The modern healthcare landscape is defined by two profound realities: the immense promise of Large Language Models (LLMs) to transform operations and the sobering truth about cyber threats. Sadly, data breaches in healthcare no longer surprise anyone. $92%$ of organizations faced a cyberattack in the past year, with an average breach costing about $9.77million. These sobering statistics underscore why any foray into AI must put data security and compliance front and center. Yet, the allure of AI, especially large language models like GPT-4 or specialized medical LLMs, is undeniable. While LLMs promise to fundamentally transform healthcare operations, the critical challenge is how to harness those benefits without accidentally tripping over HIPAA or other privacy laws. In other words, how to prepare your healthcare data for LLMs in a safe, compliant way.
Opportunities and Risks of Using LLMs in Healthcare
Let’s begin by examining the current environment. Many healthcare innovators (from Chief Data Officers to clinical informatics leaders) are exploring how large language models could vastly improve care delivery. The market already offers sophisticated AI assistants that summarize complex clinical notes, answer patient questions accurately, or flag at-risk cases in real time. All that sounds intriguing, and the initial results are highly promising. For instance, Google’s Med-PaLM 2 (an LLM specialized in the medical domain) scored $85% on U.S. medical licensing exam questions, significantly outperforming general-purpose models.
When accurately trained, LLMs can grasp deep clinical context and hold tremendous potential to streamline essential workflows in modern health systems. However, if used improperly, LLMs can introduce serious privacy and safety risks. Healthcare data is deeply sensitive and rife with Protected Health Information (PHI). This kind of personal data is strictly governed by regulations like HIPAA. A single stray note or patient identifier in the wrong context can easily trigger a reportable breach and result in fines of up to $2.13million per violation. Public chatbots and generic LLM APIs are typically not HIPAA-compliant by default, meaning organizations must proceed with extreme caution.
In short, while the transformative promise is real, the risks are equally serious. Healthcare organizations must lay a solid, compliant data foundation before deploying LLMs, or they risk devastating privacy violations, biased outcomes, and unreliable performance in a clinical setting.
In this post, we’ll explore the correct methodology to prepare healthcare data for LLM-driven projects. Let’s find an answer on how to unlock the massive value of AI while staying fully aligned with regulatory requirements.
The Two Major Data Readiness Challenges
Sphere clients, like health systems, digital health innovators, and biopharma teams, commonly share one short, insightful observation: AI projects too often fail because the data wasn’t ready. That’s why, for us, it’s essential to define what prepared data means specifically in the context of large language models.
Here are the two major challenges we consistently observe in practice:
Data is Siloed and Complex
A typical hospital system might use dozens of different software applications: EHRs, lab systems, radiology systems, pharmacy databases, billing systems, wearable device platforms, research registries, and more. Each holds pieces of patient information in different formats, and these pieces rarely “talk” to each other automatically. The result is fragmented data scattered across departments and systems, which dramatically undermines any AI model’s effectiveness.
In fact, if your LLM can’t see the full patient picture because data is siloed, its insights will be narrow and potentially misleading. Data fragmentation also translates to a lot of manual effort today (think clinicians copying info from one system to another) and missed opportunities for proactive care.
Regulations and Privacy Concerns are Restrictive
Healthcare data isn’t just any data. It’s highly sensitive and strictly regulated. Patient privacy laws like HIPAA in the U.S. (and GDPR, etc., elsewhere) impose stringent rules on how PHI is stored, used, and shared. This means that any AI project using patient data must have privacy by design. The stakes are high: breaches can lead to massive financial penalties, legal consequences, and irreparable damage to patient trust. Compliance requirements also cause hesitancy. Teams worry that introducing an AI or copying data into a new system could violate regulations, so AI pilots often stall due to uncertainty about compliance. There’s also the matter of clinical risk. If an LLM isn’t properly vetted and fine-tuned, it could hallucinate incorrect medical information or exhibit bias, which is unacceptable in clinical contexts. In summary, healthcare AI efforts live under a microscope, and rightfully so, requiring a significantly higher bar for security and accuracy than other industries.
We’ve seen too many promising AI pilots stuck in compliance reviews or derailed by missing context in siloed datasets. Our vision is to give healthcare organizations a faster, safer path to operational AI, one where data flow, security, and AI infrastructure just work, so you can focus on improving care, not building pipelines from scratch.
Stats accordingly to Definitive healthcare
Preparing Your Healthcare Data for LLMs – Step by Step Guide
To set your healthcare data up for successful use with LLMs, you must follow a structured, disciplined approach. Here’s a step-by-step guide to preparing data without breaking compliance:
1. Inventory Your Data (and Know Your PHI)
Start by identifying all the data sources you might tap for AI. This includes EHR databases, data warehouses (e.g. Snowflake, Redshift, BigQuery), claims systems, lab and imaging systems, doctor’s free-text notes, patient portal messages, etc. Catalog what data you have and where it lives.
Just as importantly, classify the sensitivity of each dataset. Know what contains PHI (names, IDs, dates, addresses, etc.) versus de-identified or anonymous data. This classification will determine how you handle the data. For any source containing PHI, mark it as high-risk and ensure you have proper patient consent or authorization to use it for your AI project if required. Getting a clear map of your data domain upfront will prevent blind spots later on.
2. Integrate and Aggregate
Your next step is to consolidate the relevant data into a unified environment where the LLM (and your data science team) can access it. This doesn’t necessarily mean dumping everything into one monolithic database, but you need seamless interoperability.
Leverage your enterprise data warehouse or data lake to bring together structured data (e.g. patient demographics, diagnoses, medications) from various systems. For unstructured data like clinical notes or radiology reports, consider an indexing or search system that can unify these text sources. Embrace key healthcare data standards during integration. For example, using the HL7 FHIR formats to represent patient records can help different systems communicate more easily.
If your organization has APIs or integration engines (like an interface engine or health information exchange), use them to pipe data into a central platform. The goal is to create a $360 view of the patient that an AI model could leverage. By aggregating data across formerly siloed sources, you’ll also uncover data quality issues earlier and ensure that your AI isn’t operating on a narrow, biased slice of information. A large hospital can generate tens of petabytes of data per year, and without unifying it, an LLM cannot possibly ingest the breadth needed for robust insights.
3. Clean, Curate, and Normalize the Data
Once data is aggregated, it’s time to clean and normalize it. This step is crucial because healthcare data from different systems can be wildly inconsistent. Normalize medical terminology and codes (e.g. ensure that lab results use standard units and naming, map equivalent medications to standard drug codes, align diagnoses to ICD or SNOMED codes, etc.). Resolve duplicate records and reconcile conflicting information (for instance, if the same patient’s data comes from multiple sources, ensure their records are merged or linked correctly).
Curate the text data as well. Clinical notes often contain typos, shorthand, or non-standard abbreviations. You may need to preprocess text (e.g. expand acronyms like “H/O” to “history of”) so that the LLM can understand it. Additionally, filter out any obviously irrelevant or low-quality data that could confuse the model (for example, extraneous monitor logs or corrupted entries).
Think of this step as data prep for a very picky consumer. The LLM will perform much better if the input data is consistent and semantically coherent. This is also a good stage to inject domain knowledge: if you have existing controlled vocabularies or knowledge graphs (like lists of medical conditions, drug interactions, etc.), aligning your data to those will provide context that an LLM can later utilize.
4. De-Identify and Protect Sensitive Information
Before any PHI touches an LLM, decide how you will protect patient privacy. There are two broad strategies: de-identification or controlled environments. De-identification means stripping out or masking personal identifiers from the data so that it’s no longer directly traceable to individuals. This can involve removing names, IDs, and contact info, as well as less obvious identifiers (dates, locations, rare conditions) that could indirectly identify someone. Automated NLP-based de-identification tools can help scan clinical text and redact or tokenize PHI (for example, replacing a patient name with a placeholder). Keep in mind that de-identification isn’t foolproof. Poor masking can leave clues that enable re-identification, so you may need expert review or formal validation (the HIPAA Safe Harbor method or expert determination statistical analysis).
The other strategy is to only use PHI in a highly secure, controlled environment. For instance, keep the data on on-premises servers or in a cloud that is configured to meet HIPAA requirements (encryption, access controls, etc.), and use an LLM system that runs entirely within that environment. In that case, you might not remove all PHI from the data, but you must ensure strict safeguards (audit logs, role-based access, authentication) are in place so the data never leaks.
Often a combination of both approaches works best. For example, de-identify data for any external or non-prod usage, and in your production AI environment apply rigorous security controls. Never send raw PHI to a third-party LLM service without a BAA in place and assurances of compliance. This step is about establishing trust: you want to be confident that feeding data to the LLM won’t result in any unauthorized exposure. Encryption of data at rest and in transit is a must, and you should treat AI outputs with equal care, since an LLM might regurgitate a patient’s info in its answer. Always review and sanitize outputs before they are shared further.
5. Fine-Tune or Choose the Right Model (Healthcare Domain Adaptation)
An LLM will perform much better on healthcare tasks if it’s familiar with medical language and context. You have a couple of options here: fine-tune a general LLM on your healthcare data, or choose a model that’s already been trained or specialized in the medical domain.
Fine-tuning means taking a base model (like an open-source LLaMA or GPT model) and further training it on a corpus of clinical text from your organization or industry. This imparts domain-specific knowledge. For example, understanding that “MICU” means Medical ICU or that “CBC” is a lab test (complete blood count). If you have the resources, fine-tuning can yield excellent results because the model learns the nuances of your data.
The other route is to leverage pre-trained healthcare LLMs. These models have already seen a lot of medical text (research papers, medical Q&As, etc.) and thus “speak the language” of healthcare out of the box. For instance, the Med-PaLM model family was specifically tuned for medical question-answering and significantly improved accuracy on clinical tasks.
Using or fine-tuning such models means your AI will be far better at understanding clinical context than a vanilla model. In short, don’t throw a generic model at highly specialized data. You’ll get shallow or incorrect answers. Instead, invest in adapting the model to the domain.
This step may involve working with your data science team or an external AI partner like Sphere to either fine-tune a model or obtain a specialized one. It’s a critical preparation step to ensure the LLM can truly comprehend the content of your data (medical jargon, clinical reasoning, etc.) and not produce dangerous hallucinations.
6. Establish Governance, Access Controls & Monitoring
Preparing data also involves putting proper governance and oversight in place. As you set up your LLM environment, implement strong access controls: limit who can access the AI system and the underlying data to only those roles that truly need it (principle of minimum necessary access). Use authentication measures like SSO and multi-factor auth for any interfaces that allow querying the model with real data.
Crucially, log every interaction with the LLM when it involves patient data. You need audit trails to know who prompted what and whether any sensitive data was output. Monitoring should be continuous: set up alerts or periodic reviews to catch anomalies (e.g. unusually large data outputs, or someone trying to download model logs). It’s wise to perform regular risk assessments on your AI workflows, much like you would on other critical systems, and to test for things like model leakage (does the model inadvertently spill out actual patient info it saw in training?). Also, incorporate a human-in-the-loop for critical use cases. For example, have clinicians review AI-generated summaries before they go into a patient chart, at least until you build enough trust in the system. By establishing governance from the start, you create a safety net that can catch compliance issues or errors early. Think of this as the “operate” part of preparation: once the data is flowing and the model is running, these controls keep it all within guardrails.
7. Pilot and Validate on Safe Use Cases
Before deploying an LLM on a high-stakes task, pilot it on a contained use case and evaluate thoroughly. Choose an application that offers clear benefit but is manageable in scope. For instance, an internal tool that lets doctors query de-identified patient notes for research insights, or an AI that drafts discharge instructions for review. In the pilot, closely monitor the LLM’s performance: Is it understanding the inputs? Are the outputs accurate and free of PHI? Is it actually saving time or effort compared to the status quo?
Use this phase to iron out any kinks in your data preparation. You might discover you need to further clean certain data fields, or that the model needs additional training on certain types of clinical notes. Also, have your compliance and IT security teams do a thorough review during the pilot: double-check that all security controls are effective, and conduct a simulated audit of the process. The pilot is your dress rehearsal. It’s much better to discover and fix issues in a small-scale test than after full deployment. Only once the pilot results are strong and compliant should you scale up to broader use (e.g. rolling out the LLM assistant to an entire hospital department). This iterative validation approach ensures you haven’t missed anything in preparation, and it builds confidence among stakeholders (both leadership and front-line users) that the AI is ready and safe.
Accelerating Compliance: How Sphere Data Agent Fast-Tracks LLM Readiness
Following these steps, you’ll transition from raw, siloed, sensitive data to an organized, model-ready, and compliant dataset that you can confidently use with LLMs. It’s a journey, often an arduous one if you do it all from scratch. In fact, custom healthcare AI projects frequently take 6–12 months of groundwork before any value is realized. The good news is that you don’t have to go it alone!
This is where specialized tools like Sphere Data Agent for Healthcare come into play, offering a shortcut without cutting corners. Sphere Data Agent is a solution built from the ground up to be “AI-ready” and “compliance-ready” for healthcare organizations. It essentially packages many of the steps we discussed into a streamlined platform. Here’s how it helps:
- Healthcare-native data models: Sphere comes with pre-built data schemas and ML models trained on clinical data and terminology. In practice, this means it already knows the difference between a “chart note” and a “lab result”, or that “HTN” means hypertension. By using pre-trained clinical NLP models, Sphere can interpret and organize your data with an understanding of healthcare context that generic tools lack. This addresses the need for domain adaptation. Your LLM projects start with a foundation that “speaks healthcare” from day one.
- Seamless integration with your tech stack: One of Sphere’s core strengths is that it connects to your existing systems rather than forcing a rip-and-replace. Whether your data lives in an Epic EHR, a Snowflake data warehouse, SQL databases, or CSV files, the Sphere Data Agent can plug in and start ingesting and harmonizing data. It’s built with connectors for common healthcare data sources (from EHRs to claims systems) and supports standards like HL7 and FHIR for interchange. By working with your current infrastructure, Sphere avoids the months of engineering typically needed to integrate siloed systems. This drastically speeds up the data aggregation phase. No need to build custom pipelines for each source.
- Built-in data pipeline and MLOps: Instead of you having to assemble a complex MLOps environment, Sphere provides an end-to-end pipeline for data cleaning, transformation, and loading into AI models. It automates many cleaning tasks (e.g. deduplicating records, normalizing code formats) and can continuously update the dataset as new data comes in. Moreover, Sphere’s platform can manage the training or fine-tuning of models on your data if needed, as well as serve the model for inference, all with monitoring in place. This means you don’t need to build your own ML infrastructure from scratch, saving enormous time and effort. In effect, Sphere acts as the “AI data plumber” connecting raw data to LLM-ready knowledge, with maintenance handled for you.
- From months to weeks – speed of deployment: Because of the above advantages, Sphere Data Agent can collapse the timeline for getting an AI initiative off the ground. In some use cases we enable a fully functional, compliant AI prototype in as little as 4 weeks. By reducing the heavy lifting of data prep and providing a ready-made compliant environment, Sphere lets your team focus on the actual AI use case (e.g. developing a predictive model or setting up an LLM chatbot) rather than plumbing and paperwork.
In sum, Sphere Data Agent for Healthcare is an example of a specialized AI vendor solution that aligns perfectly with the needs we’ve outlined. It’s like having an experienced guide who’s done this “data preparation hike” many times. It comes with maps, tools, and safety gear, so you can reach the summit (AI success) faster and without dangerous missteps.
Unlocking LLM Value Safely and Effectively
Preparing healthcare data for LLMs is a non-trivial endeavor, but it’s absolutely doable with a clear plan (and the right partners). The healthcare organizations that succeed with AI will be those that treat data preparation and governance not as afterthoughts, but as integral parts of the AI project. By investing the effort upfront to integrate siloed data, clean and standardize it, and safeguard patient privacy, you set the stage for LLM applications that can truly shine. Delivering insights without spilling secrets.
The payoff for doing this right is enormous. With a robust, compliant data pipeline in place, you can confidently deploy LLMs to tackle problems like reducing clinician documentation burden (e.g. auto-generating clinical notes), improving decision support (by synthesizing a patient’s history and highlighting risks), enhancing patient engagement (through intelligent chatbots), and much more. Clinicians and executives alike can trust the AI because they know it’s built on complete, high-quality data and operates within a secure, regulated framework. Instead of “pilot purgatory” or lingering fears about HIPAA violations, your team can move fast and innovate with peace of mind.
In the end, preparing your healthcare data for LLMs without breaking compliance is about marrying innovation with responsibility. It’s entirely possible to be bold with new AI technologies and diligent with privacy at the same time. In fact, that’s the only sustainable way forward in healthcare. Do the homework upfront, leverage available expertise, and you’ll find that you can unlock the transformative power of LLMs while keeping patient trust and regulatory compliance intact, a true win-win for your organization and the people it serves.
Sphere’s View: The Strategic Role of Synthetic Data
Many companies are now exploring synthetic data as part of their AI strategy, and with good reason. It’s a practical answer to data access issues, privacy regulations, and the need for scale. But in our experience, success with synthetic data depends less on the hype and more on making the right foundational choices.
From our work at Sphere, we’ve learned that the right partner for synthetic data projects should have three core characteristics:
- A deep understanding of real-world data complexity. It’s not just about generating data that looks plausible. It’s about preserving structure, statistical relevance, and context so your models actually learn what matters.
- Hands-on experience across different use cases. Whether it’s generating synthetic test data for QA environments or training datasets for rare-event classification, the nuances vary, and your partner needs to know how to adapt generation techniques accordingly.
- A pragmatic approach to integration. Synthetic data alone won’t solve broken pipelines or poor model evaluation. The real value comes from knowing how to plug synthetic datasets into broader AI workflows, in a way that supports governance, auditability, and performance.
We don’t see synthetic data as a one-size-fits-all solution. But when it fits, it can be a powerful tool, especially when combined with a clear understanding of where it adds value, where it doesn’t, and how to get the most out of it.
So if you’re looking into synthetic data, don’t just ask whether it’s possible. Ask how it will be used, who will create it, and what standards it needs to meet. That’s where the difference lies, not in the generation itself, but in the thinking behind it.
Frequently Asked Questions
What are the risks of using LLMs in healthcare without proper data preparation?
Using LLMs without preparing healthcare data can result in HIPAA violations, privacy breaches, biased outputs, and dangerous model hallucinations. Sensitive data like PHI must be carefully managed, and siloed systems need to be integrated for LLMs to perform effectively.
Is it possible to use LLMs in healthcare while staying HIPAA-compliant?
Yes, but it requires strict data governance. HIPAA-compliant LLM usage involves de-identification of PHI, secure environments for model deployment, access controls, and audit logging. Solutions like Sphere Data Agent provide frameworks to support this.
How do I know if my healthcare data is ready for LLMs?
Data readiness includes several factors: knowing your PHI, integrating siloed systems, cleaning and normalizing datasets, protecting sensitive data, and validating through pilots. Only after completing these steps should LLMs be introduced.
Can GPT-4 be used for clinical applications?
Not directly. Generic LLMs often lack the domain-specific knowledge required for clinical accuracy. Fine-tuning a general model or using a pre-trained medical LLM like Med-PaLM is recommended for healthcare-specific use cases.
What tools help prepare healthcare data for LLMs faster?
Sphere Data Agent for Healthcare accelerates AI deployment by integrating with EHRs, cleaning and standardizing data, ensuring compliance, and providing healthcare-native model infrastructure. It enables faster, safer LLM adoption across health systems.
How long does it take to prepare data for an LLM in healthcare?
Without the right tools, data preparation can take 6–12 months. However, platforms like Sphere Data Agent can reduce this to just 4–6 weeks by streamlining integration, de-identification, and AI-readiness processes.
What’s the best first step for healthcare organizations exploring LLMs?
Start with a low-risk pilot using de-identified data, such as auto-generating discharge summaries or internal clinical note search. Use this pilot to test your data pipeline, model behavior, and compliance readiness before scaling.
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