Interoperability is a critical factor for the success of ML applications

Machine learning (ML) and artificial intelligence (AI) are rapidly emerging as transformative technologies in healthcare, with the potential to revolutionize patient care and outcomes. The most important and often overlooked factor enabling the power of ML and AI tools is the access to aggregated data from disparate sources that has been tested, validated, and maintained for true data integrity.

As a result, interoperability will drive the ever-evolving capabilities of ML and AI solutions within the healthcare landscape.

Healthcare interoperability: Interoperability in healthcare refers to the ability to exchange data between disparate sources and applications to access consumable and actionable data. Put simply, an interoperability solution should relieve the burden of connectivity, extraction, validation, and quality assurance of data. Solutions like LKOpera, ELLKAY’s interoperability platform, work to enhance every facet of healthcare interoperability, transforming it into a complete data management platform for ML and AI solutions.

This platform empowers them to achieve their objectives of improving patient care outcomes, spanning from initial diagnosis to treatment plan modalities.

Access to data: ML applications depend on large patient data sets, extracted from the electronic health records (EHRs), practice management systems, drug and laboratory systems and other applications where patient data are stored. For ML algorithms to work to their fullest capabilities, interoperability systems need to be able to collect data from various sources, standardize it, and make it available for ML algorithms to analyze.

Imagine a scenario where a patient’s medical history is fragmented across different hospital systems, making it challenging for an ML algorithm to gain a comprehensive understanding of their health.

Interoperable systems can bridge these gaps through aggregation of the data sets, ensuring that the ML application has access to all relevant data despite differing source systems, resulting in more accurate diagnoses and treatment recommendations.

Validation and quality assurance: Connectivity and access to data is the first step to feeding ML and AI technologies, but what if the data extracted does not meet the qualitative standards that are required to accurately analyze and project new diagnosis and treatment recommendations?

When reviewing interoperability solutions, methodology of extraction, validation of data, and ongoing quality assurance are top of mind. At ELLKAY, all connections and integrations go through multi-step validation internally and externally to determine the quality of the data being accessed and to ensure there is no manipulation or errors that may cause undue burden to the recipient of the data.

For instance, a cancer diagnosis may require data sets from varying modules, such as genomics, radiology, lab and medication history, and additional patient history data.

The trajectory of the analysis from ML and AI solutions hinges on the belief that the interoperability solution and framework is delivering the complete set of data required for best-practice clinical care. The reluctance to adopt ML and AI solutions is for this exact reason – what if it only analyzes a portion of the data, and the wrong recommendation is provided? The questioning of the efficacy of these solutions starts with the effectiveness of extracting and validating the data necessary for proper use.

Data security and privacy: Lastly, while the advantages of healthcare interoperability are undeniable, it is essential to address concerns related to data security and privacy. Interoperable systems must prioritize robust security measures and adhere to strict privacy regulations, such as PIPEDA or HIPAA in the United States. This ensures that patient data remains confidential and protected from unauthorized accessibility.

Visible interoperability must be required of all connections to patient data systems to ensure there is an auditable view of who, what, and where all data elements go.

The future of healthcare is undeniably intertwined with machine learning and digital health applications. However, to harness the full potential of these innovations, we must prioritize healthcare interoperability. It is the foundation upon which accurate diagnoses, personalized treatments, efficient workflows, and groundbreaking research can thrive.

As we move forward, healthcare organizations, technology providers, and policymakers must work together to establish and maintain robust interoperable systems. By doing so, we can usher in an era of healthcare that is not only technologically advanced but also patient-centric, efficient, and secure. The importance of healthcare interoperability cannot be overstated; it is the vital connection that ensures the promise of ML and AI in digital health become a reality.

GP has over 25 years of experience in healthcare IT and has been leading ELLKAY’s interoperability solutions across all segments. He was instrumental in earning ELLKAY the position of Technical Service Provider for CommonWell Health Alliance and is involved in interoperability initiatives in the Canadian market.