Enterprise AI Analysis: Deconstructing ChatGPT's Role in Outpatient Triage

An In-Depth Look at AI Consistency for Healthcare and Custom Solutions by OwnYourAI.com

Executive Summary: Promise Meets Practicality

The study meticulously evaluates the performance of ChatGPT-3.5 and ChatGPT-4.0 in providing outpatient triage recommendations. The core objective was to measure the consistency of the AI's advicea non-negotiable requirement for any medical tool. The researchers tested both the internal consistency (does the same model version give the same answer to the same question?) and the between-version consistency (do different versions agree?).

The key takeaways for any organization considering LLM integration are profound:

• Improved but Imperfect Consistency: ChatGPT-4.0 showed significantly better internal consistency than its predecessor, a positive sign of model evolution. However, even the improved model was not perfectly consistent, raising red flags for unregulated use.
• The Versioning Dilemma: A significant lack of agreement between ChatGPT-3.5 and 4.0 highlights a major enterprise risk. Relying on a public, ever-changing model means that system behavior can shift unpredictably with each update, undermining procedural stability.
• Completeness vs. Consistency Trade-off: Counterintuitively, the older ChatGPT-3.5 model provided more 'complete' responses, even if they were less consistent. This illustrates the complex trade-offs involved in LLM performance that enterprises must navigate.

This research underscores a critical message: while generic LLMs demonstrate potential, they lack the stability and predictability for mission-critical tasks. The path to successful AI integration in healthcare lies in custom-developed, fine-tuned, and rigorously validated solutions.

Deconstructing the Data: A Look at LLM Performance Metrics

To understand the enterprise implications, we must first translate the study's academic findings into business-centric performance indicators. The data reveals critical insights into the reliability of using generic LLMs for specialized tasks.

Overall Response Quality and Completeness

The study first analyzed the basic viability of the responses. A significant portion of AI-generated outputs were either invalid or incomplete, a primary concern for any automated workflow.

Enterprise Insight: An incompleteness rate of 14.4% for ChatGPT-4.0 is unacceptable for an automated triage system. This points to the need for robust pre-processing and output validation layers in any custom AI solution, ensuring every response adheres to a strict data schema before being presented to a user or healthcare professional.

Within-Version Consistency: The Reliability Test

This metric answers the question: "If I ask the same thing multiple times, will I get the same advice?" For medical applications, the answer must be a resounding "yes." The study found that while ChatGPT-4.0 is a clear improvement, significant inconsistencies remain.

Consistency of the Top Recommendation

Focusing on just the top-recommended medical department, the consistency improves, but is still not perfect. This is a critical metric, as it's the most likely advice a patient would follow.

Enterprise Insight: A nearly 30% chance of receiving a different top recommendation from ChatGPT-4.0 on repeated queries is a critical failure point. Custom AI solutions mitigate this by using techniques like temperature scaling (setting it to 0 for deterministic outputs) and fine-tuning on specific medical protocols to enforce consistency.

Between-Version Consistency: The Stability Risk

This analysis reveals the danger of building enterprise systems on public models. The low level of agreement between versions means a simple model update from the provider could break established clinical workflows overnight.

Enterprise Insight: With nearly 55% of responses showing minimal to no agreement (scores 0 and 1), the platform risk is enormous. Businesses need version-controlled, privately hosted models. This ensures that updates are tested, validated, and deployed on the enterprise's schedule, not the provider's, maintaining operational stability and regulatory compliance.

The Enterprise AI Roadmap: From Unstable Generalist to Reliable Specialist

The study's findings are not an indictment of AI in healthcare, but a clear signal that a specialized approach is necessary. Generic models are a starting point, not the final product.

The Performance Paradox Matrix

The research highlights a classic engineering trade-off. Enterprises must balance multiple performance vectors, and off-the-shelf models force a choice. Custom solutions allow you to optimize for the quadrant that matters most to your application.

A Phased Implementation Strategy for Healthcare AI

Leveraging these insights, OwnYourAI.com advocates for a structured, four-phase approach to developing a custom AI triage solution that is safe, reliable, and effective.

Calculating the Business Value: An Interactive ROI Estimator

Beyond clinical accuracy, the business case for AI-assisted triage is compelling. It centers on enhancing operational efficiency, reducing staff workload, and improving patient flow. Use our interactive calculator to estimate the potential ROI for your organization.

Conclusion: The Case for Custom Enterprise AI

The research by Liu et al. provides invaluable, data-driven proof of a core principle we champion at OwnYourAI.com: general-purpose AI is not enterprise-ready AI. While ChatGPT's potential is undeniable, its inherent inconsistencies and the unpredictability of version updates make it unsuitable for high-stakes, regulated environments like healthcare triage out of the box.

The path forward is clear. Organizations must invest in custom solutions that are:

• Consistent & Deterministic: Engineered for predictable and repeatable outputs.
• Fine-Tuned & Domain-Specific: Trained on your proprietary data and protocols for unparalleled accuracy.
• Secure & Private: Hosted in your environment to protect sensitive data and ensure compliance.
• Version-Controlled & Stable: Managed and updated on your terms to prevent workflow disruption.