John Snow Labs Archives - | Carahsoft| Carahsoft

Healthcare organizations are under immense pressure to shrink margins, tighten regulations, improve patient expectations and utilize increasingly complex data environments. While generative artificial intelligence (GenAI) has emerged as a powerful tool, most healthcare systems still struggle to move from experimentation to measurable outcomes. Leaders are asking the same questions: Where do we start? How do we ensure security and compliance? How fast should the Return on Investment (ROI) appear?

The answer is not simply selecting a model, it is building a strategy and infrastructure that transforms AI from a promising pilot into an enterprise engine for clinical, operational and financial improvement.

Start With High-Impact Use Cases that Deliver Early ROI

The path to operationalizing GenAI begins with use cases that are narrow enough to implement quickly, but meaningful enough to prove value. Start where measurable gains are most attainable, such as document processing, contract review, claims analysis, compliance workflows and call center optimization.

One of the strongest early candidates is Protected Health Information (PHI) de-identification, where AI can accelerate research access while protecting privacy. Many organizations are also applying GenAI to claims review, using models to flag missing attachments, coding inconsistencies or errors that commonly drive costly denials. With first-pass denial rates hovering in the 17–25% range industry-wide, automating this analysis can generate immediate financial return.

These targeted wins build executive confidence, secure budget and create organizational momentum, which is critical before expanding to more complex clinical or patient-facing scenarios.

Build Trust by Grounding the Model in Your Own Data

Accuracy and trust determine whether healthcare AI is adopted or ignored. General-purpose models are not sufficient for healthcare, where language is deeply nuanced and context dependent. Instead, organizations should ground GenAI in their own governed data sources, such as Electronic Health Records (EHRs), Customer Relationship Management (CRM) platforms, care summaries, research documents or internal policies.

To achieve this, many leaders are adopting Retrieval-Augmented Generation (RAG) with vector databases, which allows models to pull precise information from internal systems in real time. Vector databases are a foundational accelerator, enabling faster, more accurate retrieval across structured and unstructured data. This approach delivers three business advantages:

Higher accuracy and confidence in model responses
Stronger control of PHI and sensitive data
Traceability, which is essential for audits, appeals and clinical validation

Grounding the model in an organization’s own data turns GenAI from a creative tool into a trusted operational system.

Use a Secure Multicloud Strategy to Reduce Risk and Increase Agility

John Snow Labs, Operationalizing Healthcare GenAI blog, embedded image, 2025

To operationalize GenAI responsibly, healthcare organizations should design for security,compliance and flexibility from day one. When separating PHI and non-PHI workloads, a multicloud strategy helps healthcare organizations:

Isolate sensitive data to minimize breach impact and simplify governance
Reduce lock-in risk and leverage the strengths of different cloud platforms
Tap into more innovative options, since each cloud offers unique AI tooling
Optimize cost and performance by matching workloads to the right environment

Multicloud design also supports stronger compliance postures by enabling auditability, identity controls, monitoring and bias/hallucination safeguards, all of which must be proven to regulators and accrediting bodies.

Avoid “Pilot Purgatory” and Build a Path to Production

Many healthcare AI programs fail not because the technology underperforms, but because the organization never assigns ownership or a path to scale. To prevent “pilot purgatory,” short-term projects that drag on without measurable outcomes, organizations should:

Create a defined production roadmap before the pilot begins
Empower a cross-functional AI Center of Excellence (COE) to own outcomes
Secure both clinical and administrative stakeholders
Treat GenAI as an enterprise capability, not a one-off project

This shift enables the same investment to support multiple use cases, expanding impact while lowering cost per interaction over time.

Continuously Measure, Optimize and Expand

An operational GenAI program is never “set it and forget it.” It is important to continuously track Key Performance Indicators (KPIs) to guide optimization and justify expansion. Recommended KPIs include:

Cost per interaction
Accuracy and confidence
Time saved per task or workflow
Time to response (latency and model speed)
User satisfaction (providers, staff and patients)

By evaluating these metrics regularly, healthcare organizations can expand from early wins to enterprise scale, from research and development to patient support, revenue cycle, compliance and beyond.

Align People, Data and Infrastructure For AI Success

Technology alone is not the determining factor of AI success in the healthcare space, alignment is. Success requires a shared vision from leadership, responsible data groundwork, a secure multicloud foundation and continuous measurement to maintain trust and value. With the right approach, GenAI can improve patient satisfaction, strengthen trust, accelerate research and innovation, reduce administrative burden and deliver measurable ROI in weeks over years.

Carahsoft and John Snow Labs help healthcare leaders accelerate this journey, combining secure infrastructure, domain-specific healthcare AI and proven deployment models. To explore how your organization can operationalize GenAI safely and effectively, watch the full webinar, “Lessons Learned from Harnessing Healthcare Generative AI in a Hybrid Multi-Cloud Environment.”

Carahsoft Technology Corp. is The Trusted Government IT Solutions Provider, supporting Public Sector organizations across Federal, State and Local Government agencies and Education and Healthcare markets. As the Master Government Aggregator for our vendor partners, including John Snow Labs, we deliver solutions for Geospatial, Cybersecurity, MultiCloud, DevSecOps, Artificial Intelligence, Customer Experience and Engagement, Open Source and more. Working with resellers, systems integrators and consultants, our sales and marketing teams provide industry leading IT products, services and training through hundreds of contract vehicles. Explore the Carahsoft Blog to learn more about the latest trends in Government technology markets and solutions, as well as Carahsoft’s ecosystem of partner thought-leaders.

Leveraging Generative AI

When implementing generative AI, healthcare organizations should consider areas to invest in, such as employee productivity or supporting healthcare providers in patient care.

Key factors to consider when leveraging generative AI:

Use case identification: Identify a challenge that generative AI can solve, but do not assume it will address all problems. Evaluate varying levels of burden reduction across use cases to determine its value.

Data: Ensure enough data is available for generative AI to provide better services. Identify inefficiencies in manual tasks and ensure data compliance, as AI results depend on learning from data.

Responsible AI: Verify that the solution follows responsible AI guidelines and Federal recommendations. Focus on accuracy, addressing hallucinations where incorrect information is provided such as responses that are grammatically correct but do not make sense or are outdated.

Total cost of ownership: Generative AI is expensive, especially regarding hardware consumption. Consider if the same problem can be solved with more optimized models, reducing the need for costly hardware.

Harnessing LLMs for Healthcare

John Snow Labs Healthcare with Generative AI Blog Embedded Image 2024

Natural language processing (NLP) has advanced significantly in recent decades, heavily relying on AI to process language. Machine learning, a core concept of AI, enables computers to learn from data using algorithms and draw independent conclusions. Large language models (LLMs) combine NLP, generative AI and machine learning to generate text from vast language datasets. LLMs support various areas in healthcare, including operational efficiency, patient care, clinical decision support and patient engagement post-discharge. AI is particularly helpful in processing large amounts of structured and unstructured data, which often goes unused.

When implementing AI in healthcare, responsible AI and data compliance are crucial. Robustness refers to how well models handle common errors like typos in healthcare documentation, ensuring they can accurately interpret how providers write and speak.

Fairness, especially in addressing biases related to age, origin or ethnicity, is also critical. Any AI model must avoid discrimination; for instance, if a model’s accuracy for female patients is lower than for males, the bias must be addressed. Coverage ensures the model understands key concepts even when phrasing changes.

Data leakage is another concern. If training data is poorly partitioned, it can lead to overfitting, where the model “learns” answers instead of predicting outcomes from historical data. Leakage can also expose personal information during training, raising privacy issues.

LLMs are often expensive, but healthcare-specific models outperform general-purpose ones in efficiency and optimization. For example, healthcare-specific models have shown better results than GPT-3.5 and GPT-4 in tasks like ICD-10 extraction and de-identification. Each model offers different accuracy and performance depending on the use case. Organizations must decide whether a pre-trained model or one trained using zero-shot learning is more suitable.

Buy Versus Build

When it comes to the “buy versus build” decision, the advantage of buying is the decreased time to production compared to building from scratch. Leveraging a task-specific medical LLM that a provider has already developed costs a healthcare organization about 10 times less than building their solution. While some staff will still be needed for DevOps to manage, maintain and deploy the infrastructure, overall staffing requirements are much lower than if building from the ground up.

Even after launching, staffing requirements are not expected to decrease. LLMs continuously evolve, requiring updates and feature enhancements. While in production, software maintenance and support costs are significantly lower—about 20 times less—than trying to train and maintain a model independently. Many organizations that build their healthcare model quickly realize training is extremely costly in terms of hardware, software and staffing.

Optimizing the Future of Healthcare

When deciding on healthcare AI solutions, especially with the rise of generative AI, every healthcare organization should assess where to begin by identifying their pain points. They must ensure they have the data required to train AI models to provide accurate insights. Healthcare AI is not just about choosing software solutions; it is about considering the total cost of ownership for both software and hardware. While hardware costs are expected to decrease, running LLMs remains a costly endeavor. If organizations can use more optimized machine learning models for specific healthcare purposes instead of LLMs, it is worth considering from a cost perspective.

Learn how to implement secure, efficient and compliant AI solutions while reducing costs and improving accuracy in healthcare applications in John Snow Labs’ webinar “De-clutter the World of Generative AI in Healthcare.”

Discover how John Snow Labs’ Medical Chatbot can transform healthcare by providing real-time, accurate and compliant information to improve patient care and streamline operations.