How can healthcare organizations leverage data to improve outcomes and efficiency, while ensuring security and compliance?
Because of inadequate management of patient admissions, hospital stays, infection control, and outbreak preparation, many healthcare institutions incur significant administrative expenditures. The Covid-19 outbreak demonstrated the inability of even well-established healthcare systems to respond to such enormous demand.
Government mandates to create health-care programs such as Medicare and Medicaid can potentially raise healthcare expenses. Organizations may focus on preventative and tailored treatment, population health programs based on predictive care, data-driven internal operational decision support, and intelligent remote monitoring of patients utilizing the internet of medical things to manage and optimize costs. However, in order to leverage the benefit of data in these projects, quick and effective decision-making is required.
The upcoming challenge for healthcare operators and providers
Medical records frequently include free-form language, such as unstructured patient notes, pharmaceutical information, physician instructions, and discharge summaries, to mention a few examples. Furthermore, huge volumes of medical imaging data, such as pictures from radiology, cardiology, oncology, and pathology, cohabit with healthcare operations data. Traditional healthcare information systems, on the other hand, are mainly packaged systems purchased from separate vendors to perform particular operational activities and implemented in silos with no integration or interoperability, resulting in segregated data collection and storage. There have been attempts to mine data using classic data warehouse approaches; however, such procedures do not suit and scale to the demands of various formats of structured, semi-structured, and unstructured data.
A lack of data analytics and business intelligence maturity is further complicated by a number of additional problems. Most healthcare companies, according to research, lack experienced analytic resources, technological integration across different platforms, systems of truth for essential data and information entities, and data governance, all of which are necessary precondition for deploying data platforms. End-user adoption, strongly perceived value of data, necessary transparency in reporting metrics, and universal acceptance of being measured using data are important organizational cultural characteristics. Finally, there is an urgent need to establish a relationship between business results and technological solutions.
One possible solution is to adopt a data mesh architecture and a data platform abstraction layer - a "Virtual Data Lakehouse".
What is a data mesh?
A data mesh is a decentralized, domain-oriented, and self-serve data architecture that treats data as a product. Instead of having a centralized data warehouse or lake that collects and processes data from different sources, a data mesh distributes the ownership and governance of data to the domain teams that produce and consume it. Each domain team is responsible for creating, maintaining, and exposing their own data products (datasets, algorithms, models, predictions) through standardized APIs and protocols. The concept of a data mesh enables a more agile, scalable, and resilient data infrastructure that can handle the increasing volume, variety, and velocity of data in health care. It also empowers domain teams to collaborate and innovate with data without being constrained by centralized bottlenecks or dependencies.
What is a data platform abstraction?
Data platform abstraction enables health care organizations to decouple data products from data platforms and provide a consistent and unified experience for data producers and consumers. For example, a domain owner can create a data product using a generic data platform interface without worrying about the underlying data platform technology or details. And the domain consumer can access a data product using the same interface regardless of where or how the data is stored or processed, whereas a data platform orchestrator can automate the provisioning and scaling of data platform adapters based on the demand and performance of data products.
Data platform abstraction can be implemented as a set of tools, libraries, frameworks, or services that enable common functionalities such as:
- Data discovery: finding and understanding available data products across the data mesh
- Data access: querying and retrieving data products using standard APIs and protocols
- Data transformation: processing and transforming data products using declarative or imperative languages
- Data orchestration: scheduling and coordinating data workflows across different platforms and systems
- Data governance: enforcing policies and rules for data quality, security, privacy, and compliance
A data platform abstraction simplifies the interaction with data products and reduces the learning curve for domain teams. It also enables interoperability and integration among different platforms and technologies without requiring costly or complex migrations or conversions.
What is a Virtual Data Lakehouse?
A virtual data lakehouse combines data mesh and cross-platform data processing technology into a federated data lake and is a great way to enhance digital transformation. A virtual data lakehouse enables to access, analyze, and process data across several data processing systems without re-platforming apps or centralizing data into a single data lake.
Blossom Sky combines data mesh and data federation with data processing, resulting in increased data scalability, increased data processing, and multiplying data analytics capabilities without losing speed, privacy, or security. A virtual data lakehouse can take advantage of huge volumes of data without sending it to a central server, a modern technology that contributes to enhanced data analytics, generative AI and federated learning (FL) developments. Our flagship product, Blossom Sky, enables companies and large organizations to apply data analytics, train machine learning (ML) or generative AI (LLM) models on distributed data pools covering many different devices, edges, data lakes, data warehouses, or data storage systems.
How can the virtual data lakehouse enable digital transformation in health care?
Data mesh and data platform abstraction are complementary concepts that drive digital transformation in health care. By adopting these concepts, health care organizations can:
- Faster time-to-value: domain teams can create and consume data products faster and easier without waiting for centralized approvals or resources
- Higher data quality: domain teams can ensure the accuracy, completeness, consistency, and timeliness of their own data products
- Greater agility: domain teams can respond to changing needs and opportunities more quickly by iterating on their own data products
- More innovation: domain teams can experiment with new ideas and solutions using their own or other domains' data products
- Better collaboration: domain teams can share and reuse data products across domains and systems through standardized interfaces
- Lower cost: domain teams can optimize the use of resources and technologies based on their own needs and preferences
Some examples of how these benefits can translate into concrete outcomes in healthcare are:
- Improved patient care: clinicians can access comprehensive and up-to-date patient records from different sources and systems through a single interface
- Enhanced research: researchers can discover and analyze relevant datasets from different domains and systems using common tools and languages
- Increased efficiency: administrators can monitor and optimize the performance and utilization of different platforms and systems using unified metrics
- Higher security: security teams can enforce consistent policies and rules for protecting sensitive data across different platforms
What are the new challenges and how to tackle them?
While a data mesh architecture and a data platform abstraction layer offer many advantages for health care organizations, they also pose some challenges that need to be addressed:
- Data quality and consistency: How to ensure that the data products produced by different domains are reliable, accurate, and compatible with each other? How to handle data conflicts and discrepancies across domains?
- Data security and privacy: How to protect the sensitive and personal data of patients and providers from unauthorized access and misuse? How to comply with the regulatory and ethical standards of healthcare data?
- Data culture and collaboration: How to foster a culture of data ownership, accountability, and collaboration among different domains? How to incentivize and reward data producers and consumers for sharing and using data?
- Data skills and literacy: How to equip the health care workforce with the necessary skills and knowledge to leverage data effectively? How to bridge the gap between the technical and clinical aspects of data?
These challenges require careful consideration and planning when implementing data mesh and data platform abstraction in health care. They also require ongoing monitoring and evaluation to ensure that the benefits outweigh the costs. 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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