How Large Language Models Are Used in Healthcare
The rise of generative AI has brought Large Language Models (LLMs) into nearly every sector, but few industries stand to benefit as much as healthcare. LLMs in Healthcare are reshaping how clinicians, health systems, payers, and patients interact with information, enabling new ways to analyze clinical data, improve efficiency, and enhance the patient experience.
In this blog, we’ll explore what LLMs in Healthcare can do, where they excel, and why their role—while promising—is still limited when it comes to complex, high-stakes functions like risk adjustment.
What Are LLMs in Healthcare?
At their core, LLMs are advanced AI models trained on massive datasets of text and medical literature. They can understand natural language, generate coherent responses, and summarize information in ways that mimic human reasoning. LLMs in Healthcare are specifically tuned to support clinical environments, often fine-tuned on medical literature, guidelines, and structured clinical notes.
Healthcare organizations are drawn to LLMs because they can bridge gaps between mountains of unstructured data—like physician notes, radiology reports, or patient portals—and the structured insights clinicians need to deliver care.
Where LLMs in Healthcare Excel
Clinical Documentation Assistance
LLMs in Healthcare can help reduce physician burnout by streamlining documentation. They generate encounter notes, discharge summaries, and patient instructions directly from dictated conversations. For example, a physician speaking to a patient about diabetes management could have an LLM produce a structured SOAP note in seconds.
Summarizing Complex Medical Information
Patients often face challenges understanding clinical jargon. LLMs in Healthcare can translate dense medical reports into plain-language summaries. For instance, a pathology report filled with technical terms can be simplified into actionable next steps for the patient.
Literature Search and Evidence Synthesis
Clinicians and researchers use LLMs in Healthcare to scan vast medical literature, guidelines, and clinical trial data. Instead of manually combing through hundreds of articles, an LLM can generate concise summaries of the latest evidence on topics like immunotherapy for lung cancer.
Patient Engagement and Virtual Assistants
LLMs in Healthcare are being deployed as chatbots or patient portals, answering questions like, “What are the side effects of my new medication?” or “When should I schedule my next mammogram?” While not a replacement for clinical judgment, they can extend access and reduce administrative strain.
Drug Discovery & Biomedical Research
LLMs are increasingly used to analyze biomedical literature, predict protein–drug interactions, and generate hypotheses for new compounds. They accelerate early-stage research by combing through massive datasets that would overwhelm traditional methods.
Real-World Examples of LLMs in Healthcare
Mayo Clinic Research: Mayo is testing how LLMs can synthesize radiology reports and improve imaging workflows.
Pharmaceutical R&D: LLMs in Healthcare are speeding up drug discovery by analyzing clinical trial data, predicting molecule interactions, and even generating regulatory submission drafts.
Patient Education: Hospitals are piloting AI chatbots that guide patients through pre-operative prep, post-discharge care, and medication adherence instructions.
The Limits of LLMs in Healthcare
While the promise is enormous, it’s critical to acknowledge the limitations. LLMs in Healthcare are not inherently accurate—they can misinterpret nuanced clinical context, or apply outdated evidence if not carefully constrained. They also struggle with compliance and audit requirements where precise linkage between diagnosis codes, clinical documentation, and payment integrity is essential.
In risk adjustment specifically, LLMs in Healthcare cannot reliably manage the granular requirements for Hierarchical Condition Category (HCC) coding, documentation integrity, or compliance with CMS regulations. The stakes are high: a miscoded chronic condition can lead not only to revenue loss but also to compliance exposure in a RADV audit.
ForeSee Delivers What Others Can’t
ForeSee Medical’s risk adjustment software with AI goes far beyond what general-purpose LLMs can do. Our platform doesn’t just summarize or draft—it leverages advanced natural language processing, machine learning, and proprietary disease-suspecting medical algorithms to deliver accurate, compliant HCC suspecting at the point of care. Unlike generic tools, ForeSee is purpose-built for Medicare Advantage and value-based care, embedding compliance and financial accuracy into every step.
Key features include:
Real-time disease suspect identification – surfacing undiagnosed and progressive conditions so providers capture the full patient complexity.
InstaVu® technology – instantly linking each diagnosis back to the exact page in the original clinical note, ensuring transparency, audit readiness, and physician confidence.
Automated mapping from CMS HCC V24 to V28 – eliminating revenue risk during the model transition with precise, up-to-date code recommendations.
HITRUST-certified EHR integration – seamlessly embedding into provider workflows with enterprise-grade security and compliance.
Point-of-care coding guidance – delivering actionable insights directly within the encounter, so providers document correctly the first time.
10x coder productivity & 20x chart review speed – proven efficiency gains that reduce administrative burden while safeguarding compliance.
Proactive compliance safeguards – minimizing RADV, OIG, and DOJ audit risk by ensuring only properly supported diagnoses are captured.
ROI-driven performance – delivering measurable lifts in RAF scores, shared-savings payouts, and long-term financial sustainability for full-risk organizations.
These are mission-critical capabilities that LLMs in Healthcare cannot replicate.
Moving Forward
LLMs in Healthcare are ushering in a new era of efficiency, accessibility, and patient engagement. They can write notes, summarize evidence, and simplify medical jargon better than ever before. But they are not designed for everything. When it comes to risk adjustment and revenue integrity, LLMs alone fall short.
That’s why some of the largest risk-bearing organizations partner with ForeSee Medical. LLMs in Healthcare are good at certain things, but ForeSee’s purpose-built HCC risk adjustment coding software does what they can’t: ensuring accurate, compliant, and optimized risk adjustment suspecting that protects revenue and supports high-quality care.
Blog by: The ForeSee Medical Team