What is interoperability?
Interoperability is the ability of applications and systems to securely and automatically exchange data irrespective of geographical, political, or organizational boundaries. Coordinated data sharing across organizations and departments is essential in several sectors for research and development as well as an improved end-user experience. Interoperability refers to the standards, protocols, technologies, and mechanisms that allow data to flow between diverse systems with minimal human intervention. It allows diverse systems to talk to each other and share information in real time. Interoperability solutions reduce data silos and help organizations achieve industry-compliant communications. This translates to increased efficiency and higher-quality service offerings.
What are the benefits of interoperability?
Interoperability allows diverse systems to develop an overlapping understanding of data specific to a particular domain. Without interoperability, systems cannot interpret and utilize data to meet common goals. For example, doctors can't use imaging data directly from MRI machines when updating the patient's health record without a common data exchange framework.
Organizations implement interoperable systems due to several advantages the mechanism provides.
Streamline data management
System interoperability allows information to propagate more cohesively without being disrupted by system incompatibility or human processes. Organizations can better manage, monitor, and protect data.
Instead of handling several fragmented data pipelines, system administrators can consolidate data access and movement from a single platform. This system also ensures information accuracy as data undergoes minimal transformation. This way, interoperable systems simplify an organization's effort to regulate data movement, manage users, protect data privacy, and comply with data security regulations.
Interoperability provides effortless data sharing among disparate systems, which improves organizational efficiency. Without interoperability, disparate systems share data with additional data manipulation and transformation steps. Additional data processing nodes present a higher likelihood of errors. Whether systemic or human-induced, inaccurate data affects the performance of downstream analytics applications.
Instead, interoperability removes repetitive, redundant data and ensures all stakeholders receive timely access to accurate and relevant information. Systems can operate in real time with minimum data processing overhead.
Data interoperability enhances an organization's ability to expand operations and adapt to dynamic market trends. With interoperable systems, organizations share data at scale without being restricted by structural and operational limitations.
For example, manufacturers expand their production capacity by adding different types of machines that communicate and understand the same protocols.
Non-interoperable systems must apply additional steps to ensure reliable and accurate data exchange. This may involve tasks like installing a middleware, which formats and distributes data between exchange points.
Installing different software components incurs additional development, operational, and maintenance costs. So, organizations shift to systems with better interoperability to reduce ongoing expenses.
What are the use cases of interoperability?
System interoperability is essential in a digitally evolving environment where organizations seek insights from data to empower decisions and achieve operational success. Next we share how system interoperability positively impacts several industries.
Medical institutions increasingly rely on interconnected medical devices and healthcare systems to collect, share, and analyze healthcare data. These network-connected systems transfer electronic health records, medical results, insurance claims, and other medical information across different healthcare departments. Healthcare interoperability allows healthcare professionals to collaborate toward better patient outcomes with prompt and reliable data.
Interoperability supports sustainable administrative approaches that governments take to improve public service deliveries. It allows various ministries and departments to implement data-driven policies through eGovernment or similar initiatives. Applying a shared data exchange framework also enhances government collaboration by overcoming language barriers.
First responders—including police, firefighters, and paramedics—require timely and accurate information to render best-effort assistance in emergencies. Instead of operating with separate systems, they use systems that share and understand common data to coordinate responses in critical situations. In emergencies, the accuracy, cohesiveness, and efficiency of interoperable systems are crucial to the affected individual's safety and well-being.
Military forces use interoperable systems to support strategic missions, training, and inter-unit collaboration. Military operations require prompt and accurate propagation of intelligence from the chain of command. Armed forces from allied nations can also host joint military exercises aided by shared tactical intelligence that interoperability provides.
Software developers use the principle of interoperability to allow software applications built with different programming frameworks to interact seamlessly. They can build software or microservices that exchange data in real time without additional middleware or coding. Through interoperable software, your end users can manage daily tasks from a single data source.
Artificial intelligence (AI) companies build deep learning models by training them with vast datasets. Data interoperability allows machine learning engineers to train several models with similar data sources, reducing training time and costs. Integrating interoperable AI models with existing enterprise applications is also easier, as they’re built to work with the same data structures and contexts.
What are the levels of interoperability?
Organizations can implement systems with four levels of interoperability.
Foundational interoperability describes computer systems that share data across a network but don't interpret it. This forms a basic level of interoperable exchange of information among disparate systems. Usually, human intervention such as manual data entry is required to bridge the understanding gap between systems.
Structural interoperability, also known as syntactic interoperability, provides a consistent data format or structure interpretable by different systems. It allows systems to retrieve and interpret data from external sources for subsequent processing.
For example, HL7 allows medical institutions to share health data securely and accurately across different departments.
Semantic interoperability enables different systems to work together with minimum interpretation. It embeds purposeful information alongside raw data in files transferred between systems.
Semantic interoperability is unambiguous and leaves no room for errors or misinterpretation. Semantically interoperable systems share the same conceptual understanding of what the underlying data conveys.
Organizational interoperability allows multiple systems to exchange interpretable data beyond their technical context. It aligns information systems on numerous aspects, including goals, workflows, and expectations unique to the organization. Organizational interoperability ensures strong data governance, collaboration, and information sharing across administrative and geographical boundaries.
How does interoperability work in healthcare?
Healthcare interoperability is the effort to balance information availability and patient privacy in medical institutions by reducing data silos.
Healthcare providers implement health information systems that give diverse teams regulated access to patient data and other electronic health information. For instance, a doctor treating a patient in an emergency room can quickly pull up a diabetic patient's recent blood sugar tests from an affiliated clinic. They could also find a cardiogram from a visit to a partner cardiologist.
At the same time, medical providers must comply with privacy and security regulations enforced to safeguard patients' interests. This becomes crucial when organizations share data with external third parties for the advancement of medical research. The exchange of de-identified patient data such as diagnostic information, genomic data, treatment regimens, and patient outcomes is an example of this.
Interoperability in healthcare works through the implementation of several industry standards and acts. These promote the secure exchange of medical data between disparate systems. We give some examples next.
Vocabulary or terminology standards represent specific agreements on terms, code sets, or descriptive representations that support health data interoperability among medical software systems. For example, the ICD-10 contains terminologies that describe symptoms, diseases, and complications.
Content standards provide a data content framework for medical systems to represent health data with a mutually agreeable format. For example, HL7 is a messaging standard that defines electronic health information's data structure and semantics. This means all health software systems can interpret the data correctly.
Transport standards are drafted to enable health information technology solutions to send and receive data reliably. For example, Digital Imaging and Communications in Medicine (DICOM) allows different imaging machines to transmit imaging data to other systems in a healthcare facility.
Privacy and security standards
Privacy standards give patients authority over how healthcare organizations collect, store, and use their personal healthcare information and medical records. For example, the Health Insurance Portability and Accountability Act (HIPAA) is a standard that regulates US healthcare providers' implementation of protectionary measures for sensitive data. This helps ensure patient safety.
Identifier standards are unique codes that allow networked computerized systems to identify a patient, medical professional, or healthcare provider. For example, hospitals use an enterprise master patient index (EMPI) to document patient care rendered by different medical departments.
How does interoperability work?
Every information system has unique hardware, software, functional, and operational specifications. To achieve interoperability, different systems must reach a certain degree of agreement when sharing data.
When building interoperable systems, engineers ensure that data can be exchanged electronically over the medium the systems are connected to. This might involve standardizing hardware communication including protocol, data format, and networking technologies. Applying industrial standards helps determine how systems communicate within a similar context.
Once foundational interoperability is established, system engineers work towards a higher level of interoperable data exchange. To do that, they may define a shared knowledge base consisting of definitions that all connected systems refer to.
The knowledge base is a common truth that streamlines how the systems interpret information from external sources without additional data manipulation. In manufacturing, for example, semantic interoperability enables data collected from production floors to be shared freely with inventory management and supply chain systems.
What are the challenges in interoperability?
Interoperability is critical to achieving the fluid information flow that unlocks the potential of operational and information systems in various industries. Organizations need help in adopting this principle for several reasons.
Managing data at scale
Implementing interoperable systems needs a coordinated effort to consolidate data from multiple sources. Legacy systems store information in siloed databases with incompatible formats. It takes skilled expertise, time, and compute resources to extract, clean, transform, and load massive amounts of disparate data sources into shared storage.
Moreover, organizations must avoid disrupting their operational workflow when transforming their data management strategies to support interoperability.
Addressing privacy concerns
Organizations must enforce security measures to safeguard user information when they enable interoperability. Such efforts become complicated with multiple systems exchanging data through complex data pipelines. Therefore, organizations augment them with appropriate security technologies and policies.
Enforcing interoperability standards
Conventionally, organizations use systems that run with their respective customized protocols and data storage structures. A common industry standard is necessary to enable systems to communicate at a high level of interoperability. Even if interoperable standards were introduced, organizations must modernize their machines, software, and data infrastructure to permit data exchange between two or more systems.
How can AWS support your interoperability requirements?
Amazon Web Services (AWS) offers AWS AppFabric to support your interoperability requirements.
AWS AppFabric is an interoperable cloud service that organizations can use to connect multiple software as a service (SaaS) applications effortlessly and reduce operational costs. AWS AppFabric uses a standard schema to help IT teams secure applications with common policies and alerts.
Here are other benefits of the service:
- AWS AppFabric improves productivity with a generative AI assistant, allowing employees to automate tasks and seek answers promptly.
- You can connect AWS AppFabric with a wide range of SaaS applications, including Asana, Slack, Jira, and Dropbox. For more details, go to AWS AppFabric Supported Applications.
- AWS AppFabric automatically ingests normalized security logs from connected applications into Amazon Simple Storage Service (Amazon S3) or through Amazon Kinesis Data Firehose to your security tool.
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