How to implement hybrid architectures in healthcare

Hybrid architecture in data management is a method that combines two different approaches to handle information efficiently: federated and centralized. In these architectures, data remains within its original, secure environment, managed by its original controllers, a characteristic of federated systems. Simultaneously, the architecture standardizes data across different sources for consistency and interoperability, akin to centralized systems. 

How does hybrid architecture operate?

According to the Preface of Hybrid Architectures for Intelligent Systems, “Hybrid architecture for intelligent systems is a new field of artificial intelligence concerned with the development of the next generation of intelligent systems.”

Hybrid architecture operates by synergistically combining the decentralized control of federated systems with the uniformity and efficiency of centralized data models. This approach is based on a secure environment under the management of local data controllers, a key aspect of federated systems. It allows healthcare providers and researchers to maintain control over their data.

The hybrid architecture applies standardized formats and protocols for data processing across different sources, akin to centralized systems. This standardization enables the consistent and interoperable use of healthcare data from diverse sources, facilitating seamless data integration and analysis.

See also: What is a hybrid entity under HIPAA?

Key aspects of hybrid architecture 

Hybrid architecture in the context of data management, particularly in sectors like healthcare, combines the features of both federated and centralized systems. This blend aims to maximize the advantages of each while minimizing their drawbacks. Key aspects of hybrid architecture include:

1. Decentralized data control (Federated aspect): In a hybrid architecture, data sources retain control over their data. This means data stays within its original environment (like a hospital or research center), under the management of local data controllers. This approach ensures data privacy and security, and for complying with various data protection regulations.
2. Centralized data standardization and integration: Even though the data is controlled locally, a hybrid system employs centralized methods for data standardization and integration. This involves transforming data from multiple sources into a common format, enabling consistency and interoperability. Standardized data formats, like the OMOP CDM in healthcare, facilitate efficient data analysis across disparate systems.
3. Secure data sharing mechanisms: Hybrid architectures use secure methods to share or access data. This can include APIs, secure data gateways, or other technologies that enable controlled and safe data sharing without the need to physically move or duplicate the data.
4. Aggregate data analysis: While individual data sources manage their data, the hybrid system allows for aggregate-level querying and analysis. This means that while detailed individual data remains secure and private, aggregate data can be used for broader analysis and insights.

See also: Using AI in patient data analysis

How to implement hybrid architectures in healthcare

Identify data sources and requirements

Start by identifying various healthcare data sources such as hospitals, clinics, research institutions, and labs. Understand the nature of data each source holds, its format, and how it's currently managed. Assess the requirements for data privacy, security, and compliance with regulations like HIPAA.

Establish federated data control

Implement a federated system where each data source retains control over its data. This involves ensuring that data remains within its local environment and under the management of the original data custodians. This approach is for maintaining privacy and adhering to local data governance policies.

Standardize data formats

Develop or adopt standardized data formats and protocols for data sharing and integration. One common approach in healthcare is mapping data to a standardized model like OMOP CDM. This ensures consistency and facilitates data interoperability among disparate systems.

Implement secure data sharing mechanisms

Create secure data sharing mechanisms that allow data to be shared or accessed without moving it from its original location. This can be achieved through APIs, secure data gateways, or other technologies that enable access to data under strict privacy and security protocols.

Develop a centralized querying system

While keeping the data decentralized, implement a centralized querying system that allows researchers and healthcare professionals to access and analyze data from multiple sources in aggregate form. This system should be able to send queries to local databases, receive responses, and aggregate the results without compromising patient identity.

Ensure data privacy and security

Implement robust security measures such as encryption, access controls, and audit trails to protect data during storage and transmission. Compliance with healthcare regulations for data protection is a must.

Incorporate advanced analytics and AI

Leverage advanced analytics and AI tools that can operate on the aggregated data to extract meaningful insights. These tools can help in predictive modeling, patient care improvement, and research.

Train stakeholders

Educate all stakeholders, including IT staff, healthcare providers, and researchers, on how the hybrid architecture works, its benefits, and the necessity of maintaining data privacy and security.

See also: HIPAA Compliant Email: The Definitive Guide

FAQs

Why is hybrid architecture important in healthcare?

It's necessary for healthcare providers to maintain control and privacy over their patient data, while still enabling the larger healthcare community to access vital aggregated data for research, analysis, and public health monitoring, especially in situations like the COVID-19 pandemic.

Does hybrid architecture compromise data security?

No, it actually enhances data security. By allowing data to stay in its original, secure environment and using secure methods for aggregate data sharing, hybrid architecture maintains high standards of data privacy and security.

Can hybrid architecture be scaled for large healthcare systems?

Yes, one of the strengths of hybrid architecture is its scalability. It can accommodate the addition of new data sources and can adapt to changing data types and regulatory requirements, making it well-suited for large, dynamic healthcare systems.

Is hybrid architecture suitable for other sectors besides healthcare?

Absolutely. While particularly beneficial in healthcare, hybrid architecture can be applied in any sector where there's a need to balance local data control with the benefits of centralized data analysis, such as finance, education, and government services.