An electronic health record, or EHR, is a digital system that stores the longitudinal health information of patients across a healthcare organization. It is the authoritative clinical record: the place where diagnoses, medications, lab results, imaging reports, clinical notes, vital signs, allergies, immunizations, and care plans are documented and maintained. For most healthcare providers, the EHR is the operational center of clinical care delivery.
The major EHR vendors in the US market include Epic, Oracle Health (formerly Cerner), Meditech, and athenahealth, each with different market segments, implementation models, and interoperability capabilities. Epic holds the largest share of large health system market, while other vendors serve specific segments including community hospitals, physician practices, and ambulatory care settings. The EHR a health system uses shapes nearly every technology integration decision that follows from it.
Integrating external technology with an EHR is one of the most technically and contractually complex undertakings in healthcare IT. EHR vendors historically controlled integration tightly through proprietary APIs, certification requirements, and per-interface fees. The mandatory adoption of FHIR APIs under federal interoperability rules has improved this substantially, but significant variation in how EHR vendors implement FHIR means that integrations often require system-specific expertise alongside standards knowledge.
For healthcare AI companies, the EHR represents both the primary data source and the primary point of workflow integration. Clinical AI tools are most valuable when they surface insights inside the clinical workflow rather than in a separate application, which requires embedding into or alongside the EHR. Getting that integration right, both technically and in terms of clinical workflow design, is one of the most significant challenges in healthcare AI deployment.