The Invisible Hand Has Arthritis: Why Free Market Capitalism is not a Panacea for U.S. Health Care

Running a health care service business is hard. Very hard. It is real-world and messy. It is a man with no insurance and a heart attack on your doorstep. It is an elderly woman who arrives senile with a broken hip and no caretaker. All of the clean simplifying assumptions written on the chalkboard at the start of my economics classes (perfect information, infinite buyers and sellers, rational players, elastic curves, a lack of externalities, homogeneity of goods or services) simply don’t apply to medicine.

Below are some useful considerations:

·         Externalities & the Common Good: Libertarians argue that if a person can’t afford health care, they shouldn’t receive it. However, U.S. society does not seem prepared to turn away the desperately ill. The Emergency Medical Treatment and Active Labor Act (EMTALA) requires most US hospitals to provide care to anyone needing emergency treatment regardless of citizenship, legal status or ability to pay.^1,2 Similarly, community hospitals can be considered a “public good”, which should fall outside of traditional market forces.

·         Perfect inelasticity of demand: Health Care is often life and death and demand curves become perfectly inelastic (straight up and down). How can you put a dollar amount on someone’s life? When you or a loved one is in the hospital, rationality is thrown out the window as fear takes hold. This reality, combined with a limited amount of suppliers, puts dramatic upwards pressure on prices.

·         Risk-pooling and third-party payers: The third-party payment system arose to pool catastrophic health care risk amongst many consumers. Now, the consumer (i.e., the patient) is not the payer (i.e., the insurance company) and the employer usually sits between the two. The relationship hinders price transparency, i.e. the relationship between subsidized premiums paid by patients and cost of services provided by hospitals & physicians.³

·         Physician/Hospital fiduciary duty: In most industries, the payer for and provider of a service agree to price ahead of time. Not in health care. Physicians and hospitals have a fiduciary duty to *rapidly* decide what and how many services to provide while ultimately receiving a financial reward for those services and potentially incurring legal liabilities if they miss something.⁴

·         Medicine is not a commodity: Medicine is complicated. It is not like drycleaning or babysitting or getting your car fixed. It is constantly advancing and certainly not a commodity. All patients have unique issues and, while there are standard treatment protocols, doctors have to quickly use their discretion and judgment at all times.

·         Lack of perfect competition: On the one hand, the government is a quasi-monopolistic buyer. Private payers often follow its lead. On the other hand, due to high infrastructure and licensing requirements, many US towns only have one or two hospital networks to supply services. There simply aren’t thousands of payers and providers driving marginal cost to equal marginal revenue, nor is there the time or inclination to shop around.

These are a few of the ways in which the simplifying assumptions of free market capitalism do not apply to health care. There are many others and they all interact with each other. The business of medicine is complicated. The better the BioPharma, Med Device, and Health Care IT industries understand this, the more likely they can all work towards driving efficiencies into and waste out of the health care system.  

Notes:

¹ http://en.wikipedia.org/wiki/Emergency_Medical_Treatment_and_Active_Labor_Act

² However, often no one reimburses hospitals for many of the related costs – it is essentially an unfunded government mandate. Many hospitals write off such care as charity or bad debt for tax purposes. Besides causing some hospitals to go bankrupt, this law has led to cost-shifting and higher rates for insured hospital patients. There are, in some cases, funds form the city or county to remunerate hospitals for indigent care. Many times these funds do not come close to completely covering the deficit. Also, not for profit hospitals have to do a certain amount of charity care to qualify for their tax exempt status. 

³ Consumer-directed health plans aim to ameliorate this situation.

⁴ This is why insurance companies often pay hospitals fixed DRG payments for a given diagnosis regardless of the actual cost of treatment to the hospital. This results in a large administrative effort to prove after the fact that the services they provided were appropriate and necessary.

Related links:

http://krugman.blogs.nytimes.com/2009/07/25/why-markets-cant-cure-healthcare/

http://e.viaminvest.com/B12Methodology/PosEcon/Table_3ExamAssumptions.asp

http://www.cdc.gov/ncidod/eid/vol7no2/scott.htm

http://www.sph.umich.edu/vbidcenter/publications/pdfs/jama1207.pdf

http://econlog.econlib.org/archives/2009/07/what_makes_heal.html

http://insureblog.blogspot.com/2009/06/economics-of-health-care.html

http://healthblog.ncpa.org/is-health-care-different/

http://www.davidhilfiker.com/docs/Economics/Constitutive%20Rules.htm

A Fish Out Of Water?: A Biotech Investor At A Mobile App Hackathon

At first (and second) glance, the worlds of biotech and mobile app development seem to mix like oil and vinegar. What do kinases, IFNs, GPCRs, RT-PCR, SNPs, DRGs, NDA, and 510K have to do with HTML5, JAVA, API, UI, WYSIWYG, Freemiums, 3G, and the Cloud? The two worlds seem polar opposites and seemingly attract people with distinctly different personalities, backgrounds, and ambitions.

·         Biotech is a steady, capital-intensive, IP-reliant industry with a fixed business model (increasingly high price/low volume), high regulation, and risk primarily dependent on technical not commercial success. Participants are used to ten or more years from product conception to commercialization.

·         Mobile App development, by contrast, is a relatively brand-new, rapid-fire, low cost industry with endless novel business models (trending towards low price/high volume), currently low regulation, and risk primarily dependent on consumer adoption. Time from product concept to commercialization could be days.

As someone who has spent the last seven years immersed in the business of biotech, I view the world of Mobile App Development with some trepidation. It seems too good to be true. In contrast to biotech, which appears to be on the flattening phase of its growth curve, Mobile Apps are the Wild West with ample room for exponential growth. Can it be real?

I decided to attend a local Mobile App Hack-a-thon sponsored by AT&T to learn more (http://mobileappsd.eventbrite.com/). The event was described as follows:

Mobile App Hackathon is an event for mobile developers to come together, network, learn about new technologies and build mobile apps. A full day of coding, drinks, food and snacks and fun contents with prizes across categories. Meet new people and teammates, start projects and work on existing ones, and learn from short talks on technologies and trends that can help you build better apps. 

Sounds great, except I know nothing about coding or developing. I built my website with a WYSIWYG (What You See Is What You Get) software program and only recently started to use Twitter and Blogger (for which I was very proud of myself). When I showed up at the event on a Saturday morning, I felt like a fish out of water.

My worries were quickly overcome, however, by the casual, open, and positive energy of the event. It turns out, by a show of hands, that many people there didn’t know how to code. Red Foundry, which has a web-based WYSIWYG App Developer Program, even gave a presentation for the non-coding App Developer (http://redfoundry.com/). In addition, plenty of expert App Developers at the event wanted to work with non-developers with good ideas for Apps. There was room, need, and even hunger for brains – not only those with HTML5 knowledge.

The next Friday, I visited ansir innovation (AI) center, a local tech accelerator with co-working space (http://aicenterca.com/), for their “Hackaway Friday” event – advertised as a casual, all-day coworking event especially for entrepreneurs and startups. Again, I felt like a biotech infiltrator who would illicit an immediate immune response from the nest of techies. Again, I was wrong. The group was casual, welcoming, and smart with a lot of positive energy and intellectual curiosity.

The moral of the story, and the point of this blog, is to encourage the biotech and other communities to get involved with the mobile one. One year ago, when a senior biotech executive encouraged me to consider software applications, I answered, “but I know nothing about that”. In reality, there are plenty of smart, casual, intellectually curious people who do know coding/developing and are eager to partner with people with good business ideas. It is yet to be seen whether mobile will live up to the hype, but I see no reason why it will not. With the biotech and other industries in a temporary (and potentially permanent) slump, it seems like a good time for biotech brains to pivot into the mobile world.  

The Digitalized U: The U.S. Health Care Consumer of 2026

What will the digitalized U.S. health care consumer of 2026 look like? Fifteen years from now, several currently emerging trends will have had time to mature, converge and disrupt the U.S. health care ecosystem, including mobile connectivity & mHealth, social media & electronic health records (EHRs), genomics/proteomics & diagnostics, consumer-controlled healthcare & insurance reform, and, not unimportantly, the rise of the BRIC countries. The system of levees and dams that currently controls and constricts innovation in the U.S. health care industry, (i.e. HIPAA, FDA Regulation, Medical Malpractice, Third-Party Payment System, Cultural Mores, Medical Paternalism, etc.), will find itself overcome by a surge of digital technology, consumer (a.k.a. voter) demand, a loosening of disease stigmas, Medicare & Medicaid cut-backs, an aging population, and a drastic need for improvement in system efficiency.  A generation of individuals raised on the internet with easy access to information, a culture of openness & sharing, and a consumer-focused economy will become middle-aged and demand more control over their own health and that of their parents. 

·         Mobile Connectivity & mHealth: The mobile health revolution is underway. Consumers are already psychologically connected to their iPhones, so a physical connection seems inevitable. mHealth will allow for continuous capture of clinical data in the home and mobile setting, an innovation which will dramatically change the treatment of chronic conditions and general health.  Sensors of all abilities will flood the market by 2026, and we will use wireless patches and implants along with environmental sensors to monitor our every biodigital datapoint, e.g., heartbeat, net calorie consumption, temperatures, insulin levels, blood pressure, brainwaves (yes, some basic thoughts), mood, stress, cholesterol levels, infections, protein-expression levels, etc. Cyborgs, here we (be)come.

·         Social Media & EHRs: The Web 2.0 revolution will be a distant memory and Web 3.0 will be underway. Compiling and utilizing international databases of consumer health care data will be considered second-nature. Population statistics applied to this data will reveal novel understandings about disease etiology & response to treatments. Clinical study implications will change the face of drug development, clinical use, and reimbursement. Social stigmas for many diseases will be reduced as a clearer picture of epidemiology and open discussion of symptoms emerges.  

·         Genomics/Proteomics & Diagnostics: A large percentage of the U.S. will have their genome sequenced, housed within global population databases, annotated for phenotype, and analyzed for revelation of new promoters, genes, SNPs, etc. Similarly, advances in microfluidics and proteomics will allow for the monitoring of a variety of protein expression signatures over time and an understanding of their association with disease. Diagnostics in general will be more accurate and ubiquitous, with OTC access. The medical profession will be incentivized to put much more emphasis on prevention vs treatment, with major implications for the drug industry.  

·         Consumer-controlled Healthcare & Insurance Reform: “Patients” of today will become the “Consumers” of tomorrow who utilize consumer driven health plans and are culturally open to out-of-pocket payments for health monitoring and preventative therapies. Instead of brick-and-mortar doctor’s offices and hospitals, health care will take place at home, in retail stores (Walmart, Walgreens, Brookstone), and online (Amazon, iTunes App Store, SaaS sites), etc. Health care consumers will take more control over their treatment plans and medical paternalism will give way to doctor-patient partnerships.

·         Rise of the BRIC Markets: India and China will be more dominant forces with innovation economies of their own. Their lower barriers to market will drive initial launches of new health care innovations outside of the U.S., with obvious successes influencing the U.S. market. The interconnectivity of the internet and digital information will allow for international human health & biomedical databases and an arbitrage on conducting research in those geographical markets with lower regulatory hurdles and more fundamental health care needs (with resulting ethical concerns).  

How the process will play out over the next 15 years remains a question, but the end result described above seems clear. Big, successful, profitable, transformative health care companies will exist in 15 years that are only glimmers in someone’s mind today. Stay tuned.

"p" or "β": How Clinical Success is Affected by the Macroeconomy

In the footnotes to my last blog post “Cost of Biotech Capital: Incorporating Development Risk into VC’s Target Return,” I contemplated which biotech start-up risks are uncorrelated with the general market and hence “diversifiable”. Many assume that a given clinical trial’s binary probability of technical or regulatory success (“p”) is one such risk.¹ As described in the last post, short-term oriented financial analysts (many at hedge & PE funds trying to gauge the outcome of binary events) assign a probability of success ("p") to a given clinical trial and do not expect that value to fluctuate with the general market (i.e. S&P 500). In the short run (event to occur in < 2 years), they are likely correct. In the long run (event to occur in >2 years), however, “p” is often influenced by general political and economic conditions. This is the timeframe considered by VCs and BioPharma companies. Below are three of the ways in which “p” can find its way into “β” in the long run and, ultimately, drive up the long term cost of capital for biotech.

(1)    FDA

FDA requirements affect clinical trial design and the choice of primary and secondary endpoints. All things equal, a stricter FDA will lead to a lower “p” or probability of what the FDA considers technical success. The FDA is in turn affected by the general political environment and hence the macroeconomy.

(2)    Availability of Capital

The free availability of capital can have dynamically opposed effects on “p”. On the one hand, more cash means better and bigger clinical trials which boosts “p”. On the other hand, too much capital leads to poor investment discipline and many companies getting funded that shouldn’t. This phenomenon is similar to what occurred in mortgages, where the free availability of low interest rate IO mortgages with no credit checks manifested in historical default rates 3-5 years later.   

(3)    Clinical Trial Recruitment

The status of the economy likely influences who and how many people agree to participate in clinical trials. One theory argues that in good times, more people can pay for experimental treatment or top-notch medical care themselves and decide not to participate in clinical trials. In bad times, the reverse may be true. Better recruitment may lead to better clinical trial results and a higher “p”. Likely (2) Availability of Capital and the existence of many competing trials is another factor in recruitment success.   

There are likely many other ways in which general economic conditions impact biopharma’s probability of technical success over time. In the long run, “p” shows up in “β”. Counter to my last blog post, this is an argument for adjusting biotech’s long-run cost of capital for binary technical risk. While 58.7% IRR still seems too high, perhaps the industry standard ~30% IRR is a reasonable happy medium.  

1.       “p” is not to be confused with the statistical term “p-value” which determines how likely a clinical trial result is due to chance.

Cost of Biotech Capital: Incorporating Development Risk into VC’s Target Return

Although the average biotech investor probably understands the term “cost of capital” and general CAPM theory, there still seems to be much confusion around how to incorporate the binary risk of drug development into discount rates and required IRRs. CAPM and portfolio theory tells us that investors are only compensated for correlation to general macroeconomic risks – i.e. risks we can’t diversify away. Thus, taking on so-called “idiosyncratic risks”, such as the binary risks of clinical trials, should not theoretically garner an investor any extra return (assuming the clinical trial risks are not correlated across a wide portfolio of investments, which may or may not be an appropriate assumption¹). Why then, do we see VCs demand gross² IRRs of 35%-60% per each investment (dramatically higher than CAPM would dictate) to compensate for drug development? The answer lies in the difference between a portfolio’s ultimate expected return (i.e. a VC investor’s cost of capital) and an individual investment’s target return.

                Andrew Metrick, my former professor at the Wharton School, clearly explains the difference in his book “Venture Capital and the Finance of Innovation” (http://www.amazon.com/Venture-Capital-Finance-Innovation-Metrick/dp/0470074280). The general financial theory he describes is also used widely in the BioPharma industry to calculate eNPV (expected or risk-adjusted NPVs). We used this theory at Genzyme, and I know it is used at many other major BioPharma companies to value their pipelines. The basic idea is to probability-adjust cash flows (or the numerator) for the binary risk of drug development (probability of technical success) and not incorporate that risk into discount rates (or the denominator). For example, to determine the value of an early-stage acquisition target, you would multiply the stream of potential cash flows by the probability of each of them actually occurring and then discount back using a discount rate determined by either (i) the general CAPM equation R_Discount = R_F + β(R_M - R_F) or (ii) a more elaborate model, such as the Pastor-Stambaugh model (PSM) that incorporates additional factors for value, size, and liquidity (for simplicity, assume no debt). The discount rate used by most BioPharma’s is ~10-15%. However, many VCs will use the following equation to move the idiosyncratic (or binary) risk (i.e. “p”) into the target discount rate (i.e. Target Return):

p/(1+ R_Discount)^Time = 1/(1+Target Return)^Time

For a project that has a p = 20% chance of exiting for some fixed amount of money and an 80% chance of exiting for $0 in Time = 5 years, with a standard R_Discount of 15%, the Target Return or Target Discount Rate = 58.7%.

.20/(1+.15)^5=1/(1+.587)^5

When VCs say their discount rate or required IRR is 58.7% to adjust for development risk, this really translates into the 15% discount rate we normally think of adjusted for probability of technical success. No investor actually expects to receive a 58.7% return for the entire portfolio. As is often said in the VC world, the winners have to make up for the losers so the returns can average out to a reasonable compensation for taking macroeconomic or non-diversifiable risk (and possibly adjustments for size, value, and liquidity).

Notes

 (1) These days, the big question is which biotech start-up risk is diversifiable and which is correlated to the general economy (i.e. macroeconomic risk) and affects β. Certainly capital availability, which plays a significant factor in the success of biotech start-ups, is greatly tied to the macroeconomy. However, this is perhaps already captured in the “size” factor of the PSM model. Other factors, such as FDA regulatory requirements or access to patients for clinical trials, are also tied to the general political environment and perhaps cannot be diversified away.

(2) Note that a VC’s gross IRR and net IRR differ due to fund management fees and carry.

Punctuated Equilibrium: Is a Meteor Heading Towards the BioPharma Ecosystem?

It is impossible to ignore the impact of social media in recent years and its powerful ability to connect people, ideas, and information across time and space. For relatively minimal time and effort, commercial websites can be created with tremendous social value (e.g. use of social media in Egypt's youth revolution). Contrast this to the aging drug development industry, which many pundits claim is facing a crisis due to a “broken business model”. Cash burn is too high, development timelines too long, approvals too infrequent, and reimbursement too low. Perhaps this is true, but pharmaceuticals are far too important to humanity for the industry to simply dissolve. Inevitably some set of phenomena will cause a rapid and dramatic change in the BioPharma ecosystem - a punctuated equilibrium of sorts. Could social media be this meteor? Could the IT world ironically give BioPharma the dose of medicine it so desperately needs?

                The striking aspect of the social media site Twitter is its openness and ability to index and communicate massive amounts of information. Similar to the invisible hand of capitalism, Twitter’s collective platform has a greater power than the sum of its individual contributors. In contrast, the BioPharma industry operates mainly in shadows and silos. At the research level, the patent system, while necessary to compensate investors for taking large and lengthy risks, clearly can stymy communication and the public’s ability to freely innovate in already staked areas. At the patient level, confidentiality and the historical difficulty of obtaining and sharing consistent digitalized clinical data are barriers to identifying large-scale trends through applying population statistics. When these barriers are overcome, the subsequent increase in the efficiency and effectiveness of drug discovery could return the industry to its boom years.

            Signs of the social media meteor can already be seen. Sage Bionetworks, a nonprofit research organization, is trying to “create an open access, integrative bionetwork evolved by contributor scientists working to eliminate human disease (http://sagebase.org/sage/index.php)”. According to their website, “The Sage Commons . . . an accessible information platform . . . will be used to integrate diverse molecular mega-datasets, to build predictive bionetworks and to offer advanced tools proven to provide unique new insights into human disease biology. Users will also be contributors that advance the knowledge base and tools through their cumulative participation. The public access goal of the Sage Commons requires the development of a new strategic and legal framework to protect the rights of contributors while providing widespread access to fundamentally non-commercial assets (http://sagebase.org/commons/index.php).”

                “Open-Source” sharing of discoveries seems anti-capitalistic. Patents should certainly continue to exist for downstream commercial developments that require millions of dollars of investment. However, many academic, corporate, and clinical datasets and scientific results will never be relevant for commercialization. A lot of information is sitting stale in a lot of labs and doctor’s offices across the globe. Like individual pieces of a large jigsaw puzzle, a clear picture of human biology only emerges when they are combined. BioPharma stakeholders will have to adjust to freely sharing basic research, datasets, and perhaps bioinformatics tools. In return, the resulting improvement in the fundamental understanding of biology will dramatically improve the success rates of patented drugs and lower costs of development. Although there will be a lot of cultural resistance at first, the social media meteor may be just what it takes to keep the struggling BioPharma ecosystem alive.

Relevant Links:

http://www.economist.com/node/2724420?story_id=2724420

http://p2pfoundation.net/Open_Source_Biotechnology

http://www.nature.com/nbt/journal/v28/n7/full/nbt0710-631.html

http://www.patentlens.net/daisy/cambia/home.html

http://mjlst.umn.edu/uploads/CS/bW/CSbWvh00RsLS5YM4raBkYg/feldman_a2.pdf

Subtle Barriers to Seemingly Win-Win BioPharma Spin-outs

Many of the R&D programs housed within big BioPharma may be developed faster, cheaper and better in the hands of a more nimble and focused start-up. These programs are often delayed or cancelled due to short term budget issues or changes in commercial strategy. Meanwhile, the IP clock runs out, researchers become demoralized, and past effort and capital is wasted. Why then, do we not see more spin-outs as part of a ritualistic spring cleaning on the part of big BioPharmas? There are six subtle reasons why seemingly win-win BioPharma spin-outs never happen.

1.       Precedent

For many BioPharma companies, there is little institutional precedent for such spin-outs (although there are admittedly notable examples, see links below). Business Development, Legal, R&D, and Commercial professionals often have scant experience preparing their own early-stage programs for someone else’s due diligence. Scientists may not have Know-how, Trade Secrets and physical materials organized and ready for transfer. Accountants may be uncomfortable with reporting requirements in light of Fin 46. Bus Dev folks may have never completed an out-license or structured a start-up. Commercial groups may lack analysis or projections for early-stage programs. This all adds up to a BioPharma deal machine whose wheels are not greased for spin-outs.

2.       Glamour

The Business Development groups of most BioPharmas are focused on M&A or in-licensing products to fill near-term holes in their parent’s income statements. Spinning-out early-stage R&D programs that the parent can no longer develop is considered neither glamorous nor the best means of career advancement. Spin-outs are also a psychological acknowledgement that someone else may be able to do a better job, a fact that can be uncomfortable to swallow.

3.       Fear

This point is perhaps the most obvious. BioPharma companies fear licensing out a potential blockbuster. After all, if they keep the R&D programs in-house, they maintain their (often valuable) option to resume developmet efforts if conditions change. Thus, BioPharma may want to include “strategic rights” or “clawback provisions” in an out-license or asset sale in addition to equity, milestone payments and royalties. These can include Right of First Refusal, Right of First Negotiation, Hard and Soft Call Options, etc. However, VCs/Angels and the management of start-ups are loath to agree to such provisions and their inclusion often halts spin-out discussions in their tracks.

4.       Entanglement

Whereas start-ups are often “project” companies focused on discrete technology bundles to be sold in five or so years, “perpetual” BioPharma companies are much more complex. Their R&D programs are often entangled with one another and may each step on some broad core set of IP or Know-how (e.g. a class of compounds or targets or manufacturing know-how). The BioPharma may not be able to effectively license out or assign IP to one program without endangering or affecting another program or restricting future activities. In addition, the BioPharma may have itself sub-licensed relevant IP without the ability to sub-sub license it. Furthermore, whereas start-ups have limited assets to worry about outside of their lead program, BioPharmas have much more cause for fear from lawsuits and other liabilities.

5.       Valuation

BioPharmas often have poured more money into a program than the VCs are willing to value in a financing round. As stated in a previous post, BioPharmas usually want to stay under 20% share ownership. With say a $10M cash Series A raise from investors, the BioPharma’s stake is equivalent to $3M (assume post of 65% investors, 20% biopharma, 15% option pool). If the BioPharma has spent $8M on the program so far, that is a hard pill to swallow even factoring in milestones and royalties. (However, it raises an interesting accounting question as the R&D has most likely already been expensed.)

6.       Management

Many of the better known BioPharma spin-outs are the result of M&A where the acquired start-up’s management team decides to spin-out remaining peripheral assets (e.g. Relypsa/Ilypsa/Amgen). In this case, the assets are unentangled and a management team is both ready, willing and able to spin them out and backed by the momentum of a recent success. It is a lot less clear who would manage the spin-outs of BioPharma’s organically grown programs.

Despite the challenges, BioPharma spin-outs can still be worthwhile endeavors. Besides preserving value and R&D momentum, they can also provide jobs during periods of corporate downsizing and give some drug programs a greater probability of success. There have been many other articles/blogs written on this topic, a few of which are linked below.

Useful Links:

http://www.nature.com/bioent/2003/030401/full/bioent727.html

http://www.signalsmag.com/signalsmag.nsf/657b06742b5748e888256570005cba01/cf05408f18adda9d8825679b0052d533?OpenDocument

http://www.fiercebiotechresearch.com/story/gsk-bets-hungry-spinouts-can-upstage-fat-pharma/2010-10-05

http://venturebeat.com/2007/10/29/amgen-relypsa-and-the-art-of-the-biotech-spinout-startup-restart/

http://www.fiercebiotech.com/story/alkermes-spinout-nabs-20m-fuel-parkinsons-program/2011-01-10