternalism that breeds dependence.  More information has been liberated for their use and technologies make it more accessible and sharable.  With the large increase in out-of-pocket financial exposure due to the new generation of health insurance plans with astonishingly high deductibles, people are more vigilant about the value of health care.  And people want convenience, eschew encumbrances, and believe in themselves to do many of the tasks previously owned by professionals in many aspects of their lives. 

They are also being equipped to be more self-reliant.  People are going to box stores like Walmart and health stores like CVS Health to receive “retail” clinic care for common ailments.   It is equivalent, quicker, more convenient, and cheaper.  And while in these stores they see an expanding display of high quality products they can use to take care of themselves.  I call these products SOPrDiMoCa, an acronym that stands for Self-Oriented Prevention, Diagnosis, Monitoring, and Care.  These tools include self-administered diagnostic tests previously controlled by doctors and labs, self-monitoring devices and coaching software to control glucose and blood pressure, smartphone apps and sensors to maintain healthy behaviors, and more.

Technology can play a strong role in bringing about this person-centered health movement by perfecting better analytics designed for people.  The business model has to change, however, from making us click to generate advertising revenues to understanding what makes us tick in order to make behavior change stick.  For example, it can produce wise information to know the individual better than she knows herself thereby providing fresh insights. It can develop “digital hugs” in order to engage the individual emotionally because that is so important for change.  And it can provide ongoing, smart coaching to help people master barriers and achieve goals. 

Investing in our health asset is fundamental to a long and healthy life.  Herophilos, a Greek physician from 335 B.C. said, “When health is absent, wisdom cannot reveal itself, art cannot manifest, strength cannot fight, wealth becomes useless, and intelligence cannot be applied.”  The surest way to reap the benefits from our birth asset is to stay healthy and manage the five behaviors of everyday life.  Increasingly, people are grabbing the baton, others are welcoming them as true partners in health, and powerful tools are emerging to equip them to be successful.  

Using Person Centered Analytics to Live Longer:  Leveraging Engagement, Behavior Change and Technology for a Healthier Life

By Dwight McNeill, PhD, MPH

Using Person Centered Analytics to Live Longer is about empowering and equipping people to take a more active role in mastering five behaviors of everyday life that cause and perpetuate most chronic illnesses. 

It is three books in one.  It provides:

-A framework for understanding why person-centered health analytics is important by describing five convergent realities:  The American way of producing health is failing, people are the drivers for improving health, converging trends demand a person-centered orientation, everyday behavior changes are the interventions that matter, and analytics provides new insights to catalyze it. 

-A toolkit for people that includes information, tools, and a quick reference guides to links that people can use on their own. 

-An opportunities guidebook for stakeholders to understand person-centered health from the person’s perspective, describes how analytics can contribute, and what actions they can take to support it. 

It is different from other books.  It goes beyond a call for action and provides tools and resources.

It describes a new generation of analytics for health.   It diverges from the usual health care analytics that focus on business intelligence for the two Ps (providers and payers) by zeroing in on the health needs of the forgotten P, people.  It is not about worshiping the art of the possible of information technology; it’s about putting analytics to work to engage people to achieve their health destiny.

The defining elements of person-centered health analytics (pchA) are:     

pc:  The focus on the person in terms of what really matters (healthy years of life) and the means to achieve it (personal behavior change). 

      h:  The focus on health that covers the continuum from wellness to sickness and places a priority on well-being and prevention. 

      A:  The focus on capturing and integrating a wide variety of health data and using connected devices, advanced computing, and social networks. 

It is published by FT Press with a release date of April 2015.  For more information on the author, Dwight McNeill, please see his author page.

Holes in the Sidewalk of Analytics

Analytics needs to walk around some holes in the sidewalk. 

A wonderful book of poems by Portia Nelson, There's a Hole in My Sidewalk: The Romance of Self-Discovery, addresses the struggle to stop falling into the same psychological/behavioral hole, how to walk around it and “go down another street”…and grow as a person.   

The field of analytics has fallen into a few big holes lately that represent both its promise and its peril.  These holes pertain to privacy, policy, and predictions.  

Privacy.  $1B. Target, the retailer, was the poster child for using big data for customer analytics to pump up sales.  It unabashedly collected lots of data on its customers, from a variety of sources, integrated it, and used it for predictive modeling to identify segments that are experiencing “moments that matter” when habits can be influenced to buy new products.  Target touts that “we’ll be sending you coupons for things you want before you even know you want them.”  For example, it developed algorithms about the probability of pregnancy and the delivery date to sell specific products that women buy at different times during their pregnancy.  It identified the women, sent them coupons, and opened its cash registers to amazing profits.  However, as we have learned, it also opened its cash registers, credit card machines, and databases to cybercriminals who stole the personal data of tens of millions of customers.  It is estimated that this error will cost Target over $1B in fraud claims.  Its stock price has fallen over 25% since the incident. 

  

The “hole” is a comfortable one for analytics.   The habit is to uncork technology before its time.   For example, the NSA exploited the technology to tap telephone calls and scrape peoples’ metadata into a database before it confronted the likelihood that world leaders and the public at large would condemn it and it could not defend it in terms of averting terrorism.  Similarly, there was a lot of talk about the “creepiness” of retailers collecting personal data on customers by whatever means possible.  The big appetite for the data to improve sales may have blinded companies from thinking about the consequences and “forgetting” the basic responsibility to protect it.  In the Target case, there are known credit card technology safeguards, including the use of a security microchip, that were ignored.  Additionally, there must be encryption protocols and firewalls to decouple data so that cybercriminals would not find personal identity information.  The simple lesson is that just because the technology exists does mean that it should be used.  Perhaps one route around the “hole” is to “count to ten” before technology genies are let out of the bottle.

Predictions:  43-8.  The great hope to demonstrate the value of analytics is (advanced) predictions.   It uses all the breadth and depth of big data to go beyond reporting on the past to predicting the future.  So, how could the predictions about the 2014 Super Bowl game between the Sea Hawks and the Broncos be so far off?   The point spread was 3 points but the actual spread was more than 10 times that as the Sea Hawks routed the Broncos and Peyton Manning from the first (mis) play of the game.   Perhaps there is a tribe of analytics “sharps” who are making it big in sports wagering but the facts are that the best of them only win about 53% of the time. 

The irony perhaps is that football, like baseball and basketball, is a fully digitized industry unlike most others including healthcare which still struggles to use electronic medical records to capture its key transactions information.  In sports, every play action on the field is captured, recorded, and discussed, resulting in a rich performance database of players in almost every conceivable context, e.g. how a baseball hitter performs relative to a specific pitcher, playing field, regular or post-season game, and so forth. 

But, it is clear from the big-miss prediction of the Super Bowl game that some important data that would improve the precision of the model are missing.  The “squares”, who rely on softer data (intuition), think they know this realty of the shortcomings of quant data, although their win rate is no better than that of the sharps.  My personal insight on this is when I was 16 years old I worked as a dog handler at a greyhound racing park.  I took a dog from its pen, to the viewing stand, into the starting gate, and picked it up at the conclusion of the race.   I knew when the dog was nervous, sick, and hyped up.  And I knew when they hit their head going into the gate that they would not recover to win the race.  

The “hole” here is the reliance on the big data that is under the lamppost.  In this case, it is the big sports data, most of which is collected…because it can be… without a model in mind and mostly for its entertainment value.  The big data presumption is that if you build it (the database), the predictions will come.  That ain’t necessarily so, even if one runs zillions of simulations on all the yottabyte of big data.  The data have to be right for the model to work.  In the case of sports, there are lots of (“soft”) untapped personal data such as health, resilience, and response to certain threats (and more) that may be important factors in big game performance.   It’s a real short circuiting of predictive modeling to be carried away with the technologies of the yottabytes while avoiding a full understanding of the phenomena under study.

Policy.  2.2/7.  The biggest analytics project in recent history is the $6 billion federal investment in the health exchanges.  The goals of the health exchanges are to enroll people in the health insurance plans of their choice, determine insurance subsidies for individuals, and inform insurance companies so that they could issue policies and bills.  The project touches on all the requisites of analytics including big data collection, multiple sources, integration, embedded algorithms, real time reporting, and state of the art software and hardware.  As everyone knows, the implementation was a terrible failure.  The CBO’s conservative estimate was that 7 million individuals would enroll in the exchanges.  Only 2.2 million did so by the end of 2013.  (This does not include Medicaid enrollment which had its own projections.)  The big federal vendor, CGI, is being blamed for the mess.  Note that CGI was also the vendor for the Commonwealth of Massachusetts which had the worst performance of all states in meeting enrollment numbers despite its long head start as the Romney reform state and its groundbreaking exchange called the Connector. New analytics vendors, including Accenture and Optum, have been brought in for the rescue.   

Was it really a result of bad software, hardware, and coding?   Was it  that the design to enroll and determine subsidies had “complexity built-in” because of the legislation that cobbled together existing cumbersome systems, e.g. private health insurance systems?  Was it because of the incessant politics of repeal that distracted policy implementation?  Yes, all of the above. 

The big “hole”, in my view, was the lack of communications between the policy makers (the business) and the technology people.  The technologists complained that the business could not make decisions and provide clear guidance.  The business expected the technology companies to know all about the complicated analytics and get the job done, on time.   This ensuing rift where each group did not know how to talk with the other is recognized as a critical failure point.  In fact, those who are stepping into the rescue role have emphasized that there will be management status checks daily “at 9 AM and 5 PM” to bring people together, know the plan, manage the project, stay focused, and solve problems.  Walking around the hole will require a better understanding as to why the business and the technology folks do not communicate well and to recognize that soft people skills can avert hard technical catastrophes.

In summary, these three holes in the sidewalk of analytics are recurrent themes and threats to fulfilling the promise of analytics.  First, the technology cannot zoom ahead of the sociology.  The need for business results cannot err on the side of the creepy use of personal data to increase sales without a full respect of the need to protect privacy and to honor customers.  Second, big data is not the answer if it is not the right data.  The full potential of predictive modeling requires more thinking and less data processing.  And lastly, the big failures in analytics have less to do with bad machines and buggy software and much more to do with people on either side of the business and technology fence just not talking with one another. 

The Flatlining of Healthcare

The business of healthcare is facing a defining moment.  For the first time in decades, the growth in healthcare expenditures continues to be slower than the rate of inflation.  In 2012 it was nearly one full percentage point lower at 3.7%.  And job growth for the industry is nearly flat at 1.4%.  How will the industry respond?  Will it conclude that this a momentary aberration and the best course is business-as-usual and protect its flank to keep the business viable during the maelstrom?  Or will it consider the likely reality that a line has been crossed and business will never be quite the same again?   

The business of healthcare, up until this point, has been reliably good as judged by its profits (as a percent of revenues) at about 7%.  But, its performance in improving health has been abysmal.  It has the poorest health outcomes when compared to peer countries and the worst efficiency of any industry.  The likelihood of getting the right treatment at the right time is just a little bit better than a coin toss.  And its consumer engagement is the worst of any industry.  It is clear that the American way of the business of healthcare is not always aligned with the production of health for people.

The paradox is that there are great opportunities to improve health outcomes and to do so at significantly less cost, thus improving economic efficiency.  But, there is one humungous fly in the ointment.  Most of these innovations will result in a big loss in the billable services which fuel revenues.

 

Many of these innovations are fueled by analytics.  I concentrate on three that hold great promise to transform health and healthcare over the next 5 years.  (See the McKinsey & Company report for more details.)  These are the big ones and there are many others that fit the category.  The top three include:

•  The combination of mobile computing devices, high-speed wireless connectivity, applications, and sensors to communicate, track, and manage all things related to health.
•  Next-generation genomic sequencing technologies, in combination with big data analytics, and technologies with the ability to modify organisms that will achieve personalized medicine customized to a “patient of one”.  
• Complex analyses and problem solving made possible by advanced computing technology, machine learning, and natural user interfaces that will automate all types of knowledge work. 

McKinsey & Company estimate that more than 20% of patients with cancer, heart disease and diabetes could receive more relevant and effective personalized care including life extension of up to two years through computer-aided differential diagnosis, connected health, sensors for remote monitoring, tailored treatments, and better communications within healthcare and to patients.  This is huge!  And, there are numerous examples of small scale, emerging solutions in all of these areas. 

But, back to that fly in the ointment.  Present worldwide annual revenues for the treatment of chronic illnesses are about $15 trillion.  A 10-20% cut would dramatically reverse the fortunes of many of those providing these services.  Diagnostic technologies will reduce the need for extra-exploratory tests.  Automation can drastically reduce the costs of knowledge workers.  And pinpoint treatments will diminish trial-and-error medicine.  

There are many challenges to operationalize these innovations.  These include the suboptimal digitization of the industry and an electronic health record that cannot yet function as an information hub.   There is a need for substantial skills to extract, aggregate, translate, and integrate multiple data sources.  Extensive research is needed to bring genomics to the bedside.  There are worrisome unintended consequences related to privacy and security.  And of course, the payment system must change to reward the production of better outcomes rather than more and more billable services.

But the biggest obstacle is the entrenched way of doing business in the healthcare industry.  As Uwe Reinhardt, the Princeton health policy sage, observes “Given that every dollar of health care spending is someone’s health care income…there must exist a surreptitious political constituency that promotes…waste.”  The American way of producing health is failing.  The standard way of providing healthcare must evolve to embrace inevitable changes to delivery and payment systems, the adoption of technologies, and the partnership with people as co-producers of health. 

When I talk with analytics leaders on the ground in prestigious healthcare organizations across the country, they have little appetite for considering that the best use of their time and talent is to improve health through analytics.  They concentrate on “business intelligence” to enhance revenues and reduce operational costs.  They do what their bosses ask of them.  And there is not a great demand of them to use analytics to dramatically improve healthcare and its outcomes in the transformational way that is possible.  

 

Some of these companies will be the last ones to have and use a BETA videocassette, a film camera, and a paper medical record.  Their strategic myopia will cause them to miss the moment and stumble in their competitive rank.   Others will “take the road less traveled by” and embrace the use of analytics, first and foremost, as a resource and support to improve outcomes.  And that will make all the difference. 

My op-ed on high insurance exchange was published by the Providence Journal and included below in this blog.  As we approach the 50th anniversary of Medicare, I wanted to reflect on its first year of implementation and enrollment of beneficiaries and compare it with the health insurance exchanges.  In summary, Medicare signed up 99% of its beneficiaries, within 9 months of President Johnson signing it into law, and did so at a cost of $45 per beneficiary (inflation adjusted).  A tough act to follow.   The exchanges have a long way to go and it is just the right time to start to consider how to increase the rate of enrollment for the 40 million Americans still without health insurance.  Please see more in my op-ed published in the editorial pages of the Providence Journal below.

Lessons of R.I.’s high exchange costs

January 26, 2014 01:00 AM

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BY DWIGHT MCNEILL

The enrollment numbers for the health insurance exchanges under Obamacare are in, and they do not paint a pretty picture. The Congressional Budget Office’s projections for enrollment were 7 million for the exchanges and 9 million for Medicaid. The actual numbers are considerably lower at 2.1 million for the exchanges and 4.4 million for Medicaid. Additionally, about 3.1 million young adults got coverage through Obamacare’s rule forcing insurers to cover dependents up to age 26.

Part of the shortfall is from the technology fumbles of getting the website up and running. But a large part of it may be because of the baked-in complexity of the reform itself.

In Rhode Island, the HealthSource RI exchange surpassed its very modest goal of insuring 10 percent of the state’s 55,000 uninsured. But other goals did not fare as well.

The cost of the exchange is very high. Given Rhode Island enrollment and costs to date and projected over the next few years (in order to spread infrastructure investments over time), the administrative cost as a percent of the total cost, including insurance premiums, is more than 15 times that of Medicare at 2 percent and three times that of private insurance (at 10-plus percent). Note that in addition to enrollment, Medicare and insurers also pay huge volumes of medical bills.

The goal of a market-based system is to use the power of competition among insurance suppliers to drive better quality at lower cost. Since Blue Cross and Blue Shield of Rhode Island is the only private insurer in the state’s exchange, this major reason for an exchange is forfeited.

How can the exchange costs be reduced? The cost of operating the Rhode Island exchange will shift from the federal government to the state in 2015. The projected yearly operating cost is about $23 million.

Three possible solutions:

•Run the exchange more efficiently. I suspect that the complexity of Obamacare and its reliance on the existing private insurance system necessitates these high costs.

•Since there is only one insurer in the exchange, perhaps it should do the enrollment, as it does for its core business.

•Divert the cost to other (out-of-state) taxpayers by shifting the exchange responsibility to the federal government, as have 23 other states.

These solutions would reduce the cost to state taxpayers and businesses but would not solve the underlying cost drivers.

The results of the experiment to use exchanges to get people insured are accumulating, and it is becoming increasingly obvious that modifications to Obamacare must be considered. The Affordable Care Act, Section 1332, supports “innovation waivers,” starting in 2017, for states to try new ways to achieve the same goals for coverage and comprehensive and affordable benefits. Some states, including Vermont, Hawaii, Oregon, New York, Washington, California, Colorado and Maryland, are viewing a single-payer system.

Medicare is a single-payer system, and is supported by the vast majority (96 percent) of seniors. When it was implemented almost 50 years ago, it signed up 99 percent of those eligible for benefits within nine months of President Johnson’s signing the bill into law.

The enrollment process had simplicity “baked in” because Social Security knew those who were eligible. The cost to enroll them was a mere fraction ($45 per enrollee) of the cost of the exchanges (estimates run from $1,000 nationally to $5,000 in Rhode Island per enrollee).

Additionally, the annual growth rate of Medicare spending per capita is projected by the CBO to be substantially lower than private health insurance spending between 2012 and 2021 (3.6 percent vs. 5 percent). And over the last 50 years, Medicare has transformed health care delivery and finance with reforms such as a prescription drug benefit, hospital diagnosis-related groups, quality measurement and transparency, and much more.

Prior to the implementation of the health insurance exchanges, there were 55 million Americans uninsured. In 2014, over 40 million remain uninsured. And it is very unlikely that most of these people will ever get health insurance.

Given the impasse in Congress, any consideration of policy modifications to improve access for the uninsured in the foreseeable future is unlikely. It is up to the states.

Rhode Island should join other leading states to address innovative ways to provide insurance more effectively and efficiently. It should not defund its exchange because, at the moment, it offers the best route to lift people out of the risk of not having insurance. But, it should set in motion a process to reincarnate the inevitable solution to health insurance, a single-payer system.

Dwight McNeill, of Little Compton, is visiting professor of health policy and population health at Suffolk University.

Person Centered Analytics for Health.

In my previous blog, Who Am I…for Health’s Sake, I suggested that we are possessed by different selves that behave in unique ways as we navigate healthcare and our health future.  These distinct selves include that of consumer, patient, citizen and customer.  Each of the four selves is well intentioned but does not live up to its potential to improve health.  They fragment our attention, limit our power, put their own needs above the rest, and derail us from taking control of our own health destiny.  In order to achieve our optimal health potential, we must be, in the words of cummings, “nobody but ourselves” and fight against the forces all around us to “make you everybody else.”

This blogs outlines a way forward that that informs, supports, and strengthens people to improve their health through analytics.

The emerging reality is that the American way of producing health is failing because of its fixation on health care, its denial that people are the active ingredient for change, and its slow uptake of technologies. The new reality is that prevention is more important than treatment, behavior change is the reliable pathway to improved outcomes, and information technologies are shifting power to people to become the primary agents of change. 

It’s about health, stupid!

There is greater appreciation that the health of Americans, ranked the lowest among wealthy nations on most measures, will not improve by spending more on health care.  Compelling evidence on the determinants of health show that personal behavior is most important in reducing premature mortality.  In fact it is about three times as important as health care.  Breakthroughs in health will happen by attending to what is obvious to prevent chronic illnesses…diet, exercise, weight, smoking and doing what the doctor says…rather than through advances in new research and clinical care.  But what is obvious has not been easy.

The science of behavior change is improving…dramatically

People need to change their behavior to achieve better health, but our track record has not been good.  We are “just human” and do not always do the rational thing, can be lazy, have other priorities, stick with our habits, and want to fit in.  And despite the best intentions of those who care for us, including providers, payers, and policy makers, we have not cracked the code.  Until now.

Behavioral economics is all the rage.  It puts together what we know about social psychology and economics to come up with powerful solutions that are working.  It digs deep into what drives behavior change and intervenes at key points.  For example, it understands that people have biases for maintaining the status quo, for the present rather than the future, and about “loss aversion”.  It knows that we have difficulty evaluating risk because we exaggerate small probabilities, we respond to positive rewards that are frequent and fun and that sometimes play on regret, and we tend to follow through with things if we make a contract to do so.   Marketers know these things and use it in advertising to make us to buy things.  It’s time for people and their advocates to embrace these tools to improve health.

Technologies put people in control

People are making more decisions for themselves rather than relying on experts because there is more information available, translated just for them, and constantly available through devices such as smartphones.  People do their banking, airline reservations, and stock trading on their own, 24/7, and they can do the same in managing their own health.  In the near future they will be aided by passive sensors that will monitor their health and have their own Siri-like advisor formulate their daily health agenda.  People stay engaged, supported, and challenged through social media and depend on the wisdom of their peers for product reviews rather than relying on marketers.  And the expanding availability of information and its democratization provide a personal analytics platform for behavior change that is more people centric, self-managed, and delivered outside of the usual healthcare structures in the living room, over the phone, and at the coffee shop.

Know me and work with me…or get lost

As the integrated self takes more control of behaviors to improve health, it will need support, but of a different kind.  People will expect everything to be customized to their needs.  They will demand accountability for products and services to work.  They will be an active participant in key decisions.  And with the convergent forces of a new priority on health outcomes and a focus on behavior change, along with enabling behavior sciences and information technologies, they will assume a central and responsible role to improve their health future.  

Stay tuned for my forthcoming book, Person Centered Analytics for Health .