Healthcare is heavily reliant on data. The collective knowledge and experience of healthcare professionals have allowed us to become more efficient in treating diseases over the years. However, the raw state of medical data has little value without effective access by analytics tools.
With hospitals becoming increasingly digitalized, healthcare professionals have access to large amounts of data. But the challenge lies in transforming this extensive data into practical insights for decision-making. This article will introduce you to the process of data integration in healthcare and provide best practices for achieving this.
What is data integration in healthcare?
Data integration involves combining information from various sources and making it accessible to different business applications. By combining data from electronic healthcare records, remote patient monitoring systems, and wearable medical devices, healthcare companies can gain a comprehensive view of their operations and make more informed decisions.
A survey by Google Cloud found that better data interoperability empowers healthcare service providers to deliver more personalized care, explore new opportunities for preventive care, and achieve operational savings. This makes healthcare data integration even more crucial with the adoption of big data analytics in the industry.
Why is data integration complex in healthcare?
Integrating healthcare data may seem straightforward, but it is far from simple. Technical limitations, legacy systems, high data variety, and lack of standardized data formats are some of the main challenges hindering effective data integration in healthcare.
Legacy systems for healthcare data management
Digital transformation in the healthcare industry is ongoing, but many institutions still rely on legacy systems. Outdated software often lacks native integrations with modern solutions, making it difficult to connect them to newly acquired technologies.
High data variety and lack of standardized data formats in healthcare
The growth of big data volumes in healthcare has resulted in real-time insights, but about 80% of the generated medical data is unstructured, making it challenging to store and query with traditional databases.
Complying with data privacy and security regulations in healthcare
Regulatory requirements like the Health Insurance Portability and Accountability Act (HIPAA) impose strict rules on patient data protection, adding complexity to healthcare data integrations.
Poor software interoperability in healthcare data systems
The lack of cross-platform interoperability and poor EMR integration with existing tech stacks present significant challenges for healthcare providers.
Lack of technical skills in healthcare data management
Data integrations in healthcare require specific technical skills, which are often hard to find. Healthcare IT teams are more accustomed to on-premises technologies, making it challenging to integrate new cloud software.
Best practices for effective data integration in healthcare
Begin with comprehensive healthcare data discovery
Data discovery is essential for identifying and classifying different data assets, establishing their storage location, lineage, and access permissions. Specialized data discovery tools can help in this process.
Standardize and map healthcare data types
Documenting the data landscape and standardizing data types is crucial for effective data integration. Structured and unstructured data require different approaches to data transformation, such as extract/transform/load (ETL) and extract/load/transform (ELT) processes.
Use APIs to enhance data extraction from legacy healthcare software
Adding an API connectivity layer can make data accessible from legacy healthcare software and protect systems from downtime due to high call volumes.
Design secure data pipelines
Well-architected data pipelines play a key role in promoting data integrity and security. They help ensure secure and rapid data transportation between different systems, prevent data duplication, and perform data enrichment.
Establish clear data governance in healthcare settings
Data governance is essential for managing data availability, usability, integrity, and security in healthcare organizations. It promotes ethical and secure data use, supporting compliance with regulations.
Data integration in healthcare can significantly impact patient care, diagnostic processes, drug approval, and overall operational decision-making. While it requires an initial investment, it ultimately saves organizations money in the long run. By following best practices, healthcare organizations can effectively integrate their data to deliver better patient outcomes and improve overall healthcare delivery.
If you’re ready to take your healthcare integration knowledge to the next level, explore the ways data analytics revolutionizes healthcare.
Edited by Shanti S Nair