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Healthcare
Tackling healthcare’s biggest burdens with generative AI
A clinician records a patient visit using the AI platform’s mobile app. 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. 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). 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.
Gen-AI technology relies on deep-learning algorithms to create new content such as text, audio, code, and more. 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. These unstructured data sets can be used independently or combined with large, structured data sets, such as insurance claims.
Use of gen AI by private payers, hospitals, and physician groups
In the near term, insurance executives, hospital administrators, and physician group operators may be able to apply gen-AI technology across the value chain. Such uses range from continuity of care to network and market insights to value-based care (see sidebar, “Potential uses of generative AI in healthcare”).
Private payers
Consumers are demanding more personalized and convenient services from their health insurance. At the same time, private payers face increasing competitive pressure and rising healthcare costs. Gen AI can help private payers’ operations perform more efficiently while also providing better service to patients and customers.
Hospitals and physician groups
Within hospitals and physician groups, gen-AI technology has the potential to affect everything from continuity of care to clinical operations and contracting to corporate functions.
Consider a hospital’s corporate functions. Back-office work and administrative functions, such as finance and staffing, provide the foundations on which a hospital system runs. But they often operate in silos, relying on manual inputs across fragmented systems that may not allow for easy data sharing or synthesis.
Gen AI has the potential to use unstructured purchasing and accounts payable data and, through gen-AI chatbots, address common hospital employee IT and HR questions, all of which could improve employee experience and reduce time and money spent on hospital administrative costs.
Clinical operations are another area ripe for the potential efficiencies that gen AI may bring. Today, hospital providers and administrative staff are required to complete dozens of forms per patient, not to mention post-visit notes, employee shift notes, and other administrative tasks that take up hours of time and can contribute to hospital employee burnout. Physician groups also contend with the burdens of this administrative work.
Gen AI could—with clinician oversight—potentially generate discharge summaries or instructions in a patient’s native language to better ensure understanding; synthesize care coordination notes or shift-hand-off notes; and create checklists, lab summaries from physician rounds, and clinical orders in real time. Gen AI’s ability to generate and synthesize language could also improve how EHRs work. EHRs allow providers to access and update patient information but typically require manual inputs and are subject to human error. Gen AI is being actively tested by hospitals and physician groups across everything from prepopulating visit summaries in the EHR to suggesting changes to documentation and providing relevant research for decision support. Some health systems have already integrated this system into their operations as part of pilot programs.