What is interoperability and how is it relevant to healthcare?

Health datasets can be incredibly complex and are often fragmented. There are often multiple data assets in a health data ecosystem tracking the process of delivering healthcare or a specfic use case, and these data assets are often stored across multiple systems.

  • Dataset complexity: When analysing health outcomes it is often necessary to look at a wide range of data assets including electronic health records of individual patients, population health data, health system usage and performance, clinical trials data and increasingly data collected from wearable technologies. Each of these datasets may describe common characteristics like dates and locations using slightly different formats, but those small differences increase complexity, making it harder to combine or compare one dataset with another.

  • Dataset fragmentation: Multiple datasets, held in multiple systems, are often used to track one process of healthcare delivery or a health intervention journey for a patient. If each of these datasets records key elements differently, for example dates, locations and names of patients, these elements must be standardised between the datasets first, in order for the data to be useful for analysis.

To help reduce complexity and fragmentation, and to enable greater reuse and sharing of data, it is important to ensure that data is interoperable.

Path.org's Digital Square defines interoperability as:

‘(...) the ability of different applications to access, exchange, integrate, and use data in a coordinated manner through the use of shared application interfaces and standards, within and across organizational, regional, and national boundaries, to provide timely and seamless portability of information and optimize health outcomes.’

In digital health systems, interoperability is essential for ensuring complete oversight of care, for example, to track outcomes post-surgery or to inform chronic disease management strategies.

Interoperability enables innovative use of data and information, and facilitates sharing across different digital platforms. This allows data collected in one system to be accessed through different systems; reducing the time and cost of data entry, increasing the availability of data and helping to assure data quality. This creates new opportunities for analytics, visualization and data-informed decisions.

There are two tools that can be used to help build interoperability of health data systems:

  1. Standards

  2. Adaptors

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