Like clinician documentation, several cases for gen AI in healthcare are emerging, to a mix of excitement and apprehension by technologists and healthcare professionals alike. Gen AI represents a meaningful new tool that can help unlock a piece of the unrealized $1 trillion of improvement potential present in the industry. These unstructured data sets can be used independently or combined with large, structured data sets, such as insurance claims. It can take unstructured data sets-information that has not been organized according to a preset model, making it difficult to analyze-and analyze them, representing a potential breakthrough for healthcare operations, which are rich in unstructured data such as clinical notes, diagnostic images, medical charts, and recordings. Gen-AI technology relies on deep-learning algorithms to create new content such as text, audio, code, and more. That near-instantaneous process makes the manual and time-consuming note-taking and administrative work that a clinician must complete for every patient interaction look archaic by comparison. Once the visit ends, the clinician reviews, on a computer, the AI-generated notes, which they can edit by voice or by typing, and submits them to the patient’s electronic health record (EHR). The platform adds the patient’s information in real time, identifying any gaps and prompting the clinician to fill them in, effectively turning the dictation into a structured note with conversational language. Here’s how it works: a clinician records a patient visit using the AI platform’s mobile app. ![]() ![]() At a convention center in Chicago in April, tens of thousands of attendees watched as a new generative-AI (gen AI) technology, enabled by GPT-4, modeled how a healthcare clinician might use new platforms to turn a patient interaction into clinician notes in seconds.
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