Using the Electronic Health Record and Artificial Intelligence to Guide Clinical Decision-Making

Abstract

Abstract Electronic medical records (EMR) have transformed how clinical information is documented, shared, and utilized over the past 60 years, and the addition of artificial intelligence (AI) has vastly broadened EMR's capabilities. Targeted comprehensive prehabilitation services can be provided to frail patients that mitigate mortality risks. Tools for early detection of sepsis and deterioration are now embedded within the EMR, with predictive models improving their accuracy compared with traditional methodology. The EMR enables protocol-driven care and templated documentation to streamline workflow and improve collaboration amongst providers. Anytime online access, referring to the EMR, enables real-time quality improvement efforts while encouraging patient engagement in their health. Large language models are being utilized to transcribe interactions with patients directly into the EMR, thereby increasing documentation efficiency and enabling more meaningful patient-provider interactions. Advanced data models are empowering patients with safer and more efficient triage of care. These advances are not without drawbacks, including data scarcity in identifying rare complications or pathologies, and a lack of transparency in algorithm development. Nonetheless, the potential of EMR and AI is expansive and will continue to develop and enhance the safety, quality, and efficiency of care while enhancing shared decision-making and provider-patient relationships.

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