Clinically Grounded Evaluation Protocol

• Clinically grounded evaluation protocols are formal methodologies that assess AI/ML systems in healthcare by anchoring criteria in authentic clinical use cases.
• They employ secure data infrastructures, deterministic tool behavior, and clinician-defined gold standards to ensure reproducible and safety-informed evaluations.
• Key applications include comprehensive error analysis, context management, and multi-step reasoning to guide model deployment and ensure clinical relevance.

A clinically grounded evaluation protocol is a formalized methodology for assessing AI and ML systems in healthcare, specifically designed to anchor evaluation criteria, workflows, and metrics in authentic clinical use cases and realities. Such protocols aim to resolve deficiencies of generic or synthetic benchmarks by ensuring that performance claims reflect genuine utility in clinical environments, reliability in edge cases, and robustness to domain-specific challenges. They combine secure infrastructure for data access, deterministic tool usage, rigorous inter-rater reliability metrics, structured workflows, and error decomposition, thereby setting a reproducible and safety-informed standard for model selection, deployment, and monitoring.

1. Foundational Principles and Motivations

Clinically grounded evaluation protocols address critical barriers in medical AI: misalignment between technical benchmarks and clinical relevance, inconsistent gold standards, privacy and security needs, and the profound complexity of real-world medical decision-making. Key motivations include:

• Clinical workflow representation: Protocols derive tested use cases and evaluation tasks directly from authentic clinical events—e.g., infection control team conferences or hospital operations—to guarantee real-world relevance and isolate points of clinical failure (Masayoshi et al., 19 Sep 2025).
• Data privacy and context fidelity: Secure access to electronic health record (EHR) systems incorporates in-hospital proxies (LiteLLM, VPN gateways), database synchronization, and explicit contractual constraints to prevent AI training on protected health information (PHI) (Masayoshi et al., 19 Sep 2025).
• Deterministic tool behavior: Clinical tool invocation is strictly governed by predefined templates, JSON schema enforcement, and domain-derived argument validation to avoid procedural ambiguity and hallucination (Masayoshi et al., 19 Sep 2025).
• Clinician-derived standards: Gold-standard outputs are curated by practicing clinicians and formatted as structured JSON or checklist references (Savkov et al., 2022, Zhou et al., 23 Jul 2025).
• Comprehensive error analysis: Protocols log every step of the model’s reasoning, tool invocation, and output integration, allowing for granular classification of argument, interpretation, format, and context errors (Masayoshi et al., 19 Sep 2025).

2. Protocol Architectures and Implementation Workflows

At the core of modern clinically grounded protocols are agent-based frameworks and modular toolchains supporting secure EHR access, unbiased data flow, and rigorous interaction cycles.

• Multi-modal infrastructure: Systems integrate EHRs synchronized into hospital data warehouses via SQL/ODBC, with proxy gateways managing connections to remote LLMs (e.g., GPT-4.1 via Azure OpenAI) (Masayoshi et al., 19 Sep 2025).
• Agent configuration: LLM agents are set up using LangGraph ReAct templates to guarantee structured Reasoning-Action-Observation loops with JSON schema-enforced outputs, promoting reproducibility and reducing output hallucination (Masayoshi et al., 19 Sep 2025).
• Custom tool ecosystem: MCP servers implement Python-based modules for clinical data retrieval, such as patient_basic_info, lab_results, bacteria_results, antibiotics_treatment, and domain-specific calculators (e.g., Cockcroft–Gault clearance) (Masayoshi et al., 19 Sep 2025).
• Workflow orchestration: Each evaluation run mirrors stepwise clinical retrieval: user prompt ingestion, LLM agent tool selection, data warehouse query execution, observation parsing, iterative tool calls, and final output consolidation (Masayoshi et al., 19 Sep 2025).

3. Task Design, Use-Case Derivation, and Complexity Stratification

Defining evaluation tasks is grounded in retrospective analytics of genuine clinical cases and stratified according to the complexity of reasoning required.

• Cohort selection: Patient populations are sourced from real cases—e.g., MRSA bacteremia treated with vancomycin—presented at clinical conferences (Masayoshi et al., 19 Sep 2025).
• Task taxonomy: Tasks are grouped as simple (single-tool calls, e.g., latest weight extraction or antibiotic enumeration) versus complex (multi-step reasoning, such as time-dependent therapy calculations or Cockcroft–Gault clearance estimation) (Masayoshi et al., 19 Sep 2025).
• Task templates: Bilingual prompt templates with embedded patient, date, and schema exemplars are deployed to standardize input across linguistic and clinical contexts (Masayoshi et al., 19 Sep 2025).
• Exclusion logic: Protocols exclude cases where clinical variables confound interpretation (e.g., patients on dialysis for CCR computation or lacking target antibiotics) (Masayoshi et al., 19 Sep 2025).

4. Evaluation Methodology, Gold Standard Construction, and Quantitative Metrics

Systematic evaluation encompasses prompt execution logging, gold-standard output collection, and adoption of established statistical measures.

• Logging and comparison: Each protocol run logs prompts, invoked tool names/arguments, raw tool outputs, and final JSON results, compared to physician-extracted gold standards using exact string matches and metric-specific list comparisons (Masayoshi et al., 19 Sep 2025).
• Metrics:
  • Accuracy: Proportion of runs matching the gold standard exactly

  $$\mathrm{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$$ - Dice coefficient: Quantifies list agreement:

  $\mathrm{Dice}(A,B) = \frac{2|A \cap B|}{|A| + |B|}$ - Cohen’s $\kappa$: Inter-rater agreement across agents and clinicians:

  $\kappa = \frac{p_o - p_e}{1 - p_e}$

where $p_o$ is observed agreement, $p_e$ is chance agreement (Masayoshi et al., 19 Sep 2025).

• Error decomposition: Result logs are analyzed for argument errors (incorrect parameter or window specification), interpretation errors (mis-parsing outputs), and context-window overruns (excessive data volume exceeding model capacity) (Masayoshi et al., 19 Sep 2025).

5. Empirical Results, Performance Analysis, and Practical Challenges

Empirical deployments of clinically grounded protocols reveal high accuracy on simple retrieval but pinpoint critical limitations in complex clinical reasoning.

Task	Metric	Performance
body_weight, antibiotics, culture_history_species	Accuracy	100% across all patients and runs
lab_data	Accuracy	≈ 93.8% (Argument Errors in date window)
culture_history_detection	Dice	0.98–1.00
calculate_ccr	Accuracy	≈ 100% (one prompt error in Japanese)
culture_neg_abx	Accuracy	40–60% (Interpretation Errors)

• Error modes: Most failures arise in multi-step reasoning tasks, attributable to mis-specified arguments or misinterpretation of complex, multi-dimensional outputs (Masayoshi et al., 19 Sep 2025).
• Context management: Lengthy prescription and laboratory histories may exceed model context windows, necessitating context extension or auxiliary summarization (Masayoshi et al., 19 Sep 2025).
• Stability: Each prompt is repeated multiple times to assess output variability and reliability (Masayoshi et al., 19 Sep 2025).
• Security and privacy: In-hospital proxy infrastructure is essential for data protection, user management, and prevention of PHI leakage in model training (Masayoshi et al., 19 Sep 2025).

6. Recommendations, Extensions, and Future Directions

Clinically grounded protocols point to best-practice recommendations and several domains for future research.

• Infrastructure: Maintain secure, in-hospital gateways, deterministic tool definitions, and strict output schemas to guarantee controlled execution and error traceability (Masayoshi et al., 19 Sep 2025).
• Workflow design: Source use cases directly from real clinical decision points; stratify tasks to distinguish retrieval from genuine reasoning (Masayoshi et al., 19 Sep 2025).
• Error logging: Comprehensive Thought/Action/Observation logging enables precise diagnosis of error sources and prompts informed refinements (Masayoshi et al., 19 Sep 2025).
• Context management: Specify retrieval windows to minimize unnecessary data; consider multi-agent architectures for condensing elongated histories (Masayoshi et al., 19 Sep 2025).
• Extensions: Broaden protocol to include LLM reasoning, fully generative report synthesis, and direct clinical decision support; conduct prospective trials to quantify impact on workflow efficiency and patient outcomes (Masayoshi et al., 19 Sep 2025).
• Generalizability: Abstract the MCP layer to enable cross-institutional deployment by adapting to heterogeneous data-source interfaces (Masayoshi et al., 19 Sep 2025).

7. Protocol Significance and Field-Wide Impact

The adoption of clinically grounded evaluation protocols sets a new benchmark for the deployment, monitoring, and assessment of AI agents in medicine.

• Blueprint for hospital AI integration: By combining secure data infrastructures, authentic clinical task sets, robust metrics, and persistent error analysis, these protocols enable reproducible and actionable evaluation for regulatory, institutional, and safety oversight (Masayoshi et al., 19 Sep 2025).
• Highlighting reasoning bottlenecks: While retrieval tasks can achieve near-perfect performance, subtleties of multi-step reasoning, temporal dependency, and argument specification remain formidable challenges, driving next-generation research on clinical reasoning models (Masayoshi et al., 19 Sep 2025).
• Standardization and reproducibility: Structured, schema-driven output expectation and logging ensure transparent model evaluation and facilitate peer comparison across systems and institutions (Masayoshi et al., 19 Sep 2025).

Clinically grounded evaluation protocols thus operationalize the translation of AI in medicine from bench to bedside, ensuring that performance metrics reflect genuine clinical impact and reliability.