Healthcare on the Ethereum Blockchain

Since Ethereum’s conception, I’ve dreamed of a blockchain-based healthcare services economy and presented the idea at BitTorrent’s headquarters 3 years ago. It’s also taken me that long to conceive of a concrete study of this protocol’s readiness for the limelight. With Ethereum’s adoption by a number of blue chip companies, including JP Morgan and Microsoft, its inevitability is clear. While still unreachably abstract to many people, I believe that healthcare’s state of disarray is a perfect environment to test the waters. As I get ready to start a Clinical Informatics fellowship at UCSF Medical Center, I’m prototyping such a blockchain-based health services marketplace and would like to humbly present the proposal to the Ethereum community for its feedback.

Pricing for healthcare services is currently based on prices determined by insurance companies’ ability to negotiate price points with groups of healthcare providers, individual providers, and healthcare systems. The lack of a true free market, and insurance companies’ administrative overhead, contribute to inflated prices for healthcare services across the board. [Figure 1: Health services marketplace in the blockchain era]

Slide1

Figure 1: Health services marketplace in the blockchain era. Red text indicates how things work presently. Green indicates how things might work in a health services economy founded on the Ethereum blockchain. Notice the absence of insurance companies in the latter, hypothetical scenario. Their role has yet to be determined. I use laboratory testing as an example, but this would apply to imaging studies, office visits, surgical procedures, and consultations.

 

Enter Ethereum, a next-generation blockchain protocol for automatically executing “smart contracts.” Autonomously executed contracts obviate the need for escrow, attorneys, and administrators. Like Bitcoin’s protocol, Ethereum is a distributed blockchain that is open source, not owned by anyone, and runs off any and all computers running the client software. Ethereum’s novelty – and power – lies in the fact that it’s a Turing-complete system. Ethereum, unlike Bitcoin has mechanisms for executing logic, so smart contracts can be written by anyone, hosted on the Ethereum blockchain, and anyone in the world can interact with these contracts with the endpoint of manipulating data and moving money in the form of Ether (also a cryptocurrency).

So why not harness the Ethereum protocol to create a distributed, open source healthcare marketplace? Without administrative overhead (which accounts for the majority of an insurance company’s expenses, which are then passed on to patients and healthcare systems) and with the freedom for any provider of healthcare services to bid for a service (imaging, lab testing, consultations, procedures…), I hypothesize that the cost of healthcare services will be reduced to approximately 10% of its current artificially inflated price. Further contributing to cost and redundancy of healthcare expenditures is data siloing, the isolation of data on servers without APIs to set them free. Many healthcare providers will agree that it’s often much easier to repeat an expensive study than obtain records of that same procedure performed at an outside hospital (even if the study was just performed hours or days ago, and oftentimes, even if the study was performed at an affiliated hospital!). Ethereum’s distributed blockchain is a global ledger of everyone’s health information. I predict that sound security protocols, which need to be developed with healthcare’s unique needs in mind, will necessitate the use of biometric data to associate data on the blockchain with individuals.

So, how can we test the former hypothesis, that Ethereum can reduce the cost of healthcare services to 10% of their current prices?

I propose simulating such a bidding system to start collecting data on the free market prices Ethereum will foster by surveying physicians based in the community, as well as groups contracting with academic medical centers. If I survey Dr. Roentgen, Dr. Tomo, and Dr. Houndsfield (and a few hundred other radiologists) asking them if they would accept $X cash payment for imaging study A, B, or C (e.g. chest x-ray, mammogram, brain MRI…) performed STAT, tomorrow, or next month, we will start to approach theoretical market price for these studies.

If you are a fellow Ethereum developer or are otherwise interested in collaborating in the spirit of establishing healthcare as a human right on the Ethereum blockchain, please send me a line! I’m dreaming up experiments and am seeking partners in code.

Portland smoke

[Daydreaming in Portland]

Omar Metwally, MD

Sunday April 30th, 2017

Portland, Oregon

 

Doctor will work for Ether: Decentralized autonomous health insurance

Once Upon a Time a young man named Vitalik Buterin presented the concept of “Ethereum” to the cryptocurrency community. Vitalik became a Thiel Fellow and persuaded the world that there’s a better way to write laws, organize ourselves politically, and conduct transactions using a Turing-complete language built on the blockchain.

I would have never imagined, when I began developing on the Ethereum platform, that the concept of a decentralized autonomous organization (DAO) would make popular media headlines so soon. This is the start of something remarkable.

When I presented my vision for decentralized autonomous health insurance in June 2014 at the BitTorrent headquarters, Chris Peel, the founder of the Ethereum Bay Area Meetup, asked me how I would realize my vision. “Well…” I began, “operating health insurance is a big undertaking!” I said, scratching my head. Two years later, the time for a better way to insure our health and pay for health services is here. By cutting out the middle people, Ethereum-powered smart contracts and DAOs promise to dramatically reduce the cost of health services. Why should most of our outrageously over-priced health insurance premiums feed bloated corporations and their executives?

Ancient Chinese physicians practiced preventive medicine par excellence. In ancient China, physicians were compensated when their patients enjoyed good health, not when they grew ill – the opposite of our reactive, fee for service-based health system. Ethereum is our opportunity to end the healthcare crisis, and it’s incumbent on us to carry forth this effort.

DOCTOR WILL WORK FOR ETHER.
NOW ACCEPTING NEW PATIENTS.

Send me a line at omar.metwally@gmail.com

Omar Metwally MD
@osmode

Carly, a voice-activated health coach for Amazon Echo

carly_cropped

Since my team won MIT’s Hacking Medicine hackathon 2 years ago with an app that generates structured documentation from an unstructured patient-doctor interaction (by passively listening and watching the interaction), I’ve taken on the challenge of natural user interfaces. Recently, APIs such as Google’s WebSpeechAPI, which I’ve used in the setting of academic research, proved their ability to convert speech to text with enough fidelity to be useful in real-life applications. With devices like Amazon Echo and wide-range microphones capable of discerning speech through ambient noise, we’ve bypassed the second major hurdle in natural user interfaces.

Now it’s time to start bidding farewell to keyboards, mouses, and ugly software that looks like an Excel spreadsheet. Instead of scrolling through a mind-numbing list of vital signs and lab values in an electronic health record, a provider should simply be able to say: How high did John Doe’s blood pressure get in the past 4 hours? or Trend Jane Doe’s creatinine level over the past week. This is what I mean by natural language interface: software that allows humans to interact with it in the same way humans think, which is through natural speech.

If you have an Amazon Echo, check out my new Alexa Skill, Carly, a voice-activated health coach for Amazon Echo. The next stop will be introducing doctors to Carly and the joy of natural user interface.

Conceptualizing health and illness through word embeddings

20 minutes into her conversation with a patient with a diagnosis of irritable bowel syndrome (IBS), Dr. Zurcher realizes that she and her patient aren’t at all on the same page. With her own concept of “IBS” in mind, she tries her best to convey the fact that IBS is a syndrome characterized by constipation and/or diarrhea . Her patient, on the other hand, is less interested in discussing his constipation or medication for IBS than he is in bringing to his doctor’s attention his crippling social anxiety, which disrupts his life much worse than any of his gastrointestinal complaints. Dr. Zurcher’s grasp of IBS as a diagnosis established according to the Rome III criteria, while medically sound, has little to do with her patient’s conceptualization of his diseases, and unless she appreciates this, the encounter is unlikely to be productive.

As much as medical schools and residencies train physicians to listen carefully to their patients, physicians invariably approach the patient encounter with an agenda (to document a patient encounter, generate ICD-10 codes, and establish a problem list and plan) that doesn’t always coincide with a patient’s agenda.

To better understand how my patients conceptualize health and illness, I trained gensim’s word2vec implementation on 2 million disease-specific tweets. The beauty of this method is its capacity to uncover both obvious and less obvious semantic relationships among words. I challenge healthcare professionals to contrast their understanding of disease with their patients’ conceptualizations of  illness.

Try searching for “heart failure”, “obesity”, “alcohol”, or “IBS”, for example. Each query returns the 10 semantically and/or lexically nearest neighbors in 100-dimensional space, along with their cosine similarity to the query term. The closer to 1.0, the closer they are in hyperspace.

Screenshot from 2016-03-04 23:56:44

Building smart contract-based health insurance

This post elaborates on a 6/19/2014 presentation I gave at the BitTorrent HQ on decentralized autonomous health insurance, and on a prior blog post. My goals here are:

  • To quantify and qualify the problems with the US healthcare system, especially as they pertain to the payer system
  • To introduce my vision for decentralized autonomous health insurance
  • To outline key components of successful health insurance systems
  • To start laying the groundwork for applying these concepts in practice
1. The U.S. spends more on healthcare than any other country — for subpar health outcomes

1.1 The U.S. spends 17.7% of its GDP on health-related expenditures, with the Netherlands (11.9%), France (11.6%), and Germany (11.3%) landing a distant second, third, and fourth place, respectively (OECD Health Data 2013). 52.2% of U.S. healthcare spending is private rather than public. The United States, Chile, and Mexico are the only OECD countries whose health expenditures are less than 50% public (OECD Health Data 2013). Despite our country’s disproportionately high healthcare spending, life expectancy among Americans has fallen to about 81 years (as of 2011), comparable to Chile and the Czech Republic and lagging behind most other OECD countries.

1.2 To change individuals’ behavior, change their incentives. To create a sustainable system, align individuals’ incentives. I’ve identified below several flaws in US healthcare’s payer systems, all of which relate directly or indirectly to financial incentives, and discuss potential solutions later in this post.

(i) Large uninsured population. According to the US Census Bureau, the number of Americans without health insurance exceeded 48 million (15.7% of the population) in 2010.

 (ii) In the US, 2.5 physicians serve 1,000 people on average, compared to the OECD average of 3.2 physicians per 1,000 people. The American Medical Association projects a shortage of 124,000 physicians (46,000 primary care physicians) by 2015.

(iii) Physicians have traditionally been incentivized financially to perform procedures, not optimize health outcomes. A surgeon is reimbursed tens of thousands of dollars for resecting tumors resulting from lifelong smoking, while general practitioners are hardly reimbursed for taking time to counsel patients on smoking cessation.

(iv) Tort laws keep physicians on the defensive and incentivize them to err on the side of excessive testing.

(v) Uneven distribution of healthcare resources

(vi) Burdensome administrative overhead to perform tasks such as preadmission certification, utilization review, membership management, collection of funds and claims, and quality assurance. Operational hurdles are a common reason for health insurance systems to fail, according to the World Bank.

(vii) Discrimination based on medical diagnoses (“pre-existing conditions”)

(viii) Disparate attitudes toward end-of-life care. Healthcare spending in the last year of life is 6 times greater on average among Medicare patients: $39,975 versus $5,993.

2. Building a smarter healthcare system

An optimal health insurance system has yet to emerge in response to the Affordable Care Act. Social and national payer systems like those in Scandinavia, Germany, and Switzerland are good working solutions, but they’re imperfect systems. Smart contracts don’t promise a utopian solution; however they allow us to iterate toward an optimal set of parameters within a dynamic actuarial system. 

2.1 An insurance system in the era of smart contracts will admit its shortcomings in terms of knowledge and financial resources and instead focus on establishing malleable rules than can be updated iteratively based on outputs such as health and financial outcomes.

2.2 Each decentralized autonomous health insurance system will be constructed in a modular fashion with a uniform parameter set, such that each system represents an actuarial experiment based on community-generated contracts.

2.3 The parameters in 2.2 and aggregate financial/health outcomes will be available publicly to facilitate iterative refinement of these smart contracts governing decentralized autonomous health insurance.

2.4 Decentralized autonomous health insurance (DAHI) will shift influence away from private insurers and toward individuals. DAHI systems hold the potential to provide just and universal access to healthcare. These smart contract-based systems will not be burdened by administrative overhead and will not be victims of political consensus or lack thereof.

2.5 Decentralized autonomous health insurance will protect the socioeconomically disadvantaged. “Unless designed to be pro-poor, health insurance can widen inequity as higher income groups are more likely to be insured and use health care services,” warn Wong et al (World Bank Health Insurance Handbook). Existing health insurance tends to draw resources away from the poor. The World Bank identifies 3 mechanisms to protect vulnerable groups, including rural communities, self-employed individuals, informal workers (“black market”), small businesses, the homeless, and orphans:

(i) Compulsory universal coverage to prevent the rich opting out of the pool
(ii) Require redistribution among multiple fund pools
(iii) Financially incentivize providers to serve poor areas

The implementation of these mechanisms, however, has proven challenging because the rules governing private health insurance favor the affluent, both in their scope and execution. DAHI governed by smart contracts will enable the formation, and more importantly the automated enforcement of, fair rules ensuring the inclusion of socioeconomically disadvantaged groups.

2.6 The World Bank identifies several key considerations in designing a sustainable health insurance system:

(i) Political feasibility and political mapping

(ii) Sociocultural norms
These can strongly influence the ultimate success or failure of health insurance programs (boxes 2.2 and 2.3). In some societies, for instance, people believe that planning for inauspicious events can harbinger bad luck. A more germane example of the importance of sociocultural norms is the debate surrounding the end-of-life care in the United States.

(iii) Financial capacity
In the context of traditional government-funded health insurance, financial capacity is determined by the number of people who buy in, the size of the formal sector of the economy that is taxable, and per capital GDP (Wong et al). The World Bank also identifies the capacity to collect, pool, and spend funds efficiently and effectively as determinants of feasible and sustainable health insurance systems.

(iv) Provider capacity
The number of healthcare professionals available to provide healthcare services

3. Guiding principles
Early adoption of DAHI will rely to a large extent on a group’s social cohesiveness. As Wang and colleagues point out, social cohesiveness generally tends to correlate positively with the success of community-based microinsurance. Individuals may be more likely to buy-in if they know that risk is being shared among individuals in their communities, to whom they can relate on a personal level. Initially, smaller insured groups will mean fewer ways for the system to fail as well as greater agility in creating and modifying rules ad libitum. Associated with smaller groups however is greater financial risk. As these groups grow larger, they also become more financially insulated. Therefore, the gradual growth in the size of an insured pool translates into an opportunity to refine gradually the mechanisms governing DAHI, which are outlined below.

3.1 Health insurance based on smart contracts provides an opportunity for societies to engineer redistribution mechanisms that preserve socioeconomically disadvantaged individuals’ access to healthcare resources. The goal here is to establish rules that promote the amalgamation of insured groups, such that wealth and resources flow from the wealthy to the poor, from the healthy to the unhealthy. Each member pays according to how much she or he can afford to pay. These rules will also promote diversification of insured pools to further strengthen the group’s financial insulation. Without such rules, smart contract-based health insurance would only recreate the same inequities in access to healthcare that traditional insurance creates, namely the formation of wealthy insured groups with the exclusion of the poor. One example of a redistribution mechanism is a quota for the sponsorship of disenfranchised individuals, in which an insured group agrees to provide X% of its funds to cover for low-income individuals who could not otherwise afford health insurance.

3.2 The decentralized nature of smart contract-powered, community-based microinsurance bypasses the political roadblocks and administrative challenges that continue to hinder the implementation of the Affordable Care Act. It’s also central to the essence of DAHI as a series of actuarial experiments that allow societies to iteratively approach an optimal set of financial parameters that maximize the just distribution of healthcare resources. One question that arises, however, is the following: what will be the role of federal and state governments? Will they hinder its implementation? If DAHI does in fact become widespread, will it constitute health insurance in the eyes of the law? I believe that governments will play the important role of “the stick” by continuing to incentivize or even mandating membership in a health insurance group, akin the the Affordable Care Act. The mechanics of health insurance, however, are best left to algorithms.
3.3 Medical practice focused on health outcomes, rather than procedures, will require a corresponding shift in reimbursement. Accountable Care Organizations reflect this tendency, which is borne from the federal government’s recent reforms. Analogously, smart contract-based DAHI will be instrumental in incentivizing providers to practice health outcomes-based medicine.

3.4 This was already stated in 1.2, but because of its centrality to the concept of smart contract-based DAHI, I repeat it here. DAHI is based on the admission that no optimal solution to the current payer problem exists. There are numerous potential solutions. Each insured group in DAHI represents a controlled experiment in health economics and policy. Therefore, DAHI must be designed modularly with replicable components, a consistent set of parameters, and transparency of both inputs and outputs to allow iteration toward optimal parameter sets.

3.5 Buy-in from providers will rely foremost on their perceived value of cryptocurrency. This is a matter of time, in my opinion, as cryptocurrencies gradually become more accessible to the general public and more prevalent in commerce. Furthermore, the rate at which DAHI grows will depend to a large extent on legislators’ reaction to smart contract-based DAHI.

4. Proposed mechanisms

Sample Serpent code is available on my Github repository:
Each DAHI system will include a set of parameters including, but not limited to, the following;

(i) Maximum/minimum number of members

(ii) Percentage of member votes necessary for approval of an insurance contract in order to become active

(iii) Enrollment period start and end

(iv) Coverage period

(v) Cancelation penalty

(vi) Premium (either a range or a fixed amount)

(vii) Adjustment formula: this allows adjusting premiums based on individuals’ relative cost to a healthcare system – without ever excluding anyone. One possibility is creating an actuarial score, for example a ratio of benefits received to premiums paid over an individual’s lifetime, which can then be transferred from one contract to the next.

(viii) Maximum plan dollar limit

(ix) Terms of care provided, including access to specialists and physician reimbursement

(x) Utilization

(xi) Number of mid-level providers and physicians from each specialty, including a voting mechanisms to allow members to vote providers into or out of a group.

(xii) Reward pool to promote good health outcomes

4.1 Both beneficiaries and providers will be subject to a peer-based application process. This will be predefined in the contract’s rules, for instance as a minimum percentage of the vote, to safeguard against discrimination.

4.2 All financial and health outcomes (in anonymous aggregate) will be publically available to allow assessment of a group’s financial feasibility/sustainability, allow quality control of providers’ practices, and promote iterative refinement of the parameter set in subsequent groups. Quality control may also take the form of reputation systems for healthcare providers.

4.3 Both patients and providers will be rewarded for good health outcomes in the form of a bonus at the end of the coverage period.

5. From theory to practice
Smart contract-based decentralized autonomous health insurance, executed correctly, can provide citizens of developed and developing countries alike equitable access to healthcare resources. It has the potential to do what traditional health insurance has frequently failed to do, namely protect the interests of the most socioeconomically vulnerable, and politically weakest, individuals. While adoption of DAHI likely will be a slow process that depends upon the rate at which cryptocurrency becomes mainstream, implementation need not be an all-or-nothing process. First steps will include running simulations and eventually using DAHI in the context of coinsurance, or health insurance for specific contexts such as outpatient visits. A few interesting open questions include how existing health insurers will react to smart contract-based health insurance, and to what extent, if any, DAHI will be truly decentralized. Ultimately, federal and state legislation governing cryptocurrency and smart contracts will shape how smart contract-based insurance is implemented.

Appendix

8. Actuarial anatomy

8.1 Risk pooling := collection of funds from members of a group to finance the cost of a catastrophic event (Wang et al, 11). This allows a group, rather than an individual, to bear the financial risk of paying for catastrophic costs. The larger and more diverse the group, demographically and economically, the more effectively the risk is spread (WHO 11). The concept of risk pooling is closely tied to moral hazard (see below): how can financial risk be distributed without corrupting individuals’ incentives to exercise precautions?

8.2 Risk aversion := converting a low-probability of a catastrophic event into a certain, low-burden event. Let’s say for instance that a dentist pays $40,000 to insure his hands over the course of 20 years. He has a 1% chance of losing one or both hands, but this would cost him $2 million in lost income. He can convert a 99% chance of making $2 million and keeping his hands (and a 1% chance of losing his hands and $2 million in income) into a 100% chance of losing $40,000.

8.3 Moral hazard : = when the behavior of an insured person changes – usually to become less risk averse – because they no longer bear the full cost of their behavior (Wang et al, 25). For example, let’s say that Larry has a family history of heart disease, and that the cost of treating a heart attack is $100,000. Given his family history, smoking habit, and sedentary lifestyle, his risk of a heart attack in the coming 10 years is 15%. Quitting smoking, losing weight, exercising, and regular checkups would reduce his risk of a heart attack to 7%. But what’s the point of going through all the hassle, Larry thinks, if his insurance will pay for it anyway? Possible solutions to the problem of moral hazard include:

(i) Coinsurance: the insured person still bears some of the costs and therefore retains incentive to adopt and maintain healthy behaviors

(ii) Preconditions to insurance: for instance, Larry’s health insurance requires him to get regular checkups and do an annual urine test to prove non-smoking status

8.4 Adverse selection := tendency of higher-risk individuals to be more likely to enroll in insurance. Unhealthy patients with worrisome family histories or concerning health findings buy health insurance, driving up the cost, whereas healthy individuals become less likely to purchase insurance as it becomes uneconomical.

9. Anatomy of health insurance
The following is an overview of actuarial terminology and common forms of health insurance.

9.1 Terminology
9.1.1 Coinsurance := health insurance plan in which the insured person pays a given percentage of medical expenses after the deductible amount, if any, is paid

9.1.2 Deductible := fixed dollar amount during the benefit period (usually one year) that an insured person pays before the insurer starts to make payments for covered medical services

9.1.3 Premium := fees paid for coverage of medical benefits per unit time

9.1.4  Copayment := health insurance plan requires insured person to pay a fixed dollar amount when a medical service is received; the insurer pays the rest
9.1.5 Gatekeeper: individual who coordinates and authorizes medical services, laboratory studies, specialist referrals, and hospitalizations. This is often the primary care provider.

9.2 Types of health insurance plans
9.2.1 Preferred provider organization (PPO)
Patients receive care from healthcare providers in a given network. Patients are incentivized to remain within the network by being charged premiums for heathcare services received from providers outside the network.

9.2.2 Health maintenance organization (HMO) is both insurer and healthcare system. This includes organizations such as Kaiser Permanente.

9.2.3 Accountable care organization (ACO). Federal or state governments pay ACOs a lump sum to provider care for X number of individuals in a given geographic area for Y units of time. This is a new reimbursement model that emerged from increasing pressure by the federal government on healthcare systems and providers to control costs and practice outcomes-focused medicine.

Acknowledgements
I’m grateful to Vitalik Buterin, Ethereum creator and recent recipient of the Thiel Fellowship, and to Christian Peel, who runs the Silicon Valley Meetup group, for their productive feedback.

References
  • OECD Health Data 2013.

http://www.oecd.org/unitedstates/Briefing-Note-USA-2013.pdf

        http://www.oecd.org/berlin/47570194.pdf

  • United States 2010 Census.
        http://www.census.gov/prod/2010pubs/p60-238.pdf
  • Wang H. et al. Health Insurance Handbook: How to make it work. World Bank Working Paper No. 219, 2012.
  • Wood, Gavin. “Ethereum: a secure decentralised transaction ledger.”
        http://gavwood.com/Paper.pdf

 

Democratizing healthcare through decentralized consensus

The concept of cryptocurrency, and more broadly, of decentralized consensus, represents a shift away from the old-world paradigm of centralized authority. My parents’ generation (and their parents’ generation) grew up accustomed to confiding their trust in infallible governments, fail-safe banks, and reputable degree-granting academic institutions to which they paid decades’ worth of savings so that their children would have a better chance in society. Although decentralized consensus is silently changing the economic underpinnings of our society, I regard cryptocurrency and decentralized consensus as safeguards of the democratic ideals espoused by our constitution. The reality is that cryptocurrency is here to stay. Paradigm shifts are a constant in human history, and I believe that the emergence of decentralized consensus will mark one of the most momentous paradigm shifts in human history.

My friends and I went to hear Andreas Antonopolous, a cryptography and cryptocurrency guru, answer Bitcoin questions yesterday. If I were to summarize the 2-hour meetup in one sentence, it would be the following: the details of how cryptocurrencies are traded are still maturing, but the concept of decentralized consensus is here to stay. Decentralized consensus holds the promise of democracy 2.0, something that’s remained a Utopian dream except in the tiny country of Switzerland. Decentralized consensus holds the promise of a better world where governments and organizations don’t steal from politically weak, defenseless individuals. As Antonopolous points out, we’re fortunate enough to have a benevolent government in the United States, but the majority of the world is not so fortunate. Decentralized consensus holds the promise of empowering people to exercise the power of their vote to truly make healthcare a human right. Before I expound on this latter point, I want to outline some technical underpinnings for the uninitiated, so bear with me.

Satoshi Nakamoto’s most remarkable achievement with Bitcoin is the cryptocurrency’s success in solving the problem of a decentralized public ledger. In the case of the US Dollar or any other currency backed by a governmental body or bank, there exists a central authority that acts as the ledger. Bitcoin’s brilliance lies in the fact that the ledger is public, encompassing potentially everyone and anyone. The blockchain ledger is the communal ledger that lends cryptocurrencies their value. It’s characterized by the following 2 criteria [4]:

  • Blocks are very difficult to discover (Difficulty Factor * 2^32 hashes)
  • Blocks are easy to validate

A Bitcoin comes into existence when a “miner” uses her/his machine (and therefore computing resources, disk space, and electrical energy) to generate new blocks that record cryptocurrency transactions. The block chain with the most cumulative computational work is accepted by consensus as the valid block. In other words,  physical energy (electricity) is converted into Bitcoins. Keep that in mind if you ever find yourself wondering whether or not cryptocurrency is “a thing.” The reward for mining Bitcoins diminishes with time, as the horizontal asymptote of ~21 million BTC is approached (around 2024).

This setup has a few interesting results with regard to game theory. While mathematicians reading this will quickly pick up on the fact that wielding >50% of mining power holds the theoretical potential to manipulate the currency, game theorists should also note that this system strongly incentivizes cooperation and veracity [2] (I won’t get into the details here, but I’ll refer you to a suggested reading list at the end of the post).

The Bitcoin protocol is not Turing-complete. Enter Ethereum, a Turing-complete protocol for scripting contracts in the blockchain. Ethereum is big. If you’re not a believer yet in Vitalik Buterin and his work, I encourage you to check out the whitepaper for an interesting read. Ethereum uses a Python-like scripting language (Serpent) to convert contracts into cryptographic building blocks. For the first time in history, parties entering into agreements are not at the mercy of inherently biased third parties. Ethereum marks an era in which algorithms — not banks, governments, or individuals — hold the power to validate and execute contracts.

One interesting result of this decentralization is the so-called Decentralized Autonomous Organization (DAO), in which each member is represented as a cryptographic public key [1]. A contract that exists as lines of code in a Turing-complete language means that we can go beyond simple two-party agreements, like this prenuptial agreement written in Ethereum, to a corporate-like structure that automates redistribution of internal capital among participants in exchange for services provided, assets, or computational power. Transactions can contain information like votes, changes in the contract (such as amendments), or adding/removing members [1]. Most importantly, this is all automated without reliance on an escrow or central authority.

The U.S. healthcare crisis has demonstrated how lawmakers, insurance companies, and healthcare systems are struggling to figure out a way to fairly distribute access to healthcare. The U.S. healthcare system was hurt by an incentive system that rewards procedures rather than quality of care and health outcomes. Recent changes in CMS reimbursement are starting to change this, prompting the emergence of Accountable Care Organizations that receive payment in exchange for providing healthcare to a fixed population, rather than on a fee-for-service basis. The healthcare system failed for the same reason the financial industry lost its credibility in the 2008 financial crisis: third parties succeed in manipulating an easily manipulable system in their favor. People were robbed blind.

I’ll give a simple example of what I’ll call Decentralized Autonomous Health Insurance. Let’s say individuals A through J enter an agreement with physicians X and Y, in which X and Y agree to provide healthcare to individuals A-J. Let’s say in this simplified example that X and Y are not reimbursed for their services, but by A-J’s health outcomes (in ancient China, physicians were paid when their patients were healthy, not when they were sick). Let’s also say that X & Y have a practice that accepts cryptocurrency as payment. Then, A-J and X&Y can pen a virtual contract with the following stipulations:

  1. A-J pay 20 Bitcoins per year to receive care from X & Y’s practice.
  2. The cost to X & Y of providing healthcare to A-J is deducted from the pool of Bitcoins in (1)
  3. X & Y will receive a minimum reimbursement of 10 Bitcoins per patient per year.
  4. If the cost of providing healthcare is less than 10 Bitcoins per person per year, the surplus is shared evenly between providers (X & Y) and patients A – J. This incentivizes patients A – J to take care of their health so they get a bonus at the end of the year, and it incentivizes X & Y to adhere to primary/preventative medicine best practices (including taking time to counsel patients).
  5. A-J can vote annually on which providers they want to provide them with healthcare.
  6. A-J can vote annually on important decisions that affect the distribution of healthcare services.

We might even imagine a scenario in which each patient’s medical record is encoded and distributed in a decentralized manner such that it exists as undecipherable bytes among millions of computers around the world, rather than behind the walls of a single healthcare system. For example, a chip could keep track of our health habits and automatically append these data to our blockchain-based medical records. These data (such as smoking and exercise habits) could then be integrated into the communal contract, so that sedentary smokers have to pay more Bitcoins per year than active non-smokers in order to receive care from X & Y. In this model, individuals’ health (not access to healthcare!) is the internal capital. Everyone is both a payer and consumer of healthcare, and everyone has the power to vote on the bounds and conditions of care provided. This type of Ethereum-based Decentralized Autonomous Health Insurance would have no administrative overhead, no bureaucracy, and no board of directors to decide who is healthy enough to be insured.

I’m less interested in the exact economics of the hypothetical example above than in the broader concept of decentralized consensus and the self-fulfilling social contract. It’s time to decentralize health insurance the same way cryptocurrency is decentralizing currency.

Cryptocurrency and Ethereum are a new social and technological frontier, which haven’t really reached mainstream yet. These young protocols still have to pass several important tests (such as reliable security mechanisms) and prove their scalability before they become widely used, but I’m optimistic. The future will be one shaped by knowledge, and less so by historical inertia. Decentralized Autonomous Organizations hold the promise of just distribution of scarce resources, including the most vital one of all: access to healthcare.

References:

  1. Ethereum Whitepaper
  2. Vitalik Buterin’s blog
  3. Bitcoin: Open source P2P money
  4. Brian Warner’s technical introduction to Bitcoin

 

[First published on my Quora blog on May 7th 2014]

Brave New Wearable World

From Google Glass to Misfit Shine, wearables refer to any hardware that can be worn around, hardware with which we interact in ways beyond the traditional keyboard and mouse/touchpad. Wearables are our daily companions, collecting information about our bodies and environments, adapting their behavior to our movements. Most of them sync via Bluetooth (or BLE) to our mobile phones. A related but different category of devices under the larger umbrella of the Internet of Things are so-called “there-ables” (a nice term used by Naveen Salvadurai).

But the reality is that wearables are still in their infancy. The term still means little to people in non-tech industries.

I started writing software in 1997 when my parents bought Sony’s PlayStation developer kit for my 12th birthday, the so-called Net Yaroze. Everything was written in the C programming back then. There was no Stackoverflow and no Googling around for sample code. When I couldn’t figure something out, I had to flip through a stack of reference manuals that shipped with the device until I eventually figured things out.

Nowadays languages like Python and Ruby are all the rage. I give credit to the open source community to the modularity of these languages, allowing people to write and contribute to libraries and frameworks that make writing software far easier than it was in the days of C. Developers can now focus on building things and rapidly prototyping rather than figuring out how to accomplish relatively low-level tasks like rendering graphics.

A similar phenomenon is emerging in the hardware world. The open source hardware community is vibrantly blossoming. Hardware hackers are sharing schematics of their creations online, and tools like Arduino, Raspberry Pi, and BeagleBone are helping engineers and non-engineers alike prototype their ideas and build devices that are turning the “Internet of Things” from a buzzword into a reality. Even Google announced a new modular phone, challenging consumers to rethink the lay “blackbox” view of hardware.

Hardware prototyping has become remarkably analogous to software development, thanks to the rise of open source hardware communities and modular prototyping platforms. This is a tremendous driving force behind the explosion of wearables. You need a team of skilled electrical, industrial, and software engineers, a good amount of capital, and a factory to manufacture a new line of activity trackers. But even the most popular wearable devices nowadays are increasingly being born as Kickstarter-funded prototypes on breadboards. The big challenge isn’t conceiving or manufacturing these wearables; the big challenge is identifying a compelling use case, and a hungry market.

Prototyping smart watches
Prototyping smart watches

One group working heroically to make hardware accessible to all is iRoboticist. I spent the past weekend in an awesome 2-day workshop run by Saurabh Palan and his team expanding my knowledge about wearable hardware and getting inspired by smart, passionate hardware hackers from all walks of life. In a few hours, I built a smartwatch prototype that can display the wearer’s heart rate, temperature, and distance walked via a 3-axis accelerometer. Oh, and it also tells time.

The greatest act of rebellion is propagating knowledge. For the sake of knowledge.

Wearables are cool, but as iRoboticist founder Saurabh Palan points out, the greatest hardware of all is the human body, which continues to inspire engineers on a daily basis. No robot is as agile as a human. No app can recognize voices or images like the human mind. I’ve yet to see a robot that can repair itself when damaged.

I’ve come to believe that designers have the most important role in product development. My experience of co-founding PulseBeat and going through the Blueprint Health incubator have convinced me of this beyond a doubt. By designer, I mean someone who works to understand users and systematically studies how they interact with hardware and software. Someone who engineers not just graphical interface or the physical form of a device, but also its functionality. I hope that someday the position of a hospital’s Chief Informatics Officer won’t be filled by (semi-)retired physicians climbing the organizational ladder, but by true designers passionate about the interaction between patients, physicians, nurses, software, and hardware.

I’ll wrap up this post with a final thought on the most important of questions in tech entrepreneurship, the so-what question. From venture capitalists to physicians to journalists, people frequently ask me what I think about wearable technology and where it’s going. Technology is like the restless child running laps around our wizened, slow-moving society of bureaucrats and reactionary institutions. The majority of new technologies, regardless of how much Silicon Valley hypes them and infuses them with venture funds, never find a place in the real world. They either fizzle out or become sustenance for bigger fish. But that doesn’t matter — it’s part of the process of innovation. That’s why I chose tech over academia. Throw a lot of spaghetti against the wall until a strand sticks.

Building software and hardware is my passion. The Valley is filled with very talented engineers and hackers. It’s burgeoning with bullish investors eager to invest in new ideas and wide-eyed founders. But the most valuable thing I learned from my time at Blueprint Health was the fact that the biggest challenge any startup faces is not an engineering or funding question; it’s the so-what question.

We’ll eventually have miniature, implantable, and edible hardware with dozens of sensors onboard. But collecting data is not enough. Even doing analytics on these data is not enough. There’s a huge mismatch between sensors and actuators in health tech. It’s not enough to know that a patient’s health has changed for the worst. The trillion dollar question is, what will your app or hardware do to turn a patient’s health around, whether it’s delivering healthcare workers (what my startup does), delivering therapeutics, or changing people’s behavior.

So you built a cool app or a beautiful new wearable device. But what problem is it solving? Whose pain is it alleviating? To paraphrase Sam Altman, are you selling a painkiller or a vitamin?

We need to get patients, engineers, and healthcare professionals to sit down at the same table more often. The process of product development is radically different in health tech than in other industries. I think the reason so few people have really figured out health tech lies in the fact that it’s hard to get these 3 parties, who have disparate pain points, to participate equally in the development of consumer health apps and wearables. I’m working hard to change this.

[First published on my Quora blog on April 21st 2014]

The Holy Grail: automating documentation of patient encounters

Ask any healthcare professional to make one wish, and more often than not it’ll be something along the lines of, “I wish I had an app to write my notes for me.” Documenting patient encounters is time-consuming, annoying, and takes away time/attention that could be better spent communicating face-to-face with patients. This past weekend, I peeled myself away from PulseBeat-related work to spend the weekend in Boston participating in MIT’s healthcare hackathon. I went with Google Glass developer extraordinaire, John Rodley (TwiAge, a fellow Blueprint startup), and met several other talented people there. After 2 days of hacking, we walked away with a functional prototype of a Glass app that does just that — and won First Place. More importantly, I learned a lot and walked away feeling even hungrier to change healthcare as we knew it.

[First published on my Quora blog on March 18th 2014]

The Winning Team
The Winning Team at MIT’s Hacking Medicine Hackathon (March 2014)