Leveraging AI and Machine Learning to Advance Interoperability in Healthcare

Navigating the healthcare system is often a complex journey involving multiple physicians from hospitals, clinics, and general practices. At each junction, healthcare providers collect data that serve as pieces in a patient’s medical puzzle. When all of that data can be shared at each point, the puzzle is complete and practitioners can better diagnose, care for, and treat that patient. However, a lack of interoperability inhibits the sharing of data across providers, meaning pieces of the puzzle can go unseen and potentially impact patient health. True interoperability requires two parts: syntactic and semantic. Syntactic interoperability requires a common structure so that data can be exchanged and interpreted between health information technology (IT) systems, while semantic interoperability requires a common language so that the meaning of data is transferred along with the data itself.  This combination supports data fluidity.  But for this to work, organizations must look to technologies like artificial intelligence (AI) and machine learning (ML) to apply across that data to shift the industry from a fee-for-service where government agencies reimburse healthcare providers based on the number of services they provide or procedures ordered – to a value-based model that puts focus back on the patient.

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