AI training over private sensitive distributed data sources
Nowadays, more and more organizations want to run analytics over distributed datasources: either because the data volume or data privacy constraints prevent organizations from moving the data into a single place. For example, healthcare companies are increasingly using machine/deep learning (ML/DL) to learn diagnosis and prediction models from their customer base. Machine- and deep learning have helped medical organizations to better schedule surgical operations or predict patient re-hospitalizations after an organ transplant much better.
Blossom Sky as digital health tech enabler
Digital health is the use of information and communication technologies to improve health outcomes, access, quality and efficiency of health care. Digital health encompasses a wide range of applications, such as telemedicine, mobile health, electronic health records, wearable devices, artificial intelligence and big data analytics.
One of the key challenges in digital health is to integrate and coordinate the various technologies and stakeholders involved in the health care ecosystem. This is where Blossom Sky comes in. Blossom Sky is a digital health tech enabler that provides end-to-end solutions for health care providers, payers, patients and innovators. The platform connects and orchestrates the different digital health soft components, such as apps, platforms, data sources and services. The platform enables seamless data exchange, interoperability, security and compliance across the digital health value chain. We also provide consulting and development services to help clients design, implement and scale their digital health initiatives.
Use Case: Blossom Sky improves global research
Long Covid is a term that describes the persistent symptoms that some people experience after recovering from Covid-19. These symptoms can include fatigue, brain fog, shortness of breath, chest pain, and more. According to a recent study, about 10% of Covid-19 patients develop long Covid, and the condition can last for months or even years.
Decentralized data processing allow healthcare providers to analyze the patient data, such as medical imaging or electronic health records, directly on the patient's device or on a secure server within the healthcare facility. This eliminates the need to transfer the data to a remote location, which reduces the risk of data breaches and ensures compliance with HIPAA regulations. Additionally, in situ data processing can also include techniques like data de-identification, encryption and access control to further protect the sensitive data.
As an example, a pharmaceutical company would like to know how effective each COVID-19 vaccine type could be for certain population segments in the North American area. The data science team will require access to all vaccination centers and health centers to know what type of vaccine each citizen has taken, as well as information about the health of vaccinated citizens and possible hospitalizations. This means the company needs access to HIPAA data from health centers in targeted areas. Additionally, to study different segments of citizens who belong to different municipalities, the data scientist will have to analyze data from all municipalities in the area. This use case implies multiple data sets, located in different data silos; it is highly privacy-relevant because any violation of regulation would result in fines, while the company's reputation is at stake due to its public image. Thanks to Blossom Sky and Federated Learning, the data will be processed in situ. This approach avoids data-source breaches and regulatory issues. A typical data pipeline could look like the one we used in that use case:
Decentralized data processing is a promising solution for long Covid research, as it can overcome some of the limitations of centralized data processing methods. By leveraging the power of distributed computing and blockchain technology, decentralized data processing can enable faster, cheaper, and more secure data collection and analysis for long Covid research. This can ultimately lead to better understanding and treatment of Covid and improve the quality of life of millions of people affected by this condition.
The increasing need for federated data lakes in healthcare
One of the challenges of studying long Covid is that it is a heterogeneous and complex phenomenon that affects different people in different ways. There is no clear definition or diagnostic criteria for long Covid, and the underlying mechanisms and causes are still unknown. This makes it difficult to collect and analyze data from diverse sources and populations, and to identify patterns and trends that can help understand and treat long Covid.
This is where decentralized data processing can play a key role. Decentralized data processing is a way of processing data that does not rely on a central authority or server, but rather on a network of distributed nodes that can communicate and collaborate with each other. Decentralized data processing can offer several benefits for long Covid research, such as:
- Privacy and security: Decentralized data processing can protect the privacy and security of the data providers, such as patients, doctors, researchers, and health organizations. By using encryption, hashing, and peer-to-peer protocols, decentralized data processing can ensure that the data is only accessible and shared by authorized parties, and that no single entity can control or manipulate the data.
- Scalability and efficiency: Decentralized data processing can handle large volumes of data from multiple sources and locations, without compromising the speed or quality of the analysis. By using parallel computing, distributed ledger technology, and smart contracts, decentralized data processing can optimize the use of resources and reduce the costs and risks of data storage and transmission.
- Collaboration and innovation: Decentralized data processing can foster collaboration and innovation among the stakeholders involved in long Covid research. By creating a common platform and standard for data sharing and processing, decentralized data processing can enable cross-disciplinary and cross-border cooperation, as well as incentivize participation and contribution from various actors.
In summary, decentralized data analytics is a powerful tool that allows for the collection and analysis of data directly in the field, without the need to transfer samples or data to a remote location. This approach has many benefits, including improved accuracy, reduced costs, and minimized impact on the environment. Additionally, in situ data processing can ensure compliance with regulations such as HIPAA, and it can also provide real-time monitoring and control, which can be useful in many industries such as healthcare, manufacturing, finance, IoT, transportation, logistics and energy. Furthermore, when combined with federated learning, in situ data processing can enable the sharing of best practices and improved decision-making across multiple sites or devices, which can lead to increased efficiency and improved outcomes.
Data mesh and data platform abstraction are not silver bullets or one-size-fits-all solutions. They require careful planning, design, implementation, and governance. They also require a cultural shift from centralized to decentralized data ownership and collaboration. DataBloom's Virtual Data Lakehouse offers a promising vision for how organizations can harness the power of data to deliver better value for their providers, partners, and stakeholders. Be sure you undergo a brief consultation with your DataBloom AI representative to address the challenges of implementing Blossom Sky into your data strategies.
Blossom Sky stands for federated data lake technology, data collaboration, increased efficiency, and helping to create new insights by breaking data silos in a unified manner through a single system view. The platform is designed to adapt to a wide variety of AI algorithms and models. Blossom Sky integrates with all major data processing and streaming frameworks like Databricks, Snowflake, Cloudera, Hadoop, Teradata, Oracle, Apache Flink as well as AI systems like Tensorflow, Pandas, PyTorch.
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