The Future of Safe, Scalable AI in Care – Becker’s Hospital Review

Artificial intelligence is often positioned as a silver bullet for solving healthcare’s capacity challenges—from managing patient volume to automating administrative work and supporting diagnosis. But too often, AI tools are black boxes, raising real concerns about transparency, accuracy, and safety in clinical settings.

This is particularly challenging with the rise of one of the most popular forms of AI. Large language models (LLMs) are impressive in their ability to generate conversational responses to a broad range of questions. However, due to their broad nature and lack of transparency, they are prone to producing hallucinations—factually incorrect or misleading information. In a field as sensitive as healthcare, there is no room for error.

To harness AI’s potential while minimizing risk, health systems need more than one-size-fits-all models. They need an AI architecture purpose-built for clinical use to apply the right kind of AI to the right task, supported by clinical logic. That means moving beyond generic models toward a clinically validated, end-to-end framework that can scale safely and effectively across the full care journey.

Three Types of AI—and Why Healthcare Needs All of Them

No single type of AI is equipped to meet the full spectrum of clinical and operational needs in healthcare. To deliver safe, scalable, and effective care, health systems need a multi-model approach—one that combines the strengths of three distinct types of AI, each applied where it performs best.

This approach boosts efficiency while raising the bar for safety, clinical accuracy, and patient experience—without requiring additional staff.

Powering the Entire Care Journey

In addition to leveraging multiple forms of AI, effective implementation starts at the very first patient interaction and continues through diagnosis, treatment, and follow-up, and powers this continuum in three key ways:

1. Smarter Access at the Digital Front Door®

From the moment a patient visits a health system’s site, conversational AI answers general questions while natural language processing (NLP) streamlines navigation with symptom checking, scheduling, bill pay, and more. If an inquiry is clinical, self-service experiences should seamlessly transition from LLM and NLP to a clinical expert system, ensuring patients receive appropriate guidance and are directed to the right level of care. By guiding patients to the right care at the right time, healthcare organizations can accelerate acquisition while reducing leakage and call center overhead.

2. Faster, Consumer-Grade Care Delivery

Effective AI also supports triage and accelerates care. Structured, clinical-based questionnaires powered by a clinical expert system gather symptom and medical history data via adaptive interviews, producing structured notes that providers can review asynchronously. At Fabric, this model enables providers to make diagnoses and treatment decisions in as little as 89 seconds for asynchronous virtual care—up to 10 times faster than traditional visits while maintaining safety or quality.

This approach increases patient throughput, drives visit volume, and expands clinical capacity without requiring additional staff.

3. Continuous, Automated, and Personalized Patient Engagement

Effective AI also powers experiences beyond the visit. It automates follow-ups, delivers personalized check-ins, and proactively identifies patients at risk of falling through the cracks. Automated engagement, powered by conversational AI, improves treatment adherence, reduces no-shows, surfaces potential issues early, and drives patient loyalty—without additional burdens on staff.

Introducing Fabric Hybrid AI

This comprehensive multimodal approach is the foundation of Fabric Hybrid AI—a unified system integrating multiple AI models across the care continuum. This approach enhances patient and member experiences, reduces the administrative burden on clinicians and health systems, and drives both patient acquisition and long-term loyalty, all while relieving pressure on clinical teams.

Fabric Hybrid AI brings together an LLM, NLP, and a clinical expert system—each applied precisely where it performs best. Instead of relying on a single AI model to manage every interaction, Hybrid AI dynamically selects the right intelligence based on patient intent, context, and clinical complexity.

Built for Safety and Scale

Above all else, AI built for healthcare needs to be safe and effective. Fabric’s clinical expert system is reviewed and updated by licensed clinicians and overseen by a Clinical Quality Advisory Council composed of over 18 CMOs and medical directors from top U.S. health systems. This ensures every AI-driven interaction aligns with best practices and clinical standards.

A study conducted by Baylor Scott & White confirmed that Fabric’s clinical expert system is as effective in diagnosing patients as face-to-face care.

The Future of AI in Healthcare