II.  National Health Policy: Highlights & And Our Future

IIi.  The Cambrian Explosion and A Rapid-Learning Society by Lynn Etheredge

III.  Why Did Novartis Pay Trump’s Lawyer $1.2 Million? Look At Its Drug Prices



 Lynn Etheredge heads the Rapid Learning Project. His career started at the White House Office of Management and Budget (OMB), where he was OMB’s principal analyst for Medicare and Medicaid and led its staff work on national health insurance proposals. Lynn headed OMB’s professional health staff in the Carter and Reagan administrations. His contributions have ranged broadly across Medicare, Medicaid, health insurance coverage, retirement and pension policies, budget policy, and information technology. Lynn Etheredge proposed the concept of the “rapid-learning health system” in a special issue of Health Affairs in 2007, and is collaborating widely in developing this approach. Rapid learning initiatives are now generating comparative effectiveness research, a national system of learning networks and research registries, national biobanks with linked electronic health record and genomic data, a new Medicare and Medicaid Innovation Center (with $10 billion of funds), and rapid-learning systems for cancer care and pediatrics. He serves on the editorial board of Health Affairs and is author of more than 85 publications. He is a graduate of Swarthmore College.

II. National Health Policy: Highlights & And Our Future

National Health Policy: Highlights & And Our Future

From cottage industry to 4th Industrial Revolution

Lynn Etheredge Swarthmore College June 2, 2018


  • The US health system is a key issue for national social and economic welfare, health status, scientific progress, budget and tax policies, and retirement security, as well as our (dysfunctional) political system

  • The US health system is now big business, influenced by business strategies as well as bio-medical science, physicians, and patients

  • We are at the start of bio-medical and rapid-learning revolutions, driven by genomics and ”precision medicine” advances, digital technologies (computers, Internet, big data, data science/AI), and rapid learning networks -- possibly a 4th industrial revolution, a global bio-economy, The Cambrian Explosion and a Rapid-Learning Society (website)

  • Big politics + big business + big science: What does this mean for us, the next 10 years? Benefits and risks -- from politics, markets & science.

Bio-Medicine -- Highlights

  • US is world leader in bio-medical research (NIH $30 B) but with large gaps in evidence-based medical care, long delays in science advancesàpatients. A “rapid-learning health system” proposed (2007), for using digital technologies, with electronic health records for all (2009, $70B), national research databases & learning networks. Also UK & EU.

  • ”Precision medicine” (2011): most diagnostic codes, based on symptoms, are inaccurate (or wrong). (Dx also used for billing). Diseases will be increasingly defined by personal genomics & proteomics, therapies will be molecularly designed and targeted for personalized medicine. We will need large national databases and learning systems to make this work.

  • CRISPR-Cas9 (2012) – fast, accurate gene-editing technology; custom design of cells and what they do; cells as “factories of the future” & bio-economy

  • Genome sequencing costs: 2001 ($100M), 2007 ($10M), 2018 (<$1,000)

Bio-Medicine -- Highlights

  • Human genome sequences: 2018 – 10 yrs for 10, 000 patients (PanCancer Atlas); 2025: 60 M, 80% in health delivery systems. (40 countries). 257 genetic tests (2014)à75,000 (2018) (10,000 unique). Data science/AI advances.

  • Led by cancer and rare disease patients. 3% of cancer patients in trialsànearly 100%, with comprehensive, comparable data & predictive models (Bayesian statistics). Oncology “CancerLinQ” networks community oncologists and patients into real-time learning system. 1,000 new targeted therapies in trials

  • With electronic health records, national patient care now becomes part of a “rapid learning health system”. Beyond selected patients in academic centers, and studies. New national research databases now include 300M+ patient years of data, 100M+ patients. 1 M “All of US” bio-bank.

  • Swarthmore: new Biology + Engineering building, computer science as 2nd major. 50% of MIT faculty and students now doing biology. Davos forum: 4th Industrial Revolution (after steam, electricity, computers & IT).

Health Insurance -- Highlights

  • The US has been the only major country without a commitment to assure accessible, affordable healthcare as a right of its citizens. Avoidable illness & economic distress. Provider cost-shifting to insured, burdens on state and local safety nets & other public spending. After 40 years, historic gains (2010)

  • Proposals (failed) from Presidents Nixon, Ford, Carter, Clinton to expand coverage w/ cost controls. Congressional leadership, e.g. Sen. Kennedy, Rep. Waxman (Medicaid mothers & kids, CHIP). Heading toward 50 M uninsured, growing 1 M/yr by start of Obama administration (2009)

  • Obama plan (ACA, 2010) combined coverage elements from predecessors: tax credits for affordable private insurance, Medicaid for lower-income, health exchanges for sign-up & choice. Negotiated health provider support by added revenue from coverage, offset w/ modest Medicare future rate restraint and “pay for value” reforms.

Health Insurance -- Highlights

  • Uninsured <65): 44M (2013)à26M (2017). More coverage as ACA “hold out” states (18) expand coverage. Obama legislation has been our generation’s landmark social legislation – so far. (Social Security 1935, Medicare/Medicaid 1965)

  • Medicaid’s expansion (Federal-state): 20M (1980), 32M (2000), 74M (2018). Plus long term care coverage for nearly all Americans, now about 50% home & community based. From welfare add-on to largest US health program. David Smith, Judith Moore Medicaid Politics and Policy (2017, 2nd edition). Medicare added disabled, Rx, hospice, home health, private health plan options

  • Federal deficits of > $1 T/yr and Republican agenda to cut ACA enrollment to pay for tax cuts challenge coverage gains.

Health Expenditures -- Highlights

  • The US health system is the most expensive in the world, $3.5 T (2017) 17.9% of GDP v. $75 B (1970) 6.9% of GDP. OECD average: 9.0%, Canada 10.6%, Germany 11.3%, UK 9.7%. A key difference is higher US prices ( versus greater volume of services or value). Technology use is a driver. Health sector is a major employer with well-paying secure jobs.

  • Health spending increases have slowed a lot from 7.3%/yr (1990-20007) to 4.2%/yr (2008-2016) but still faster than GDP, wages, profits, tax revenues

  • Most workers find health insurance premiums increasingly difficult to afford. Average family premium = $18,764 (69% employer paid 2017) v. median household income = $59,039 (2016) . The employer-paid premiums ($12,947) are part of total compensation, reduce workers wages.

Health Expenditures -- Highlights

  • Government programs shifted from open-ended insurance to price-setting in 1980s; Clinton “managed competition” proposal led private employers to shift from open-ended insurance to “managed care” in 1990s. Great consolidation among private insurers. Providers respond with greater consolidation, led by hospital mergers and buying physician practices. One-third of Medicare & two-thirds of Medicaid enrollees now in “managed care” plans. ”Regulatory capture” + inefficient markets + unapplied antitrust.

  • US real GDP/capita: 2.2%/yr (1947-2000), .9%.yr (2001-2016), <1%/yr (future) A fundamental problem for US future. Unless economics policies and performance change, this will seriously challenge the ability to afford future health care, retiree benefits, education, R&D, infrastructure, world leadership v China (10%/yr GDP growth for 30 years), and other expenditures,

Our Future - ?

  • US healthcare has “modernized” since the (somewhat sleepy) 1960s paper- based, cottage industry & traditional insurance. It now has capitated, organized systems of care, with tools and incentives to improve health, quality and efficiency, e.g. electronic health records, quality metrics, practice guidelines, a comparative effectiveness studies agency, a $10 B innovation center to support expansion of best practices. Plus “big data” science, Watson, AI , a ”rapid learning health system” infrastructure.

  • How our care will change:

– Precision & personalized medicine: electronic health records + genetic sequencing, on-line physician and patient desktop access to world’s bio- medical science base, patients-like-me databases and networks, AI and predictive models, new options including clinical trials & patient-centered learning systems. All of US to 40 M. More effective treatments, prevention, even disease reversal.

Our Future - ?

• How our care will change:

  • -  Proteomics & prevention: Star Trek diagnostics: 50 organ protein assays for 50 organ systems = 2500 from a few drops of blood, advice on what to eat for lunch, prevention & treatment, possibly reversal

  • -  Full digital options; Fit-bits, smart phones & watches, wearables, home monitors, many Apps, Project Baseline (Stanford, Duke & Google). Genomics, proteomics, metabolomics, microbiome, epigenomics, immunophenotyping, and much more. First 10,000 cohort now enrolling

  • -  Cancer, chronic and rare diseases – precision medicine, genetics and targeted therapies, more and better treatment and prevention options, predictive models, national/global rapid learning networks and databases using AI

Our Future -- ?

• How our care will change:

  • -  Aging reversal: e.g. NAD+, 40 products in trials

  • -  Epidemics/pandemics: universal flu vaccine, pandemics <60 days

  • -  Nutrition: new bio-engineered products & GMOs

  • -  Retirement communities: 15+ yrs life extension, domestic robots, etc.

  • -  Regenerative medicine: (‘self-healing”): 700 companies

Our Future -- ?

  • Eric Topol: the bio-medical revolution & digital futures will greatly improve our health, empty most hospital beds. Physicians will see fewer patients. Health spending may drop dramatically. (The Creative Destruction of Medicine).

  • PBS futures speaker predicts life expectancy will rise to 100+, we will get healthier, more vigorous and active over the next 10 years, as chronic disease, anti-aging and regenerative medicine come on-line. (However, we may need to keep working in a “gig” economy to afford all that we want to do!)

  • How well will new ”rapid-learning health system” work? How will physicians and patients respond? Will we revitalize economic growth and widely-shared prosperity?

  • We may be much healthier in 10 years, whether richer or poorer. A lot depends on science, economics & politics. What future will we create?

III.  The Cambrian Explosion and A Rapid-Learning Society by Lynn M. Etheredge

Working Paper
October 2016

    An extraordinary period -- about 541-485 million years ago – known as the Cambrian Explosion -- launched the most rapid evolution of living organisms’ capabilities, variety, behavior, interactions, and potential in Earth’s history. Four lessons from the Cambrian transformation – that dramatically boosted the range and amount of useful data and data processing capacity of living organisms – offer a strategic framework for creating a new Cambrian explosion, our future rapid-learning society.
    This summer and fall, the Natural History Museum, London is presenting an exhibition on “Colour and Vision: Through The Eyes of Nature” on the genesis of these revolutionary changes – the development of eyes (and brains) that can form images. Today, thanks to the Cambrian Explosion, 96% of all species now have such capacities, and we live in a vibrantly colorful – and much changed – world.   
    Vision created enormous new competitive advantages to find food and resources, acquire mates, avoid predators, and communicate.  Organisms developed that could move at speed on earth, in water, in trees and in the air. Predator-prey eco-systems and evolutionary “arms races” among species accelerated. Many kinds of eyes developed from a small genetic toolkit, varying in structure (simple vs compound), number, size, location, independence, wavelengths (color) sensitivity, focus ability, and specialized adaptations for different species and ecological niches. Pre-Cambrian living organisms (typically single-cell or colonies of single cells) quickly evolved into the rich diversity of complex, adaptive, and moving animals (and their ecosystems) that we know today. Nearly all major groups of modern animals appear within just a few million years   With animal visual sensing in many wavelengths, flowers evolved with vivid colors to attract pollinators.
    During the last two decades, human beings have launched a massive increase in our modern “vision” capacities for learning about and from our world through digital technologies and rapid-learning initiatives. 
    These new capabilities for our individual and collective brains have exploded at a speed and scale that justifies a “Cambrian explosion” comparison. A reported 90% of American adults now own a cellular phone, and about 65% of them are smart phones.   Apple reports more than 2 million Apps available for its iPhone.   On-line free courses offer state-of-the-art information for everyone.   A new generation of global companies now serve, accelerate, and share these kinds of developments, among them new household words like Amazon, Apple, Google, Facebook, Microsoft, Baidu.  There are new capabilities and possibilities, nearly everywhere on the planet, for nearly everyone.  
    Almost everything can now be observed, recorded, and studied, if we choose to do so -- from sub-microscopic to intergalactic scale – with “big data” databases (pre-designed and pre-populated to answer questions of interest), learning networks and high speed computers (operating at petabyte speed – a quadrillion operations per second). What was been learned can be shared with others anywhere in the world within a few seconds.  
    For the last decade, I have been working with colleagues to create a “rapid learning health system” – an intentional “Cambrian explosion” to accelerate biomedical sciences and personalize healthcare as rapidly as possible. It was launched in a special issue of Health Affairs, January 2007.   Our top science agencies, e.g. the National Institutes of Health and National Cancer Institute, the Food and Drug Administration, the Patient-Centered Outcomes Research Institute and its PCORNet learning networks, and our largest health programs, e.g. the $10 billion Innovation Center for Medicare and Medicaid, are now leading the way.   The potential benefits of these learning revolutions are extraordinary.
    Our rapid-learning health system can be a “flagship” to inform and inspire cross-sector conversations for similar Cambrian-explosion initiatives, in sciences, economics, education, and all sectors -- to create a rapid-learning society.
Four Lessons from the Cambrian Explosion
    To advance cross-sector conversations, let me offer four suggestions for how the Cambrian era can illuminate our own age and offer insights for shaping our collective futures.
    First, progress need not be at a glacial pace, in small increments and localized. Explosive change is possible, on a global scale. Our new digital technologies and rapid-learning capabilities are already showing a potential for a new Cambrian age.
    Second, there are great benefits – who would want to go back to the pre-Cambrian ? – but also risks from upgrading information-gathering and learning capabilities on a global scale. The earlier Cambrian age saw many new predators, intense rivalries, and arms races, many winners and losers. Today’s early adopters include national security states moving quickly toward universal surveillance, social media used in politics and popular revolutions (e.g. Arab Spring), warnings of cyber-warfare, private companies collecting vast amounts of personal data, international cyber-espionage on businesses, and hacking. We will need on-going vigilance and (sometimes) mid-course corrections.
    Third, human beings have - so far - been the biggest winners from the Cambrian explosion and 500 million more years of evolution. However, our visual capacities were not decisive.  Many other species have better vision, in terms of acuity, sensitivity to different colors, numbers of eyes, 360-degree vision, and more. The decisive human advantage has been combining: (1) our vision potential; (2) our unrivalled expansion of brain capacity for visual pattern recognition, memory, thinking, communicating and learning; and (3) our collective abilities – and public policies -- to create learning societies.
    The full package took time to develop. Writing – a key use of visual abilities to record information, learn and communicate – wasn’t invented until about 3200 BC.   Today’s learning society – with many institutions designed for the discovery and use of new knowledge – emerged from the Industrial Revolution starting about 1750, 250 years ago. It includes near universal education – using visual inputs (reading) as a primary tool over the first 16 years of life -- to transmit knowledge and develop brains, higher education universities, national research funding, intentional design of large research databases, experiments and scientific methods, rapid advances in software, computing and communications technologies (including the Internet and world wide web), implementation sciences, a global economic system that rewards developing, producing and marketing useful new products, and much else. All these status quo elements should now be viewed as akin to inherited “legacy software”, pre-Cambrian systems – the best that could be done at the time. To get the full potential benefits of a new Cambrian revolution -- to become a fully rapid-learning society – we need to upgrade all elements of today’s learning systems. The agenda will include new open science databases, new software capabilities, new learning organizations, professions, and systems, new public policies, and implementation strategies.
    Finally, a new “science of rapid-learning systems” needs new mental models and ways of thinking that include the insights of evolution (e.g. the Cambrian era), ecology, and rapid-learning successes. My paper “11 Models For A Science Of Rapid-Learning Systems” (2015) provides an overview of new ways of thinking that have proved useful for the rapid learning project. It suggests ecology studies as a promising source for more ideas and insights.   Joel Mokyr wrote a review of dialogues between biology and economics as of a decade ago; one of his take-aways was that; “While the exchanges between economics and biology can sometimes be hazardous and misleading, quite a lot could be learned by economists…” 
    The Cambrian Explosion shows us the extraordinary power and benefits of vastly increasing the amount and range of useful data and data processing for living organisms. It also reminds us about risks of predator behavior and need for societal vigilance. Our society now has wonderful opportunities to leave slow rates of progress behind and to transform our scientific, social, economic, education, and other institutions.  Many of our public and private leaders can have key roles in designing and bringing about a new Cambrian explosion, an intended rapid-learning society.


III.  Why Did Novartis Pay Trump’s Lawyer $1.2 Million? Look At Its Drug Prices

By Jay Hancock MAY 11, 2018

Swiss pharmaceutical giant Novartis' campus in Basel, Switzerland (Fabrice Co!rini/AFP/Getty Images)

President Donald Trump didn’t mention Novartis or other drugmakers by name last year when he said the industry is “getting away with murder.”

Yet executives at the Switzerland-based pharmaceutical giant shelled out $1.2 million to Trump lawyer Michael Cohen to “advise” its executives on health policy and what was happening in the Trump White House.

Novartis paid more money to Cohen than did any of his clients revealed thus far.

The company said it quickly determined he was unable to deliver the help but paid the full amount owed in his contract. “We made a mistake” in hiring him, CEO Vasant Narasimhan told Novartis employees on Thursday.

Hiring the president’s personal attorney matches a history of aggressively courting government officials by a corporation with much to lose in the debate over high drug prices.

Novartis has nine blockbuster drugs generating around $1 billion or more in annual sales and priced so high in some cases that patients have trouble affording them even with insurance. Another nine drugs produce more than $500 million in sales.

High costs and copayments for Novartis’ Gleevec, which treats a form of leukemia, are associated with patients delaying or skipping doses, said researcher Stacie Dusetzina of Vanderbilt University.

Gleevec often must be taken for life and costs $148,000 a year — three times more than when it came out, according to Connecture, which provides technology to help people save money on prescriptions.

Novartis also makes drugs for psoriasis and multiple sclerosis that cost more than $100,000 a year. The price tag for Kymriah, a Novartis leukemia treatment approved last year, is $475,000.

The company earned $7.7 billion in profits last year on worldwide sales of $49 billion.

Novartis’ political action committee has been a sizable contributor on Capitol Hill, donating $204,500 last year to candidates for federal office and other political causes.

The company spent $8.8 million lobbying U.S. lawmakers in 2017, its highest amount ever, according to the Center for Responsive Politics. That doesn’t count the previously undisclosed payments to Cohen, which the company said were for consulting, not lobbying.

One issue that especially interests the company: the importation of drugs from Canada and other countries, which would undercut its high U.S. prices and badly hurt profits. Novartis sells its drugs for a fraction of U.S. prices in other developed countries. In 2015, Gleevec sold for $38,000 a year in Canada while a generic version of the same drug sold for only $8,800.

Importation was one of Novartis’ most lobbied issues last year. A few of Novartis’ blockbusters:






$2.1 billion


Multiple sclerosis



$3.2 billion





$1.9 billion





$1.9 billion

*Three-year increase

Source: Novartis; average wholesale price from Connecture • Get the data • Created with Datawrapper

KHN’s coverage of prescription drug development, costs and pricing is supported by the Laura and John Arnold Foundation.

Jay Hancock:, @JayHancock1

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