Nokia and Equideum Partner to Optimize and Analyze Health Data

K. Weldon
K. Weldon

Summary Bullets:             

  • The Nokia Bell Labs and Equideum Health collaboration will leverage data generated from wearables and home health devices.
  • Analytics will be fundamental in aiding clinicians, pharmaceutical companies, and researchers to rapidly gain insight from the data as well as shorten clinical trial timelines.

In April 2022, Nokia Bell Labs and Equideum Health announced a partnership focused on empowering individuals to own and benefit from their personal health data. The collaboration will leverage the rapidly expanding datasets generated from wearables and other edge devices, including the growing set of in-home medical devices. The central premise is that while health data is increasing exponentially, no one has figured out a way to collect it, centralize it, and use it for near real-time meaningful insights. Edge computing, AI, ML, and blockchain technologies are now available to accomplish this by collecting and analyzing diverse data types from a wide variety of devices (e.g., wearables, sensors, smartphones, and video feeds). The partners also expect to empower a flood of innovation, without companies worrying about sharing proprietary information or individuals worrying about sharing their personal information without knowing who has access to it. Beneficiaries of this vision will include consumers, healthcare providers, pharmaceutical companies, researchers, institutions, medical device manufacturers, and potentially a slew of startups excited about access to reams of high quality, verifiable health data.

The announcement focuses on edge computing to handle large volumes of data locally (e.g., with compute and storage on the device itself, or with data sent to local/regional data centers or potentially home or enterprise access points) and to ensure privacy for individuals that consent to provide their data. However, the use of AI and ML algorithms at the edge will also be instrumental for real-time analysis of these large volumes of decentralized and aggregated health data. The analytics will be fundamental in aiding clinicians, pharmaceutical companies, and researchers to rapidly gain insights from the data as well as shorten clinical trial timelines. 

Nokia will also be contributing its Nokia Data Marketplace (NDM), a SaaS offering implemented within a public Ethereum blockchain architecture to support self-sovereign identity, data, and privacy preservation. Equideum will use NDM as a fundamental element of its new Equideum Exchange, which allows individuals to monetize their personal health data; it also enables enterprises that are part of Equideum’s person-centered Data Integrity and Learning Networks (DILNs) to benefit from the new data economy. 

The first of two projects on the partners’ roadmap includes Nokia Bell Labs’ ‘earable’ prototype, a smart device worn in the ear that uses signal processing and on-device ML for cognitive augmentation. The earable will be integrated as a representative edge device within Equideum Health’s planned direct-to-consumer offering. The second project will be an implementation of a collaborative ML environment that preserves the privacy of user data and of the AI/ML models during training and inference.Nokia and Equideum have specific plans for their partnership, but theirs is a wide-ranging vision to do what many in the public and private sector have theorized about for years: to collect vast volumes of decentralized health data from explicitly consenting individuals, aggregate this data on a regional or global basis, and then apply analytics to the data to gain insights and improve health outcomes while simultaneously allowing diverse constituencies to monetize the data. Nokia has more plans for this kind of data gathering/monetizing than its partnership with Equideum. It hopes to replicate the data sharing and analysis model in other verticals including government, airlines, other healthcare initiatives, and telecommunications.

Both partners are concerned about how to convince consumers that they can safely provide their personal data and benefit from it; they have not clarified exactly what the consumer will ‘get’ from their contribution. In theory, consumers might literally get paid; consumers will likely get analysis of their own aggregated healthcare data, which is now likely fragmented into digital or non-digital notes in diverse doctors’ offices or collected by separate smartphone apps, but not shared with the consumer in detail. Consumers will contribute to the improvement of their own and others’ health outcomes and help companies provide better health products and services. Clearly this messaging needs to be better expressed by the partners.

Technology vendors and operators should be analyzing what their role should be in this ‘new’ data economy and how they can monetize the high volumes of data that can be collected, centralized, and analyzed in key verticals. They already take part by providing technologies such as wearables; edge computing, AI, ML, and blockchain technologies; 5G; and related orchestration and service initiatives. While operators already play a role, they need to strategize to see if they can find a larger seat at the table when it comes to data monetization, especially if a fair share of the data is coming from their own smartphones, wearables, and connected home medical devices and is carried on its networks.

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