{"id":20923,"date":"2025-03-06T11:00:04","date_gmt":"2025-03-06T11:00:04","guid":{"rendered":"https:\/\/www.equalexperts.com\/?p=20923"},"modified":"2025-03-27T12:53:56","modified_gmt":"2025-03-27T12:53:56","slug":"engineering-genai-systems-a-systematic-approach-through-healthcare-documentation-part-2","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/data-ai\/engineering-genai-systems-a-systematic-approach-through-healthcare-documentation-part-2\/","title":{"rendered":"Engineering GenAI systems: A systematic approach through healthcare documentation (Part 2)"},"content":{"rendered":"

“Look how well it extracted the patient\u2019s history!”<\/p>\n

The physician\u2019s enthusiasm was palpable as they reviewed our initial prototype. At that moment, I witnessed a common pattern in enterprise AI implementations: the seductive power of early demos overshadowing the engineering rigour needed for production systems. While our proof-of-concept (discussed in Part 1<\/a>) demonstrated the foundational capability of large language models to parse clinical conversations, the path from a promising demo to a production-ready system requires sophisticated validation infrastructure.<\/p>\n

Continuing the systematic approach from Part 1, we now face a core engineering task: building an evaluation framework robust enough to stress-test our solution across diverse clinical scenarios. Each variation introduces potential failure modes that must be systematically validated, from edge cases in medical terminology to speciality-specific documentation patterns.<\/p>\n

Our evaluation framework comprises 3 components: Golden datasets that accurately represent the diversity and complexity of real-world clinical documentation; Comprehensive metrics that quantify technical and clinical quality aspects; and Evaluations, often called Evals, which validate system behaviour.<\/p>\n

Let\u2019s see how these components work together in practice.<\/p>\n

Building a golden dataset for SOAP note generation<\/h2>\n

At the core of any robust evaluation framework lies the golden dataset \u2014 a curated collection of inputs and their corresponding reference outputs that serves as ground truth for system validation. While the concept sounds straightforward, creating a reliable golden dataset often reveals a complex interplay between theoretical requirements and practical constraints.<\/p>\n

In our medical documentation system, we identified a solid foundation: a diverse set of patient-doctor dialogues. However, the critical missing piece was the corresponding reference SOAP notes \u2014 the \u201cgold standard\u201d outputs that would validate our system\u2019s performance \u2014 generating these reference notes required (again) collaboration with a physician due to the domain expertise needed.<\/p>\n

Rather than pursuing the traditional (and time-intensive) route of manual annotation, I developed a hybrid approach that leveraged AI capabilities. I used a simple crafted prompt to generate initial SOAP notes drafts:<\/p>\n

\"The<\/p>\n

combined with a structured JSON schema:<\/p>\n

\"An<\/p>\n

for LLM output formatting (using structured outputs from OpenAI), provided a low-effort starting point for physician review.<\/p>\n

While production systems typically demand hundreds of validated examples, we strategically limited our initial dataset to 25 entries for the sake of my exploration. However, even with this concentrated scope, we maintained rigorous selection criteria:<\/p>\n

    \n
  1. Representational diversity: Included varied dialogue patterns and medical conditions to stress-test the system\u2019s adaptability<\/li>\n
  2. Edge case coverage: Deliberately incorporated complex scenarios that challenge common assumptions<\/li>\n<\/ol>\n

    The process revealed why golden datasets, despite their critical importance, often become a bottleneck in AI system development. They demand a rare combination of domain expertise, technical precision, and substantial time investment \u2014 it took a few hours to review the 25 entries, even with the synthetic drafts. Yet, this foundation proves invaluable when scaling systems from promising prototypes to production-ready solutions.<\/p>\n

    This golden dataset would serve multiple critical functions in our use case. The question became: how do we leverage this carefully curated foundation to build a comprehensive evaluation framework? This leads us to our next challenge: implementing systematic evaluation strategies.<\/p>\n

    Evals<\/h2>\n

    Evals are fundamentally tests, but they represent an evolution in testing methodology driven by the unique challenges of LLM-based systems. While traditional software tests verify deterministic behaviours against fixed expectations, Evals handle language model outputs’ inherent variability and contextual nature. There are two types of evals:<\/p>\n

    Deterministic Evals:\u00a0these provide unambiguous pass\/fail signals for structured fields:<\/p>\n