new dokchain: intelligent automation in healthcare … · 2018. 2. 6. · dokchain: intelligent...

DOKCHAIN: INTELLIGENT AUTOMATION IN HEALTHCARETRANSACTION PROCESSING

W. Bryan Smith, PhD; Chief Scientist, PokitDok; [email protected]

Contents

Summary 1Motivation 1Cost Savings Estimates 2New Market Opportunities 4DokChain Alliance 6Protocol 6Use Cases 6DokChain Currency: Cure Coin 10Cure Regulation and Minting 12Closing 15

Summary

DokChain is a distributed network of transaction processors operating on financial andclinical data in the healthcare industry. Our goal is to deploy distributed ledger technologyacross a broad range of industry participants to bring intelligent and dynamic automation tofour core use cases that span healthcare encounters: context-relevant identity management;autonomous transaction validation and processing; prior authorizations; and event-drivensupply chain management. The initial implementationi employs proof of elapsed time (PoET)consensus among hosting nodes to generate an encrypted, immutable log of every transactionin the system, using on-chain pointers to an off-chain, distributed file system for data storage,access, and analysis. The result of our implementation is a new kind of healthcare economy,in which data and services are quantifiable and exchangeable, with strong guarantees aroundboth the security and privacy of sensitive information as well as the longitudinal auditabilityof transaction history.

Motivation

The healthcare industry is encumbered by operational inefficiencies and prone to costlyerrors, resulting in tremendous loss of both financial as well as human capital. While a matterof global importance, the issue is particularly acute in the United States, where the nationalexpenditure on healthcare is disproportionately large (Figure 1). Note that moving the U.S.to the nearest contour in the distribution would require more than half a trillion dollarsin cost reductions. Among the many reasons often given for the waste seen in healthcare

Date: September 26, 2017.iimplementation details, including available consensus algorithms, minting protocol, etc are subject to change

1

2 W. Bryan Smith, PhD; Chief Scientist, PokitDok; [email protected]

spending, it is clear that poor information access and management across the ecosystem isa common factor. The current state of affairs is, however, not for lack of trying. Indeed,a tremendous amount of regulatory and policy-driven effort has been spent attempting tosolve the issues that plague the industry. The list of such initiatives includes “meaningfuluse” mandates around legacy technologies, payment incentive models based on “value” versus“volume,” and large scale insurance market intervention (i.e. the Affordable Care Act, ACA).However, by many measures, these efforts have largely failed to deliver on their respectiveobjectives. While it is clear that these failures are due in part to behaviorally complex (andoften misaligned) incentives throughout the existing system, we believe that a truly connectedand intelligent information infrastructure will cure a lot of what ails healthcare.

Figure 1. Distribution of global healthcare spend as a percent of GDP (vertical axis), byGDP per capita. Each colored circle represents a country: size is proportional to population;color shows life expectancy at birth, as indicated by the scale at right. The three oval contoursrepresent 1, 2, and 3 standard deviations on the covariance distribution. An interactiveversion is available here. Data from worldbank.org.

Cost Savings Estimates

In a very real sense, the simple statement “healthcare in the U.S. is a complete clus-terf#@k” is sufficient motivation for creating DokChain. However, to try and more formallyframe the potential value of the DokChain network, we begin with an estimate of the ef-ficiency gains the system is expected to realize across the healthcare industry. Healthcarespending is rapidly approaching 20% of the U.S. GDP, with the average healthcare spendglobally at only about 6.7% of GDP. Yes, you read that correctly: the US spends nearlythree times the global average on healthcare, even after adjusting for population size and

https://dokchain.com/wp-content/viz/GlobalHealthSpendGDP.htm

http://data.worldbank.org/

DOKCHAIN: INTELLIGENT AUTOMATION IN HEALTHCARE TRANSACTION PROCESSING 3

economic productivity, often with inferior outcomes. What’s more, the U.S. National Health-care Expenditure report from the Centers for Medicare and Medicaid Services (CMS) projectsthat healthcare spend will grow 1.3% faster than GDP over the next decade.

Although these statistics are alarming, it is difficult to define exactly how large healthcarespending should be, given the combined size and relative wealth of the US population. Someargue that perhaps healthcare spend should be much larger in the U.S. compared to othercountries for myriad reasons, including the extensive R&D activities and technology-heavyprocesses that comprise significant aspects of the U.S. ecosystem. Such debates notwith-standing, it is our position that there remain obvious sources of waste in the U.S. healthcaresystem. Depending on which sources of waste one intends to tackle, estimates of the totalpotential cost savings range from the low hundreds of billions to a trillion dollars or more.We focus here on three categories of waste for our initial estimates: manual transactionprocessing; care coordination; and administrative complexity ii.

Eliminating Manual Transactions. According to the most recent statistics from the Councilfor Affordable Quality Healthcare (CAQH Index report, 2016), while the industry has seenwidespread adoption of electronic transaction systems over the last 20 years, manual transac-tion processes still account for roughly $10 billioniii in unnecessary spend. The ‘gate keeper’transaction of this set, eligibility and benefit verification, accounts for more than half of thesavings potential, as summarized briefly in Figure 2.

Figure 2. CAQH estimates of savings potential due to eradicating manual transactionprocesses.

Close the Gaps in Care Coordination. The waste associated with failures in care coordina-tion is attributed to patients “falling through the cracks” in the fragmented care deliveryprocesses common throughout the system today. This typically manifests as a loss of con-tinuity of both patient data and provider assessment as a consumer moves from a primarycare provider to one specialist and then the next throughout a complex episode of care. The

iiThe text in this section borrows heavily from the 2016 CAQH Index and Berwick and Hackbarth, 2012iiiAll dollar values are adjusted to 2017 levels assuming 5% annual growth in healthcare spend


consequences include increased readmissions, declines in functional status, and increased de-pendency, especially among the chronically ill. For example, 30-day readmission rates amongfee-for-service Medicare recipients have been estimated at about 20%, with roughly 75% ofthose determined to be “potentially avoidable.” In aggregate, the estimated savings to berealized by eliminating this class of waste range from $32 to $58 billion.

Automate Away Administrative Complexity. The result of governments, accreditation agen-cies, payers, and others entities actively creating rules and processes that actually decreaseefficiency is among the largest sources of waste to be addressed. Administrative complexity isthe stuff that makes the everyday consumer and provider experiences in the U.S. healthcaresystem as terrible as they tend to be. This includes such factors as providers being forcedto use electronic health record (EHR) software that conflicts with their preferred workflows,consumers not understanding out of pocket contributions at time of service, and official re-quirements to conduct business via legacy technologies that stifle innovation. It is estimatedthat solving the problems related to administrative complexity will reduce waste by $137 to$500 billion.

In summary, by addressing only these three classes of waste in the existing system, weestimate a total addressable market at somewhere between $180 and $570 billion. The sixtypes of waste, adapted from the Berwick and Hackbarth paper, are shown in Figure 3 forreference.


New Market Opportunities

While it is clear that there are massive efficiency gains to be made, effectively and safelyrealizing those gains remains a significant economic challenge. To quote Berwick and Hack-barth:

The potential economic dislocations are severe and require mitigation throughcareful transition strategies.

And so, while we continue working diligently with corporate, legal, and regulatory partners toensure that appropriate transition strategies are being pursued, we have also identified somekey opportunities that exist now, and that will emerge as a result of large-scale distributednetwork adoption in healthcare. A few of these opportunities are listed briefly here, some ofwhich are discussed in detail in the Use Cases section of this paper:


Use Case Prioritization and Governance Definition. Founding members of the DokChainAlliance will have a unique role in defining the specifics of initial use cases, and will worktogether to determine the optimal governance structure for overseeing the development andoperation of the Network. Because the Network will only be open to Founders during theinitial period of testing and optimization, the ability to directly have a large scale impact onthe outcome of the system will be the sole privilege of these first adopters. While provisionsfor modifying the Network have been incorporated into the protocol, such modifications willbe minor once the initial, closed period of optimization has lapsed.

Information Asset Monetization. As is often described in other blockchain implementations,one of the most intriguing opportunities to be explored is the different ways information as-sets can be defined and monetized. One example of this, in which an individual user’s identityprovides monetization opportunities both for the individual as well as the participating en-tities who provide identifying information from their respective domains, is described in ouridentity management use case below. There will also be significant value creation in theeventual emergence of smart contract markets, wherein users will bid on the right to usedifferent contracts to conduct their transactions, relying on full transparency around the rel-evant attributes of the various contracts being used to set a fair price. Other informationasset classes and mechanisms for monetization are left as an exercise for the reader.

Outcomes Based Optimization. An obvious feature of an integrated health information net-work as described here is that the potential for outcomes-based optimization may finally berealized. Not only will all the relevant data to conduct such optimizations be available ata population level, but true personalized medicine approaches at an individual level can beapproached with confidence, knowing that the user identities and the data surrounding theirepisodes of care have been verified and reliably logged in the system via distributed consen-sus. The industry-horizontal nature of the DokChain network will enable outcomes basedoptimization in a much broader business context as well. One such example, event-drivensupply chain optimization, is defined as a primary use case below.

Consumer-Driven Healthcare Markets. As consumers take charge of their personal healthinformation, and begin to understand the value of that information as an asset, having accessto a platform where that value can be exchanged for goods and services provides tremendousopportunity for the emergence of true markets for health services. As discussed elsewhere,rising plan deductibles and a growing consumer awareness of varying levels of quality of careare already providing massive incentives to drive these markets. The DokChain networkprovides the information access and transparency required in order to capitalize on thoseexisting incentives. We see these markets evolving in such a way that the very nature ofhealthcare service payment will change dramatically, shifting from the existing model basedon premiums and deductibles to one where “payers” make real-time bids to pay for serviceson behalf of consumers in exchange for interest payments over time.

Cryptocurrency Sale. The initial role of the in-Network cryptocurrency is to function as aB2B token used as a means of “paying” for transactions and rewarding Founding Membersfor their efforts during the initial, closed period of Network optimization. Once the Networkis open to a larger group of participants, however, the currency accrued by Founders maybe sold as part of the onboarding process for new network participants, or traded for othercurrencies on a public exchange.


DokChain Alliance

The DokChain Alliance was established in early 2016 with the intent of creating a fullyinterconnected health information economy, focused on the security, efficiency, verac-ity, and transparency of information transfer across the network. The Alliance has pulledtogether an unprecedented breadth of industry representatives, spanning not only the entirehealthcare ecosystem, but also financial services, consumer electronics and software, hard-ware manufacturers, credit bureaus, third party data providers, and others. The group hasconvened quarterly to discuss the Network protocol, use case prioritization, and governance,which are summarized here.

Protocol

The initial implementation of DokChain is a private, permissioned, distributed ledger oftransactions, using on-chain references to an off-chain distributed file system to store theactual data payloads associated with each transaction. Currently the data store is imple-mented using IPFS/IPLD, though the system can flexibly accommodate any similar content-addressed storage model. This approach trivially addresses content scalability and dataprivacy concerns, as the distributed ledger is not used directly as a data storage mechanism.For the testnet implementation, transactions are processed via execution of smart contractsby a Proof of Elapsed Time (PoET) selected leader. Every contract — including the networkprotocol, governance structure, membership agreements, et cetera — exists as a signed ob-ject on the DokChain ledger, removing any ambiguity as to the authenticity and reliabilityof these objects.

At a high level of abstraction, a transaction contains the following required elements:

{‘sender’:DKID, ‘recipient’:DKID, ‘payload’:blob, ‘contract’:address, ‘coin’:float}

The processes governing issuance and authentication of each identity (DKID) are describedin detail in the Use Case section below. The payload of a transaction as it is posted tothe network may contain the data upon which the designated smart contract will operate,although only a hash of the data is written to the distributed ledger. Figure 4 outlines ahigh level description of the protocol as currently defined. Additional details of the currentprotocol have been previously described in a blog post by Ted Tanner, PokitDok co-founderand CTO.

Use Cases

A fundamental consideration driving the initial DokChain use cases is a strong notionof information asset ownership and rights management. In the DokChain Network,digital asset ownership is conferred at time of issuance, based entirely on the identities (i.e.pubkeys) of the transacting parties, with partial asset ownership defined explicitly in theparameters of the contract being used. For example, protected health information (PHI) isowned by the individual consumer from whom the data are collected, and the componentsof the data to be shared with a provider or payer are defined explicitly, both at the time ofinitially writing the data to the system, but also upon subsequent access for use in analysisor auditing. It is our expectation that this more “appropriate” information asset ownershipmodel will incentivize a shift toward a more central role for consumer-driven digital assetmanagement in optimizing care coordination and delivery.

https://github.com/ipfs/ipfs

https://github.com/ipld/specs/tree/master/ipld

https://fullmetalhealth.com/full-consensus-distributed-ledger-hardware-acceleration/


Figure 4. DokChain Pseudocode

Contextually Relevant Identity Management Protocol. The cornerstone of the Dok-Chain network is a multi-party, contextually-relevant identity management protocol (CRIMP)that will ultimately incorporate active key management and key recovery functionality as im-plicit attributes of each identity in the system. Multi-party is as expected: multiple thirdparty entities in the network can confirm the identity of an individual user. Contextualrelevance here refers to the fact that different elements of a user identity may be needed indifferent contexts. A typical example given is the context of ordering an alcoholic beverage:the only identifying information the vendor needs to know is that the person requesting thedrink is older than what the local age limit requires. Yet today, the customer surrenders anidentification card that contains their full name, address, weight, height, and other pieces ofsensitive information that have no relevance to their immediate request. We have developedan identity management solution that, initially, allows the user to decide what they are will-ing to share of their personal record based on the context of each encounter. As the systemevolves, intelligent agents will evolve in parallel, learning how to manage the complexity ofthis information on behalf of the human user. The system is described in a high level of de-tail here, with implementation specifics made available in the DokChain code base to hostingnodes as appropriate.


(a) Multiparty Identity (b) Contextual Identity

Figure 5. A user identity (A) is defined as the entirety of all information known aboutthem from many different sources. Subsets of identity information are combined together(B)to match different contexts.

An identity in the DokChain Network comprises a set of key-pairs for each individual,where each key-pair can be used to unlock a distinct subset of information to be used indifferent transaction contexts. Given the extent to which consumer engagement is a drivingfactor in Network adoption, it is essential that users of the system have access to the mostrobust identity verification and validation system in existence, without having to rememberany complex passphrases. We also believe users should not have to concern themselves withthe potential consequences of losing their private keys.

To that end, the DokChain identity management system takes full advantage of the factthat, at least in the digital world, you truly are what you do. The entirety of your digital inter-actions, not just within any single industry vertical, creates the strongest possible signature ofwho you are as an individual. Medical records, e-commerce clickstreams, government-issuedidentity cards, biometrics: anything that increases confidence in determining the uniquenessof an individual can be incorporated. Our solution leverages the horizontal nature of theDokChain Alliance directly, in that the protocol integrates information about users frommyriad third party sources (see Figure 5). The power of our approach becomes increasinglyobvious as compromised legacy systems continue to leak personal information about usersworldwide. We assume that at some point strong identifiers, as well as their links to sensitiveaccount numbers, will effectively be public knowledge as a consequence of these hacks. Oursystem provides mechanisms to ensure identity verification and proofing even in this scenario.

We are pursuing a variation of a proof-of-work consensus component in the validation ofeach identity, whereby the private key is partitioned among a group of user-defined trustedparties, and according to a process such as Shamir’s Secret Sharing Scheme, the key isregenerated from a subset of those partitions. This mechanism is initially being used for key


recovery in the case of accidental loss, but will eventually function as an implicit componentof identity verification that occurs each time an identity is invoked in a transaction. Thework done is a combination of signing, encrypting, and reassembling the private key, as wellas moving the requisite subset of key partitions across the network in real time for eachvalidation and verification event.

The consensus identity that emerges from this process is given a confidence score that isinterpreted as a multi-party attestation that the attributes in a given user vector accuratelyand uniquely identify the individual. Transacting parties can confirm one another’s iden-tity via a consensus score alone as a base case, or may choose to request access to additionalidentifying information, including the identities of the validating parties, as their preferences,and budget, dictate. In addition to providing unprecedented identity security, this creates acompletely new information asset monetization channel as both users and their ‘validatingsponsors’ can expect to be compensated for providing such detailed, verifiable identity infor-mation. Placeholder examples of how such scores may be computed are shown for referencebelow.

Define a user vector, u, an m-dimensional vector of identity query attributes, q ⊆ u,and a set of N identity service providers, p. Then a consensus score, Sqp, representingthe confidence over p that q corresponds to u, may be generally stated as

Sqp =1

N

N∑

i=1

Fn(pi,q)

Where, in a naive implementation, Fn(·) simply represents each identity provider’s confidencethat q = u. Alternatively, we may consider a model that computes a weighted aggregateover each element of q:

Sqp =1

N

N∑

i=1

Pr(q | pi) ·wT

where w is an m-dimensional weight vector (i.e. some information-theoretic prior) over theelements of q that is updated over time, and as a function of the (inferred) context of thequery.

Efficient Prior Authorization and Referral. One of the more costly, complex, and todate overwhelmingly manual transactions in the healthcare industry is the prior authorizationand referral process (of which the X12 278 transaction is a component). Today, it is estimatedthat these transactions cost the payers several tens of dollars per transaction, a cost that canbe cut in half with basic process improvement and automation. We are developing smartcontract-based automation workflows that may reduce costs by an additional factor of 5 or10.

Autonomous Claims Adjudication. Medical claims processing is a notoriously compli-cated process, with coding, billing and reimbursement processes representing significant per-centages of the business overhead for providers. Somewhat spurred on by antiquated regu-latory policies, the system today sees clearinghouses, third party administrators, and otherintermediaries operating under the assumption of risk management on behalf of their respec-tive customers. As things have evolved, however, these intermediaries are not proving theirvalue proposition, tending instead to increase the complexity and reduce the transparency ofconducting business.


To some extent, this issue is currently being addressed in the form of auto-adjudicationsoftware and protocols. Auto-adjudication refers to computational claim processing, withoutany human role in reviewing or paying the claim. Great strides have been made in thisarena, with United Healthcare (the largest private health carrier in the U.S.) claiming that86% of electronically submitted claims to their systems are auto-adjudicated, while only 70%of manually submitted claims achieve this status.

While this is a solid start, it is worth noting that 14% of claims at United Healthcare isstill a massive volume of claims, and that United is far ahead of other major carriers in thisregard. In addition, auto-adjudication rule sets and the software used to manage them oftenrequire human intervention, which is simply deferring the manual portion of the task to adifferent step in the process.

The DokChain Alliance members have agreed that an artificially intelligent, smart contract-mediated claims adjudication protocol is a highly desirable use case to pursue. Such a systemwill allow for near-realtime adjudication and remittance of submitted claims, saving time andmoney for payers and providers, and reducing both the uncertainty as well as latency of outof pocket expenses for consumers. Further, because the system will be running within theDokChain distributed Network, updated rules and optimal payment schemes can be learnedin real-time over a broad range of plans and health systems.

Event-Driven Supply Chain Management. In the fragmented care delivery systemstoday, consumers are often shuffled from one office to another, sometimes carrying their owndiagnostic test results (e.g. MRI images) along with them by hand, being forced to drive toa pharmacy or equipment supply house that is far away from where they’ve been seen bytheir provider, only to find that what they need is out of stock or not exactly what is neededfor their specific condition. Imagine instead the benefits of a situation where everything aconsumer needs after a doctor’s office or hospital visit is already at their door by the time theyarrive home. This improves not only convenience and consumer satisfaction, but adherenceto prescribed treatments will increase dramatically as well.

Making this situation a reality is the goal of this DokChain use case. Again leveragingthe horizontal nature of the distributed network, a consumer’s preferred retail vendor canbe granted access to their Personal Health Record to know when they have been seen by aprovider (an ‘event’), as much detail about the nature of that encounter as the consumerwishes to share, and therefore what drugs and other consumables as well as durable medicalgoods they need to continue treatment at home.

DokChain Currency: Cure Coin

We are defining an internal currency, herein denoted as Cure, as opposed to using Bitcoinor Ethereum, in an effort to isolate the currency value from market forces outside of theDokChain network. For example, the exchange rate of Bitcoin with respect to other currenciesis extremely volatile, subject to regulatory statements by governments, and protocol decisionsof a small group of developers and/or miners, none of which have any relevance to theDokChain Network. While the currency may be listed and traded on an exchange at a laterdate, thereby creating additional monetization strategies, we do not intend to focus on thisaspect of the currency in our MVP implementation.

The role of a virtual currency in the DokChain Network is three-fold:

(1) To signal transaction value in ongoing operations of the Network


(2) A method for seeding the Network with relevant assets(3) To incentivize Founding Members to participate in optimizing the Network

In order to bootstrap the Network, we are initially conducting a private, B2B token sale toten founding partners selected from participants in the DokChain Alliance. Each founderwill contribute a minimum of $1M in the form of some combination of cash, press, and otherassets. While minimum and maximum asset contributions are negotiated on an individualbasis, a $250k minimum cash contribution is a strict requirement. A non-exhaustive exampleof assets considered is shown in Figure 6. Asset exchange for Cure in a future public coinsale will be evaluated in an information theoretic framework, and priced according to therelative increase in system functionality and/or information content provided by that assetat that point in time. As with all other governance components of DokChain, such exchangecontracts will be encoded on-chain for reference.


DokChain members are granted access to the Network by purchasing Cure to be used intransaction valuations. In addition to coins awarded for transaction processing as describedin the protocol above, each hosting node is issued a stream of coins at a variable rate that isdetermined by that node’s participation in the Network. “Participation“ is defined by beinga source of transaction activity, processing transactions, validating user identities, and othergeneral functional attributes of the system.

In our testnet implementation, there is no distinction between nodes operating as trans-action processors and nodes operating as validators, though a future implementation is in-tended to accommodate such a distinction. As we move toward such a system, the sum ofthe available coins in a block of transactions will be differentially shared among the processor


(PoET-selected leader) and any participating validators, with the processor being compen-sated slightly more than each of the validators. A trivial differential payout scheme is includedhere to illustrate a simple version of how such payouts could be easily managed:

Let N be the number of validators participating in mining any given block, rs the sum ofcoin reward associated with the block, and mp the multiple over each validator payout that ispaid to the transaction processor. Then the reward for the processor, rp, and each validator,rv, can be computed as:

rp =mprs

N + mp

and rv =rs − rpN

The processor bonus, mp, can vary randomly according a known distribution whose param-eters are determined as a function of the “Network Health.” Ideally the payout bonus forprocessors asymptotically approaches 1.0 over time, as the tasks of processing and validatingapproach parity due to maturation of a smart contract marketplace within the system.

Cure Regulation and Minting

The DokChain system requires an in-network currency, herein referred to as cure, in orderfor transactions to be processed: some non-zero amount of cure is posted with, and spentin the processing of, every transaction. Cure can be purchased directly via fiat currencies onan exchange, acquired via like-kind asset exchange according to on-chain asset valuation

contracts, or earned over time by hosting a node in the DokChain Network. Here wedescribe in detail the guiding principles underlying the rate at which cure is issued to hostingnodes over time.

Because the DokChain protocol does not rely on proof-of-work mining, we do not define apriori the total amount of currency that will be available in the future, but instead define amonetary policy by which currency is minted. The currency issuance at any point in time willbe a function of three metrics: (1) the performance of nodes operating the network; (2) theperformance of the network as a whole; and (3) a model of external economic factors. Thegoal is to modulate the rate of cure minting to reflect the value being created and capturedby the operation of DokChain, at the scale of both individual nodes as well as the entireNetwork. We further explicitly regulate currency flow at a rate that is inversely proportionalto the derivative of an external economic factors model. This has the effect of incentivizingthe purchase of cure when the larger-scale economy is in decline, but induces scarcity intimes of strong economic growth outside the system.

Sources of Supply.

Cure Pre-Sale. All cure coin is issued via a contract included in the initialization of theDokChain Network, modifiable only via a four-fifths majority vote among Network mem-bers. The initial 1e9 cure coins, herein denoted as C0, are distributed to the initial 10DokChain Alliance Founding Members in exchange for the assets they each contribute to theinitialization of the Network. This is designated as the cure pre-sale. The current proposedallocation in the pre-sale is 35.2% to PokitDok, Inc, with the remaining 64.8% split equallyamong the other 9 Founding Members, for a stake of 7.2% per Founder.


Transaction Fees. The smart contracts that broker each transaction include provisions forcure disbursement, including a minimum required cure quantity to be paid to the hostingnodes of the DokChain Network. In the simplest version of disbursement, the sum of thiscurrency is split equally among all transaction processors operating the Network. This dis-bursement model illustrates at least one reason we are considering a consensus algorithm thatdoes not require all processors to validate every block: as the number of hosts grows, theshare of cure each node expects to obtain via this supply channel will decrease, potentiallydis-incentivizing network expansion.

Cure ‘Minting’. In addition to the initial currency allocated at the time of joining the net-work, all hosting nodes will accrue cure according to a monetary policy or minting function,which is described in detail below. For the time being, and until it is determined whether it ispractical (or desirable) to distinguish between transaction processors and transaction

validators, as well as whether we pursue a quorum-style consensus algorithm, it is assumedthat the total amount of cure minted at any point in time is disbursed equally among thehosting nodes.

Node Performance Score. Given that the hosting nodes are by definition the entitiesoperating the network, there is clearly a relationship between the global network score andthe node performance score, but we parameterize them separately. The normalized nodeoperations score, Snd ∈ (0, 1], is a measure of how well the nodes are performing the tasksexpected of them, such as the percentage of transactions in a block that they participated inprocessing/validating, etc. We expect nodes to behave properly, if not ideally, even at timezero, when the network is initialized. Snd will be initialized at a fairly high value, increasingat an unknown rate over time, currently modeled as a linear ramp from 0.67 to 1.0 over aten year period (Figure 7, green).

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5

0.0

0.2

0.4

0.6

0.8

1.0SnetSnod

Figure 7. Examples of Snt and Snd used in this paper.

Network Operations Score. The currency minting rate needs to reflect the overall func-tion of the DokChain Network in ways that are easily understood by all participants andinterested parties. The normalized network operations score, Snt ∈ (0, 1], captures aspectsof the function of the network as a whole. This metric is initially dominated by global net-work statistics such as the total transaction volume processed by the network, normalized tothe 20B transactions per year in the healthcare ecosystem as estimated by CAQH. The rateof cure minting is relatively large when this value is small, decreasing to zero as the ratio


Snd/Snt approaches 1. The network will be initialized with Snt set to 0.01, a value that weexpect will increase at an unknown rate over time, currently modeled as a gamma cumulativedistribution function ramping from 0.01 to 0.97 over a ten year period (Figure 7, black). Wedefine a requirement that Snt < Snd, which makes intuitive sense given that the networkperformance score should not be higher than the average score of the nodes operating thenetwork.

External Economic Factors Score. We think it is important to explicitly include a termin the cure minting function that takes into account external economic factors. This is a vitalcomponent to the idea that the amount of cure being minted is in some way proportionalto both the value being created by the system (the node and network scores) but also the“captured value” of, for example, the eroding market share and/or reduced economic outputof entities that are disintermediated by the success of the DokChain Network. Candidatefeatures in such a model include the labor participation rate, the equity prices of companiesthat currently operate as intermediaries, etc. For the bootstrap analysis included in thispaper, the daily diff computed over 10 years worth of historical NASDAQ closing price datawas modeled with a Laplacian distribution and randomly sampled to provide a workingexample of an Sec function, as shown in Figure 8.

−6 −4 −2 0 2 40.0

0.2

0.4

0.6

0.8

1.0NASDAQ Diff (10yrs)

Laplacian Model

(a) Normalized Histograms

−4 −3 −2 −1 0 1 2 3 4

0.0

0.2

0.4

0.6

0.8

1.0NASDAQ Diff (10yrs)

Laplacian Model

(b) Cumulative Distribution Functions

Figure 8. Distributions of the NASDAQ daily diff data and a maximum likelihood-estimated Laplacian model.

Putting it all together. As shown in Figure 9, we can simply compute the amount of cureto be minted at any point in time t as

Ct = C0

(Sndt

Sntt

− 1

)e−dSect

Another possibility to consider is that we define an ideal currency accumulation functionwith predetermined values at any point in time, CI , and then mint cure according to thedifference between the ideal cure and the sum of currency influx via direct purchase andlike-kind asset exchange, denoted as Cx:

Ct = CI − Cx

To actually be implemented, however, this approach requires definition of a max currencyamount, a path we currently do not favor.


0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5

time (years)

11

12

13

14

15

16

cum

ulat

ive

sum

ofcu

rren

cy(l

og10

)

C0

(SndSnt− 1)e−dSect

median

mean

(a) Time included in the exponent.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5

time (years)

0.0

0.2

0.4

0.6

0.8

cum

ulat

ive

sum

ofcu

rren

cy

×1013C0

(SndSnt− 1)e−dSec

(b) Time excluded from the exponent.

Figure 9. Two versions of cure accumulation. Dashed lines represent 95% confidenceintervals based on 10k random samples from the Sec Laplacian model.

Closing

Healthcare in the U.S. is a $3.5 trillion industry, growing at a rate expected to exceed GDPgrowth for at least the next decade. Much of the waste in the industry is directly attributableto intermediaries such as clearinghouses and outdated ‘technology’ vendors, making it ripe forblockchain-based disruption. Here we have described a distributed network implementationthat eliminates these irrelevant intermediaries and empowers consumers, providers, payers,and other interested parties to create a new healthcare information economy. Our goal is notto change healthcare, but to replace it completely with something that we know will workbetter. Readers interested in participating in the DokChain Alliance can visit DokChain.comto learn more.

http://dokchain.com

new dokchain: intelligent automation in healthcare … · 2018. 2. 6. · dokchain: intelligent...

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