When tasked with developing an AI-driven eligibility process for the US healthcare system, one of our crucial decisions was determining the appropriate architectural approach. Understanding the distinction between different AI implementation frameworks was essential for making informed choices that would serve our needs.
Understanding AI Implementation Frameworks
In AI systems, the term “agent” carries various interpretations. While some view agents as fully autonomous systems capable of independent operation over extended periods, others see them as more structured implementations following predetermined paths. Following Anthropic’s guidelines, we can categorize AI implementations into three main frameworks:
- Simple LLM Calls: Direct prompt-response interactions for straightforward tasks
- Workflows: Orchestrated systems where LLMs and tools follow predefined code paths
- Agents: Systems where LLMs autonomously direct their processes and tool usage
Making the Right Choice
The golden rule in LLM-based applications is to opt for the simplest solution that meets the requirements. This often means avoiding unnecessary complexity and only scaling up when more straightforward solutions prove insufficient. While agentic systems can offer enhanced task performance, they typically come with increased latency and costs.
For well-defined tasks, workflows provide predictability and consistency. Agents, however, excel when flexibility and autonomous decision-making are essential. In other cases, optimizing single LLM calls with proper retrieval and contextual examples suffices.
Our Implementation Decisions
In our healthcare eligibility system, we faced two distinct challenges that required different approaches:
- Communication Interface: Our system integrates an agent-based approach to handle interactions with patients, clinics, and insurance companies. Depending on the communication context, the agent determines the optimal channel—email, SMS, WhatsApp, or phone calls—to request or validate information. We utilize an AI-powered calling service (Bland.ai) for voice interactions that autonomously makes calls, asks for relevant details, and transcribes the conversation. The extracted data is then processed using a language model (LLM) to structure and update the necessary records. This approach ensures efficient, context-aware communication while minimizing manual intervention.
- Eligibility Processing: Here, we chose a workflow-based implementation. The eligibility determination process follows specific, well-defined rules and regulations. A workflow approach allowed us to:
- Maintain strict control over the process flow
- Reduce the risk of hallucination or incorrect interpretations
- Ensure consistency in decision-making
- Implement clear audit trails
This hybrid approach exemplifies how different AI implementation patterns can be combined effectively within a single system, each serving its optimal use case. The key is not to build the most sophisticated system possible but to build the right one for each specific need.
Our experience reinforces the importance of maintaining design simplicity while ensuring transparency in system operations. By choosing between agents and workflows based on specific requirements, we created a system that balances flexibility with reliability in serving our healthcare eligibility needs.
Understanding the Problem Domain: A Data-First Approach
Before diving into architectural decisions or choosing AI implementation patterns, we need to understand the importance of the healthcare eligibility domain. As the saying goes, ‘A problem well-stated is half-solved.’ This principle guided our initial approach.
Data Discovery Phase
Our journey began with data exploration, not with AI model selection. We identified Synthea as a valuable source of synthetic Electronic Health Records (EHR) data. This synthetic data provided a comprehensive view of the healthcare ecosystem, including:
- Patient Demographics and History
- Claims and Transaction Records
- Medical Observations and Conditions
- Provider and Organization Information
- Insurance and Payer Details
- Treatment and Medication Records
- Care Plans and Procedures
Data Modeling for Practical Implementation
After analyzing these comprehensive datasets, we focused on creating a simplified yet realistic model to meet our eligibility determination needs. We designed a streamlined database schema that captured the essential elements while maintaining practical utility.
This approach reinforces a crucial lesson in AI implementation: start with understanding your data and problem domain. AI models, whether simple LLM calls, workflows, or agents, are just tools in your development toolkit. Just as a carpenter wouldn’t start by choosing between a hammer and a saw before understanding what they’re building, we shouldn’t begin by deciding on AI implementation patterns before thoroughly understanding the problem space.
The quality and relevance of your data, along with a deep understanding of your problem domain, should drive your architectural decisions. In our case, this data-first approach helped us:
- Identify the key data points needed for eligibility determination
- Understand the relationships between different healthcare entities
- Recognize patterns in claims processing and approval
- Define clear business rules based on real-world scenarios
This foundation proved invaluable when we later moved to selecting and implementing appropriate AI patterns for different aspects of our system. Stay tuned for part two of this series, where we’ll dive deeper into the solution process, tools and frameworks, and challenges we had to tackle along the way. And of course, contact us to help you build AI solutions for healthcare.