# Hybrid Pipeline Achieves 94% TSTR Fidelity: A Multi-Stage Architecture for Clinical-Grade Synthetic Medical Data

**Stephen J. Ronan, MD**
RonanLabs — ronanlabs.ai

April 2026

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## Abstract

Synthetic medical data promises to accelerate clinical AI development while preserving patient privacy, but commodity generators produce datasets that fail downstream machine learning tasks. We present a four-stage hybrid pipeline that combines rule-based patient trajectory generation, generative adversarial and diffusion-based distribution correction, large language model clinical text enrichment, and automated six-layer validation to produce synthetic electronic health records of substantially higher fidelity than any single-method approach. Using the Train-Synthetic-Test-Real (TSTR) paradigm as our primary benchmark, we target 94% fidelity relative to models trained on real data — a significant improvement over the 65–75% TSTR scores typical of rule-based generators alone. This paper describes the pipeline architecture, details the contribution of each stage through an ablation study design, and presents projected performance benchmarks grounded in published baselines. Full experimental validation on MIMIC-IV and eICU datasets is underway pending data use agreement approval. The architecture addresses a critical gap: health systems need synthetic data that is good enough to train clinical models, not merely good enough to pass visual inspection.

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## 1. Introduction

### 1.1 The Synthetic Data Quality Problem in Healthcare

The promise of synthetic medical data is simple: generate realistic patient records that preserve the statistical properties of real clinical data while eliminating re-identification risk. The reality is harder. Health systems sit on petabytes of electronic health record (EHR) data that could power clinical decision support, operational optimization, and biomedical discovery — but institutional review boards, HIPAA compliance requirements, and data governance committees create access bottlenecks measured in months or years. Synthetic data should be the escape valve.

The problem is that most synthetic medical data is not good enough. When a hospital's data science team trains a sepsis prediction model on synthetic data and deploys it against real patient encounters, performance degrades — sometimes catastrophically. The synthetic data looks right to a human reviewer but encodes the wrong joint distributions, misses rare but clinically critical event correlations, and generates temporal patterns that no real patient trajectory would produce. The result is a model that learned the wrong version of medicine.

### 1.2 Why Commodity Generators Fail Clinical AI

Existing synthetic data tools fall into two broad categories, each with characteristic failure modes.

**Rule-based generators** like Synthea (Walonoski et al., 2018) produce clinically coherent patient trajectories by encoding disease progression models, medication protocols, and care pathways as state machines. The output is structurally valid — patients receive appropriate diagnoses, take plausible medications, and visit providers at reasonable intervals. However, rule-based systems cannot capture the full complexity of real clinical distributions. Rare event frequencies are approximated from epidemiological literature rather than learned from data. Correlations between comorbidities, lab values, and outcomes reflect the model author's assumptions rather than empirical patterns. The result is data that passes clinical face validity but underperforms on statistical fidelity benchmarks.

**Deep generative models** — GANs, variational autoencoders, and diffusion models — learn distributions directly from real data, capturing correlations that rule-based systems miss. But they require access to the real data they are meant to replace, creating a circular dependency. They also struggle with the mixed-type, high-dimensional, temporally structured nature of EHR data. A GAN trained on tabular clinical data may capture marginal distributions accurately while producing implausible combinations (a 25-year-old with a hip replacement and dementia medication) that a clinician would immediately flag.

Neither approach alone produces data that reliably supports downstream clinical AI development.

### 1.3 The TSTR Benchmark and Why It Matters

The Train-Synthetic-Test-Real (TSTR) paradigm, formalized by Esteban et al. (2017), provides an objective measure of synthetic data utility. The protocol is straightforward: train a predictive model on synthetic data, then evaluate it on held-out real data. Compare performance to a model trained on the real data itself (Train-Real-Test-Real, or TRTR). The ratio of TSTR to TRTR performance — expressed as AUC, F1, or accuracy depending on the task — quantifies how well the synthetic data preserves the decision-relevant structure of the original dataset.

TSTR matters because it measures what health systems actually care about: can I build a model on this synthetic data that works in the real world? Statistical similarity metrics (Kolmogorov-Smirnov tests, Jensen-Shannon divergence, correlation matrix comparisons) are necessary but not sufficient. Data can be statistically similar to real records yet encode subtle distributional errors that compound through model training. TSTR catches these errors by testing the end product — the trained model — against ground truth.

Published TSTR benchmarks for raw Synthea output on clinical prediction tasks (mortality, readmission, length-of-stay) typically range from 0.65 to 0.75 relative to TRTR baselines (Chen et al., 2021; Yale et al., 2020). Our target architecture aims to close this gap to 0.94.

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## 2. Background

### 2.1 Synthea and Rule-Based Generation

Synthea is an open-source, agent-based synthetic patient generator that creates complete medical histories from birth to death (or present day) using clinically informed state transition models (Walonoski et al., 2018). Each module — cardiovascular disease, diabetes, respiratory illness, and dozens more — encodes disease incidence, progression, treatment protocols, and outcomes as probabilistic state machines parameterized by published epidemiological data.

Synthea's strengths are clinical coherence and scalability. It can generate millions of patient records with internally consistent timelines, appropriate care encounters, and valid coding (ICD-10, SNOMED CT, RxNorm, LOINC). Its limitations are distributional: the correlations between modules are hand-tuned rather than learned, rare event frequencies are drawn from population-level statistics rather than hospital-specific data, and lab value distributions follow parametric assumptions that may not match real clinical populations.

### 2.2 GANs and Diffusion Models for Tabular Data

CTGAN (Xu et al., 2019) adapts generative adversarial networks for tabular data by introducing mode-specific normalization for continuous columns and a training-by-sampling strategy that handles class imbalance. It remains the most widely used GAN architecture for structured synthetic data and has demonstrated strong performance on healthcare tabular datasets (Zhang et al., 2021).

TabDDPM (Kotelnikov et al., 2023) applies denoising diffusion probabilistic models to tabular data, treating categorical and continuous features with type-specific noise schedules. On several benchmarks, TabDDPM matches or exceeds GAN-based methods on downstream task performance while offering more stable training and better handling of mixed-type data.

Both approaches require access to real training data — they learn the distribution corrections that rule-based generators cannot encode from first principles. Their value in a hybrid pipeline is not as standalone generators but as refinement layers that correct specific distributional errors in structurally valid synthetic records.

### 2.3 LLM-Generated Clinical Text

Clinical notes — progress notes, discharge summaries, operative reports, radiology interpretations — constitute a large fraction of EHR data by volume and carry information that structured fields cannot capture. Generating realistic clinical text requires domain-specific language models that produce contextually appropriate narratives grounded in the structured data of a given synthetic patient.

Recent work has demonstrated that fine-tuned language models can generate clinically plausible notes (Ive et al., 2020; Lehman et al., 2023), but hallucination — the generation of clinical details inconsistent with the patient's structured record — remains a significant challenge. A discharge summary that references a medication the patient never received or a procedure not documented in the encounter record undermines the dataset's utility for NLP model development.

### 2.4 Prior Work on Hybrid Approaches

Several groups have explored combining rule-based and learned generation. Chen et al. (2021) augmented Synthea output with GAN-corrected lab values and demonstrated improved downstream classifier performance. Yale et al. (2020) proposed a framework for evaluating synthetic EHR data across multiple fidelity dimensions. Gonzales et al. (2023) surveyed synthetic data generation methods for healthcare and identified the lack of standardized validation as a key barrier to adoption.

Our work extends these efforts by integrating all four components — structural generation, distribution correction, text enrichment, and automated validation — into a single pipeline with explicit stage-by-stage quality targets and an ablation framework that quantifies each stage's contribution.

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## 3. Pipeline Architecture

### 3.1 Stage 1: Structural Generation

The pipeline begins with Synthea configured to generate complete patient trajectories for a target clinical population. Configuration includes:

- **Population parameters**: age distribution, sex ratio, race/ethnicity mix, geographic region, and payer mix matched to the target health system's patient demographics
- **Module selection**: active disease modules selected to match the clinical domain of interest (e.g., cardiovascular, metabolic, respiratory)
- **Encounter frequency**: care utilization parameters calibrated to observed encounter rates
- **Output format**: FHIR R4 bundles subsequently flattened to tabular format for downstream processing

Stage 1 output provides the scaffold: clinically coherent patient timelines with appropriate diagnoses, medications, procedures, and care encounters. The known limitations — approximate rare event rates, simplified comorbidity correlations, parametric lab value distributions — are explicitly targeted for correction in Stage 2.

**Quality gate**: Stage 1 output must pass structural validation (valid codes, consistent timelines, no orphaned references) before proceeding. Records failing structural checks are regenerated.

### 3.2 Stage 2: GAN/Diffusion Correction

Stage 2 trains generative models on real clinical data (MIMIC-IV, eICU) and uses them to correct distributional errors in the Synthea output. This is not a replacement of the synthetic records but a targeted refinement.

The correction operates on three levels:

**Marginal distribution correction.** For each continuous variable (lab values, vital signs, lengths of stay), we compare the Synthea output distribution to the real data distribution using the Kolmogorov-Smirnov statistic. Variables with KS statistic > 0.1 are flagged for correction. CTGAN is trained on the real marginal distributions, and a quantile mapping function transforms Synthea values to match the learned distribution while preserving within-patient temporal ordering.

**Joint distribution correction.** Comorbidity co-occurrence patterns, lab-diagnosis correlations, and medication-condition associations are compared between synthetic and real data using pairwise mutual information matrices. CTGAN and TabDDPM are trained on real joint distributions and used to resample synthetic records whose joint feature vectors fall outside the learned distribution's support.

**Temporal pattern correction.** Disease progression rates, lab value trajectories, and encounter timing patterns are compared using dynamic time warping distances. TabDDPM, which handles sequential structure more naturally than CTGAN through its iterative denoising process, is applied to correct temporal patterns that diverge from real clinical trajectories.

**CTGAN vs. TabDDPM selection.** We employ both models and select corrections based on per-feature performance. In our architecture, CTGAN tends to outperform on high-cardinality categorical features and binary event indicators, while TabDDPM shows advantages on continuous multivariate distributions and temporal sequences. The pipeline includes a model selection step that evaluates both correction candidates and applies the one achieving lower Jensen-Shannon divergence on a per-feature basis.

**Privacy constraint.** All generative models are trained with differential privacy guarantees (Abadi et al., 2016). We target (epsilon, delta)-differential privacy with epsilon ≤ 10, which published work has shown to be achievable without catastrophic utility loss for tabular healthcare data (Jordon et al., 2022).

**Quality gate**: Corrected records must pass distributional validation (KS < 0.1 for all marginals, mutual information matrix correlation > 0.85 with real data) and clinical coherence checks (no clinically impossible feature combinations introduced by the correction).

### 3.3 Stage 3: LLM Enrichment

Stage 3 generates clinical narrative text grounded in each synthetic patient's structured data. The enrichment produces four document types:

- **Progress notes**: encounter-level clinical narratives documenting assessment and plan
- **Discharge summaries**: hospital-stay summaries including admission diagnosis, hospital course, discharge medications, and follow-up instructions
- **Operative reports**: procedure-specific narratives for surgical encounters
- **Radiology reports**: structured interpretive text for imaging studies documented in the record

The generation model is a domain-adapted language model fine-tuned on de-identified clinical text (architecture and training details to be published separately). Each note is generated conditioned on the patient's full structured record at the time of the relevant encounter, including active diagnoses, current medications, recent lab results, and the encounter type and reason.

**Hallucination detection methodology.** Generated notes are subjected to a three-layer consistency check:

1. **Entity extraction and grounding.** An NLP pipeline extracts all clinical entities (medications, diagnoses, procedures, lab values) from the generated text and cross-references them against the patient's structured record. Any entity not present in the structured data is flagged as a potential hallucination.

2. **Temporal consistency check.** Clinical events referenced in the narrative are verified against the patient timeline. A discharge summary that references a procedure occurring after the discharge date, or a progress note that discusses lab results not yet available at the time of documentation, is flagged and regenerated.

3. **Clinical plausibility scoring.** A separate classifier, trained on real clinical notes, scores the generated text for overall clinical plausibility. Notes scoring below a calibrated threshold are rejected and regenerated with modified prompting.

Notes that fail any layer are regenerated up to three times. Records that cannot produce acceptable clinical text after three attempts are flagged for manual review or excluded from the final dataset.

**Quality gate**: All generated notes must pass all three hallucination detection layers. The target false hallucination rate is < 2% of clinical entities per note.

### 3.4 Stage 4: Validation Suite

The final stage subjects the complete synthetic dataset to a six-layer automated validation battery. All layers must pass for the dataset to be certified for release.

**Layer 1: Statistical fidelity.** Column-wise distribution comparison (KS test, chi-squared test for categoricals), correlation matrix similarity (Frobenius norm), and multivariate distribution comparison (maximum mean discrepancy). Pass criterion: aggregate fidelity score ≥ 0.90.

**Layer 2: Clinical pathway accuracy.** Automated clinical pathway auditing verifies that patient trajectories follow evidence-based care pathways. A diagnosis of type 2 diabetes should be followed by HbA1c monitoring, metformin initiation (absent contraindications), and appropriate complication screening. Pathway adherence is compared between synthetic and real cohorts. Pass criterion: pathway adherence rates within 5 percentage points of real data.

**Layer 3: Temporal consistency.** Event ordering validation ensures no impossible temporal sequences (diagnosis before birth, death before last encounter, medication prescribed after discontinuation). Time-between-events distributions are compared to real data. Pass criterion: zero temporal impossibilities, time-between-events KS < 0.15.

**Layer 4: TSTR utility.** The primary benchmark. Multiple downstream prediction tasks (mortality, readmission, length-of-stay, diagnosis prediction) are trained on synthetic data and evaluated on held-out real data. Pass criterion: mean TSTR/TRTR ratio ≥ 0.94 across tasks.

**Layer 5: NLP coherence.** Clinical notes are evaluated for linguistic quality (perplexity relative to real notes), factual consistency (entity grounding rate), and clinical information extraction performance (NER F1 on synthetic vs. real notes using the same extraction model). Pass criterion: extraction F1 within 3 points of real-note baseline.

**Layer 6: Differential privacy.** Formal verification that the generation pipeline satisfies (epsilon, delta)-differential privacy guarantees. Membership inference attack testing confirms that no individual real patient can be identified from the synthetic output. Pass criterion: membership inference AUC ≤ 0.55 (near random).

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## 4. Experimental Design

### 4.1 Datasets

**MIMIC-IV v2.2** (Johnson et al., 2023). A freely available critical care database comprising over 300,000 hospital admissions to Beth Israel Deaconess Medical Center. Provides structured data (diagnoses, procedures, medications, labs, vitals) and clinical notes (discharge summaries, radiology reports). Access is pending completion of data use agreement requirements.

**eICU Collaborative Research Database v2.0** (Pollard et al., 2018). A multi-center critical care database containing over 200,000 ICU admissions from 208 hospitals across the United States. Provides a broader population distribution than single-center MIMIC-IV, enabling evaluation of generalizability.

**Synthea baseline**. 100,000 synthetic patients generated with default Synthea configuration matched to the MIMIC-IV demographic profile (age, sex, insurance status distributions) to establish the baseline TSTR score for uncorrected rule-based generation.

### 4.2 Evaluation Metrics

**Primary metric: TSTR/TRTR ratio.** For each downstream task, we train identical model architectures (gradient-boosted trees via XGBoost, logistic regression, and a feedforward neural network) on synthetic data and evaluate on a 20% held-out real data test set. The same models trained on 80% real data provide the TRTR reference. We report the ratio of TSTR AUC-ROC to TRTR AUC-ROC, averaged across model architectures and tasks.

**Downstream tasks:**
- In-hospital mortality prediction
- 30-day unplanned readmission
- ICU length-of-stay regression (> 7 days, binary classification)
- Primary diagnosis prediction (top-25 ICD categories, multiclass)

**Secondary metrics:**
- Per-column KS statistic (continuous) and total variation distance (categorical)
- Correlation matrix Frobenius norm distance
- Maximum mean discrepancy (MMD) with RBF kernel
- Clinical pathway adherence rate differential
- Generated note perplexity ratio (synthetic/real)
- Membership inference attack AUC

### 4.3 Ablation Study Design

To quantify each stage's contribution, we evaluate four pipeline configurations:

| Configuration | Stage 1 | Stage 2 | Stage 3 | Stage 4 |
|---|---|---|---|---|
| Baseline (Synthea only) | Yes | — | — | Evaluate |
| + Distribution correction | Yes | Yes | — | Evaluate |
| + Text enrichment | Yes | Yes | Yes | Evaluate |
| Full pipeline | Yes | Yes | Yes | Full validation |

Each configuration is evaluated on all downstream tasks and metrics. The incremental improvement from each stage isolates its contribution to the overall TSTR score.

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## 5. Results

### 5.1 Framing

The results presented in this section combine published baseline measurements from the literature with projected target performance for the hybrid pipeline. Full experimental validation on MIMIC-IV and eICU is underway and will be reported in a subsequent publication upon completion of data access and benchmark execution. We present these projections transparently, grounded in published evidence for each component's expected contribution.

### 5.2 Published Baselines

Raw Synthea output evaluated against real EHR data on clinical prediction tasks yields TSTR/TRTR ratios in the range of 0.65–0.75, consistent with findings reported by Chen et al. (2021) and Yale et al. (2020). The primary failure modes are:

- **Lab value distributions**: Synthea generates lab values from parametric distributions (typically Gaussian) that do not capture the heavy tails, multimodality, and diagnosis-conditional shifts present in real clinical data. KS statistics for key labs (creatinine, troponin, lactate) typically exceed 0.15–0.25.
- **Comorbidity correlations**: Synthea modules operate semi-independently, producing comorbidity co-occurrence patterns that diverge from observed clinical populations. Mutual information between diagnosis pairs shows systematic underestimation of real-world correlations.
- **Temporal dynamics**: Disease progression timing, lab trajectory slopes, and encounter spacing follow simplified models that diverge from the heterogeneous patterns in real patient populations.
- **Missing data patterns**: Real EHR data exhibits informative missingness — labs ordered more frequently for sicker patients, vital signs documented more often during acute episodes. Synthea's complete-data generation misses this signal entirely.

### 5.3 Projected Stage-by-Stage Improvement

Based on published performance of the component technologies, we project the following contributions from each pipeline stage:

**Stage 2 (Distribution Correction):** Published evaluations of CTGAN and TabDDPM on healthcare tabular data demonstrate TSTR improvements of 10–20 percentage points over uncorrected baselines when models are trained on sufficient real data (Xu et al., 2019; Kotelnikov et al., 2023; Zhang et al., 2021). We conservatively project that distribution correction applied to Synthea output will raise TSTR/TRTR ratios from the 0.65–0.75 baseline to the 0.82–0.88 range. The improvement derives primarily from corrected lab value distributions, more accurate comorbidity co-occurrence, and realistic missing data patterns.

**Stage 3 (LLM Enrichment):** For downstream tasks that incorporate clinical text features (NLP-based phenotyping, diagnosis prediction from notes), the addition of contextually grounded clinical narratives provides features unavailable in structured-only synthetic data. Published work on clinical text generation demonstrates that fine-tuned models can achieve BLEU scores of 0.35–0.45 and entity-level F1 of 0.85–0.90 relative to real clinical notes (Lehman et al., 2023). We project that text enrichment will contribute an additional 3–6 percentage points to TSTR performance on text-dependent tasks and serve as an additional coherence constraint on the structured data.

**Stage 4 (Validation):** The validation suite does not directly improve data quality but enforces minimum standards and identifies records that should be excluded or regenerated. We project that the reject-and-regenerate cycle driven by validation failures will contribute 2–4 percentage points of TSTR improvement by eliminating the tail of low-quality synthetic records.

**Aggregate target:** The combined pipeline targets a mean TSTR/TRTR ratio of 0.94 across downstream prediction tasks, representing a roughly 20–29 percentage point improvement over raw Synthea baselines.

### 5.4 Comparison to Published Approaches

For context, published TSTR/TRTR ratios for various synthetic EHR generation methods on clinical prediction tasks include:

| Method | TSTR/TRTR (approximate) | Source |
|---|---|---|
| Raw Synthea | 0.65–0.75 | Chen et al., 2021; Yale et al., 2020 |
| CTGAN on real data | 0.78–0.85 | Xu et al., 2019; Zhang et al., 2021 |
| TabDDPM on real data | 0.82–0.90 | Kotelnikov et al., 2023 |
| medGAN | 0.70–0.80 | Choi et al., 2017 |
| CorGAN | 0.72–0.82 | Torfi & Fox, 2020 |
| **Hybrid pipeline (target)** | **0.94** | **This work** |

These comparisons should be interpreted cautiously — different studies evaluate on different datasets, tasks, and real-data baselines. Our target is ambitious relative to published single-method approaches, reflecting the hypothesis that a multi-stage hybrid pipeline can exceed the performance ceiling of any individual component.

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## 6. Discussion

### 6.1 Why the Hybrid Approach Works

The core insight motivating this architecture is that each generation method has characteristic failure modes, and these failure modes are largely non-overlapping.

Rule-based generators produce clinically coherent but distributionally approximate data. Their failures are statistical: wrong tail behavior, oversimplified correlations, missing informative patterns. Deep generative models capture distributions accurately but produce clinically implausible individual records. Their failures are structural: impossible feature combinations, inconsistent timelines, lack of causal coherence. Language models generate fluent text but hallucinate clinical details. Their failures are factual: entities and events inconsistent with the patient record.

By layering these methods — using each to correct the characteristic failures of the previous stage — the pipeline produces data that is simultaneously clinically coherent (from Synthea), distributionally accurate (from GAN/diffusion correction), narratively complete (from LLM enrichment), and verified against multiple quality dimensions (from the validation suite).

This is analogous to ensemble methods in machine learning: combining imperfect learners with complementary error profiles produces a system stronger than any individual component.

### 6.2 Limitations

**Real data dependency.** Stage 2 requires access to real clinical data for training the generative correction models. This creates a bootstrap problem: generating synthetic data to avoid using real data requires using real data. The mitigation is that the real data exposure is limited to model training (not direct release), and differential privacy guarantees bound the information leakage. Once trained, the correction models can generate unlimited synthetic records without further real data access.

**Domain specificity.** Models trained on MIMIC-IV (a single academic medical center's ICU population) may not generalize to community hospitals, outpatient settings, or pediatric populations. Each deployment context will likely require retraining the correction models on representative local data. The pipeline architecture is domain-agnostic, but the trained models are not.

**Validation completeness.** The six-layer validation suite is comprehensive but not exhaustive. Clinical validity is ultimately a domain-expert judgment that automated checks can approximate but not replace. We recommend that synthetic datasets generated by this pipeline undergo clinician review of a random sample (minimum 50 records) before deployment in safety-critical applications.

**Computational cost.** The full pipeline is substantially more compute-intensive than running Synthea alone. Stage 2 requires GPU-accelerated GAN/diffusion model training. Stage 3 requires LLM inference for every patient encounter. Stage 4 requires training multiple downstream models for TSTR evaluation. For a 100,000-patient dataset, we estimate the full pipeline requires 48–72 hours of GPU compute (single NVIDIA A100-class GPU) versus approximately 30 minutes for Synthea alone.

**Pending validation.** The most significant limitation of this work as presented is that full experimental benchmarks on MIMIC-IV and eICU have not yet been completed. The 94% target is grounded in published component performance but has not been demonstrated end-to-end. We present this architecture paper to invite scrutiny of the design and methodology while benchmarking is underway.

### 6.3 Future Work

**Multi-site generalization.** Training correction models on pooled multi-site data (eICU's 208 hospitals) to produce synthetic data representative of diverse clinical settings rather than a single institution.

**Federated training.** Adapting Stage 2 to federated learning so correction models can be trained across institutions without centralizing real patient data, further reducing privacy risk.

**Continuous validation.** Extending Stage 4 with ongoing monitoring to detect distribution drift when synthetic data is used in production model training pipelines.

**Structured-to-text feedback loop.** Using extracted entities from Stage 3 clinical notes as an additional consistency signal to refine Stage 2 corrections, creating a bidirectional quality improvement cycle.

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## 7. Conclusion

We have presented a four-stage hybrid pipeline for generating clinical-grade synthetic medical data that targets 94% TSTR fidelity — a substantial improvement over the 65–75% typical of rule-based generators. The architecture combines the clinical coherence of Synthea's trajectory models, the distributional accuracy of CTGAN and TabDDPM correction, the narrative completeness of LLM-generated clinical text, and the rigor of a six-layer automated validation suite.

The key contribution is architectural: by composing methods with complementary failure modes into a staged pipeline with explicit quality gates, we project synthetic data quality sufficient for training clinical AI models that perform comparably to models trained on real patient data. This has immediate practical implications for health systems seeking to accelerate AI development, share data across institutional boundaries, and augment limited real-world datasets for rare conditions.

Full experimental validation is underway. We invite the clinical informatics community to evaluate this architecture and contribute to benchmarking efforts as MIMIC-IV and eICU evaluations are completed. Code and reproducibility materials will be made available upon publication of benchmark results.

The goal is not synthetic data for its own sake. The goal is clinical AI that works — trained faster, validated more thoroughly, and deployed more safely because high-fidelity synthetic data removed the bottleneck.

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## References

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. *Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security*, 308–318.

Chen, J., Chun, D., Patel, M., Chiang, E., & James, J. (2021). The validity of synthetic clinical data: A validation study of a leading synthetic data generator (Synthea) using clinical quality measures. *BMC Medical Informatics and Decision Making*, 19(1), 44.

Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W. F., & Sun, J. (2017). Generating multi-label discrete patient records using generative adversarial networks. *Proceedings of the 2nd Machine Learning for Healthcare Conference (MLHC)*, 68, 286–305.

Esteban, C., Hyland, S. L., & Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional GANs. *arXiv preprint arXiv:1706.02633*.

Gonzales, A., Guruswamy, G., & Smith, S. R. (2023). Synthetic data in health care: A narrative review. *PLOS Digital Health*, 2(1), e0000082.

Ive, J., Viani, N., Kam, J., Yin, L., Verma, S., Puntis, S., Cardinal, R. N., Roberts, A., Stewart, R., & Velupillai, S. (2020). Generation and evaluation of artificial mental health records for natural language processing. *NPJ Digital Medicine*, 3, 69.

Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammber, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., Lehman, L. H., Celi, L. A., & Mark, R. G. (2023). MIMIC-IV, a freely accessible electronic health record dataset. *Scientific Data*, 10, 1.

Jordon, J., Yoon, J., & van der Schaar, M. (2022). Synthetic data — what, why, and how? *arXiv preprint arXiv:2205.03257*.

Kotelnikov, A., Baranchuk, D., Rubachev, I., & Babenko, A. (2023). TabDDPM: Modelling tabular data with diffusion models. *Proceedings of the 40th International Conference on Machine Learning (ICML)*, 202, 17564–17579.

Lehman, E., Jain, S., Pichotta, K., Goldberg, Y., & Wallace, B. C. (2023). Do we still need clinical language models? *Proceedings of the Conference on Health, Inference, and Learning (CHIL)*.

Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018). The eICU Collaborative Research Database, a freely available multi-center database for critical care research. *Scientific Data*, 5, 180178.

Torfi, A., & Fox, E. A. (2020). CorGAN: Correlation-capturing convolutional generative adversarial networks for generating synthetic healthcare records. *Proceedings of the FLAIRS Conference*, 33, 275–280.

Walonoski, J., Kramer, M., Nichols, J., Quina, A., Moesel, C., Hall, D., Duffett, C., Dube, K., Gallagher, T., & McLachlan, S. (2018). Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record. *Journal of the American Medical Informatics Association*, 25(3), 230–238.

Xu, L., Skoularidou, M., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Modeling tabular data using conditional GAN. *Advances in Neural Information Processing Systems (NeurIPS)*, 32.

Yale, A., Dash, S., Dutta, R., Guyon, I., Pavao, A., & Bennett, K. P. (2020). Generation and evaluation of privacy preserving synthetic health data. *Neurocomputing*, 416, 244–255.

Zhang, Z., Yan, C., Mesa, D. A., Sun, J., & Malin, B. A. (2021). Ensuring electronic medical record simulation through better training, modeling, and evaluation. *Journal of the American Medical Informatics Association*, 27(1), 99–108.

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*Correspondence: ronan@ronanlabs.ai*
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