A scoping review of electronic health records interoperability levels, expectations, approaches, and problems

To achieve useful interoperability between electronic health record (EHR) systems, many approaches have been proposed. To date, none has prevailed as a clear solution. This scoping review studies 24 publications from 2014 to 2023. The aim is to streamline the understanding of current EHR interoperability expectations, practices, and problems, highlight learnings from the Levels of Conceptual Interoperability Model (LCIM), and suggest means for expediting EHR interoperability. Four interoperability levels are visible in EHR compared to seven in the LCIM: technical/foundational, syntactic/structural, semantic, and process/organization. Semantic interoperability-preserving meaning of exchanged data-is the main focus and the problem to solve. Its many expectations cause implementation difficulty. Standardization of data structures, transfer protocols, terminologies, vocabularies, and ontologies are the most common approach, but there is a lack of consensus on standards. Emerging approaches include fuzzy ontologies, natural language processing, and bidirectional transformation. Standardized data structure is not a prerequisite to useful EHR interoperability. Focusing on the state of health records rather than full system integration can expedite interoperability. Different use cases can benefit from various approaches. Artificial intelligence shows promise for handling semi-structured or unstructured data. Stronger regulations may be necessary to guide ongoing integrations.