BitDoctor Blockchain Technology Stack
Decentralized Health Data Management System
Last updated
Decentralized Health Data Management System
Last updated
This document outlines the architecture for BitDoctor.AI, a decentralized system leveraging blockchain technology for managing health data, analysis, and rewarding contributors. The system incorporates Decentralized Physical Infrastructure Networks (DePIN) and Decentralized Science (DeSCI) principles to create a transparent, secure, and community-driven ecosystem. Additionally, it introduces a Flywheel Effect to drive network growth and a Data Protocol Vision that invites developers and researchers to build on top of anonymized health data.
Note
1) The final choice of blockchain architecture is TBD. We are still evaluating whether to deploy on our own native blockchain or use an L2 solution (e.g., Base) or Solana.
2) The LIV points mentioned in this document are in-game points used to determine weightage for future airdrops; they are not the final token.
BitDoctor Smartphone AI Doctor: - Collect health metrics such as heart rate, blood pressure, heart attack risk, diabetes risk, etc. - Forward the data to the system via the BitDoctor Gateway.
b) BitDoctor Gateway - Entry Point: Acts as the entry point for the collected data. - Processing and Forwarding: Blockchain (TBD): For creating a decentralized identity (DID) and storing data hashes. Centralized Databases or IPFS/Arweave: For scalable storage of health data.
c) Blockchain (TBD) - Data Integrity: Ensures data integrity and security. - Decentralized Identity (DID): DID and data hashes are stored for transparency and validation. - Reward System: Handles the reward system via a Reward Contract (using LIV points for distribution weightage, until final token is determined).
d) Data Storage - Filecoin/Arweave: Decentralized storage solutions for health data. - Centralized Database: Backup or complementary storage for certain data needs.
e) BitDoctor Aggregator - Data Aggregation and Analysis: Aggregates health data and analyzes it. - Outputs: Medical reports. Propensity for diseases. - Sharing: Shares data with medical providers and AI agents for further use.
f) Consumers - Stakeholders: Medical Institutions, End Users, and Health Agencies. - Access: These stakeholders pay (in tokens or other agreed-upon means) to access medical reports and health data insights.
g) AI Agents - Enhanced Models: Use the aggregated data to enhance AI-driven medical models for diagnostics or predictive analytics. - Tracking: Contributors to these models are tracked using the Model Contributor Tracking system.
h) Reward System - Contributor Rewards: Individuals providing BitDoctor application data are rewarded with LIV points (which may later translate to token allocations). - Management: Data Contributor Validator: Validates data contributions. Reward Contract: Allocates points based on contributions.
i) Medical Providers Access: Gain access to aggregated data and reports for diagnostics or treatment recommendations.
a) Distributed Data Collection - Leverages BitDoctor application as physical infrastructure to collect health data. - Incentivizes device owners through LIV points to participate in a contributing their data.
b) Infrastructure for AI Training - Uses distributed computing and data storage networks (e.g., Filecoin, Arweave) to support massive AI model training workloads. - Reduces dependency on centralized entities, democratizing access to AI capabilities.
c) Network Security and Integrity - Ensures data collected from physical infrastructure is securely stored and accessible only through permissioned blockchain-based mechanisms.
a) Open Data for Research - Health data aggregated through BitDoctor is anonymized and shared with the global scientific community for disease research and predictive model development. - Researchers can access data via a pay mechanism (or token model), ensuring contributors are rewarded fairly for their participation.
b) Transparent Model Development All AI models developed using contributor data are anonymized and governed on-chain, ensuring transparency and accountability.
c) Collaborative Research Incentives Encourages scientific collaborations by rewarding contributors whose data significantly enhances research outcomes, fostering innovation in healthcare.
d) DeSCI Tokenomics A portion of institutional payments for medical data or AI models is reserved for funding decentralized scientific initiatives.
Ensures contributors of data or models are recognized and rewarded.
DID and wallet information is used to ensure transparency in reward distribution (through LIV points).
b) Profit Distribution Contributors to the AI models generating profits are identified, and LIV-based rewards are allocated accordingly.
a) DID Creation - When a contributor logs in via the mobile app, a unique Decentralized Identifier (DID) is created (e.g., did:bitdoc:12345abc). - The DID tracks the contributor’s data contributions, which are stored along with metadata about the data (e.g., type, volume, timestamp, relevance). - The DID does not equate to a wallet but acts as a bridge between the contributor’s data and the reward (points) allocation process.
b) Wallet Linking - Contributors link their DID to a wallet address through the app. Mapping between the DID and the wallet is securely stored: {
"DID": "did:bitdoc:12345abc",
"Wallet": "0x12345abcdef67890"
} - If a wallet is not linked, the allocated points remain tied to the DID until claimed or converted (upon token launch).
Contributor Participation Contributor A uploads health data (e.g., heart rate, blood pressure) via their BitDoctor application.
The system assigns this data to their DID (did:bitdoc:12345abc) and logs it with metadata:
{
"DID": "did:bitdoc:12345abc", "Data": { "Type": "Heart Rate, Blood Pressure", "Volume": "1,000 records", "Timestamp": "2025-01-01T10:00:00Z",
"Relevance": "High"
}
}
Massive Data Pool The Stroke Prediction Model is trained using data from 50,000 contributors. The system logs each DID whose data was used for training: {
"Model": "Stroke Prediction Model", "Contributors": [ { "DID": "did:bitdoc:12345abc", "Volume": 1000, "Relevance": "High" },
{ "DID": "did:bitdoc:67890xyz", "Volume": 500, "Relevance": "Medium" }
]
}
a) Institution Payment - Suppose a hospital pays a certain amount (e.g., 100,000 tokens or a stablecoin equivalent) for access to the Stroke Prediction Model / data. - A portion of that payment (e.g., 50%) is allocated to the reward pool for contributors.
b) Reward Pool Allocation - LIV points are allocated based on: Volume of data contributed. Relevance of the data (e.g., stroke-specific metrics may carry more weight). - Example Allocation:
Contributor A’s data volume: 1,000 records.
Total data volume: 50,000,000 records (all contributors combined).
Contributor A’s share: - Adjustments for relevance: High relevance multiplier (e.g., ×2): Contributor A earns 2 LIV points.
c) Reward Storage
Allocated points are stored on the blockchain (or sidechain) under the contributor’s DID until they are claimed:
{
"DID":
"did:bitdoc:12345abc",
"Allocated Points": 2
}
a) Contributor Logs In Contributor A logs into the system app and links their DID (did:bitdoc:12345abc) to their wallet (0x12345abcdef67890).
b) Mapping on Blockchain
The system records:
{
"DID": "did:bitdoc:12345abc",
"Wallet": "0x12345abcdef67890"
}
c) Claiming Process - Contributor A logs into the app and view the LIV points. Claimable tokens are determined based on the LIV points of the contributor. - Contributor A clicks on the claim button and the system validates their wallet and processes the transaction, transferring tokens from the reward pool to their wallet.
d) Audit Log
Blockchain transaction log: {
"Transaction ID": "tx123456",
"DID":
"did:bitdoc:12345abc",
"Wallet":
"0x12345abcdef67890", "Points
Claimed": 2,
"Timestamp": "2025-01-02T12:00:00Z"
}
As mentioned, the final choice of blockchain architecture is still under discussion (native chain vs. L2 solutions like Base vs. Solana). The LIV points described here are not the final token but an in-game metric for airdrop weightage. Below is a conceptual overview, which may evolve:
Proposed Token Structure (Subject to Change)
a) BitDoctor's Future Token (TBD) - The primary token within the BitDoctor ecosystem will be determined after the chain choice (native vs. L2). - This token could be used for: i) Payments for accessing medical reports. ii) Rewards to contributors (possibly converting LIV points at a certain ratio).
b) Gas Fee Mechanism - If BitDoctor launches its own network, gas fees could be denominated in the BitDoctor token or in a well-adopted gas token (e.g., ETH on L2, SOL on Solana). - Until then, LIV points will serve as an internal reward and accounting mechanism.
a) Bridge to Popular Blockchains - To enhance liquidity in the future, bridging mechanisms may be implemented, allowing the final token to be interoperable with popular chains (e.g., Ethereum, BSC, Solana). - LIV points, as an internal measurement, would eventually convert or unlock the final token.
b) Seamless Conversion A bridging or conversion contract could facilitate the movement of tokens between ecosystems, ensuring holders can leverage multiple blockchain networks.
a) Efficiency (If Native or L2) - A streamlined chain or L2 could reduce transaction costs and congestion.
b) Flexibility - Cross-chain bridges provide users with greater liquidity options.
c) Sustainability - A well-designed token model ensures long-term viability for data contributors and ecosystem partners.
a) Incentivizing Data Submission - Contributors are rewarded (via LIV points) for submitting health data. - This incentivizes individuals to upload data more frequently, improving the quantity and quality of the dataset.
b) Enhanced Data Value - As data volume and quality increase, the health dataset becomes more valuable. - A larger and richer dataset attracts more medical institutions and researchers, who purchase access to the data and AI models.
c) Platform Revenue Growth - With more data and growing demand, BitDoctor’s revenue from data sales and AI model subscriptions increases. - Part of these revenues is channeled back into the reward pool.
d) Increased Incentives for Members - As revenues grow, the system can offer higher rewards (via more LIV points which gives weightage for future token airdrops) to new and existing contributors. - This further encourages more data submissions and participant growth.
e) Reinforcing the Cycle The cycle repeats, creating a positive feedback loop: More data leads to more value, which leads to more revenue, which leads to more rewards, and thus encourages even more data contributions.
a) Protocol Approach - Over time, BitDoctor aims to evolve into a comprehensive data protocol, aggregating vast amounts of anonymized health data. - This protocol will serve as a foundation layer upon which developers, researchers, and AI model creators can build.
b) Developer Ecosystem - By exposing APIs and smart contract interfaces, developers can create applications or tools that leverage anonymized health data. - Examples include: i) Advanced AI-driven diagnostics tools. ii) Research platforms for epidemiological studies. iii) Customized health and wellness apps offering personalized recommendations.
c) Open Collaboration - Researchers and AI model developers are invited to build on top of the protocol. - DeSCI principles ensure that data remains open, anonymized, and securely permissioned, fostering a global community of innovators in healthcare.
a) Privacy by Design All data made available to third-party developers will be anonymized and stripped of personally identifiable information (PII).
b) Enhanced Research Outcomes The richness of the dataset, combined with its anonymized nature, fosters cutting-edge research and AI model development without compromising individual privacy.
BitDoctor.AI aims to revolutionize healthcare data management by combining decentralized technology, robust incentive mechanisms, and privacy-focused data sharing. Through its Flywheel Effect, the platform drives continuous growth and increased value for all participants. By evolving into a data protocol, BitDoctor invites developers, researchers, and AI innovators to build transformative healthcare solutions on top of a vast pool of anonymized health data—ultimately improving global health outcomes while ensuring contributors are fairly rewarded via LIV points (and, in the future, a fully realized token).
