Every Hospital Needs an AI Strategy—And Why It’s Not as Simple as Dropping an LLM on Your EHR

Imagine a hospital where clinicians spend more time with patients and less on paperwork, where AI prevents critical errors before they happen, and where operational inefficiencies are a thing of the past. That future isn’t years away—it’s happening now. But only for hospitals with a defined AI strategy.

According to McKinsey & Company AI-driven solutions could save the U.S. healthcare system up to $360 billion annually by streamlining administrative processes, improving diagnostics, and optimizing resource allocation.

Yet, many hospitals are still struggling to define a clear AI strategy.

Hospitals Are Drowning in Data but Searching for Meaningful Metrics

Electronic health records (EHRs) contain vast amounts of patient and operational data, but much of it is unstructured, inconsistent, and difficult to extract actionable insights from. Hospitals face a paradox: they are overwhelmed with data but lack the tools to make sense of it in real time. Without a defined AI strategy, this data remains an untapped asset, limiting clinical decision-making, increasing operational inefficiencies, and contributing to provider burnout.

In a survey of more than 20,000 physicians across 30 specialties, nearly a third of physicians said they spend 20 hours or more a week on paperwork and administrative tasks—a problem AI-driven solutions could help mitigate by automating documentation and streamlining workflows. Without AI, hospitals risk further straining their already overburdened staff.

Can You Just Put an LLM on an EHR and Let It Run?

The short answer: no.

Large language models (LLMs) are powerful, but they require structured, clean data to function effectively when dealing with something as complex as healthcare data. EHR data is notoriously messy, filled with inconsistent terminology, duplications, and unstructured clinician notes. Without preprocessing, an LLM would generate unreliable, misleading, or even dangerous responses.

Moreover, hospitals that implement AI without a structured approach often face unexpected costs, compliance risks, and disappointing results. According to Gartner nearly 85% of AI models fail due to issues like quality data integration and governance. In other words, simply dropping an LLM onto an EHR isn’t a strategy—it’s a gamble.

A Model for Success: How Avant-garde Built an AI-Powered Chatbot

Avant-garde has tackled this challenge head-on. Instead of blindly applying an LLM to raw EHR data, we implemented a multi-step, structured AI approach:

• Preprocessing: Using a combination of machine learning (ML) algorithms, rule-based coding, and physician data scientist reviews to group similar procedures together, ensuring data consistency.
• Categorization: Leveraging natural language processing (NLP) and ML to classify medical supplies, documentation, and clinical queries.
• Structured Queries: Once cleaned, the structured data allows the chatbot to provide real, reliable answers to clinical and operational questions.

Using this structured AI strategy, Avant-garde launched its chatbot, SID, in the summer of 2024. SID is designed to help hospital administrators, clinicians, and operational teams quickly extract meaningful insights from complex datasets.

For instance, Cleveland Clinic used AI-driven workflow optimization to cut patient discharge times by 12%, saving $60 million annually in operational costs.

"SID has transformed how we analyze large volumes of real-time discrete data fields that otherwise would take hours or days to compile."

— Chuck Schwab, Healthcare Executive at IBJI

The Takeaway: Every Hospital Needs a Thoughtful AI Strategy

AI has the potential to revolutionize healthcare, but only when applied with intention and structure. Hospitals that invest in data cleaning, categorization, and structured query models (or work with vendors who do) will be the ones that truly harness AI’s potential to drive efficiency and improve patient care.

Hospitals that fail to define their AI strategy risk falling behind. As data complexity grows, so do the inefficiencies—making AI not just an advantage, but a necessity. The question isn’t whether hospitals should adopt AI—it’s whether they can afford not to.

To learn more about how Avant-garde Health’s software can help your hospital or health system achieve its financial goals – schedule a free demo today.