EthMed

Tools to enable visualization, management for developers to build solutions in medicine, improve transparency in drug testing with Eth, IPFS, Chainlink, Optimism, SxT, Flow, FVM, Truflation, VerseDEX

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Inspiration

Institutional health records are not always open to other institutions, and institutional or personal health records are not always interoperable. These issues of care coordination are well known in healthcare, and are fundamental problems in medicine. Given its size and prominence, pharma and drug research organizations seeks to always be guarded against information security threats—from both external and internal actors. This means that they are required to sift through billions of events generated by network devices, endpoint solutions, and enterprise software. Collecting and aggregating EHR is a complex activity for drug design, development.

What it does

Developer Tools to enable medical trial testing, clinical trials via EMTTRs (Electronic Medicine Trial and Test Records as a Service) and EHR and Radiology services, Chainlink, Flow, Truflation, SxT, Optimism, Ethereum and IPFS/Filecoin.

Our solution enables the bottom of pyramid through empowering pharma companies and the medical eco-system to do medicine trial testing and clinical trials via blockchain and IPFS enabled EMTTRs(Electronic Medicine Trial and Test Records as a Service), EHR and Radiology services on the decentralized cloud, Chainlink VRF, Optimism, Ethereum, Embark, Filecoin and IPFS eco-system tools. EMTTRs as a service aims at providing

■ Secure data storage, transparent data movement and data authenticity;

■ Improving Data Transparency in Drug Testing;

■ Enabling healthcare community by empowering pharma companies & the medical eco-system to do medicine trial testing securely, transparently using smart contracts compiled on EVM and FVM.

We are developing a technical solution using Ethereum blockchain developer library along with open source spreadsheet engine, AWS Comprehend and AWS APIs to enable secure data storage, transparent data movement and data authenticity. We enable pharma and drug design companies through EHR and Radiology services on the cloud. Further, we utilize a decentralize twitter application for health counselling, drug design and trials.

Screencasts:

EMTTR Demo Video for anonymization and encryption of medical data and dapp on decentralized medical counseling (dapp, security): https://www.youtube.com/watch?v=rJRRlaVQSMY

EMTTR Demo Video for bulk query, retrieve of medical data & dapp using OP, Eth, Embark, IPFS (dapp, portal): https://www.youtube.com/watch?v=BUiVvhuUdrE

Pre-Hackathon Pitch Video: https://www.youtube.com/watch?v=aIq3JiTlNVA&t=6s

The solution needs to be maintained by regulators (e.g. MHRA, FDA), pharma and contract research organisations (CRO), which could be used in parallel with traditional clinical data management systems, framing the process as a transactional inter-organisational record keeping model between untrusted participants, thereby empowering global medical eco-system. Features: Just in Time Service: Availability of pharma companies medicine records across different stakeholder through secure blockchain network. • Record Management: Quality documentation reduces the issues regarding testing procedures and standardization. • Research: Enabling healthcare community by empowering pharma companies & the medical eco-system to do medicine trial testing securely, transparently. • Data Security: Efficiently sharing of data (including personal data), privacy concerns and patient enrollment strategies. • Transparency: Improving Data Transparency in Drug Testing Using Blockchain.

Explanation: Data Transparency : Availability of patient’s medical records across different stakeholder through secure Ethereum and Near blockchain network. The platform utilizes Ethereum, Chainlink blockchain, IPFS via NFT.Storage, Nucypher i.e. patients and organizations who place their data on the exchange will be able to control which consortium entities have permission to access information.

Data Uniformity : Data is processed to make it uniform and stored in PACS (Picture Archiving Communication System) so that it can be utilized by different stakeholders on verified request. Also records are encrypted to avoid any tampering of the data over course of time.

Data Analytics : With the help of computer aided detection and machine learning algorithms, data can be further used for analysis and early prediction, drug discovery and development.

A greater and more seamless flow of information within a digital health care infrastructure, created by electronic health records (EHRs), encompasses and leverages digital progress and can transform the way care is delivered and compensated. EHRs helps in improved care coordination. EHRs helps in making health care ecosystem proactive, accessible and authentic. EHRs with the help of computer aided detection will help in early prediction and diagnosis of diseases.

We have developed our solution based on a variety of blockchain protocols and solutions.

Unstructured clinical text such as physicians notes, discharge summaries, test results, and case notes can be derived using AWS Comprehend. Amazon Comprehend Medical uses natural language processing (NLP) models to detect entities, which are textual references to medical information such as medical conditions, medications, or Protected Health Information (PHI).

How we built it

We developed the tool using ethereum blockchain network, store on IPFS/Filecoin via NFT.Storage, DAOTooling using Chainlink, use NuCypher protocol for encryption, open source spreadsheet, open source PACS (Picture Archiving and Communication Systems) solution, Chainlink for storing offchain metadata, NFT smart contract application using Near blockchain, Verse DEX tools for TPA claims and payments and AWS for running the backend of medication log, medical suite, patient log, blood sugar log, blood pressure register.

Chainlink: Workflow tool using ethereum blockchain network, store on IPFS/Filecoin via NFT.Storage, Chainlink for storing offchain metadata, Chainlink Data Feeds and automation for TPA claims and payments.

We are utilizing Chainlink VRF, services, external adapter and automation tools as follows:

Research Contract Bill Generation: We are utilizing Chainlink Mix to work with Chainlink smart contracts. The bill script will deploy a smart contract to goerli and get a Random number via Chainlink VRF, which can used to identify a unique transaction/order number for the research contract bill.

Parametric Insurance Solution for medicine discovery with special procedures. We are extending the chainlink insurance solution for medical contract researchers.

NFT and Certificate for Good research services in drug discovery: https://github.com/aspiringsecurity/EMTTR/tree/main/dapp_suite/NFT-giveaway-certificate-service-rating

Drug Discovery Service Providers (CRO) and Pharma Service Organizations Payouts: Chainlink Parametric Insurance dapp at https://github.com/aspiringsecurity/EMTTR/tree/main/Drug-Discovery-service-payout

Truflation Market Insight modules for Chainlink services and Covalent End-points/Dashboard: https://github.com/aspiringsecurity/EMTTR/tree/main/covalent-nft-dashboard/truflation-insight

Space and Time: Dynamic NFT generator for DICOM images using SxT and Chainlink at https://github.com/aspiringsecurity/EMTTR/tree/main/EHRs/EMTTR-SxT-dNFT

Flow Blockchain: Flow Modules integration with NFT and Certificate for Good research services in drug discovery: https://github.com/aspiringsecurity/EMTTR/tree/main/dapp_suite/NFT-giveaway-certificate-service-rating/flow-modules .

Verse DEX tokens: Creating custom EMTTR token at Verse for DICOM images. Please visit: https://github.com/aspiringsecurity/EMTTR/tree/main/Drug-Discovery-service-payout/Token-verse-dex

Chainlink VRF: We utilized Chainlink VRF (Verifiable Random Function) to enable provably fair and verifiable random number generator (RNG) that further enables smart contracts to access random values without compromising security or usability for contract research management. For each request, Chainlink VRF generates one or more random values and cryptographic proof of how those values were determined. The proof is published and verified on-chain before any consuming applications can use it. This process ensures that results cannot be tampered with or manipulated by any single entity including oracle operators, users, or smart contract developers. At this juncture, we are planning to use only subscription supported network for Chainlink VRF.

  • Flow Blockchain Modules:

We are utilizing Flow Blockchain with Chainlink services for the following requirements:

USDC enablement with Cadence: We are utilizing USDC implementation using Cadence smart contracts mainly for Government organizations, who would only prefer to use USDC stable coin for providing government biotechnology grants (DBT) to research organizations. Government organizations can send a USDC from source-chain to destination-chain and distribute it equally among all accounts specified for biotechnology grant or reimbursement using call contract with token. Cross chain lending platform: We can supply collateral and borrow tokens from a satellite chain to a fork of Ethereum's mainnet using existing Compound Protocol.

Niftory's APIs and wallet apis: We are using Niftory's API to develop no code, low code analytics tooling using an open source analytics and visualization tool, namely SocialCalc, which enables tabulation, organization, collaboration, visualization, graphing and charting. We are utilzing wallet apis for sending NFTs and facilitate peer to peer payments for pharma organizations and CROs (contract research organizations).

FlowNS: We are using FlowNS to develop an NFT storefront and marketplace for NFTs representing anonymized DICOM images for the following modalities (CT Scan, MRI, X Rays).

We are extending and adapting the Flow blockchain project to enable cross blockchain interoperability and transaction management with Ethereum and Filecoin/IPFS storage. Also, we are extending the project for L2 technologies like Metis to develop rollup capabilities to use decentralize NFT voting for contract research work in biotechnology.

Flowty: We are learning to integrate SocialCalc analytics tooling with flowty services. We are also learning to use secondary marketplace of flowty with SocialCalc to enable sharing of medi assist credits which can be redeemed in various pharma stores.

Improving data transparency during drug testing: We are enabling an Ethereum blockchain network maintained by regulators (e.g. MHRA, FDA), pharma and contract research organizations (CROs), to be used in parallel with traditional clinical data management systems (CDMS), framing the process as a transactional inter- organizational record keeping model between untrusted participants.

A hierarchical arrangement of two core types of smart contract is required: (i) A regulator contract, holding a data structure containing clinical trial authorization (CTA) details. This contract is owned and updated by regulators based on off-chain licensing agreements, and includes a container used to store trial contracts. (ii) A trial contract, deployed by CROs using a function within the regulator contract, dependent on permission logic determined using the CTA data structure. Contains a data structure used to store the trial protocol, using IPFS8 or Ethereum’s native Swarm protocol where large file storage is required, with permission logic requiring protocol deposition and endpoint definition prior to the storage of subjects within a container. Subjects are added by CROs using a function within the trial contract, with permission logic restricting the calling of this function outside of the recruitment period defined in the protocol. The subject data structure contains anonymized subject information, consent documentation, and a container allowing storage of successive clinical measurements. Individual measurements are recorded, with full timestamping, in a format such as string- encoded JavaScript Object Notation (JSON), providing a flexible schema that can be adapted to any study type. Should data privacy be required, strings can be encrypted using public key encryption, with regulators holding a distinct private key for each trial con- tract, or using more elaborate techniques such as zero-knowledge proofs and homomorphic encryption as they become available.

Source code written in JavaScript and the Solidity smart contract programming language is provided under Data and software availability, allowing contracts to be implemented, and data to be written to and read from the blockchain. The scripts perform the following steps:

• Start JavaScript implementations of Ethereum and IPFS nodes, each connecting to local private networks.

• Deploy a regulator contract. A trial proposal, including proto- col documentation, is subsequently submitted to this contract by a CRO, with the documentation being stored using IPFS.

• If the proposal is accepted by the regulator, a trial contract is created. This contract is owned and administered by the CRO.

• Subjects are appended to the trial contract up until the trial start data. Synthetic data is then appended for each of the subjects, up until the trial end date.

• Finally, a script is provided to read all the data from the blockchain, providing a summary of each trial, and details of each subject and data points that have been added, with full timestamping.

Challenges we ran into

  1. AWS deployment: We were using PV (paravirtual) based classic instance. We were required to convert PV (paravirtual) instance to an HVM (Hardware Virtual Machine) and convert it from a classic instance to a VPC instance. We faced a number of challenges in converting our PV instance to an HVM instance as our instance was not reachable via ssh. We arrived at a good conclusion on the issue by doing the following:
  2. Created an ami of current classic instance.
  3. Launch a new instance from this ami after shifting to vpc as the base with new VPC security groups and updated volume.

  4. The newly launched instance is working and we are able to ssh and has the code base. We purchased the developer support plan using AWS credits and have being shared key pointers on converting our VPC based PV instance to a VPC based HVM instance. This challenge would not have been solved without the support of AWS team.

  5. The third party administrators (TPAs) have to manually upload the medical history data to create the medi-claim/insurance based NFT. It is not a problem for a small set of vehicles where we can use csv to json conversion using open source libraries like phpexcel in ethercalc but problematic for larger number of patients. At this juncture, the TPAs have to manually connect their wallet and create the medi-claim/insurance claim NFT.

Accomplishments that we're proud of

A greater and more seamless flow of information within a digital drug discovery infrastructure, created by electronic medicine trial and test records as a service (EMTTRs), encompasses and leverages digital progress and can transform the way medicines are developed, tested and distributed to improve the global health economy and achievement of Sustainable Development Goals in Healthcare. Our blockchain solution, EHR as a service, can help in:

  1. Legitimate storage of medical and environment records, medical invoices and environment data using open source PACS and IPFS for storage and react based spreadsheet application.
  2. Records movement in a secure way using NuCypher Re-encryption Protocol, Query and Retrieve application on bootstrap and Orthanc DICOM open source solution.
  3. Health counseling and environment awareness steps, preventive treatment and remediation over the ethereum blockchain network using a decentralized twitter application and Embark tools. EHRs can help in data cleansing, creating EHR, records legibility and coordination with hospitals, schools, pharmacies, diagnostic clinics, in case when movement of records is needed. The EHR application can be accessible using Internet browser, all the data is on the cloud.

What we learned

We can utilize SocialCalc, Machine Learning Models coupled with Chainlink,Optimism, Ethereum, Near blockchain, Tron DAO, NFT.Storage, network tools, ethereum based infrastructure tools for analysis and prediction of incidents to provide early stage detection and prevention of accidents. We also witnessed the great eco-system available to developers to learn and contribute in the Ethereum eco-system.

What's next for EthMed

We are evolving the solution in the following phases: PHASE I – Requirements Analysis and Design (1 months)

  • Demonstrate the solution using spreadsheet and PACS software on cloud connected devices, Android phones, iPhones, first generation tablets.
  • Procuring server hosting for storing and utilizing images and associated video data to prove that real time monitoring is viable.
  • Set up timeline for planning the 2023 winter deployments and user training session
  • Participate in community events organized by the incubator.

PHASE II (winter deployments and user training session) (5 months) -Create PoC based on designed specification. Complete the design of web interface.

  • Initial user testing, and POC refinement -Prototype Release- Start the pilot trials with 2-3 vendors. -Prototype Manufacturing at the Vendors location. -Prototype validation and assembly: Final Prototype assembly and validation; Refinements based on the mechanical prototype; Final Engineering CAD release.
  • Manage and provide the hands-on task of exporting images and video reports from the customer to SEETA medical cloud system.
  • Create and deploy a gateway service in the customer geo-location that will enable the continuing export of images and video reports to the SEETA medical cloud system.
  • Completion of supporting collateral required to fulfil services and deliverables such as the equipment, supplies and other open source software tools.
  • Continue the collection, data organization and management of images and associated video report data to improve computer aided detection using deep learning algorithms and integrate them with the platform.
  • Survey on community’s needs, user interaction, selection of vendors and quality diagnostic centers where we could deploy full-scale pilot, possibly focused on mobile-platform
  • organize a hardware agnostic program
  • Enable pilot users to be developers of web based platform and contribute in improving the existing deep learning algorithms using websites like Kaggle.
  • Focus on making the platform interoperable with a variety of vendor systems in different housing societies.

PHASE III (6 months)

  • Winter 2023-2024 deployment in hospitals, diagnostic labs, clinics, pharma companies and drug design and development institutions.

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