What’s on Santa’s List for Analytics?

 It’s that time of year again, when stathead’s thoughts turn to sugar plums and new gadgets to make their jobs more interesting and productive.  It’s amazing how technology keeps getting better and cheaper.  Recall that Moore’s law predicted that chip performance would double every two years, which would increase processing speed, memory capacity, sensors, and even the pixels in digital cameras proportionately. For example, comparing the IBM PC released in August 1981 with the Apple iPhone 4 released in June 2010, the CPU clock speed of the PC was 4.77MHz compared to iPhone at 1GHz; the processor instruction size was 16 bits for the PC and 128 bits for the iPhone; the storage capacity of the PC was 160KB and that of the iPhone (base model) was 16GB; and the installed memory (RAM) was 64KB for the PC and 512MB for the iPhone.⁹ Additionally, the list price on release of the PC was $3,000 (or about $7500 adjusted for inflation) and the iPhone was $199, or about 2.5% of the cost of the original PC. This exponential growth in computing performance has driven the impact of digital devices from computers to household appliances in every segment of the world economy.

So, what’s in the Holiday Gift Catalogues for Analytics this year?  Here is a sampler of my five favorites.

NoSQL (Not Only SQL) databases are an alternative to traditional, relational databases and are especially suited for unstructured big data, Web 2.0, and mobile applications. It uses open source software that supports distributed processing. It scales “out” to the cloud, rather than “up” with more servers. It has fewer data model restrictions than relational databases management systems, which allows more agile changes and less need for database administrators. It can use low cost commodity hardware. The bottom line is that it is faster and much cheaper. Examples of popular NoSQL databases include Cassandra, Hadoop, and BigTable. Companies that use it include Facebook, Netflix, LinkedIn, and Twitter. For more information see the NoSQL website, which touts itself as “your ultimate guide to the non-relational universe.” 

High performance computing (HPC) allows users to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. This is the computing capability needed for mining mountains of data. This capacity can be provided by dedicated computer clusters or by cloud clusters. Dedicated, custom-built, supercomputer infrastructure requires significant capital investments, long procurement times, long queues, and extensive database management. Buying HPC services from the cloud provides definite cost advantages, short lead teams, access to the scale required for a given project, and on-demand capacity. An example of such an offering is from Amazon Web Services called Cluster Compute Instances.  In healthcare, the biopharma sector uses HPC for genome analysis. Other industries, including oil and gas, financial services, and manufacturing, use it for modeling.

  

The idea that machines could replace humans for certain functions has been around a long time. And it certainly has become commonplace in industries such as automotive with robots on the assembly line. But can the machines actually “learn” and improve functioning on their own beyond being explicitly programmed? There are good examples of this with Google Search and Amazon purchasing recommendations, and with voice and facial recognition applications. In healthcare, IBM demonstrated a compelling use of machine learning (and natural language processing and predictive analytics) with its Watson technology by beating two grand champions on the Jeopardy! TV quiz show. IBM is working on healthcare solutions.  It has partnered with is Memorial Sloan-Kettering Cancer Center to have the technology gather and assimilate information from the research literature and from the Center’s clinical experience documented in its medical records and other files to “bring up-to-date knowledge to the bedside of every cancer patient.” Watson might be able to do this through its capabilities to read and understand language, interact with humans, remember everything, and provide answers to real-time questions. How the information will be delivered to the physician, how it might transform the practice of medicine, and whether physicians will embrace the technology are all important, open questions.

The Internet has transformed the way businesses communicate, market, do commerce with customers, and collect data about them. In retail, clicks are challenging the bricks. What could be more indicative of shifting paradigms than the collapse of the structures in which people do business (stores). One example is the capability to do virtually instantaneous randomized trials of alternative Web site features, e.g., how to get the most contributions during a political campaign. Another is Web page “scraping” in which all types of data about people’s Web wanderings are turned into ratings about their suitability for a job, a loan, and a date.

More than half of the adult population in the United States have smartphones.  Facebook has more than 1 billion monthly users.  The hot combination of these clicks and mobile produces a platform for easy, convenient, and quick communications that also enable e-commerce, uber-targeted marketing, location monitoring, and much more. An opportunity going forward in healthcare is to create closer relationships with people to help them get healthier by tapping into data that are freely exchanged and by supporting the continual, fast evolution of new applications to support health.

   

Technology has been awesome in increasing computing capacity with hardware (speed, memory, storage, access, etc.) and with software (to manage all the data and make sense of it). But if the technology is so great, why is the uptake of healthcare analytics so low in comparison to its potential and relative to the performance of other industries? The answer is complicated but one compelling reason is that the technology of making change happen, of getting from a good idea to its being embedded in operations, is unappreciated and untapped.  And analysts are enamored with computing technology and may take their eyes off the prize…making behavioral changes to improve clinical and business outcomes.

This holiday gift idea is cheaper than all the others and may be more consequential.  A guide is available in my book, A Framework for Applying Analytics in Healthcare:  What Can be Learned from the Best Practices in Retail, Banking, Politics and Sports.  

What gold mining can tell us about healthcare yottabytes?

The data gold rush in healthcare is on. It's the wild, wild West.  We are told that the data produced in U.S. healthcare will soon be counted in yottabytes, or a million trillion megabytes, or 1,000,000,000,000,000,000,000,000 bytes. McKinsey & Company asserts that creative and thoughtful extraction of all the healthcare big data is worth at least $300 billion a year. Where are all these data coming from, what value is being extracted from them, and where are the untapped opportunities?

Where’s the present data coming from:

• Transactions.  The traditional sources of usable (structured) healthcare data come mostly from billing (claims) data.
• Electronic medical records.  EMRs produce useful clinical data in mostly unstructured and semi-structured data.
• Machine-emitted.  Most of the yottabytes come from this source, which includes readings from medical sensors and “scrapings” from Web and social media sources including clickstream and social interaction data.
• Biometric devices.  These are all the findings from medical measurements such as blood pressure readings and x-rays and other monitors of everything from steps taken (e.g., fitbit) to places visited (GPS).
• Research.  Data on individuals from clinical trials, registries, and other sources.
• DNA sequencing.  Genomic data to support personalized medicine are not widely available now but are on the verge of becoming accessible and reasonably priced.

Is it producing big value today?

It is very hard to know because much of the analytics activity in healthcare is about digitizing the business and building data warehouses, but not using it to add value to the business or to clinical outcomes.  It is also difficult to know how much is being spent on analytics.  So, let’s get back to gold mining to infer some answers.

   

There are 2,500 metric tons of gold produced annually. At the current price of $1,300 per ounce this amounts to revenues of about $100 billion. It takes, on average, 30 tons of rock to produce one ounce of gold. Hence, the final product amounts to .000001042 of the rock that needs to be worked through to harvest it. There are also by-products of this extensive mining including the use of cyanide to extract it and huge open pits and large mounds of waste rock across the countryside where it is produced. The mining processes include huge investments in monster shovels and trucks to extract and transport rock to the plant and warehouse for processing and storing. Gold mining is hypothesis-driven, that is, mine rock in a specific place and in a specific way and you get gold. This is quite different from a hypothesis-free approach, which is to take all the rock and do a lot of tests on it to see whether there is anything in it of value.

Yotta-driven analytics in healthcare is mostly hypothesis-free, akin to analyzing the whole mountain and looking to discover “similarities” that may provide new understandings about the delivery of healthcare. The monster computing technology available today, at relatively low costs, can enable seemingly limitless simulations to do this.  So, the question is, how much rock will it take to find the gold in healthcare? Will the conversion rate be +/- .000001042?

Some of the gold from the healthcare data rush is palpable and it is “small.” The integration of genomic data with clinical data could lead to answers to important questions, such as whether a certain chromosomal variation is related to a disease, which could then fuel individually tailored treatments. For example, Tamoxifen has been an effective drug for the treatment of breast cancer. On average, about 80% of patients benefit from it. The potential with personalized treatment is to become 100% effective in 80% of patients because genetic markers can improve the knowledge of who does and does not benefit from the treatment. There are many instances of “small” hypothesis-driven data that can have a precise impact on business and health outcomes. Other rock in the yotta may not be as clearly useful. For example, much of the yotta is comprised of data emitted from machines, and much more research needs to be conducted to home in on likely ways it can contribute.

Are there other mines that healthcare is missing?

There are two types of data missing from the previous list. These data do not necessarily add a lot to the yotta stats. They are “small” and have specific and targeted purposes. These include extra industry personal data and people-generated data.

Extra Industry Personal Data

The world is full of relevant data and a lot of it resides outside of health care. External data can address specific health care issues, for example, to change people’s behavior, ranging from marketing to early detection of diseases. These data come from privately aggregated and publicly available databases on a wide range of personal attributes that can define microsegments that can be precisely targeted with specific interventions to improve health. For example, data on height and weight are available from external sources (and not easily collected or extracted from usual health care data) and can be used to calculate the body mass index (BMI) to determine premorbid obesity. Additionally, when personal data are integrated with medical data and in combination with the right channel, especially mobile, it can produce a much better identification of high-risk patients, with more effective interventions mapped to their specific needs, and include closer monitoring over time.

People Generated Data

Another source of untapped data is people. This is another type of “small data” with big potential benefits. Most of the data sources listed previously do not involve the active participation of people. The real potential lies in gathering much more relevant data from individuals with their consent and engendering their partnership to engage in data-sharing activities that help them improve their life. After all, people know more about their own health and illnesses and can monitor it better than any doctor could possibly hope to do. There is much more to be learned from a person’s head than from their data streams. There are indications that this is happening without, and perhaps in spite of, the active strategies of traditional health care. For example, networks of patients with the same condition are sharing data and creating large databases that are beginning to approximate crowd-sourced clinical outcomes research. For example, as of the end of 2011, PatientsLikeMe had more than 120,000 patients in 500 different condition groups; ACOR (Association of Cancer Online Resources) had more than 100,000 patients in 127 cancer support groups; 23andMe has more than 100,000 members in their genomic database. People also engage in their own data sharing through mobile and social media. And people have been responsive to surveys when the purpose is big (like polling in a presidential campaign) and when the rewards for participation are adequate.

Conclusion

A mountain of data is available for analytics in healthcare.  Some of it is really big and has unknown uses but is intriguing, and the technology may be able to find the gold although the conversion rate may be infinitesimally small. Some of it is small and can have immediate applications to produce value. And some that is potentially very valuable and comes directly form people is not included in the count and is not collected. Certainly, healthcare lags other industries in its use of big data because of the challenges with complex and unstructured data, the reluctance to use external data, data integration issues, and concerns about patient confidentiality. And IT folks say there is enough unused healthcare industry data to keep them busy for a very long time. Threading the needle for the most productive use of data, whether big or small, hypothesis driven or free, depends on analytics making the case that it is worth the investment and an innovation worth adopting.

This blog is an extract from Dwight’s new (edited) book, Analytics in Healthcare and the LifeSciences.

Social Justice and Economics Haunt the Exchanges

The exchanges give older people a bad deal in terms of social justice and economics.

As the data on the pricing of plans offered by the new health insurance exchanges become available to consumers and as the exchanges become more transparent about their metrics of success and how their operations are performing, opportunities for improvement are becoming clear.  This blog focuses on how one health insurance exchange, Healthsoure RI, gives older people a bad deal in terms of social justice and economics…and what to do about it.  

Age Discrimination

Reed Abelson’s New York Times article, “In New Health Law, A Bridge to Medicare”, proclaims that early retirees are the “big winners” with the new health insurance exchanges because they are better off than they were before.  True, they are better off now that Obamacare makes it illegal for health insurers to deny coverage on the basis of pre-existing conditions.  Also, their premium rates may be better because the age bands for rating insurance are limited to “only” three, meaning that early retirees are subject to three times the premium cost compared to twenty-somethings for the same insurance product. 

I noted in an op-ed in the Providence Journal, “Don’t Balance Healthcare on the Backs of the Elderly”, that the premium and out-of-pocket costs for medical care for a mid-level plan from Healthsource RI, could amount to almost a third of the income for sick, older people who do not qualify for subsidies.  I interpreted this as a social justice concern.  Abelson’s “better off” argument just does not cut it when it comes to discrimination.   Age rating should go the way of other forms of discrimination that were made illegal under Obamacare including for gender, health status, and pre-existing conditions. 

Economics

If the rates are so bad for older people on the exchanges, how do they compare to the rates currently available on the open market?  The press is touting big drops in the rates for insurance at the exchanges.  Another piece by Abelson (and Rabin), “Health Plan Cost for New Yorkers Set to Fall 50%”, asserts that “individuals buying health insurance on their own will see their premiums tumble next year in New York State” and one interviewee for the piece said that “health insurance has suddenly become affordable in New York.”

What's true for the average New Yorker, if there is one, is not true for all.  There is certainly variation around any average and older people will be at the tail of the distribution.  HealthPocket, which provides consumers complete information about the insurance options available to them, addressed the issue of affordability of insurance plans on the exchanges for older people in four states, including California, Connecticut, Ohio, and Rhode Island.   Their analysis concluded that “anyone expecting major price declines for older consumers will be disappointed”.  

I went to the HealthPocket website to find the prices for plans on the market today for older people in my state, Rhode Island.  Compared to the plans and prices on the exchange, the value of the insurance is better off than on the exchange.  (Note that I was unsuccessful in finding the age band rates for New York on the NY State of Health website or on the Internet.)  Here’s the data:  The mid-level silver plan referenced above (the plan where the costs could amount to a third of income) from Healthsoure RI is called VantageBlue Direct and offered by Blue Cross and Blue Shield of Rhode Island.  It has a $3000 deductible and a $6000 out-of-pocket limit with a cost for a 64 year old of $8366 per year.  A VantageBlue Direct plan, available directly from the same insurance carrier, has a $1000 deductible and a $3000 out-of-pocket maximum per year and costs $7608 for the same age (without a pre-existing condition).   That’s quite a savings, over $700 per year for insurance and a big reduction in the out-of-pocket max of $2000.  In addition to the name of the plan being exactly the same, the benefits and copays are very similar.  For example, the co-pays for seeing a primary care physician or a specialist are the same.  The co-pay for hospitalization and emergency room visits are exactly the same.  But the big issue for a sick older person when it comes to insurance is the annual out-of-pocket maximum and the off-exchange deal is much better.

An important goal of the state exchanges is to negotiate rates locally so that citizens get the best deals, as is done by large employers, the Federal Employees Health Benefit Plan, and state governments.   Although this may be true on the exchanges for the average citizen, it is not true for older people.  They will face affordability sticker shock whether on the exchange or not.  If they were in these other group plans as above, they would not pay more because age rating is just not the norm.

Very High Administrative Costs

The federal government has pumped a lot of money into the exchanges.  It will have provided the State of Rhode Island with over $100 million by 2015 when it will cut the umbilical cord and expect the exchanges to be self-sustaining.  Even the Commonwealth of Massachusetts, which has been running an exchange for five years and is the model for the country, received over $136 million from the federal government between February 2012 and January 2013.  

The Rhode Island exchange is an expensive program for a small state.  It is budgeted at $28 million per year for 2013 and 2014.  State legislators are concerned about the budget for 2015 when they will be on the hook for paying for it.  In a letter to the editor to the Providence Journal, “Whopping Costs for RI Exchange”,  I noted that if the program achieves its stated primary goal of a 10 percent reduction in the number of people uninsured by 2015, the cost of operating the exchange amounts to almost 30 percent of its overall expenditures (using its own projections of "target populations" and "potential premium range" and adding in premiums for Medicaid to determine expenditures). Even if it achieved 25 percent in years to come, the administrative cost would still be more than ten times that of Medicare at 1.4 percent of its total $549 billion in expenditures and twice what private health insurers are allowed spend on administration and profit (the medical loss ratio). 

I also noted that the administrative costs to get people insured amounts to more than the costs to actually insure them. Even if the exchange were to achieve an unrealistically high 50 percent reduction in the number of people uninsured, or about 50,000 people, by 2020, at a total cost of more than $200 million between the federal government at $100 million and the exchange costs between 2015 and 2020 of over $100 million, the cost per new enrollee would be $4,000 which happens to be higher than the average cost of the health insurance for a year.

Making Exchanges Better

It is important to step back and acknowledge that the overarching goal of the exchanges is to get many more people insured.  And, it is important for the exchanges to determine the subsidies for those in need and to enroll people in a fair and efficient way.  Obamacare has done almost everything politically feasible in terms of legislation to make this happen.  But, what is decreed in Washington is implemented locally. 

There are models for exchanges that do not jeopardize social justice, have a proven record of economic results and customer satisfaction, and do so at low administrative costs.  The best example is Medicare.  It has low administrative costs and has kept healthcare cost increases lower than the private sector over the long term.   It does not discriminate on age in its pricing.  It serves a social good that is paid for by the ability to pay, not by charging more to the sick or the old.  Most retirees love the program, even those who dislike government “intruding” in their lives.  Their common refrain is, “Do not take my Medicare away.” 

As the exchanges come on line in just a few weeks they will provide transparency about prices for insurance, their administrative costs, and who the winners and losers will be.  It is important to recognize that the exchanges are an additional overlay to a relatively high-cost private health insurance system.  In hindsight, it might have been better to allow older people to enroll early in Medicare as they can do with Social Security.  It might have been better to have a single payer system.  But, “it is what it is” after 50 years of debate.  What is critical for the exchanges to accomplish in order to be self-sustaining beyond 2015 is to stop age discrimination and adopt community rating, reduce administrative costs by achieving economies of scale across states, and make a difference in peoples’ lives by assuring sufficient and affordable health insurance benefits.

Exchange This!

As the prices for insurance products on the health insurance exchanges become public, there is a growing awareness of affordability sticker shock that threatens its economic and moral sustainability.   We all know that health care costs are way out of line and are the primary reasons for high insurance costs. The private insurance model we continue to depend upon cannot elude this problem.  Somebody must pay.

Subsidies will help ease the affordability problem for most people (now) seeking insurance on the exchanges. But, for those who do not qualify for subsidies (those with an income of more than $45,000 per year) the premium prices and ratcheted-down benefit plans will be too much to bear and these people may continue to go without insurance.  One group in particular is being discriminated against.  My op-ed , "Don't Balance Health Care on Elderly Backs", published in the Providence Journal, is reprinted below.

Dwight McNeill: Don’t balance health care on elderly backs

September 09, 2013 01:00 AM

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BY DWIGHT MCNEILL

Felice Freyer’s Sept. 1 article (“Tall ambitions for Obamacare in R.I. — more than insurance”) was meant “to describe . . . not critique” the vision of HealthSource RI, the health insurance exchange for Rhode Island. But we need to be critical thinkers and reporters about features of the exchanges that may lead to their demise.

One longstanding concern about private insurance is charging people more for their personal characteristics. Buying insurance from the exchanges should be just like buying products over the Internet through Amazon.com. But there is one big difference. When you go to Amazon.com to buy a television set you do not have to pay three times what somebody else pays just because you are older.

The newly published rates from HealthSource RI charge older people three times those of younger people, as the law intended. For example, if one picked the mid-level “silver” plan, the yearly cost would be $8,388 for the 64-plus individual and $2,784 for the 24-year-old.

For the older person making $45,000 and not eligible for subsidies, this amounts to 19 percent of income. For the young person, this amounts to 6 percent of income. Additionally, most of the mid-level “silver” plans offered have a deductible of $3,000 and an annual limit of $6,000. Older people are much more likely to have health conditions such as a chronic illness and a hospitalization that could quickly add up to the limit of $6,000. So, for a sick older person, the total impact of age-related pricing and a not-so-generous benefit plan could amount to over $14,000 out-of-pocket, or almost one-third of income.

An Obamacare hallmark was to eliminate most forms of insurance discrimination, including by gender, health status and pre-existing conditions. But age discrimination lives on. Why? It is not the norm around the world, under employers’ plans, in Medicare or the insurance program for Congress, and many states do not vary employee cost for insurance by age.

Peer countries that have a government-run insurance plan or offer supplemental private insurance also consider it illegal to price insurance according to age. In an ironic twist, Obamacare allows for increased insurance rates of up to 150 percent for people who smoke, but the smokers can evade this if they enroll in a wellness program. Older people cannot do anything to erase the toll of aging.

Age rating of insurance represents an American tension that plays out in many forms of public policy. Is health care and its insurance a right or a privilege? Are we a nation of individuals who “bowl alone” and take responsibility for our own risks and rewards, or are we a community of people that has shared goals and responsibilities?

Many believe that health care is a social good, like education and good infrastructure, and should be financed on the basis of the ability to pay, not on one’s use of services. If we extended the logic of use rating, shouldn’t there be extra fees for public education for families with children?

Age discrimination is the last vestige of an archaic private health-insurance system. This Achilles heel of the exchanges should go the way of the reversal of the “doughnut hole” for Medicare Part D before it. The sick or other population groups should not be singled out to balance the books for public policy.

Dwight McNeill, of Little Compton, is a visiting professor of population health and health policy at Suffolk University and author of the book “A Framework for Applying Analytics in Healthcare: What Can Be Learned from Best Practices in Retail, Banking, Politics and Sports.”

Pogo Knows Analytics

To borrow a phrase from Pogo, “We have met the enemy and he is us.”  Those of us practicing analytics must change the ways we do our job.  Quite frankly, there are huge opportunities to improve healthcare and many analytics solutions to support it, but we are only scratching the surface of our potential.  We need to expand the scope of our work from doing things with data to using data to change the organization for the better, as depicted below.

But this is an overwhelming list of functions and competencies.  No one person can do it all.  It requires a village (team) to have all the skills and accomplish all the functions.  Some suggest that a new chief is needed to lead us.

Salvation from the CAO

CAO stands for Chief Analytics Officer.  (It also stands for Chief Administrative Officer.)   The role is new for the C-suite.  Michael Bloomberg, the data-driven mayor of New York City, in his last State of the City Address, appointed the city’s first ever CAO, Michael Flowers, to “improve the way all agencies share information and to make the data available to the public so that the community can hold the city accountable.”   Flowers used to be the city’s Analytics Director.  It’s not clear why there was a title change. The job description sounds better in the previous job.  “Mr. Flowers leads a team of data scientists in analyzing city data from over 20 city agencies to allocate its resources quickly and efficiently to prevent fire, crime, safety hazards, and unhealthy conditions.”

Chiefs are becoming very popular.  Forbes lists new C-suite titles including Chief Internet Evangelist, Chief Happiness Officer, Chief Privacy Officer, Chief Digital Officer, Chief Knowledge Officer and Chief Customer Officer among others.  In healthcare, the new CAO role joins forces with other information leaders including the CIO (information), CDO (data), CIO (innovation), CMIO (medical informatics), and CNIO (nursing informatics).  I bet there are more to come. 

I guess the reason for chiefs is to bring visibility to the function, get the ear of the CEO, collaborate with peer chiefs for the good of the enterprise, provide better management oversight, and be accountable for results.  All good things and it’s important that analytics is recognized as an important function along with the dozens of others.  And it is good to have the executive talent.  But, leadership is not just for the few chiefs.  It’s for all of us.

Remake ourselves

Mahatma Ghandi said “Be the change that you wish to see in the world.”  We need to expand our technical and people skills to increase the utility of analytics in healthcare.  We need to work locally and make teams work better through communications and collaboration and dedication to a common goal.  We need to focus on the immediate tasks at hand such as working through an algorithm or building a database and also be sure there is a receptor site to absorb our work.  We need to visualize how analytics improves business and society.    We need to lead by our own example.

This blog is an abstract from my chapter, Health Analytics:  The Way Forward in the forthcoming book I am editing, Analytics in Healthcare and the Life Sciences: Strategies,Implementation Methods, and Best Practices, to be published in December 2013.

Don’t Play, Be Happy

Employers can get out of healthcare and do right for employees, the company, and country.  Most employers are reluctant agents in providing health insurance and would be happy to find a clean exit.  Obamacare gives them the opportunity.  Let me be clear.  It is not about evading responsibility, e.g. reducing employee working hours to avoid providing insurance or spinning off separate companies to get the number of employees down to under 50, but about making wise investment decisions for the benefit of all stakeholders. 

Employers got into the health insurance role during WWII in a deal to provide tax free benefits during a wage freeze.  The freeze thawed but the “temporary” measure has continued for 70 years.   It costs the U.S. Treasury $260 billion in reduced tax revenues and is the largest single tax expenditure.  Most economists would agree that providing insurance through employers does not make sense.  Employees do not realize that benefits are provided in lieu of increased wages.  Employers are sick of the volatility of premium increases and increased medical costs.  But, if employers leveraged this tax advantage to actively improve health and productivity it might be a good deal, but few do.

 

Large employers who do not “play” in providing insurance benefits must “pay” a penalty of $2000 per employee per year starting in 2015.  Yet, the average insurance premium per employee in 2013 is over $11,000 with the employer paying about $9000.  Do the math.  According to a survey by McKinsey & Company, between 30 and 60 percent of employers will stop offering health insurance after 2014 and at least 30% would gain economically even if they paid the penalty and made employees whole through other benefits or increased wages.

How can this be?  The bottom line is that the health insurance exchanges provide subsidies to low to mid income people (up to 400% of the federal poverty level or about $46,000 per individual or $94,000 for a family of four) such that their out of pocket insurance expenses are low and in some cases can be zero (if they chose a plan of lower cost compared to the benchmark plan).  About 67% of households have income below the 400% level.  Other advantages of the exchanges are that people also have a choice of plans to meet their needs, not usually available from employers.  And their total out of pocket costs for health care expenses are capped at lower levels than most employer plans.  So, the majority of Americans could be better off in an exchange if the employer provided a minimal subsidy in addition to the large subsidy provided by the government.  Higher paid employees may need alternative benefits or compensation to remain whole.

The pay or play rules for large employers have been delayed until 2015.  And it is possible that some state exchanges will make insurance available to employees of large companies in 2017 which could be funded by employers on a tax free basis with a health reimbursement arrangement.  So, many employers may be wise to do watchful waiting and see how the exchanges emerge and succeed over the next few years.

The inevitable slide away from traditional employer sponsored health insurance is underway and will be accelerated with the presumed success of the exchanges.  Some employers may find a competitive advantage to pull the trigger earlier rather than later.  The competitive advantage will come from lower benefit costs and from providing a better deal for their employees which may reinforce the primary purpose of compensation…to attract and retain employees.

Healthcare Spring

Thanks to the New York Times for its continuing, excellent reporting on exploitative pricing in the American healthcare system as exemplified by Elizabeth Rosenthal’s article, “American Way of Birth, Costliest in the World.”  But, healthcare is not alone.   All industries have taken a turn for the worse in sticking it to customers in a variety of ways including extra fees whenever they can get away with it, data mining to manipulate buying habits, advertising chicanery, “self-service” to deflect costs and aggravation, lack of transparency, and more.  This is all done in the name of “optimizing revenue enhancement” to improve business outcomes.

The paradox in healthcare is that while healthcare is a profitable industry, ranking 14^th among the top 35 industries, its outcomes are the worst of its peer wealthy countries and its efficiency is the worst of any industry.  Healthcare is also different in that it is not selling laundry detergent, airline tickets, or car loans.  It is selling a solution to a basic human need based on the belief that without your health you have nothing.

Another paradox is that healthcare uses science more than any other industry to understand the causes of diseases and treatments.  But when it gets to the business side of delivering on the research in hospitals and doctors’ offices, people have just over a 50/50 chance of getting the right care at the right time. 

We have known how to fix these seemingly intractable problems for a long time, e.g. with global payments based on outcomes.  But markets cannot be depended on to do the right thing for society and governments are shackled from doing more largely because of special interests.  What are other alternatives?

One obvious solution would be for healthcare businesses to recognize the upside opportunity to distinguish themselves in the market by filling the void and actually producing exceptional outcomes at a fair cost.   Could a company differentiate itself if it demonstrated that its members experience better health and outcomes than the competition?  Statements like “Our health plan members live 5 years longer” or “Our heart attack patients live longer with a better well-being” should get some traction in the marketplace.  This would be substantially more creditable and honest than paid advertisements for “America’s Best Doctors”.  Healthcare companies have the data.  Why don’t they use it for this purpose?  I suspect that they cannot show the difference in outcomes to their advantage.

A second possible solution would be for government to step in and correct market inefficiencies and abuses.  Obamacare was a solid step in the right direction.  Much more is needed.  But, this Congress will not allow any more action on healthcare or seemingly anything else for that matter.

A third is to recognize the customer as more than something to be exploited.  After cutting costs following the Great Recession, many companies say they understand that the route to profitability is by growing the top line by understanding and serving the customer and actually having a relationship with them rather than selling them things through mass, impersonal, customization.  So far, this appears to be more of a marketing campaign than a true actualization of the appreciation of the customer.  Mahatma Ghandi talked about customers in a very respectful way, “We are not doing him a favor by serving him.  He is doing us a favor by giving us an opportunity to do so.”

Absent any action by the healthcare industry or by government, what is needed is a “Healthcare Spring”.  People need to stand up and revolt against an “oppressive regime” that robs them of wage increases (eaten up on healthcare insurance), does not produce on its purpose and promise to improve health, and does it all in an authoritative way that lacks transparency and citizen input.  They need to say “no” to the covert collection of their personal data which is used to manipulate rather than to heal.  And they need to recognize their own contributions to the pricing wars by acknowledging their own behavior to aggressively shop on price (for most products) and to demand more and more useless health care because “it is better to be safe than sorry”.  With the Healthcare Spring comes the reality that people need to step out of the shadows and engage in the co-production of their own health and make trade-offs that are right for them, their communities, and the Nation.

Big Data for Customers, First and Foremost

Knowing the customer is key to winning in the marketplace.  Those companies who know more, sell more, and can achieve long term loyalty and optimal lifetime value. 

Knowing citizen customers in the public government sphere has been demonstrated to be important in winning elections and in preventing terrorist attacks and finding the bad guys. 

Knowing involves more information.  The quest for knowing-it-all means that we live in a surveillance society.  There’s not much that we do that is not digitized.  Data streams that feed surveillance are far flung and include mobile communications, web interactions, biomedical devices, social media, video surveillance, and much more.  This boundless personal data has become increasingly more manageable and valuable due to significant strides in technology. 

Knowing-it-all is all about creating…intimacy.  The more intimate knowledge of one’s behavior and habits leads to more success in selling products and services because the selling can be (almost) customized to a market of one.  But the intimacy is often strange.  It’s not like we are having a conversation!   Firms and government can know more and more about you without you even knowing you have a suitor, or consenting to it for that matter.  The data that streams off you is snatched.  It is free “natural resource” to those doing the snatching.  But it is yours.  The relationship has not been reciprocal.  And who wants that kind of a relationship?

Turns out that, so far, people don’t seem to mind.  Polls show that people are not terribly concerned about the government’s collection of “metadata” on everyone’s phone calls as long as it put to good purposes like averting another terrorist attack and does not result in an FBI agent lurking in the yard or taking out your daughter’s hard drive.  And people seem to yawn at the notion that every keystroke is processed by cookie crawlers that make judgments about one’s fitness for an receiving an advertisement, job or loan. 

But there may be a looming possibility of a Data Spring.  Like the Arab Spring and uprisings in other countries where citizens reach a tipping point and reject authority that does not respect the will of the people, a Data Spring may erupt if just a few examples of privacy abuse cause the big data spigot to be turned off.    

   

It is time to think of customer analytics as serving the need to improve the lives of customers and the common good of citizens, first and foremost.  We have a saying in healthcare, "nothing about me without me"--which means that patients ought to be in control of decisions about their own lives, including their own information, and the focus of all interactions should be to improve health outcomes.  It's about engaging them in the co-production of something that is worthwhile in their lives.  Clearly, the business will prosper by taking care of the needs of customers first.  Customers will get something of value and may welcome the sharing of much more information to achieve mutual goals.  In fact, there is much more valuable data in their heads than could ever be scraped from their Big Data streams. 

So, the old customer analytics were quite passive, covert, and rather manipulative for customers.  It is the province of the marketing department.  It is all about gathering information on purchasing behavior and integrating it with models about habits and then intervening with the right message at the right time, and probably with a coupon offering, to sell them something.   The new customer analytics is dedicated to customers, engages them, and is reciprocal in terms of sharing of information and creating value.  It is about intimacy and a new relationship with customers.  Mahatma Ghandi talked about customers in a very respectful way, “He is not an outsider to our business; He is part of it.  We are not doing him a favor by serving him; He is doing us a favor by giving us an opportunity to do so.”  From this clear statement of values, transformation can occur to improve the top line for business and the health of populations. 

World Series Analytics

One knows much, much more about the performance of a baseball player, who entertains us, than about our physician, who directs our care that can mean the difference between life and death.  In my book A Framework for Applying Analytics in Healthcare:  What Can Be Learned from the Best Practices in Retail, Banking, Politics, and Sports, I address this paradox and conclude with a counter-intuitive idea. 

On the one hand there is a mesmerizing array of performance data on athletes.  It is used for recruiting athletes, but mostly it is for entertainment.  Because of the abundance of data, choosing a baseball player is so much easier than choosing a physician. When you go to a health plan or medical group Web site to choose a doctor, the information provided is comically bereft of relevant detail. One can learn about the medical school attended, specialty, languages spoken, gender, and office address, but absolutely nothing on the performance of doctors...not even how many times the doctor has been at bat, never mind whether the doctor is a champion in his or her field.

The information on doctor performance is collected but it is not made available to the public.  Same with teachers.  There is a tremendous reluctance to hold teachers and doctors accountable for outcomes for many reasons.

But, healthcare might have it right and can teach sports something to improve its industry. Healthcare, like sports, should be considered a team sport, and measurement and management should be directed accordingly. Putting the metrics on the team’s performance rather than individual actors might lead to better coordination, efficiency, and outcomes in healthcare...and in sports. And sports team management is beginning to realize that as well. Although sports generates a lot of data and it appears to know a lot about data on the surface and in TV commentary, its use of advanced analytics to improve the competitiveness of the teams and to produce value for the business is in its infancy…just like in healthcare.