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 .

Who Am I…for Health’s Sake?

Sybil is a true story about a woman possessed with sixteen different personalities spanning the intensely dramatic Vanessa to the vivacious Marjorie.  The psychiatric term is dissociative identity disorder which is characterized by at least two identities that alternatively control a person’s behavior.  After considerable treatment, Sybil’s different selves were able to reconcile and Sybil combined them into an integrated self that relieved her turmoil and improved her well-being.

In healthcare, we are possessed by different selves that behave in unique ways.  These distinct selves include that of consumer, patient, citizen and customer.  As with Sybil, we need to understand what our separate selves are up to and evolve an integrated self that minimizes distractions, takes control, and focuses on a healthy future.

Our four selves in health:

1. Consumer:  America is a shopping nation and we rely on our consumer self when we buy goods and services.  The term “consumer” comes from economics and is predicated on the theory of choice.  The theory states that when people have choice and information on price and quality they make rational decisions to optimize their welfare and to stimulate competition to improve efficiency.  The theory works well in most industries, like retail, but poorly in healthcare because the requirements for a healthy market are distorted:  a) There is little consumer choice (employers (mostly) pick health insurance plans, plans select networks and doctors, and doctors pick specialists, hospitals, and treatments), b) people pay for things with other people’s money (insurance and government subsidies), and c) information for consumer decision-making is either absent, irrelevant, or difficult to understand. Nevertheless, we persist in our belief that a consumer-driven, market-based approach produces superior results compared to alternatives including a government approach. 

The latest example is the health insurance exchanges.  Its primary goal is get more people insured and to make markets work better by rewarding insurers that satisfy consumer needs better than the competition.  But, there is little choice among insurers in most markets.  The choice is often among products offered by a single insurer.   The information is limited.  Yes, there is information on prices.  But, there is no information on insurers’ performance in improving health and customer experience and whether one’s doctors are in the narrower networks offered. 

Also, the lower cost insurance plans conceal hidden costs in the form of much higher out-of-pocket costs.  The “new normal” deductible is $2500+ for the individual silver benchmark plan. Classic research from RAND shows that people with deductible plans at this high level use doctors and prescription drugs significantly less and do not discriminate on the services they cut, whether effective or wasteful.  This has led people to question outlandish medical charges, which is good, and for some to “take out their own stitches”, which is not.

Peoples’ welfare is improved through the exchanges mostly because insurance is more affordable due to subsidies and not because their actions as consumers are inducing more competition to drive down costs and improve quality.  So, the consumer self turns out to be an ineffective role that causes a good deal of frustration and churn and a distraction from focusing on what matters most. 

2. Patient:   Our patient self emerges when we receive medical care.  It is defined by the discipline of medicine which takes a disease orientation that relies on deep knowledge of the science of diagnosis and treatment to make people well.  The expert role of the physician defines the pact between patients and doctors:  Doctors “know best” and assume an authoritarian role and patients comply with their doctor’s “orders” and “prescriptions” and assume a dependent role.

     

There are two limitations of the patient self.  The first is that the medical model works well when there is a known treatment for a specific diagnosis.  But in many cases, when the outcomes of alternative treatments are equivalent or equivocal, the choice of treatment should have more to do with the wishes and tradeoffs of the person rather than the opinions of the doctor.  This is when patients and doctors need to practice shared decision making.  But the medical model has not relented much to a patient-centered model that truly empowers patients in decision making. 

The other limitation is that the medical model only works well when people are sick.  But the majority of sick care today does not stem from pathogens or mysterious medical causes.  Most is for chronic illnesses that are caused by individual’s behavior where prevention and self-monitoring are more important than treatment.  And the medical model has not been very successful in shaping people’s behaviors.  For example, the probability that people will take their medications is 50/50.  And admonitions to eat well and take off pounds do not go far enough.  So, the patient self needs to evolve dramatically to be more actively involved in co-producing health.

3. Citizen:  Our citizen self is expressed when we vote to have others represent our views in the political process and when we participate directly as a member of a community.   It is based on the discipline of political science and the premise that democracy leads to improvements in the status quo. 

Health care has certainly been on the political agenda for the last few election cycles and is positioned for the next. But the citizen self has been relatively passive and on the receiving end of thunderous propaganda from special interests to garner support for their positions.  A slim majority votes in presidential elections and far fewer are involved at the state and local levels.

Although the citizen self is dormant today, there was a time during the 1960s and 1970s when it was in full flower.  One example was the Oregon Health Plan which included a great deal of citizen deliberation about setting priorities for healthcare including what services would be paid for under Medicaid.  The belief was that the only way to control costs was to understand that resources are limited, trade-offs are needed, and the political process must activate deep citizen participation to succeed.   This movement reached its pinnacle during the Great Society era and died off when market oriented approaches to societal challenges supplanted government approaches in the early 1980s. 

The citizen self has been hollowed out and will be not be resurrected unless the playing field is leveled and those in power invite genuine participation.

4. Customer:  A customer is similar to a consumer in that they both buy goods and services but is different because of the underlying discipline that defines it, business.  Business relates to customers in two distinct ways.  In one way, business reveres the customer and keeps them happy with low prices, high quality, and good service in order to build loyalty and profits.  In this view, the customer is always right and close relationships with them can reveal how to improve products and services and develop new ones that meet demand.  In the words of Mahatma Gandhi, “A customer is the most important visitor on our premises…We are not doing him a favor by serving him. He is doing us a favor by giving us an opportunity to do so.”

In the other way, business uses marketing and advertising tactics to deceive the customer.   They use intrusive ways to gather more information about them, without consent, to know them “intimately” in order to sell more.

In healthcare, people are seldom referred to as customers.  After all, they do not account for much of the buying.  Employers buy from insurers, insurers pay doctors, doctors determine treatments.  In business, leverage comes to those with the most money in play.  People are bit players and more likely to be on the receiving end which limits the opportunities for influence and maximizes the likelihood of manipulation.

Our integrated self

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.  To achieve an integrated self, one must understand and balance competing demands and align these with an overarching conviction to achieve our full human potential.  This is not easy.  As  e.e cummings said, “ To be nobody-but-yourself--in a world which is doing its best, night and day, to make you everybody else--means to fight the hardest battle which any human being can fight; and never stop fighting.” 

The good news is that there are developments in three areas that are converging to make the fight winnable.  In my next blog, I will address these promising solutions.

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.