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Ever Medical Network

Decentralizing Global Healthcare Protocols for Self-sovereign Health Data Ownership

A strategy towards innovation of healthcare to increase access, lower cost and improve care outcomes through the intermediation of structural data flow in a decentralized open source system

August 2017, October 2018, January 2020

Historical Document

This whitepaper (v0.9) documents the founding vision from 2017-2020. While the core thesis remains — that healthcare costs are unsustainable and disruptive innovation via decentralized health data exchange is the path forward — the technical implementation has since evolved into EDH (Ever Decentralized Health), which uses EVM-compatible blockchain, IPFS with end-to-end encryption (NaCl/TweetNaCl cryptographic primitives), and Biological Identity Attestation (BIA) for genomic-anchored decentralized identity. The architecture sections below reflect the original 2017 thinking; see the BIA Quickstart and API Reference for the current technical design.

Abstract​

Despite advancements in the standard of care and state of medical technology, the global healthcare industry has yet to address persistent market inefficiencies. The lack of adequate data access, control, and interoperability stands as one of the main inefficiencies, with the misalignment of incentives among industry stakeholders as the underlying cause. This ultimately leads to losses of economic welfare for all industry stakeholders including but not limited to patients, medical professionals, healthcare institutions, and insurance providers. A decentralized open source global infrastructure can optimize economic welfare by improving security, transparency, and efficiency of information flow while addressing the interests of all stakeholders. The goal is to free up siloed and hoarded data in a secured, governed and verifiable way — enabling a true transformation of health data and leading the health industry to the next step of technological efficiency in the value chain. Once entrenched data is excavated and data flow channels are created, the values and ownership of the data can be fully redistributed back to users in a model of full self-sovereign health data ownership.

At the heart of this ecosystem lies a stack of decentralized technologies: a blockchain-based ledger for access control and audit trails, decentralized database and file storage systems, improved orchestration services, and the ability to host services in a decentralized manner for individual or institutional healthcare. This includes a sub-layer encompassing a network of care providers and facilities that enable collection, analysis, storage, and sharing of personal health data for telemedicine service or clinical tests. A protocol layer serves as a gateway platform for any other decentralized applications to be integrated with the different layers within the network.

This document identifies the operational burdens that hinder the efficiency of the healthcare industry. By combining economic analysis and technical expertise in medical operations and decentralized technology, a pragmatic approach is outlined for streamlining operational processes for all healthcare stakeholders. The framework's vast possibilities for further use cases and potential to generate positive externalities for the global economy are also explored.

Table of Contents​

Overview of Healthcare Industry​

The modern healthcare industry has been undoubtedly effective in improving health and quality of life for the global population, leading to a substantial increase in life expectancy and reduction in premature deaths in less than half a century. However, this technological advancement has come with a costly price, as global healthcare expenditure has grown at a rate exceeding that of global gross domestic product ("GDP").

Consider the following trends in healthcare expenditure as a percentage of GDP: in the US, it increased from 13% to over 17% from 2000 to 2010; in the UK, it increased from 7% to over 9% from 2000 to 2014. Similar increases were observed not only across OECDs, but also emerging economies.

Moreover, the alarming trend of healthcare consuming an increasing share of resources is apparent at the global scale. According to the World Health Organization ("WHO"), global healthcare expenditure totaled US$7.5 trillion in 2013, and these figures have continued to accelerate. The staggering figure has been growing at over 6% per annum for more than 20 years. For reference, global GDP growth never exceeded 5% during the same period.

Aging population is a main driver of the growing healthcare expenditure. Individuals over 65 years of age require more medical care than younger demographics, therefore accounting for a higher share of healthcare expenditure. This issue is especially evident among OECDs (e.g. Japan, Italy) and will be faced by emerging economies as they all inevitably undergo demographic transitions (e.g. China, India, Brazil).

Another main driver of the growth in healthcare expenditure is the increased incidence of chronic diseases. This is, unfortunately, a negative byproduct of economic development, as sedentary lifestyles and unhealthy diets become increasingly prevalent. Chronic diseases account for the vast majority of healthcare expenditure in developed nations. Furthermore, the global populations suffering from diabetes and obesity continue to increase annually.

Although exogenous factors mentioned above add strains to the global healthcare industry, it is the endogenous factors that contribute to structural inefficiencies which hinder the industry from relieving such strains efficiently. The legacy healthcare infrastructure is plagued with operational burdens and redundant procedures that constrain time- and cost-efficiency of the global healthcare industry.

Examples of endogenous complications include: frictional and repetitive paperwork and verifications procedures by insurers and other parties significantly complicates the healthcare payments process; regulatory burdens regarding cyber security and data ownership leading to complications such as health data silos and opaque information transparency; restricted supply of capable healthcare workers.

The myriad of endogenous complications within the healthcare industry creates unquantifiable economic burdens. Due to the relative inelasticity of demand for healthcare, such economic inefficiencies are ultimately passed down to end users in the form of increasingly unsustainable cost of care.

The healthcare industry is a complex ecosystem with multiple stakeholder groups pursuing differentiated, and often conflicting, interests. Stakeholders can be patients, care providers, insurance providers, research organizations and policymakers etc.

Interwoven and intricate relationships between the stakeholder groups create persistent challenges in accessibility, control, and security of data, contributing to imperfect information. One underlying cause of such economic inefficiencies is incentive misalignment.

Inefficiencies of Current Healthcare Infrastructure​

Healthcare chief executives have consistently expressed demand for upgrades in information systems, big data analytics, and health data storage technologies, yet adoption remains slow. Overall, the healthcare industry has historically invested only 2% to 3% of total revenues in information technology. Although this has led to an increase in digitization of health data, the information is heavily siloed and owned exclusively by many individual institutions, preventing information transfer and transparency.

Central health and government organizations have spent a significant amount of time and money to set up and manage traditional information systems and data exchanges. However, their outdated approach proves inefficient as they're continuously required to deploy resources to troubleshoot issues, update field parameters, perform backup and recovery measures, and extract information for reporting purposes.

Federal laws and incentive programs have made healthcare data more accessible, in response to hospital pushback regarding EMR implementation. However, the vast majority of hospital systems still cannot easily or safely share their data. As a result, doctors spend more time typing than actually talking to patients, contributing to rising physician burnout rates.

These facts present sufficient evidence that the modern healthcare industry has yet to effectively accommodate and adopt digital transformation. As a result, many pain points faced by various stakeholder groups remain unsolved.

Patient Access to EMR​

Patients have limited access to their EMR. In order to receive a personalized and complete continuum of care, it is essential for patients to have easy, digitized access to their medical records. Studies have shown that by facilitating access to EMR, patients can better understand their own conditions and enhance communications with care providers. Furthermore, complete ownership means that patients can more efficiently explore alternative diagnostic and treatment options. Although this is an area of increasing need, it is not appropriately addressed by the industry.

Finding the Right Care Provider​

Patients also lack the tools and services to help them efficiently identify the ideal care provider, especially for specific symptoms or illnesses. Patients often have to visit several candidates before finding the appropriate professional. Online reviews can be unreliable, and relevant recommendations by word of mouth are hard to come by.

Even after identifying the ideal care provider, patients are often placed on long waiting lists — a challenge that persists across healthcare systems worldwide.

Care Provider Challenges​

While patients experience difficulties in optimizing their choice of care providers, many of such providers also face the reciprocal problem of reaching out to relevant patients. For many individual medical professionals and smaller organizations, online ad space is expensive. Skilled individual professionals and small but capable organizations are often disadvantaged against larger institutions with regards to patient outreach and exposure.

Efficiently reaching out to relevant patients is one problem, effectively building trust and maintaining reputation is another. Although plenty of online medical professional profile platforms exist, the return on investment is questionable, and registering professional profiles on multiple platforms is not economically viable due to the high costs.

When care providers have no upfront information regarding patient appointment history, further inefficiencies may arise. Care providers are not compensated for patients that are often late to or absent from their appointments, leading to a substantial loss of potential revenue both directly and indirectly.

Insurance Complications​

There are also various complications when it comes to insurance. Pre-authorization to determine if a patient's health insurance covers a particular medical expenditure can be tedious, involving many parties such as the consumer, provider, and payer. Insurance coverage can vary based on the relationship between the payer and provider (e.g. in-network and out-of-network). Timing is oftentimes a critical issue depending on the medical case, as pre-authorization may persist through the full revenue cycle until the provider receives payment. Moreover, healthcare insurance payers have convoluted billing requirements and regularly delay or deny claim payment, leading to considerable cash flow difficulties experienced by many healthcare providers.

Record Keeping and Security​

Record keeping and security are another challenge in clinician visits. The healthcare industry has substantial record keeping requirements, and this issue is exaggerated for concierge and cross-country healthcare providers. Home healthcare records, usually kept on paper, are often lost, leading to incorrect billing or non-payment. Furthermore, regulatory violations may occur in processing. Privacy and security of health data remains a major risk — both from external hacking attacks on digitized records and from internal mishandling of physical documents.

Data Opacity and Fraud​

The lack of data transparency and interoperability in the healthcare system leads to ambiguous data ownership, prevalence of data silos, and data complexity. This opacity breeds various forms of moral hazard, including medical fraud, unnecessary or inadequate patient care, and improper billing. In the US alone, the estimated total cost of insurance fraud exceeds US$40 billion per year. Ultimately, these problems arising from asymmetric information lead to substantial inefficiencies. The society incurs higher premiums and out-of-pocket expenses, as well as reduced benefits and coverage for both employer-sponsored and individually-purchased health insurance. Many patients are excluded from the insurance market due to unaffordable premiums.

Pharmaceutical and Supplier Challenges​

Suppliers and pharmaceutical companies also face challenges in the healthcare system. Researching patients on a global scale is difficult and costly, requiring multiple research and marketing firms, increasing expenses and management overhead.

Reaching care providers globally is also inefficient and slow. In some countries, prescription drugs could only be directly advertised to doctors. Companies would have to use closed community websites to contact physicians in each country separately or medical sales representatives to visit medical professionals in person, a slow and expensive process.

Moreover, companies are unable to serve niche markets which are fragmented across geographical regions. For orphan or rare diseases, patients are not concentrated in a geographical area, making it much more difficult to reach them. The expenses of hiring multiple research companies and the project management resources make it impractical to carry out these studies, preventing the development of such drugs.

Incentive Misalignment among Industry Stakeholders​

The endogenous inefficiencies of the healthcare industry are caused by a fundamental misalignment in incentives between the industry stakeholders. There are no immediate needs or incentives for health institutions to facilitate easy access and sharing of medical records. In fact, it is in their interest to withhold the data exclusively as it enables them to improve patient retention, boost their medical research database, or generate additional revenue from selling or analysing it. Meanwhile, the users stand to fully benefit from having accessible health records that they own themselves, allowing for interoperability across their many different care procedures. This creates a scenario where health data is increasingly forced into closed centralized systems. This incentive mismatch has been replicated across the healthcare industry, creating interwoven and intricate market inefficiencies.

This is why improving access to healthcare remains the key challenge: "The global shortage of general and specialist staff is increasing patient waiting times and affecting their access to diagnosis and treatment."

Increasing the number of care workers presents a logical but partial solution. Technology — especially telemedicine, mobile health, and AI-assisted diagnostics — could help in a major way.

Furthermore, patients were the early adopters of shared knowledge and lifestyle values, as they created online portals to support and share information. Patient communities work as health data repositories, a fact that reinforces and empowers patients as the real owners of medical information.

Although physicians have started portals for collaborative virtual diagnosis of difficult medical cases, adoption remains low. The sharing economy has gone beyond knowledge repositories: it has enabled faster genomic research and disease outbreak detection through global data collaboration tools.

A decentralization methodology can align all stakeholders' incentives and create an on-going self-enforcing system, governed by the stakeholders themselves. The goal is a system where stakeholders can focus on forwarding their personal agenda whilst benefiting the system as a whole through constant improvement, science-driven care, and shared value — aligning physicians, patients, health systems, payers, advocacy groups, and insurers toward shared goals.

Transforming the Medical Industry​

"To bring healthcare to your own hands, repair the fractured healthcare infrastructure and provide a more efficient system that aligns the incentives for all healthcare stakeholders"

The healthcare infrastructure can be improved through a new model of decentralization that acts to align incentives and reallocate control to the rightful stakeholders — viewing health data as a 'public good' or 'information good' yet semantically governed and resistant to free-riding. This should be supported by a wide variety of disruptive innovations in decentralized technology, healthcare delivery, and the underlying fabric of infrastructure technologies. The proposal is to create a layered network stack that helps both care administration networks create flow of data and patients to truly own their health data in a fully decentralized means.

The Information Ecosystem​

Large healthcare institutions such as hospital conglomerates, contracted research organizations, pharmaceutical companies and public healthcare entities are the owners of the majority of individual health data. As these institutions have different objectives, the misalignment of incentives presents a barrier to efficient transfer of information. Therefore, an infrastructure needs to be built in order to support the existing ecosystem and facilitate information flow.

The EDH ecosystem allows individuals and institutions to take sole access and control over their health data. In response to the high privacy risks in current EMR systems as well as aforementioned issues of data ownership, data isolation, and lack of accountability, the notion of self-sovereignty as a solution has gained significant traction. The network grants individuals self-sovereign ownership of data through Personal Health Records ("PHR"), a blockchain-anchored health data schema. Meanwhile, institutions leverage Provider Data Management protocols to create data repositories with permission keys generated and controlled by the creators. Transmissions of data between hospitals — especially in multilateral health networks such as public health networks within a rural province — yield unprecedented efficiency and process optimization for hospital internal production lines, leading to many positive externalities for the whole system.

Apart from granting healthcare stakeholders more control over their information, the bundled protocols present opportunities to expand newer socio-economic channels of data flow. New data economies can be formed such as third-party application layers or algorithm curation layers. All stakeholders may collaborate liberally without ceding control to any centralized intermediaries.

Due to the highly sensitive nature of individual healthcare information, countries across the world have adopted many policies to promote utmost privacy and security. One notable example is the Health Insurance Portability and Accountability Act ("HIPAA") established in 1996. However, healthcare institutions have failed to prevent all unauthorized access to this data, as exemplified by numerous high-profile data breaches affecting millions of individual healthcare records. A decentralized ecosystem can mitigate this risk by transferring ownership from centralized bodies to the individuals themselves — granting access and ownership to its rightful owners, the patients.

Concerns around privacy and security have led to major obstacles to sharing data, as healthcare organizations worry about HIPAA or similar regulations. While breaches are a valid concern, data sharing under proper precautions and consent is not a violation of HIPAA. Blockchain can provide a more secure environment to store and access data. To ensure information is shared only with authorized users, patients can grant access to their information to specified physicians and insurers through cryptographic key management.

As all access to the data is recorded via an immutable ledger and distributed file storage system, sharing of access is enabled among network stakeholders. This creates a completely transparent and permissioned system allowing for the safe deposits of data.

Certifications of health institutions and medical practitioners can be verified by third-party organizations such as central health authorities, consensus-recognized health institutions, or decentralized stakeholder governance. This transforms the network model into a universally recognized platform for the credentials of all healthcare stakeholders, reinforcing its credibility as a trusted system for health information exchanges.

Trust issues between entities may be solved through automated data verification on the ledger. No intermediary is needed. Participants have control over who accesses the data, which is tamper-resistant once stored.

The completed vision is an open-source platform that allows other platforms to use or build applications upon it. The primary objective is to standardize global health information storage through blockchain and distributed systems and the global health interactions protocols through decentralized models.

The platform is not just for health professionals, but also for patients. Users are able to store any health-related data such as Patient Generated Health Data ("PGHR") on their own PHR repository. PHRs are designed to help patients keep track of their health and wellness condition through multi-interface platforms created by third-party developers to empower the whole ecosystem.

Personal Health Records: Bringing Healthcare to Your Own Hands​

The vision is to bring healthcare data to your own hands, where users have self-sovereign ownership of their data and can prevent unauthorized access. This creates the ultimate privacy for a person's most important data — health records.

The system can aggregate clinical data across the multiple care organizations that a patient has visited over their lifetime, as well as data from wearables such as fitness trackers or heart monitors and patient self-generated health data such as self-diagnosis or self-tracking.

PHR is an online document that contains data concerning the patient's health and healthcare information across a period of time. PHR differs from EMR in that PHRs are maintained and owned by the patient while EMRs are maintained and owned by the hospital. Health data included in a PHR might be outcome data on a certain test, lab results, and biometric data from electronic devices such as a digital weighing scale, or health monitor gadget. Information could be from a variety of sources.

Patient-generated data can also be added to the user's PHR. Consensus data standards are applied to new data entering the user's repository, resulting in consistently formatted data creating a readable and consistent data environment from every input source.

The aim of PHRs is to provide information that is easily understandable by providing a summary of a patient's entire record. In this way, patients would be able to closely monitor their health records and make informed decisions, particularly if they suffer from chronic diseases that require constant attention. Patients could also offer their PHRs to professionals and clinicians who could make better treatment decisions when provided with more continuous, longitudinal data. PHRs may be particularly useful if patients go to new hospitals who do not have their medical record. As doctors often ask patients about their allergies and drug interactions, PHRs could also provide a profile for doctors to help them prescribe medication and treatments. In case of emergencies, PHRs could quickly provide critical information for proper diagnosis or treatment due to their accessible nature.

"80% of health is what you and your friends, relatives, relationships achieve together at home, work, away from home and in your life. It is not available in a bottle from the GP or hospital." — Dr Richard Fitton

Individuals could also use PHRs to manage the health of their family — such as young children and elderly parents. Administrative workload for doctors is decreased, communication between patients and doctors is improved, and operational efficiency for hospitals is increased.

In addition to expanding and educating patients on health knowledge, PHRs also play an important behavioural role. As all health information is stored in the PHR and only a few clicks away, patients are able to instantly check the status of their health. This creates a sort of 'reminder' for the patient to lead a healthy lifestyle as they continuously aim to improve their PHR.

"With 60 to 70 percent of premature deaths caused by detrimental health behaviours, it is vital that people engage more with improving their own health." — The King's Fund, 2014

PHR allows people to specifically choose what is best for their health and improve their health through education, involvement, and action.

Patient-Generated Health Records (PGHR)​

The proliferation of wearable and connected medical devices continues to accelerate. Modern wearable medical devices — from FDA-cleared smartwatches capable of ECG and blood oxygen monitoring, to continuous glucose monitors (CGMs), implantable cardiac monitors, and smart inhalers — are generating unprecedented volumes of patient health data. These devices track vital signs, physical activity, sleep patterns, blood glucose trends, and more. The data and analytics can be linked to EMRs, benefiting patients in monitoring their personal health, aiding doctors in prescribing personalized medicine, and providing insurance providers with insights into cost of care.

However, security and privacy concerns remain significant. Medical device vulnerabilities have been repeatedly demonstrated — from compromised drug infusion pumps to cardiac device exploits. Connected medical devices are here to stay, and the attack surface grows exponentially. Current cyber security solutions for identity management remain inefficient and lack the ability to track failure and accountability immediately.

In addition to compromise of medical devices, there are several privacy concerns with health data collected from both Medical IoT Devices and EMR systems. Patients are concerned about the lack of transparency in which healthcare stakeholders have access to their data and how their data is used. Over 300 EMR systems utilize centralized architecture which are prone to single point of failure and suffer from lack of interoperability, resulting in the lack of a holistic view of personal health. Moreover, even though many health providers follow rules or laws such as HIPAA, there are still many entities which are not covered by any laws. Therefore, it is crucial that any entity that has access to the data should be accountable for their operations on the data, and all operations should be auditable.

A decentralized network's capability to capture data provenance facilitates secure tracking of medical devices from production to ongoing use. The provenance information encoded in the blockchain provides immutable and reliable workflow with a trusted ground truth. This ground truth can be used for transparency, traceability and accountability when any device malfunctions accidentally or as a result of a security attack. The capability is also useful for autonomous monitoring and preventive maintenance of medical devices. Compared to existing cyber defense solutions, distributed consensus protocols and cryptographic techniques with decentralized control reduce cyber threat risks for medical devices while streamlining secure tracking, reducing costs, and improving patient privacy through secure and targeted data access.

Health Institutions Data Interoperability​

In another cornerstone of healthcare industry data lies the care providers and other healthcare institutions. With misalignments of incentives between each institution and body, healthcare data has become dispersed, fragmented and ultimately siloed. Each institution hoards an uncountable amount of data as there are no proportionate reliable and secured data channels to utilize. Furthermore, data ownership is sensitive and disorganized, with different lines drawn by different legal zones dictating the structural ownership of the data. With its immutability, tamper-proof design and fault tolerance attributes, a decentralized network can provide a flexible structured solution for institutions and stakeholders to organize and structure current data to the rightful ownerships. Data segregation can be done in a much more transparent manner with the correct keys being held by the correct people — transparency helps foster a vibrant schema for data flows to be promulgated.

As data is segregated and becomes interconnected through real demand and supply of use, the security of the flow must be ensured. A decentralized blockchain network provides heightened data security and integrity. The open network protocol acts as the foundational tool for institutions to secure their data in these connected environments. The network is not owned or controlled by any single entity, allowing for open, transparent, self-sovereign use by institutions with full access to data based on cryptographic keys created at time of data repository creation.

Healthcare Providers​

A decentralized network can provide a sophisticated solution to issues such as health provider misinformation. Care provider data directories — among the most accessed functions on care insurers' platforms — are frequently out of sync with reality. Misinformation is rampant across different directories, with inaccuracies in addresses, language capabilities, hospital affiliations, patient capacity, acceptability of new patients, and contact numbers.

Such misinformation makes it hard for patients and members to access or efficiently receive effective care and generally creates unexpected cost outcomes. The misinformation issue arises from a lack of interconnectivity in provider data information; insurers rely on care providers to iterate on their directory information and often receive information from fragmented and conflicting points of contact with frequent time lags.

A blockchain-anchored system empowers institutions to take control and publish their own self-sovereign healthcare data to every stakeholder and network connection without the hassle of having to update every single directory. Institutions such as care providers can utilize the open APIs of their own permissioned and embeddable data repository, which can then be used to connect to insurers or authority listing services at their own request — a self-sovereign single point of access for provider data.

In addition, the network can be used to automate processes and reduce costs. Institutions can reduce repetitive reconciliation costs by replacing redundant cross-stakeholder payment process touch points with automated transaction flows via programmable contracts. With data hosted on self-sovereign yet networked repositories, institutions can create their own rules of processing data and payment transactions.

Another benefit is the improvement of care provided to patients. Previously there has been a lack of efficient clinical data aggregators across different care organizations, where records are kept both horizontally and longitudinally. Coupling such records with data from wearables such as fitness trackers or heart monitors could create a very comprehensive and well-rounded informed diagnosis and improved outcomes of care. The accessibility to clinical data and new research could also improve the effectiveness of care procedures and create more understanding on personalized precision-medicine initiatives.

Lastly, HIPAA and health data information regulations create a paramount barrier in data flow. Care providers and institutions must adhere to strict privacy and security guidelines set out by respective local authorities. A decentralized network can provide a transparent environment for institutional and patient data to be securely stored and accessed. The missing links for creating fair, structured and transparent data ownership can be solved by creating an open network that is adjustable and adaptive to local rules and regulations.

Structural Disintermediation of Data Flows​

Healthcare information intermediaries such as third-party data administrators, data clearinghouses and health information exchanges are mostly non value-creating, forcing stakeholders to incur unnecessary costs. Eliminating such intermediaries could create faster, non-bottlenecked data flow which helps foster the new global health data economy.

Moreover, the whole health data economy benefits from transmigrating data flow to the correct sources of ownership, creating wider and more channels of data streams. Some personal data kept within institutions is often sensitive with huge penalties if breached (such as HIPAA settlement fines), creating a situation where the institution chooses to hoard the data as they bear the burden of responsibility in keeping the data secure. By disintermediating the data structurally, the network can transfer ownership and responsibility to the patients themselves, creating a self-sovereign health data repository. This could potentially unlock the massive hoard of data kept by institutions throughout the years.

Architecture Overview​

The network architecture is a layered system described as follows:

  • Decentralized Identities and Public Key Infrastructure — A signature chain model for storing patient identity and identification for querying purposes, anchored on blockchain. The current implementation uses Biological Identity Attestation (BIA) with did:bio decentralized identifiers and verifiable credentials.

  • Data Sharing and Hosting Mechanism — A cryptographic file sharing model using end-to-end encrypted distributed file storage for PHR, giving full ownership of Personal Health Data back to the patients.

  • Blockchain Consensus Layer — Used for decentralized access control, document notarization, and audit trail anchoring.

  • Database Caching Layer — Used to quickly fetch aggregated cached data for the protocol layer as necessary.

  • Device Pods Layer — Point of care testing and IoMT; physical "health pods" that enable collection, analysis, storage, and sharing of personal health data for telemedicine service or clinical tests.

  • Protocol Layer — Gateway backend service for any healthcare application to interact with the network, with internal communications via message queuing protocols. The protocols are designed to be hosted on containerized orchestration services.

Network Layers​

The medical network consists of multiple layers which interact to create a sophisticated stack that solves key overarching issues:

  1. Physical/Care Administration Layer — The distributed database layer, where each care stakeholder in the network can operate and run their own distributed database cluster.

  2. Connector Layer — Includes the application marketplace as a user-friendly interface and integrated clients which connect the blockchain layer to end-users. This is the layer where decentralized health application API protocols are developed.

  3. Data/Blockchain Layer — The permissioned data control layer with byzantine fault tolerant consensus modules, access management, trusted execution environment for indexed data, and IPFS file distribution for end-user self-sovereign ownership through content-addressed storage for record keeping and integrity protection.

Governance and Design Philosophy​

Open Governance and Incentive Alignment​

Decentralized networks inherently require clear governance structures to achieve mutually beneficial goals. Blockchain and byzantine fault tolerance protocols have shifted the paradigm by enabling transparent, verifiable governance mechanisms built on immutability and cryptographic trust. For a decentralized health network to avoid monopolization by any single actor, it should adopt an open governance model where medical stakeholders and network participants can validate data and participate in network governance for the continued, adaptive and perpetual running of the open network.

Design Principles​

  • Decentralization — Distributed Privacy and Access Control: a decentralized permission management protocol deals with each personal health data request. Data access records are stored to provide traceable logs, using blockchain to preserve immutability.

  • Consensus — The consensus model preserves the sanctity of trust on the network. The consensus protocol must satisfy three properties:

    • Safety: The protocol must be safe and deterministic
    • Liveliness: The protocol promises liveliness of all non-invalid nodes
    • Fault Tolerance: The protocol provides high tolerance to network-wide failure

  • Transparency — The network routinely self-audits the ecosystem, reconciling transactions at regular intervals.

  • Open Source — A decentralized yet closed-source application requires users to trust that the application is decentralized. Closed-source applications act as a barrier to adoption. All core infrastructure should be open source and auditable.

  • Identity and Access — The identity and accessibility model encompasses public/permissionless, private/permissioned, and consortium configurations. The current implementation uses BIA (Biological Identity Attestation) for genomic-anchored decentralized identity.

  • Immutability — Once data or information is written to the blockchain, it cannot be altered. This is highly beneficial for auditing health data access and provenance.

  • Privacy-Preserving by Default — The system is designed so that data sovereignty remains with the individual. All encryption happens client-side — no backend server ever sees plaintext health data.

  • Scalable Data Processing — The volume of health data collected from wearable devices and user input scales greatly, requiring high-throughput algorithms and infrastructure to maintain efficiency.

  • Scalable Federated Data Learning — Implementation of federated machine learning models that allow utilization of data value whilst maintaining full self-sovereignty and federation of data ownership. Modern approaches include differential privacy, secure multi-party computation, and federated learning with privacy guarantees — enabling AI model training across institutions without raw data ever leaving the patient's control.

System Stakeholders​

Patients​

System users collect data from Medical IoT Devices which monitor health data such as walking distance, sleeping conditions, and heartbeat. The data is then uploaded via the mobile application to storage hosted on a decentralized platform. The user is the owner of personal health data and is responsible for granting, denying and revoking data access from any other parties.

Third Party Medical Devices SDK​

Third party device SDKs serve to transform original health information into human readable format, and then the data is synchronized by the user to their account. Each account is associated with a set of third party devices. When a piece of health data is generated, it is uploaded to the blockchain network for record keeping and integrity protection.

Healthcare Provider and Care Network​

Healthcare providers such as doctors, or care networks like a cluster of hospitals and clinics, are appointed by a user to perform medical tests, give suggestions or provide medical treatment. Every data request and the corresponding data access is recorded on the blockchain.

Insurance Company​

Users may request a health insurance quote from health insurance companies or agents. To provide better insurance policies, insurance companies request data access from users including health data from Medical IoT Devices and medical treatment history. Users cannot hide or modify medical treatment history data since it is permanently recorded on the blockchain network and integrity is ensured.

Governance Bodies​

For full decentralization, governance stakeholders must be established through transparent, community-driven models. Stakeholders must have clear incentive alignment through meaningful participation in network governance — such as running nodes, validating data, or contributing to protocol development.

Blockchain Network​

The blockchain network serves three purposes: integrity protection of health data, decentralized permission management for data access, and auditable access logging.

Cloud Database​

The cloud database stores user health related data, data requests, data access records and data access control policy. Data access is accountable and traceable.

System Architecture​

The platform is a decentralized medical system supported by a consensus model, distributed database and file storage systems, and a permissioned blockchain with programmable contract modules and bundled protocols created for medical use-cases.

Consensus Network Layer​

The core consensus layer is designed to expand and encapsulate different Care Administration Networks, allowing new participants to join and host their own network using the open stack. Each participant has their own set of private, permissioned interactions, yet is able to interact with different clusters of networks beyond their own. This is the true power of decentralization — commoditizing complex health information and giving it back to people as a public good in the form of full personal access to their own health data.

The consensus model is designed to be blockchain-agnostic due to the rapidly adaptive nature of decentralized technologies. The current EDH implementation uses an EVM-compatible blockchain, but the architecture supports migration to whatever protocol best meets the network's evolving requirements.

Technology Stack​

Blockchain technology has provided scope for decentralized distributed storage of PHR, allowing for self-sovereign ownership of health data and enabling interoperability through verifiable collection of logs from data access to fine-grained verification of actions by any stakeholder or peer within the network.

Blockchain Layer​

The original vision proposed Hyperledger Fabric as the core permissioned blockchain. The current EDH implementation has evolved to use an EVM-compatible chain with smart contracts (Solidity/Hardhat), which provides broader ecosystem compatibility while maintaining permissioned access control. Key components include:

  • Smart Contracts — EDHAccountRegistry for account management, SeedVault for encrypted multi-device seed recovery, AccessLogAnchor for tamper-proof audit trails
  • Meta-Transactions — EIP-2771 trusted forwarder pattern enabling gasless transactions for end users
  • Access Control — Cryptographic key-based access management ensuring PHRs are fully controlled by patients

IPFS​

IPFS is used as the distributed storage layer for the PHR module. Storing PHRs on the blockchain directly would be expensive and cumbersome. IPFS provides distributed storage of large files while providing a resilient network with persistent availability. IPFS provides all files with a cryptographic hash — a unique fingerprint that allows each file to be accessible through a content identifier.

Encryption​

The current implementation uses NaCl (TweetNaCl) cryptographic primitives: SecretBox (XSalsa20-Poly1305) for symmetric encryption, Box (Curve25519-ECDH) for asymmetric encryption, and Scrypt for key derivation. All encryption happens client-side — the backend never sees plaintext health data. Asymmetric encryption is coupled with symmetric encryption to allow multiple authorized parties to access the same data.

Key Establishment​

  • Account Seed — Derived from user password via Scrypt, forming the root of the key hierarchy
  • Encryption Key Pair — Derived from account seed for asymmetric (Box) operations
  • Account Secret — Derived from account seed for account-level operations
  • Device Keys — Each device generates its own key pair; account seeds are boxed (encrypted) to each device's public key for multi-device recovery

GDPR Compliance​

The General Data Protection Regulation ("GDPR") has become a de facto global standard for data protection. With security built at the core of the infrastructure design, the network ensures GDPR compliance through client-side encryption, user-controlled data access, and the ability for users to revoke access at any time.

Distributed Storage​

EMRs should not be stored directly on a blockchain record. The data is disseminated into metadata which goes into each block and real data sets which are written into distributed storage. For larger health data such as X-rays, content is stored on IPFS with content-addressed pointers for data relations and blockchain records for validity and access control.

Access Control​

Cryptographic access control lists are used to set rules for patient, practitioner and researcher access. Smart contracts handle the business logic, enabling management of ledger state through transactions and ensuring that only authorized parties can access specific health records.

Platform​

The platform aims to provide the user interface and full functionalities of the protocol for all stakeholders within the network, including provider and services portals, researcher interfaces, and end-user platforms.

Health Applications​

Health applications built on the network protocol aim to leverage decentralized technology for mass adoption of PHR, accessible care, and aligned incentives to create better care outcomes for everyone. Applications are structured around three pillars:

  1. Initiation — Decentralized telemedicine platforms for initial consultations
  2. Contact-Point — Partnered care providers for primary care, including remote and home-visit models
  3. Care Providers Listing — Treatment and provider discovery platforms

Open-Source App Marketplace​

Third-party application developers can utilize the network API protocol to create their own applications. Tapping into the decentralized health data ecosystem gives rise to many possibilities for specialized healthcare applications.

Network Stakeholder Trust Indicators (NSTI)​

One of the most important aspects of the healthcare industry is trust. The global healthcare trust standard is fragmented and unreliable, often plagued by mismatched information leading to market inefficiencies.

The network introduces a universal trust indicator system API operating across all application layers through verifiable, on-chain reputation tracking. The trust indicator of each care provider is designed to be universal across multiple platforms through an embedded format, creating a transferable medium that serves as a single source of truth. Trust is established through verifiable credentials, peer attestations, and transparent track records — with the overall system design pattern being to limit possible bad actions, impose cost for bad behaviour, and reward good behaviour, ultimately reducing bad actors within the system.

Curated Content Publishing (CCP)​

A knowledge-sharing platform where different stakeholders are able to contribute and interact, with a two-layer moderation system: automatic filtering via verification algorithm, and manual reporting with bipartisan resolution.

Medical Tribune Consultation System (MTCS)​

Users in the care providers portal have the option of allowing third-party members to message them at varying price ranges. A more accomplished doctor could charge higher for consultations. This acts as both an anti-spam function and screening function for important messages.

Decentralized Health Data Marketplace​

Evidence For A Marketplace​

Every $1 that pharmaceutical firms spend on R&D for a New Molecular Entity (NME), $19 is spent on data acquisition, market research, and communication activities. In the US alone these costs are estimated to be $27 billion. The difference in cost stems from the epistemic risk involved in prolonged R&D times; it takes an average of 10-12 years before revenue on an NME is generated. Despite high investments, the majority of drugs rarely make it out of clinical trials.

Value of Health Data Marketplace​

The health data repository bridges the pharmaceutical industry's gap in knowledge and production by:

  • Introducing feasibility studies for analysis of patient populations and study sites
  • Establishing patient cohorts and inclusion/exclusion criteria
  • Supporting molecular pathway and gene variant analysis for smaller population studies
  • Enabling adaptive clinical trials for better understanding of pharmacogenomics
  • Decreasing time for patient enrollment into clinical trials
  • Maintaining an anonymous health repository for academic research

Mechanism​

  1. Pharmaceutical firms or academic institutions create a query profile of clinical attributes needed
  2. An intermediary function compares anonymized patient records to research attributes
  3. A data access request is created based on available matching data
  4. An independent ethics committee reviews the access request
  5. If ethically sound and regulation-compliant, patients can review what data is being requested
  6. If patients choose to enroll, a consent agreement is recorded on-chain
  7. Patients are compensated for their participation

Health Information Matching​

As the network allows for patient self-sovereign owned health metadata engagement, at users' consent, users can find relevant local care providers matching their specific needs. Rather than traditional advertising, this is a matching platform of supply by care providers to demand of needs by patients.

Survey Based Research​

Phase IV post-marketing surveillance (PMS) provides continued safety surveillance in a natural environment on a much larger sample size. The network survey tool aggregates information from users and compensates them for participation.

Open Research Repository​

The system maintains a complete database pointer logged on the blockchain. The data is deposited on specific blocks, published and timestamped, with links directed to relevant clinical trials. This opens possibilities for research institutions and pharmaceutical firms to access relevant anonymized data.

Gamification Rewards Engine​

A tool to create better engagement between socio-stakeholders in the network. Health and wellness can be achieved in an interactive and enjoyable way through gamified health surveys and health level point systems.

Identity Management and Biological Identity Attestation​

Identity fraud is a major problem across multiple industries. The network provides a biometric key management protocol for third-party services that want to utilize user's biometric data to verify identity at the repository owner's consent.

The current implementation has evolved this vision into Biological Identity Attestation (BIA) — a comprehensive framework for genomic-anchored decentralized identity. BIA introduces several key innovations:

  • Genomic Identity Anchoring — Using short tandem repeat (STR) markers and single nucleotide polymorphisms (SNPs) to create a unique biological identity commitment via Poseidon hashing, without storing raw genomic data on-chain
  • did:bio DID Method — A W3C-compliant Decentralized Identifier method that binds a user's biological identity to their cryptographic keys, enabling globally unique, self-sovereign identity
  • Verifiable Credentials — W3C Verifiable Credentials for health attestations, genomic proofs, and institutional certifications — all cryptographically signed and independently verifiable
  • Zero-Knowledge Proofs — Proving properties about one's biological identity (e.g., genetic predispositions, kinship) without revealing the underlying genomic data
  • Liveness Attestation — Multi-modal verification combining biometric challenges with genomic commitment proofs to prevent identity spoofing
  • OIDC Integration — OpenID Connect bridge allowing existing healthcare systems to authenticate users via their biological identity without requiring full blockchain integration

Furthermore, this identity framework opens up possibilities such as digital inheritance — through identity verification matching users' genetic coherence, mitigating cases such as accidental death and locked-up digital health assets.

See the BIA Quickstart for the complete technical design.

Genomic Sequencing​

Personalized medicine — medical treatments tailored to the specific traits of each patient — has moved from theoretical promise to clinical reality. Due to variation in the genetic makeup of each individual, certain drugs or therapies may only be effective for a specific group of people. Through genetic mapping, diseases may be predicted prior to the development of symptoms, and pharmacogenomic profiling can guide drug selection and dosing.

The price of genomic sequencing has decreased from $10 million two decades ago to under $200 per genome today, making population-scale genomics feasible for the first time. Advances in CRISPR gene editing, polygenic risk scores, and multi-omics integration are accelerating the transition from reactive to predictive healthcare. However, the massive challenge is now in managing the bulk of information being produced and ensuring that individuals retain sovereignty over their most sensitive biological data.

A sequenced genome associated with a person is highly sensitive data. By knowing a person's genetic markers, predispositions to illness or longevity factors could be predicted. A decentralized genomic data channel — where individuals control access to their genomic data via cryptographic keys and biological identity attestation — opens up pathways for faster development and advancement of genomic medicine. The network provides a solution to privacy and data ownership concerns by bringing a secure and decentralized system where individuals can verify and control exactly which entities may access their data, for what purpose, and for how long.

Health Insurance Integration​

Through decentralizing the healthcare industry while enhancing data provenance, the network becomes a solution for sharing sensitive medical data among insurers, providers, patients, and other industry stakeholders.

Key Insurance Applications​

  • Identity — Portable, secure, globally available store of personal data for simplified verification processes, anchored by biological identity attestation
  • Space — Swift interaction between dispersed participants, widening the network of insurers across geographies
  • Time — Immutable permanent record enabling real-time risk assessment and claims adjudication
  • Mutuality — Entities can interact without a central processor, reducing monopolistic bottlenecks

Dynamic Modeling and Value-Based Care​

Active users may generate highly trustable health profiles. Insurance premiums can be automatically risk-adjusted according to user input data, creating an incentive for users to improve their health through measurable goals and milestones. This aligns with the broader industry shift toward value-based care — where reimbursement is tied to patient outcomes rather than volume of services rendered. Real-world data (RWD) captured through PHRs and connected devices provides the evidentiary foundation for these models.

Decentralized Pool of Health Policies​

Users can form small local groups to negotiate bundled hospital services for insured payment, verified through the network.

Peer-to-Peer Insurance​

The network can support alternative risk transfer vehicles such as P2P or crowdfunded insurance exchanges. Programmable contracts could replace many administrative functions of claims settlement, making P2P business models possible.

Parametric and Micro Insurance​

Programmable contracts and real-time data feeds could enable new models such as parametric insurance — where payouts are automatically triggered by predefined conditions (e.g., hospitalization exceeding a threshold) — and micro or pay-per-use insurance, automatically adjusting premiums based on real-time health data.

Pathway for Global Expansion and Accessibilities Schemes​

Through the rise of m-health adoption and positioning of smart health applications, the network envisions scaling to become a globally recognized health data hub repository and healthcare ecosystem standard.

Adoption Angles​

  1. Patient-Led Adoption — Empowering patients with self-sovereign stored healthcare data and health tracking through universal hospital standards for lab tests, operation notes, prescriptions, test imaging and diagnosis notes.

  2. Multi-Utility Functions — Disruptive utilities for the current global healthcare infrastructure including medical tourism, telemedicine, and genetic sequencing access.

  3. Data Aggregation Engine — Incentive programmes from third-party stakeholders to encourage health data storage on the network, creating richer datasets for research and better care outcomes.

  4. Serving the Underserved — The network's fully traceable and transparent nature enables cross-country charitable donation tracking for healthcare and autonomous crowdfunding for social or philanthropic causes.

Citations​

  1. "Health expenditure, total (% of GDP)", The World Bank, 2015
  2. The World Bank, 2015
  3. World GDP growth data, World Bank
  4. "Chronic Disease Prevention and Health Promotion", Centers for Disease Control and Prevention, 2015
  5. "IDF Diabetes Atlas: Sixth Edition", International Diabetes Federation, 2014
  6. Physician burnout statistics, 2011-2014
  7. US insurance fraud estimated costs, FBI reports
  8. US Justice Department false billing costs, 2016
  9. Medicare Fraud Strike Force, US Department of Justice
  10. Medical marketing industry, USA
  11. "The global shortage of general and specialist staff", World Economic Forum Healthcare White Paper, 2016
  12. Phase IV PMS clinical trial statistics
  13. Personalized medicine definition, FDA
  14. Pharmacogenomics definition, National Institutes of Health
  15. Genomic sequencing cost trends, National Human Genome Research Institute

Additional References​

  • Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap
  • "National health accounts", World Health Organization, 2015
  • "World Population Ageing 2013", United Nations
  • "National Health Expenditure Projections 2012-2022", Centers for Medicare & Medicaid Services
  • "Easy-Read Version: Five Year Forward View", NHS England, 2014
  • "Readmissions Reductions Program", Centers for Medicare & Medicaid Services