Clinical Trial Recruitment with LLMs: Structured Evaluation Guide for Singapore Hospitals

Clinical Trial Recruitment with LLMs: Structured Evaluation Guide for Singapore Hospitals

Clinical trial recruitment remains one of the most operationally challenging bottlenecks in hospital research. A recent study demonstrated that large language models (LLMs) can identify eligible ICU patients for ventilator-induced diaphragm dysfunction trials [1], joining a growing list of clinical applications—from urine drug test sign-outs [2] to mental health screening [3]. But two preprints published in the last week reveal a critical gap: LLMs can achieve 60–70% diagnostic accuracy while exhibiting fundamentally inconsistent clinical reasoning [4][5]. For Singapore hospitals evaluating LLM deployment in trial recruitment, consent workflows, or eligibility screening, this matters more than benchmark scores suggest.

Key takeaways

• Accuracy alone masks reasoning instability: LLMs reach 60–70% diagnostic accuracy on complex cases, but structured graph analysis reveals they lack consistent clinical reasoning pathways—a critical risk for trial eligibility decisions [5]
• Fluency creates evaluation illusions: Well-structured LLM explanations appear clinically convincing even when the final conclusion is incorrect, demanding human-in-the-loop evaluation frameworks [4]
• Trial recruitment is a high-stakes use case: Unlike ambient documentation, eligibility screening directly impacts patient safety, research validity, and regulatory compliance—requiring structured reasoning evaluation before deployment
• Singapore hospitals need domain-grounded evaluation: Generic benchmark performance does not predict reliability in local clinical workflows; structured evaluation with clinical reasoning graphs and human review loops is essential
• Governance precedes deployment: LLM trial recruitment systems require explicit reasoning audit trails, clinician override mechanisms, and continuous monitoring—not just accuracy thresholds

Why trial recruitment exposes LLM reasoning gaps

Clinical trial recruitment in Singapore hospitals involves multi-step reasoning: parsing inclusion/exclusion criteria, interpreting lab values in clinical context, assessing contraindications, and documenting eligibility rationale for research ethics boards. The recent ICU trial recruitment study [1] demonstrated feasibility, but did not evaluate reasoning consistency—the focus of two new preprints.

Researchers at multiple institutions introduced clinical reasoning graphs: structured representations extracted from free-text LLM diagnostic traces using a domain-grounded ontology with 5 node types (symptoms, findings, diagnoses, tests, treatments) and 7 edge types (causal, temporal, contradictory relationships) [5]. When applied to complex clinical cases, the analysis revealed a troubling pattern: LLMs achieve moderate diagnostic accuracy but exhibit competence without consistency. The same model produces different reasoning pathways for identical cases, contradicts its own intermediate conclusions, and generates clinically implausible causal chains—even when the final diagnosis happens to be correct.

For trial recruitment, this instability is unacceptable. An LLM might correctly identify a patient as eligible for a sepsis trial but justify the decision with contradictory reasoning about infection timing or organ dysfunction criteria. If a research coordinator or ethics board reviews the rationale, the inconsistency undermines trust. If no human reviews the reasoning—only the binary eligibility decision—the system becomes a black box that occasionally fails in unpredictable ways.

The fluency illusion: why human review is not enough

A parallel preprint introduced CLExEval, a human-in-the-loop framework specifically designed to evaluate LLM clinical reasoning [4]. The core insight: fluent and well-structured explanations can appear clinically convincing even when the final diagnosis is incorrect. This "evaluation illusion" means that simply asking a clinician to review LLM output is insufficient—the review process itself must be structured.

CLExEval decomposes clinical reasoning into explicit steps: differential diagnosis generation, evidence synthesis, hypothesis ranking, and final conclusion. Human evaluators assess each step independently, rather than judging the overall narrative. Early results show that LLMs frequently produce coherent-sounding explanations that fail on specific reasoning steps—contradicting lab values, ignoring temporal sequences, or conflating similar conditions.

For Singapore hospitals, this has immediate implications for clinical AI services design. If you deploy an LLM for trial recruitment:

1. Do not rely on clinician spot-checks of final eligibility decisions—the fluency illusion means errors will be missed
2. Implement structured review workflows that force evaluators to assess intermediate reasoning steps (e.g., "Did the model correctly interpret the creatinine trend?" not "Does this eligibility summary sound reasonable?")
3. Log reasoning traces, not just outputs—you need audit trails that expose contradictions and implausible causal chains

This aligns with the governance-first approach we've advocated in health data infrastructure planning: deployment readiness depends on evaluation infrastructure, not just model performance.

Structured evaluation framework for trial recruitment LLMs

Based on the clinical reasoning graph methodology [5] and human-in-the-loop evaluation design [4], we propose a practical evaluation framework for Singapore hospitals assessing LLM trial recruitment systems:

Pre-deployment evaluation checklist

1. Reasoning consistency testing
- Run the same eligibility case through the LLM 10 times with temperature > 0
- Extract reasoning graphs from each trace (symptoms → findings → eligibility conclusion)
- Flag cases where the model produces contradictory causal chains or different eligibility decisions
- Acceptance criterion: <5% inconsistency rate on high-stakes inclusion/exclusion criteria (e.g., organ dysfunction thresholds, contraindicated medications)

2. Structured human review protocol
- Decompose eligibility reasoning into discrete steps: (a) criterion interpretation, (b) data extraction from EHR, (c) clinical context assessment, (d) final eligibility determination
- Train research coordinators to evaluate each step independently, not the overall narrative
- Use a scoring rubric: "Criterion correctly interpreted" / "Data accurately extracted" / "Clinical context appropriately considered" / "Final decision justified by evidence"
- Acceptance criterion: >90% agreement between LLM reasoning steps and expert review on a validation set of 50 cases

3. Edge case stress testing
- Construct adversarial cases: borderline lab values, conflicting inclusion/exclusion criteria, missing data, ambiguous clinical notes
- Evaluate whether the LLM flags uncertainty or produces overconfident incorrect eligibility decisions
- Acceptance criterion: LLM must explicitly flag uncertainty ("Unable to determine eligibility—requires clinician review") in >80% of ambiguous cases

4. Reasoning audit trail validation
- Verify that the system logs intermediate reasoning steps, not just final eligibility decisions
- Confirm that logs include: (a) which EHR data fields were accessed, (b) how inclusion/exclusion criteria were interpreted, (c) what clinical context influenced the decision
- Acceptance criterion: 100% of eligibility decisions must have retrievable reasoning traces for ethics board review

Post-deployment monitoring

• Weekly reasoning graph analysis: Sample 20 eligibility decisions, extract reasoning graphs, flag new patterns of inconsistency
• Monthly clinician review: Research coordinators review a random sample of LLM eligibility decisions using the structured evaluation protocol
• Quarterly model revalidation: Re-run the pre-deployment checklist on new cases to detect reasoning drift

This framework is more rigorous than typical LLM deployment protocols because trial recruitment is a high-stakes, high-scrutiny use case. Unlike ambient documentation (where errors are caught during clinician review) or operational forecasting (where errors average out over populations), trial recruitment errors directly impact individual patients and research validity.

Why this matters in Singapore

Singapore's clinical research ecosystem is a national priority, with hospital clusters competing for international trial partnerships and Ministry of Health funding for investigator-initiated studies. LLM-assisted recruitment could accelerate trial enrollment, reduce coordinator workload, and improve eligibility screening consistency—but only if the systems are trustworthy.

The regulatory environment adds complexity. While the Health Sciences Authority (HSA) does not currently classify trial recruitment software as a medical device, any system that influences patient care decisions (e.g., eligibility for experimental treatments) falls under hospital governance and research ethics oversight. If an LLM incorrectly excludes an eligible patient or includes an ineligible one, the principal investigator and institutional review board are accountable.

Singapore hospitals also face practical constraints that amplify LLM reasoning risks:

• Multi-lingual EHR data: Clinical notes mix English, medical abbreviations, and occasionally Mandarin or Malay—increasing the risk of misinterpretation
• Heterogeneous data quality: Public hospital EHRs vary in structured data completeness; LLMs may hallucinate missing values or misinterpret free-text notes
• Small trial populations: Many Singapore trials target rare diseases or specific ethnic subgroups—meaning eligibility errors have outsized impact on enrollment feasibility

These factors make structured reasoning evaluation non-negotiable. A model that performs well on US-based clinical benchmarks may exhibit different failure modes on Singapore EHR data, and you will not detect this with accuracy metrics alone.

What to do next

If your hospital is evaluating LLM deployment for trial recruitment, consent workflows, or eligibility screening:

1. Adopt structured reasoning evaluation before deployment: Implement the pre-deployment checklist above—do not rely on diagnostic accuracy benchmarks or vendor-provided performance claims
2. Design human-in-the-loop review workflows: Train research coordinators to evaluate intermediate reasoning steps, not just final eligibility decisions; use the CLExEval decomposition approach [4]
3. Require reasoning audit trails: Ensure the LLM system logs intermediate reasoning steps, data sources, and uncertainty flags—not just binary eligibility outputs
4. Pilot on low-stakes trials first: Start with observational studies or trials with broad inclusion criteria, where eligibility errors have lower patient safety impact; validate reasoning consistency before expanding to interventional trials
5. Establish continuous monitoring protocols: Implement weekly reasoning graph analysis and monthly clinician review; treat LLM trial recruitment as a high-risk clinical AI application requiring ongoing governance

For hospitals without in-house AI evaluation expertise, consider partnering with clinical AI deployment consultancies that specialize in healthcare AI governance and structured LLM evaluation. The upfront investment in rigorous evaluation infrastructure is far lower than the cost of trial delays, ethics board scrutiny, or patient safety incidents caused by unreliable reasoning.

If you are planning LLM deployment for clinical workflows in Singapore, start a project with structured evaluation design—before procurement, not after.

FAQ

Can we use existing LLM benchmarks to validate trial recruitment systems?

No. Generic medical benchmarks (e.g., USMLE-style questions) measure factual knowledge and diagnostic accuracy, not reasoning consistency or reliability in specific clinical workflows. Trial recruitment requires multi-step reasoning over real EHR data, with explicit audit trails for ethics review. You need domain-specific evaluation using clinical reasoning graphs [5] and structured human review protocols [4], not benchmark leaderboards.

How is this different from ambient clinical documentation AI?

Ambient documentation systems generate draft clinical notes that clinicians review and edit before signing—the human review step catches errors. Trial recruitment systems make eligibility decisions that directly influence patient enrollment, often without line-by-line clinician review of the reasoning. This higher autonomy demands higher reasoning reliability and structured evaluation before deployment. See our previous analysis of why diagnostic accuracy alone fails medical LLM evaluation.

What if our LLM vendor claims 95% accuracy on internal validation?

Accuracy on vendor-selected test cases does not predict reasoning consistency or reliability on your hospital's EHR data. The recent preprints [4][5] show that LLMs achieve moderate accuracy while exhibiting fundamentally inconsistent reasoning pathways. Demand access to reasoning traces, not just accuracy metrics, and conduct independent validation using the structured evaluation framework above. If the vendor cannot provide reasoning audit trails, the system is not deployment-ready for high-stakes clinical workflows.

Should we wait for better LLM reasoning capabilities before deploying trial recruitment systems?

Not necessarily—but you must deploy with appropriate guardrails. Use LLMs as decision support tools (flagging potentially eligible patients for coordinator review) rather than autonomous decision-makers. Implement structured human review workflows and continuous reasoning monitoring. The technology is useful today for augmenting coordinator workflows, but not yet reliable enough for fully autonomous eligibility screening. The key is matching system autonomy to reasoning reliability, which requires structured evaluation.

Sources

[1] Large language models for optimizing clinical trial recruitment in ICUs: application to ventilator-induced diaphragm dysfunction. Critical Care, June 11, 2026. https://doi.org/10.1186/s13054-026-06127-1

[2] Development and Implementation of an AI System for Generating Clinical Urine Drug Test Sign-Outs. JAMA Network Open, June 1, 2026. https://pubmed.ncbi.nlm.nih.gov/42334849/

[3] Feasibility and user evaluation of HopeBot: An LLM-powered conversational chatbot for depression screening. PLOS Digital Health, June 25, 2026. https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001446

[4] CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning. arXiv preprint, June 30, 2026. https://arxiv.org/abs/2606.31608v1

[5] Clinical Reasoning Graphs: Structured Evaluation of LLM Diagnostic Reasoning Reveals Competence Without Consistency. arXiv preprint, June 29, 2026. https://arxiv.org/abs/2606.29876v1

[6] NIST AI Risk Management Framework. NIST. https://www.nist.gov/itl/ai-risk-management-framework