I’ll Have a SOPrDiMoCa

SOPrDiMoCa is not a popular travel destination in Italy, nor is it a seasonal coffee from Starbucks. It is an acronym for Self- Oriented Prevention, Diagnosis, Monitoring, and Care.

SOPrDiMoCa tools help people assess their own risk factors; sort out symptoms and learn what to do next; monitor a wide variety of signs, symptoms, and life events; and adjust their own care. All of these tasks can be done effectively and safely in real time and in one’s own home, and they can save time and cost less than going to a doctor’s office. According to Christensen, Grossman, and Hwang in their book, The Innovator’s Prescription:  A Disruptive Solution for Health Care, “Following the diagnosis and treatment by physicians, in many instances physicians can’t add much additional value beyond teaching patients broad categories of do’s and don’ts. Patients and their families typically must distill from their own experience algorithms of diet and activity that minimize the severity of their symptoms. Patients with these behavior–intensive diseases can generally formulate better algorithms of care through trial and error than their physicians can.”

The trend away from professionalism and centralization and toward simplicity, convenience, and a consumer-focused market is, according to Christensen et al, a natural progression during the “second and third waves of growth” of all industries.  For example, people are questioning why some of the things done in doctors’ offices have to be done there. Alternatively, they go to box stores like Walmart and health stores like CVS Health to receive “retail” clinic care for common ailments. In most cases, it is equivalent, quicker, more convenient, and cheaper. And while they are in these stores, they see a growing list of high-quality products that they can use to test and take care of themselves in aisle 7, SOPrDiMoCa.

The availability of home testing tools is expanding quickly. For example, OPTUM, a subsidiary of UnitedHealthcare, provides an At-Home Kit for members for biometrics. “This easy, self-administered test offers remote employees, spouses and new hires a private way to ‘self-screen, leveraging step-by-step instructions and a screening kit delivered directly to their doorstep.”  The Public Health Foundation of India deploys an android-based mobile system called the Swasthya Slate, which can perform 33 tests, including blood pressure, glucose, hemoglobin, and ECG. It can also test for pregnancy, dengue, and malaria. It retails for Rs 25,000 (or about $400) and has been tested and approved for use by community health workers.  A proponent says, “When we get sick, we won’t need to go—in high temperature and in severe pain—to our doctors’ offices, only to wait in line with patients who have other diseases that we may catch. Our doctors will come to us over the Internet.”  SimulConsult is a diagnostic tool that ingests the complete body of research literature for certain disorders.  The tool prompts doctors to enter information about the patient’s condition. It then generates hypotheses with associated probabilities about what the patient may have.  With a little more translation and technology to make it simpler, it could become a tool for people to use.

People have already adopted a more self-reliant role in other aspects of their lives. They use data and tools, make their own decisions, and prefer to “do-it-yourself” instead of relying on professionals to do their finances (e.g., online banking, electronic tax preparation and filing), travel (e.g., navigating directions, using online travel services), education (e.g., online coursework and degrees), shopping (e.g., buying online), and more. In these areas, technological advances have equipped and enabled people to take on these functions and have “changed cultural expectations regarding what people can learn, know, and do.”  The notion that people will not use data and tools to take a more active and decisive role in their lives has been debunked.

From Slick and Click...to Tick and Stick

Despite the tens of thousands health apps, it is clear that digital health developers are not winning over consumers.   A killer app has not swept the market. Even the commanding iPhone 6 and IOS8 and the Apple Watch may not carry HealthKit in its bountiful wake to ignite consumers’ interest in a digital health revolution.

Thus far, digital health technology for consumers has been slick and focused on making people click.  It is premised on the belief that the great successes with functions and apps delivered through smartphones can spill over into health. But, what people like to do on smartphones is mostly entertainment and social communications: Technology developers:

• Miscalculate that the technology, such as the-data-platform, is a “big deal” and will interest consumers as much as it does techie developers.
• Presume that the click mentality for revenue production derived from advertising and will work in health.
• Perpetuate uni-focused apps, like hailing an Uber cab, buying a product on Amazon, or making an airline reservation, will also work in health. 

These conventions are constraining and not an easy transplant for the health market. Apps perpetuate the characterization of people as extremely distractible, only capable of handling one simple function at a time, and not willing to pay for digital services while accepting the hidden costs of marketing intrusion.

When it comes to changing people’s behavior, technology has to understand what makes people tick and how to make them stick. The big draw of activity sensors seems to be that it lets people know when they reach 10,000 steps with its beeps and this insight can be shared with friends and family.  But, is this simple model really enough to hook people on a sustained program of activity for health? I think not.  Technology can offer so much more. 

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.