1. Systems must constantly tune the balance of benefits, costs, and risks.  Unfortunately, the stakeholders in our health system are generally only thinking about one at a time.  This creates much of the inability to communicate and make progress.
2. Disruptors don’t have the same chance of winning as in other industries.  When you make the hurdles to disrupt too high, you lock in the incumbents.
3. Technology is beginning to create some paradigm shifts.  The existing structures will likely blow up quickly vs. transition slowly
4. The Architecture for Personalized Health will look very different than that for our current system.  This will be a focus of an upcoming series on what a transition to personalized health will truly require and who will win and lose.

Related articles

• Exploring What We Don’t Know at TEDMED 2011 (rwjfblogs.typepad.com)
• You say you want a (healthcare) revolution: TEDMED 2011 (oslersbeast.wordpress.com)
• TEDMED Announces Move to Washington, DC; the John F. Kennedy Center for Performing Arts Will Host TEDMED 2012 Conference (prnewswire.com)
• TEDMED Day 1 – Pop It, Lock It, and Take a Statin (medgadget.com)

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Terrific Clay Shirky talk on how the reducing cost of communication will change the dynamics of institutions — namely that the coordination cost of large institutions will become higher than the connecting and collaboration costs of small teams and volunteer individuals.

How does an institutional model (thinking in the world of FTEs) begin to understand the contributions of individuals on a power law curve? Competing organizational structures are going to be interesting…

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Culture

Just came across Netflix’s internal presentation on their culture.  It’s a pretty fascinating look at their approach to personnel.  Looks a lot more similar to a sports franchise than a public company.

This would indicate that the War for Talent is alive and well…and that companies are much better adding a few very talented individuals over many averagely talented individuals.

In the realm of the information society, where power laws rule over averages and medians, it seems that organization of organizations is beginning to shift.

Related articles

• Seven Secrets to Netflix’s Success (gigaom.com)
• Here’s How Reed Hastings And Netflix Won (businessinsider.com)
• Silicon Alley Insider: HOW NETFLIX CONQUERED THE WORLD: The Management Secrets That Propelled A DVD-By-Mail Company To Greatness (businessinsider.com)
• INSTANT MBA: Don’t Hire Too Many Highly-Talented People (businessinsider.com)
• Reed Hastings at D9 Conference: Netflix Will Remain Complement, Not Competitor, to Cable (robhof.com)
• Netflix’s Formula for Innovation (blogs.wsj.com)

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One panel that really brought home the challenges of making a better system discussed whether providers would be able to bear risk successfully.

The assumptions of the Accountable Care model are that providers are going to want to take risk.  At some level, you can gain share and the provider would see a bonus for better performance.  At a deeper level, the provider may capitate or truly create a risk-bearing entity that enables it to truly win or lose based on the management of total costs.

A few things hit home:

1. Providers (with a few exceptions) don’t know how to manage risk or even measure exposure.  They’re used to cash being paid for services.  Adding negative risk on already thin margins is likely to bring bankruptcy or bail out as soon as the first misteps or uneven catastrophic loss hits the balance sheet.  Insurers have much more padded balance sheets for a reason.  A strong reinsurance market that understands the risk to these risk-taking health entities will need to be created (which then limits the risk to providers)…making the scheme of providers bearing risk only partially relevant
2. There is no objective metric for sharing risk.  A “currency” type metric similar to Nielsen scores (television) or FICO scores (credit history) will need to be put in place to enable the providers and payers to trade appropriately (and at scale) on the basis of risk adjusted individuals or populations (and risk reduction through better care).
3. Almost no new entity (not already very far down the path before the legislation) should want to become an ACO in the near future based on the current regs.  The economics aren’t clear in a positive direction and there are too many requirements vs. projectable upside.  I find this sad.

More to come soon.

Related articles

• Accountable Care Organizations (ACOs) (medpromgmt.wordpress.com)
• Accountable Care Organizations – What Do They Mean For the Average Citizen-Patient? (medcitynews.com)

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Salmon Khan offers an inspiring TED Talk highlighting his approach to transforming education through technology.  He founded Khan Academy after realizing that his cousins preferred his YouTube video interaction over his actual interaction.  By enabling students to learn at their own pace and review things they didn’t quite get, his platform give students the flexibility to achieve mastery in every topic before moving on.  In a normal classroom, you get your grade and move on regardless of your mastery of the subject covered.

By focusing on proven mastery of a subject (get it right 10 times in a row) over one-size-fits all, Khan has leveraged technology to create a disruptive model of education.  I think it’s a great starting point and I look forward to seeing competition around student mastery of education over the current metrics we use today.

Related articles

• Yes, The Khan Academy IS the Future of Education (video) (singularityhub.com)
• Salman Khan Describes the Video Education Revolution at TED (video) (singularityhub.com)
• Misconceptions about the Khan Academy (shipordie.com)
• Khan Academy is the Best Gift (neouto.wordpress.com)
• Khan Academy Is 21st Century Education (kwiksocial.com)
• Khan Academy: The self-paced lecture (thebrightlibertarian.blogspot.com)
• The Future of Education: Online and Quantified (quantifiedself.com)
• Khan Academy (jeninec.wordpress.com)
• Khan Academy’s Founder Journey (andrebastary.com)
• Perspective – The Autodidact and Khan Academy (practicaltheory.org)
• Salman Khan – Let’s Use Video to Reinvent Education (freetech4teachers.com)
• Educational Innovation, Technology and Entrepreneurship (crystalkeyministries.wordpress.com)
• Salman Khan: Video in Education (malbell.wordpress.com)
• Can Technology Reinvent Education? (crystalkeyministries.wordpress.com)
• Let’s use video to reinvent education: Salman Khan on TED.com (ted.com)

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In our previous post, we outlined the rationale for having a Personal Vitality Score.  Here, we will outline the components that could be used to create one.

Objective of the Personal Vitality Score

The goal for the Personal Vitality Score is to create an objective, up-to-date score that summarizes the health of an individual.  Much as a FICO score measures credit-worthiness, an objective individual-level Vitality Score can deliver a snapshot of where a person is and what services/ products will most help them to improve their health.  We believe that a Personal Vitality Score -type currency is required to create the metrics for a health system that would focus on improving the health of an individual (and populations).

Components of the Personal Vitality Score

A Personal Vitality Score would summarize the health status of an individual.  It would need to combine multiple elements:

1. Current state of an individual’s health, based on their individual health information
2. Programs and treatments the individual is enrolled/ engaged in
3. Projected health of the individual given (1) and (2)

1. Individual Health Information

The Individual Health Information dataset provides a current picture of the individual’s health.  For it to do so, it must combine what is known about the person’s current status with a mix of how they live and their medical history.  Far beyond the hospital-centric EMR, this dataset is a synthesis of all the factors known about this individual that impact their health.  It combines information across:

a) Daily Activities and Lifestyle

b) Personal Characteristics and Abilities

c) Medical History

For this snapshot to be relevant, this information must be updated in a regular fashion.  A data collection infrastructure to capture and synthesize relevant information must be put in place for this comprehensive snapshot to come together.  We’ll give some examples of what this may look like below:

1a. Daily Activities and Lifestyle

This category captures the day to day “healthstream” of choices made by an individual and activities they participate in.  We know that things like diet and exercise have a tremendous impact on health status, and this category enables us to capture that information.  Likely sources for this information include:

• Quantified Self  and Activity tracking websites like DailyBurn, Nike+, BodyBugg, diet logs, etc
• Health profile updates from profiles on medical sites like PatientsLikeMe
• Wireless health data including sensors, scales, and monitors
• Grocery purchases logged on credit cards or store loyalty cards
• Health club visits and digital workout logs

1b. Personal Characteristics and Abilities

We must know an individual’s baseline to appropriately understand their risks and their potential.  This category captures the underlying characteristics of the individual and their current capabilities, including:

• Genome and genetic markers
• Physical and mental capabilities: IQ test, reading levels, abilities to perform activities of daily living, fitness, visual acuity, etc
• Molecular performance: activity levels or protein expression of various enzymes and protein markers
• Life skills
• Weight/ body composition

1c. Medical History

The presence of disease markers or diagnosed conditions can significantly impact health risks and future capabilities.  This category captures diagnosed conditions, disease markers, family history, and other relevant components of the medical history.  Sources could include things like:

• Personal Health Record
• Electronic Medical Record
• Aggregators like Microsoft HealthVault or Google Health
• Profiles from disease communities like ACOR or PatientsLikeMe

2. Program / Treatment Enrollment and Engagement

The programs and treatments being utilized or considered are elements that can change the trajectory of someone’s future health.  They may impact the individual by improving their capabilities or reducing their risks.  Today, programs and treatments are often reviewed in isolation.  In tomorrow’s world, the total impact of the program to the individual, which may be measured by the Personal Vitality Score, is what will matter.  Impact will depend on the:

• potential improvement and side effects of the program or treatment chosen
• context of the individual
• degree of engagement
• interaction with other programs or treatments

As we better understand the relative impact of various programs and treatments on individuals, we will finally begin to tap the potential of comparative effectiveness research and understand the opportunity cost of choosing one program over another.

The set of data captured in this database includes:

a) Participation in programs and treatments: CPT codes for procedures, prescription data, use of herbal medicines, treatments by allied health providers, etc

b) Engagement and adherence: Prescription fill data, office visits, program specific metrics, self-reported participation, etc.

c) Options considered: List of alternatives under consideration

d) Treatment interaction: Likely interactions between treatments and programs in which individual is enrolled, cumulative burden of all treatments and programs enrolled, etc.

3. Projected Individual Health Outcomes

Creating a personalized projection for future health capabilities and risks must mine what we know from longitudinal studies and treatment databases and combine it with what we know about the individual.

The approach required to create a projected individual health outcome is the opposite of the one used to create a clinical trial.  In the clinical trial, we look to isolate a variable across a number of people.  In this projection, we look to drill down into the segment specific data for all the conditions and treatments pertaining to the individual and synthesize them.

This category will require development of detailed, individualized algorithms that can mine deep datasets including:

• Segmented population databases enabling projection for the individual based on a number of relevant factors (e.g., Framingham)
• Deep longitudinal datasets from disease-oriented communities (e.g., PatientsLikeMe, ACOR), clinical trials, payers, and providers
• Segmented or individualized clinical trial data (e.g., enabling drill-down by various factors)
• Provider-specific and treatment-specific outcome data

Vitality Algorithm

The Vitality Algorithm pulls these three disparate streams of information together into one comprehensive score with a number of sub-elements.  The algorithm is likely to be a work in progress — updated as information needs and relative importance change over time — and so a structure that balances an accepted standard that may be replaced with disruptors is likely to be required.  It is unlikely that a structure based on consensus or a scoring system based on a monopoly held by a nonprofit or government will be dynamic enough to keep up with the advances.  Therefore, a dedicated scoring model that gains share but faces multiple disruptors (e.g., FICO) is likely to become the best answer over time.

Synthesis

The Personal Vitality Score creates a synthesis of a person’s health by combining in one score: 1) a personalized view across all known health data, 2) what we know about the individual, and 3) what actions the individual is taking.

By packaging all these elements together, we get a robust picture of an individual’s health, where it is going, and what can and is being done to improve it.  Abstracting a level, we can create a Vitality Index across a population, which would help to allocate resources in the most effective way.

The creation of this Personal Vitality Score enables us to measure value creation in health and could create an ecosystem with the appropriate incentives to improve health in a given individual or population.  What that new ecosystem looks like is the subject of our next post.

Related articles

• Measuring Health Value – A “Personal Vitality Score” | Health Information Economy (goelinsights.com)
• Creating Fundamentally Valuable Information in Health | Health Information Economy (goelinsights.com)
• Robert Houtenbos: The Value of Openness: The PatientsLikeMe Blog ” How Patients Like You Use Your Treatments in the Real World: Two New Studies (patientslikeme.com)
• VA Blue Button moves ahead with vets health records (cnewmark.com)
• Will mHealth Apps and Devices empower ePatients for Wellness and Disease Management? A Case Study (andyde.blogspot.com)

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Measuring Health Improvement

As we discussed in the previous post, the fundamental value of health services are things that improve our health.  As we define the foundations of a Health Information Economy, information that has intrinsic value includes:

• our current and future health status
• our current and future health-related capabilities
• the personal  impact of any health product or service under consideration
• total cost of any health product or service utilized or under consideration

Having this information at an individual level enables the appropriate decisions to be made for each specific individual.  In a rational system, one would expect that an aggregate of the decisions to improve the health of each individual would move the needle on a community level index.

Defining the Personal Vitality Score

The Personal Vitality Score is analogous to a “FICO” score for your health.  It is a single number that synthesizes the various elements of your health status, risks, and capabilities into a number that goes up as your health improves and declines as your health deteriorates.

Today, we do not have a Personal Vitality Score to help us understand where we stand and how important potential interventions may be for us.  The implication is that we are inundated with things that might save our life or may be wildly irrelevant, and yet we are told to “ask our doctor”.  Since our doctor can’t keep all the relevant factors for you in his/her head, we often do not prioritize the interventions, tests, or activities that have a disproportionate impact on our health.

The requirements for an actionable Personal Vitality Score are the following:

• Dynamic: updates as our health status changes (for good and bad), and as new data comes out
• Auto-populating: data continually updated without need for additional data entry (e.g., accepts feeds)
• Accurate: take in enough relevant data points, with good enough processing algorithms, to appropriately report current and future health status
• Actionable: outline concrete things individuals can do to improve their status
• Comprehensive: takes into account elements spanning wellness to specific illnesses

Implications of a Personal Vitality Score

As we think about a health information economy, a Personal Vitality Score is one starting point in terms of creating the currencies that enable companies to capture the full value of the “healthstreams” produced for each individual and across populations.  This becomes a foundational block for rewarding Accountable Care models, creating actionable “comparative effectiveness” maps for individuals facing treatment choices, and creates a currency and value curves for companies that want to make a business out of superior real-world impact.

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What is Fundamentally Valuable in Health?

Those who find better, cheaper approaches to improve health should be rewarded, right?  Therefore, there should be a market for information that enables these systems to be put in place.

Unfortunately, the marketplace in healthcare (with a focus on the US) does not measure nor reward the improvement of health.  Today’s fragmented health systems are dominated by the needs of large players, each attempting to maximize their share of the pie for the specific transactions and services they perform.  In such an environment, the actual value of the services delivered to any individual matter much less than the negotiating clout of each of the players and structure dictated by the biggest payer — the government, regardless of the impact to the health of any specific individual or community.

For the purposes of this exercise, we will not start from this current model as a given.  Instead, we will assert that each individual would value the best possible health outcomes for them at the lowest total cost to them and their family.  As individuals aggregate into groups, the same standard should hold, but at the group level.  By this definition, value would be created as organizations discovered how to maximize the outcomes for individuals along some pricing curve.

As we look to create a sustainable Health Information Economy, we assert that in order for this new economy to exhibit rational and ethically consistent behavior, there must be rewards for those that can demonstrably improve the health of individuals and populations while holding down cost.  A Health Information Economy based on a currency of improved health value creates rational incentives down the line.

How would one measure this currency?  We’ll explore some possibilities in our next post.

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We’ve decided to put together an entry for the Economist-Innocentive Health Information Economy Challenge and hope that you’ll help us sharpen out thinking on various elements as we try to put it all together.  While the prize money isn’t substantial ($10,000), it’s an area we certainly have a number of strong perspectives around.  It’s a topic that seems to engage all of our areas of focus: innovation and strategy in health information technology.

Strategic perspectives, of course, are not well-written prose, so we hope that you will help point out the logical inconsistencies in our upcoming writings and help us battle-test these perspectives so we can put them all together into one well-crafted position paper (which we will of course share after our submission).

Look forward to your feedback and hopefully to some friendly sparring.

Best,

Vijay

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Pundits appear to resoundingly pan them for poor taste and insensitivity (Conan spoof below).

What shocked me was the extremely poor sales positioning and ad copy for a company who’s core competency is supposed to be sales positioning and ad copy.  Perhaps those skills matter less than the extreme discounts that they broker for consumers?  If so, this could make for some unhappy shareholders in the year or so following the Groupon IPO.

Why?
As Verizon has emphasized in it’s wars with AT&T, it’s all about the network.

Groupon and the rest of the group buying clones have 2 audiences to sell to:

1. Consumers
2. Businesses

The pitch to consumers is pretty straightforward to date: we’ll get you great deals (50+% off) at high quality businesses

The pitch to businesses is a little bit more complicated…and here’s where I see the potential for failure: we’ll bring you high quality consumer traffic and exposure at no risk to you.

Now given the spot below (one of Groupon’s unlaunched commercials), they bring up some pretty serious questions about the value of the consumers in their network

Let’s break it down:

Businesses want to be able to justify the offers they are putting out over Groupon and other group buying sites as low risk today and highly profitable vs. other forms of marketing.  Generally you would expect the campaign to reach positive Return on Investment (ROI) over 12 months or so after launch.

Here’s where the network makes the difference.

Good network case: (and why the growth to date has been so explosive)

• Brand recognition: Positive write up brings brand awareness to target customer segment
• Upfront cost: None for the campaign
• Variable cost: Negative revenue impact for current customers taking deal.  Incremental losses for new customers utilizing deal (if margins on sales <75%*)
• Profitability drivers:
  • PR value: Your brand gets a positive writeup emailed to X subscribers in your area, of which a reasonable percent are potential customers or referrers
  • Return customer rate: Substantial percent of people introduced to the business like it, and return.  Multiplied by any friends they also bring to the business.
  • Addition Purchase: Coupon holders feel so good about the deal, that they spend additional money (Groupon states an expectation of 50% over coupon value)

Summary: Free publicity and a one-time deal is actually high value customer acquisition

*Assuming 50% share between business and Groupon after 50% discount

Bad network case: (and where networks effects could make investors try to sell at 50-90% off today’s valuation)

• Brand recognition: Email deal to non-target customers brings in people from the wrong target demographic.  Their displeasure with your offering is multiplied by the number of bad reviews and negative word of mouth they spread about the business.
• Upfront cost: None for marketing.
• Variable cost: Negative revenue impact for customers taking deal and customers displaced (or negatively impacted by) by non-target but deal-seeking audience utilizing coupon.
• Profitability wreckers:
  • Substantial incremental losses on deal itself (for products with margins <75%* or for limited capacity venues)
  • Negative PR/ reviews from existing customers impacted by overwhelmed staff and from the deal-seeking audience who are a poor fit for the business
  • Minimal return customers…they’re deal seekers who never want to pay full price
  • Minimal purchases over coupon value…heck they don’t even like to tip on the full price

Summary: The locusts descended and stripped your establishment only to move on to the next one

Word of caution.

If the Bad Network case ends up evolving, businesses will rightly reject the current model.  That could mean a number of things…decreasing quality or percentage of offers, substantial reductions to current share of revenue, high turnover among establishments = high sales/marketing costs.  Any of these would substantially impact the sustainability of the growth curves being seen and turn the Group Purchasing sector into the coupon mailer v2… don’t see any $15B valuations in that space.

Related articles

• I’m Willing Tibet This Won’t End Well For Groupon (ecorazzi.com)
• Catherine Ventura: Groupon and Kenneth Cole: Trafficking in Human Misery (huffingtonpost.com)
• Groupon Defends Its Super Bowl Ads (webpronews.com)
• http://blog.dogster.com/2010/03/23/the-achilles-heel-of-gilt-groupon-long-term-volume/

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