Why Grounding Artificial Intelligence in Curated Knowledge is Necessary in Healthcare

Author: Charles Lagor, Semedy Inc.

Large Language Models are powerful—but without access to current, institution-specific knowledge, they can lead clinicians in the wrong direction. Here is how Semedy's Knowledge Management System with a Model Context Protocol interface changes that.

Artificial Intelligence (AI) has arrived in healthcare. Physicians look up drug interactions mid-consultation, nurses query care protocols in real time, and hospital administrators lean on AI-assisted tools to streamline complex workflows. Large Language Models (LLMs) sit at the center of this transition—and for good reason. LLMs are articulate, fast, and impressively broad in their knowledge. But breadth is not the same as accuracy. And in healthcare, the gap between the two can have negative consequences.

The hidden problem with limited knowledge

LLMs are trained on massive datasets—but those datasets have a cutoff date. As a result, models will have knowledge gaps. Instead of flagging their own ignorance, they will answer confidently, drawing on whatever they “know”—which may be wrong, outdated, or simply not applicable to a clinical context. This is not a hypothetical risk. It is a structural limitation of how LLMs work and can be illustrated in two scenarios.

Scenario one: outdated clinical knowledge

Consider a clinician who asks an AI assistant about prescribing Cardamyst (etripamil) —a medication approved for Paroxysmal Supraventricular Tachycardia (PVST) treatment in 2025. A model trained before the drug approval date will draw on its best guess and might assume etripamil to be isoproterenol, a beta-adrenergic agonist used in bradycardia (actual answer from an LLM). That answer is wrong. Etripamil is a calcium channel blocker specifically indicated for Paroxysmal Supraventricular Tachycardia (PVST). The consequences of acting on incorrect information in a clinical setting are significant.

The solution is not a smarter LLM — it is a better-informed one. If the same LLM from the above example is directed to the frequently updated medication knowledge base in Semedy's Knowledge Management System (KMS) via a Model Context Protocol (MCP) interface, the LLM will gain real-time access to curated, up-to-date clinical knowledge. It does not need to have been trained on etripamil. The LLM would retrieve the latest knowledge from the KMS and apply the new information to the specific scenario at hand. For example, if a patient had Wolff-Parkinson-White syndrome — a contraindication for etripamil — the LLM would advise the clinician not to prescribe etripamil to the patient.

Scenario two: absence of institutional context

Even when an LLM's general knowledge is current, it will not know local care practices for a specific clinical setting. Ask an LLM about an institution's general orthopedic surgery order set and it will return a reasonable-sounding list: preoperative antibiotics, pain analgesics, anti-inflammatories, anticoagulants. Plausible — but generic. What clinicians actually need are the specific orders with formulation, dosage, and administration that their institution has adopted.

To meet the clinicians needs, an organization represents its local policies and protocols, including care plans, pathways, and order sets as knowledge assets within Semedy’s KMS. The LLM can then dynamically retrieve the relevant information via the MCP interface and properly assist clinicians with information about preferred tests, procedures, and drugs, aligned with local practices.

The value of a curated knowledge base

Semedy's KMS is built on knowledge graph technology. Rather than storing information in tables, KMS represents clinical knowledge as a web of interconnected concepts linked by semantic relationships. This means information can be retrieved by meaning and relationships — not just by matching keywords — enabling far more precise and context-aware answers.

The MCP is the bridge between that knowledge graph and any LLM. Through the MCP interface, an LLM can dynamically query a KMS in real-time, retrieving the relevant and up-to-date knowledge it needs to answer clinical questions correctly. The KMS can represent multiple types of knowledge assets from multiple clinical domains. Thus, the synergy between the KMS and the LLM does not need to be limited to medications or order sets. The knowledge within the KMS does not go stale, it can grow and evolve following the needs of the organization. Moreover, the organization remains in control of its own knowledge, retaining the ability to define and promote high quality care.

The result is not just a smarter AI assistant. It is one that is always current, always contextually grounded, and always drawing from knowledge that an organization has reviewed and approved.

Conclusion

Healthcare AI that produces confident but incorrect answers is not just an inconvenience—it erodes trust, introduces risk, and undermines the clinical workflows it was meant to support. The solution is not to distrust AI. It is to give AI access to the right knowledge at the right time.

Semedy's KMS with MCP addresses two major dimensions of the AI knowledge problem: the temporal gap created by training cutoffs, and the contextual gap created by institutional specificity and local needs. Together, these capabilities enable organizations to deploy LLM-powered tools that are genuinely fit for clinical use—accurate, current, and aligned with the way an institution delivers optimal care.

AI in healthcare is not going away. Organizations that get the most value from it will be those that invest not just in AI models, but in the knowledge that powers them.

Ready to see how KMS with MCP can ground your clinical AI in knowledge that is always current and always yours?

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