Healthcare and Data Sovereignty

From: thepipeline_xyz

Healthcare and data sovereignty are critical concepts, especially with the increasing digitalization of personal health information and the rise of decentralized technologies. The aim is to empower individuals with control over their sensitive medical data while ensuring its utility for medical advancements and public health.

Challenges with Centralized Healthcare Data

Traditional healthcare systems often suffer from data silos, meaning patient records are not easily shared between different hospitals or providers [01:16:08]. This fragmentation can lead to sub-optimal treatment, especially in emerging economies where public health systems are already strained. The global cost of this problem is estimated at $200 billion annually, with over 30,000 lives lost as a direct result [01:16:37].

Beyond fragmentation, centralized data storage poses significant security risks. A notable example is the 2023 23andMe hack, which exposed 7 million patient genomic records [01:16:48].

Decentralized Solution: Ever Network

Ever Network proposes a decentralized healthcare data storage protocol built on Monad to address these challenges [01:17:09]. The core of their solution is to return data sovereignty to the users [01:17:18].

Key aspects of their approach:

• User Control Patients can own their medical data, deciding where it is stored, who can access it, and how it is used [01:17:32].
• Encrypted Storage The protocol is built on an encrypted layer of IPFS (InterPlanetary File System) [01:17:22].
• Data Interaction Users can interact with their own data through a chatbot, for instance, to understand symptoms without needing to consult a doctor, avoiding unreliable sources like WebMD [01:17:45].
• Monetization Users have the opportunity to monetize their data by licensing it to pharmaceutical companies, insurance providers, and drug researchers. This can also lead to recruitment for clinical trials [01:18:14].

Ever Network’s Traction and Strategy

Ever Network began as a SaaS company in 2019 and has already secured government contracts, including with the Thai government for medical record management [01:18:37]. Their network includes over 650 hospitals and holds more than 10 million patient records [01:18:47]. They have also signed a contract with the Indonesian government and are pursuing partnerships with the Colombian government and Japanese hospitals [01:18:58].

Their go-to-market strategy is three-pronged:

1. B2B Partnerships Expanding their hospital network globally, particularly in emerging economies [01:19:16].
2. Web3 Integration Empowering users to leverage wearable data from devices like Aura rings, Whoop, and Apple Watches. This lifestyle data can be combined with health records, offering users rewards for participation [01:39:30]. They believe web3 users are more health-centric and willing to provide data for insights and rewards [02:23:05].
3. Physical Activations Conducting events where people can onboard by, for example, getting a retina scan to detect diseases and immediately access the application [02:23:41].

Currently, the private key for a patient’s data is held by the originating hospital, though Ever Network plans to transition this control to the patient in the future [02:15:37]. The primary target users for direct patient engagement are individuals with chronic conditions like type 2 diabetes, who require long-term data tracking [02:22:07].

Primus and Data Verification

Primus (formerly Parallax) is a cryptography technology provider that aims to make world data usable on-chain by enabling the verification and computation of any web3 data [02:21:43]. They identify a limited amount of data currently available on-chain and the complexity of bringing web2 data into web3 securely [02:22:08].

Primus’s vision is to build a cryptographic layer for secure data intelligence by connecting authenticated, high-value data from Web2 to Web3 and AI applications in a trustless and secure way [02:23:03].

Their core technologies include:

• MPC TLS / ZK TLS Used for verifying data flow from Web2 to Web3. Primus’s protocol is 10 times faster than existing solutions like TLS Notary [02:23:30].
• ZK FHE (Zero-Knowledge Fully Homomorphic Encryption) Enables verifiable computation on any encrypted data. Their design offers over 300 times improvement in computation time compared to alternatives [02:24:06].

Primus focuses on data verification on-chain, confidentiality, enterprise-level solutions, and confidential AI [02:24:25]. In the short term, they will provide data verification capabilities for building digital goods marketplaces, verifiable data marketplaces, and prediction markets [02:24:47]. This technology can support use cases like those of Paper Plane (off-chain data with on-chain service) and Ever Network (healthcare data verification) by ensuring only verifiable and authenticated data enters the system [02:27:58].

Long-term goals include deploying confidential AI applications using ZK FHE and FHE VM [02:25:24]. The company emphasizes its speed of innovation and ability to handle large-scale memory data, such as chat GPT, picture, and video data, which other TLS solutions cannot [02:29:25].

Pulse and Data Privacy in Wearable Technology

Pulse is a deepin network that uses native wearables to unlock the value of health data, emphasizing user control and privacy [02:31:17]. They highlight how companies currently monetize user health data (from hospitals, wearables, DNA tests) without providing any compensation to the data generators [02:31:36].

The health data market is significant, valued at $69billionandprojectedtogrowto$540 billion over the next decade, driven by demand from AI applications, longevity research, personalized healthcare, and consumer self-care [02:32:14].

Pulse’s solution:

• Data Aggregation Aggregates health data from various sources including wearables, DNA, blood work, and health records [02:32:01].
• On-chain Encryption Data is encrypted on-chain, and users can share it with third parties in exchange for rewards [02:32:06].
• Consumer Application Their consumer app acts as a gateway for users to contribute data and receive insights [02:33:52].
• Native Wearable Device Pulse offers its own wearable device, designed for “productivity hackers,” that tracks sleep, heart rate, calories, and energy levels [02:34:11].
• Inbuilt Hardware Wallet The device includes a secure enclave where users create their private keys, which act as certifiers and encryption keys for their data. This ensures that even Pulse does not have access to user data without explicit permission, enabling decentralization from day one [02:34:48].
• Monetization Model Third parties (e.g., insurance companies, researchers) can make bids for data from Pulse users on-chain [02:39:08]. Users receive notifications and can accept or deny these bids. If not enough users accept, the third party must offer more attractive terms [02:39:21]. This process is designed so that companies only receive aggregated, anonymized answers to their questions (e.g., “how many users in Bangkok run 1 km”) without accessing any personal identifiable information, protecting data privacy [02:39:30].

Pulse has demonstrated strong early traction, achieving $150,000 in sales in 21 days for their wearables and engaging over 1,000 members in a fitness competition with the Monad community [02:35:23].

Totem and Conversational Data Monetization

Totem focuses on leveraging daily conversations, turning them into meaningful, private, and monetizable assets under user control through an AI Deepin Network [03:39:53]. They differentiate themselves from competitors like Masa and Grass, which focus on low-value browser data, by targeting the voice data market, estimated at over $100 billion [03:40:23].

Challenges in this market include fragmented tools for capturing, processing, and monetizing voice data, and significant privacy concerns limiting participation [03:40:51].

Totem’s comprehensive solution:

• Hardware Device A small device that attaches to a phone, capturing voice data [03:41:11].
• Real-time Processing Converts voice data into real-time transcription, translation, and summarization [03:41:20].
• Privacy-preserving Encryption The processed data is encrypted using Zero-Knowledge Proofs (ZKPs) to become anonymized data sets [03:41:26].
• On-chain Upload Users can upload these anonymized data sets on-chain via Monad’s fast throughput, exchanging them for tokens [03:41:32].
• AI Model Optimization Utilizes AI models like Meta’s LLaMA, optimized by experts from Google’s Gemini and Alibaba [03:42:32].
• User Control and Opt-in/Opt-out Users have complete control over their data, with clear opt-in and opt-out options for recording and uploading [03:47:08]. This allows users to use transcription or note-taking features without uploading raw data to the model [03:47:41].

Totem’s monetization model includes device sales ($199), subscription services for premium features, and selling anonymized data sets to AI companies for model training [03:42:58]. They target both traditional consumers and crypto-native users through direct sales, major distributors, and cross-cultural/industrial events [03:43:24].