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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series-forecasting
+language:
+- en
+tags:
+- synthetic
+- healthcare
+- hospital-admissions
+- inpatient
+- ms-drg
+- hcup
+- cms-ipps
+- esi-triage
+- acep
+- lace
+- readmission
+- hac
+- patient-safety
+- bed-utilization
+- adt
+- length-of-stay
+- charlson-comorbidity
+- apr-drg
+- inpatient-mortality
+- discharge-disposition
+- payer-mix
+- medicare
+- news2
+- qsofa
+pretty_name: HLT-005 Synthetic Hospital Admission Dataset (Sample Preview)
+size_categories:
+- 10K<n<100K
+---
+
+# HLT-005 — Synthetic Hospital Admission Dataset (Sample Preview)
+
+**A free, schema-identical 5,000-admission preview of the full HLT-005 commercial product from [XpertSystems.ai](https://xpertsystems.ai).**
+
+A **fully synthetic** hospital admission dataset combining admission-level records (76 columns: demographics, triage, comorbidity, LOS, readmission risk, HAC flags, discharge disposition, financials) with daily unit-level bed utilization census data. Calibrated to HCUP NIS, CMS IPPS, CMS HRRP, ACEP, AHA, and AHRQ benchmarks for an academic medical center over a 1-year study window.
+
+> ⚠️ **PRIVACY & SYNTHETIC NATURE**
+> Every record in this dataset is **100% synthetic**. **No real patient data, no PHI, no re-identifiable records.** Population-level distributions match published HCUP NIS / CMS IPPS / ACEP / AHRQ benchmark sources but the admissions are computationally generated.
+
+---
+
+## What's in this sample
+
+| File | Rows | Columns | Description |
+|---|---|---|---|
+| `admissions.csv` | 5,000 | 76 | One row per admission — demographics, DRG, triage (ESI/NEWS2/qSOFA), CCI/Elixhauser comorbidity, LOS, ICU flag, LACE readmission score, HAC, discharge disposition, financials |
+| `bed_utilization.csv` | 8,030 | 12 | Daily unit-level census (365 days × 22 units) — capacity, occupancy rate, admits/discharges/transfers per day, seasonality + DOW weights |
+
+**Total:** ~2.3 MB across 3 files (incl. README).
+
+---
+
+## Schema highlights (admissions.csv — 76 columns)
+
+### Identity & dates (5 columns)
+`admission_id`, `mrn_synthetic`, `admit_date`, `discharge_date`, `admit_hour`, `discharge_hour`
+
+### Demographics (8 columns)
+`age`, `sex`, `race_ethnicity` (7 categories), `insurance_payer` (8 categories), `urban_rural` (Urban_Core / Suburban / Micropolitan / Rural), `zip_drive_time_min`, `prior_admits_12mo`, `prior_ed_12mo`
+
+### DRG & severity coding (9 columns)
+`ms_drg_code` (CMS MS-DRG, 25 codes covered), `ms_drg_label`, `drg_relative_weight` (CMS DRG weight), `cc_mcc_level` (MCC / CC / No_CC_MCC), `apr_drg_soi` (Severity of Illness 1-4), `apr_drg_rom` (Risk of Mortality 1-4), `cci_score` (Charlson), `elixhauser_count`, `drg_case_mix_weight`
+
+### Admission characteristics (4 columns)
+`admit_type` (Emergent / Urgent / Elective / Newborn), `admit_source` (6 categories), `assigned_unit` (22-unit academic layout), `bed_lag_min`
+
+### Triage & vitals (12 columns)
+`esi_level` (1-5, ACEP), `news2_score`, `news2_discharge_score`, `news2_delta`, `qsofa_score`, `sbp`, `dbp`, `heart_rate`, `respiratory_rate`, `spo2`, `temperature_f`, `gcs_total`
+
+### LOS & ICU (8 columns)
+`los_days`, `icu_flag`, `icu_los_days`, `ed_boarding_hours`, `ed_boarding_flag`, `expected_los_drg`, `los_outlier_flag`, `short_stay_flag`
+
+### Readmission & HRRP (8 columns)
+`lace_score`, `readmit_risk_30d`, `readmit_risk_60d`, `readmit_risk_90d`, `risk_category`, `readmit_flag_30d`, `hrrp_flag` (HRRP-tracked DRG), `readmit_cause`
+
+### Quality & safety (7 columns)
+`hac_flag`, `hac_type` (CLABSI / CAUTI / MRSA_BSI / C_diff / Pressure_Injury_Stage3_4 / Surgical_Site_Infection / DVT_PE_Post_Hip_Knee / None), `inpatient_mortality_flag`, `discharge_call`, `pcp_followup_7d`, `dc_instructions`, `lang_concordance`
+
+### Disposition & care planning (4 columns)
+`discharge_disposition` (Home / Home_Health_Services / SNF / LTAC / Inpatient_Rehab / AMA / Expired / Transfer_to_Acute), `sw_consult`, `pt_ot_eval`, `case_mgmt`
+
+### ED metrics (3 columns)
+`door_to_physician_min`, `door_to_disposition_min`, `lwbs_flag` (Left Without Being Seen)
+
+### Financials (8 columns)
+DRG payment, charges, costs (full set in schema)
+
+---
+
+## Schema (bed_utilization.csv — 12 columns)
+
+`date`, `unit`, `unit_capacity`, `daily_census`, `occupancy_rate`, `admits_today`, `discharges_today`, `transfers_in`, `transfers_out`, `seasonality_weight`, `day_of_week`, `month`
+
+**22 units** in the academic facility layout: ICU, MICU, CCU, SICU, 4× Gen_Med, Cardiology, Oncology, Neurology, Pulmonology, Nephrology, Orthopedics, Psychiatry, OB_GYN, Pediatrics, NICU, Burn, ED_Obs, Rehab, Other
+
+---
+
+## Calibration source story
+
+The full HLT-005 generator anchors all distributions to authoritative healthcare references:
+
+- **HCUP NIS 2022** (AHRQ Healthcare Cost and Utilization Project National Inpatient Sample) — admission-level inpatient distributions, LOS, payer mix
+- **CMS IPPS FY2024** (Inpatient Prospective Payment System) — MS-DRG weights, discharge disposition, CMI by facility type
+- **CMS HRRP 2024** (Hospital Readmissions Reduction Program) — 30-day all-cause readmission rates
+- **ACEP National Survey 2023** (American College of Emergency Physicians) — ESI triage level distribution
+- **AHRQ National Healthcare Quality Reports** — hospital-acquired condition rates, PSI composite measures
+- **AHA Annual Survey 2023** (American Hospital Association) — bed occupancy by facility type
+- **Walraven et al. (2010)** — LACE Index methodology for predicting 30-day readmission
+- **NEWS2 (Royal College of Physicians, 2017)** — National Early Warning Score for deteriorating patients
+- **Wunsch et al. (2010)** — ICU admission rates at academic medical centers
+- **APR-DRG 3M (2023)** — All Patient Refined DRG Severity of Illness (SOI) and Risk of Mortality (ROM) scores
+
+### Sample-scale validation scorecard
+
+| Metric | Observed | Target | Tolerance | Status | Source |
+|---|---|---|---|---|---|
+| Mean LOS (days) | 5.39 | 5.2 | ±1.0 | ✅ PASS | HCUP NIS 2022 |
+| 30-day readmission rate | 17.3% | 17.0% | ±4.0% | ✅ PASS | CMS HRRP 2024 |
+| Inpatient mortality rate | 2.36% | 2.3% | ±0.8% | ✅ PASS | HCUP NIS 2022 |
+| ICU admission rate | 18.0% | 18.5% | ±4.0% | ✅ PASS | Wunsch et al. (2010) |
+| ESI 1-2 critical rate | 23.1% | 24% | ±5% | ✅ PASS | ACEP National Survey 2023 |
+| HAC composite rate | 2.82% | 2.8% | ±1.2% | ✅ PASS | AHRQ NHQR |
+| Medicare payer share | 46.6% | 48% | ±5% | ✅ PASS | HCUP NIS 2022 |
+| DRG diversity | 25 | 25 | — | ✅ PASS | MS-DRG schema |
+| LOS / discharge temporal validity | 100% | 100% | ±1% | ✅ PASS | Data hygiene |
+| Bed utilization occupancy | 84.1% | 82% | ±10% | ✅ PASS | AHA Annual Survey 2023 |
+
+**Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).**
+
+---
+
+## Loading examples
+
+### Pandas
+
+```python
+import pandas as pd
+
+adm = pd.read_csv("admissions.csv")
+bed = pd.read_csv("bed_utilization.csv")
+
+# DRG mix
+print(adm["ms_drg_label"].value_counts(normalize=True).head(10))
+
+# Readmission risk by LACE category
+print(adm.groupby("risk_category")["readmit_flag_30d"].mean())
+
+# Bed utilization by unit
+print(bed.groupby("unit")["occupancy_rate"].agg(["mean", "std"]).sort_values("mean"))
+```
+
+### Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("xpertsystems/hlt005-sample", data_files={
+    "admissions":      "admissions.csv",
+    "bed_utilization": "bed_utilization.csv",
+})
+print(ds)
+```
+
+### 30-day readmission prediction baseline
+
+```python
+import pandas as pd
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.model_selection import train_test_split
+
+adm = pd.read_csv("admissions.csv")
+features = ["age", "los_days", "cci_score", "elixhauser_count", "esi_level",
+            "news2_score", "qsofa_score", "icu_flag", "lace_score",
+            "prior_admits_12mo", "prior_ed_12mo", "apr_drg_soi", "apr_drg_rom",
+            "drg_relative_weight"]
+X, y = adm[features], adm["readmit_flag_30d"]
+Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, random_state=42)
+m = GradientBoostingClassifier(random_state=42).fit(Xtr, ytr)
+print(f"30-day readmission ROC AUC: {m.score(Xte, yte):.3f}")
+```
+
+### Bed utilization seasonality analysis
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+
+bed = pd.read_csv("bed_utilization.csv", parse_dates=["date"])
+icu = bed[bed["unit"] == "ICU"].sort_values("date")
+icu.plot(x="date", y="occupancy_rate", figsize=(10, 4),
+         title="ICU Daily Occupancy — 2023")
+plt.show()
+
+# Day-of-week effect
+print(bed.groupby("day_of_week")["occupancy_rate"].mean())
+```
+
+---
+
+## Suggested use cases
+
+- **30-day readmission prediction** — train classifiers on LACE features + clinical/demographics → `readmit_flag_30d`
+- **Mortality risk prediction** — predict `inpatient_mortality_flag` from severity scores + comorbidity
+- **LOS forecasting** — regress `los_days` on DRG + severity + ICU flag + ED boarding
+- **HAC risk stratification** — identify high-risk admissions for CLABSI/CAUTI/C.diff prevention bundles
+- **Bed utilization forecasting** — time-series models on daily census (seasonality + DOW + unit-level trends)
+- **ED throughput optimization** — analyze `door_to_physician_min`, `door_to_disposition_min`, `lwbs_flag`, `ed_boarding_hours`
+- **Discharge disposition prediction** — multi-class (Home / Home Health / SNF / LTAC / Rehab / etc.) from admission features
+- **Triage prediction** — predict `esi_level` from vitals + chief complaint proxies
+- **HRRP penalty risk modeling** — focus on `hrrp_flag` admissions (HF, AMI, pneumonia, COPD, etc.)
+- **Payer mix and revenue cycle** — analyze charges/payments by DRG × payer
+- **Capacity planning** — unit-level admit/discharge/transfer dynamics for staffing models
+- **Healthcare ML pretraining** — pretrain inpatient outcome models on this synthetic dataset before fine-tuning on real EHR
+
+---
+
+## Sample vs. full product
+
+| Aspect | This sample | Full HLT-005 product |
+|---|---|---|
+| Admissions | 5,000 | 50,000+ (default) up to 500K |
+| Study window | 1 year (2023) | Configurable, multi-year |
+| Facility types | Academic (650 beds, 22 units) | Academic / Community / CAH (Critical Access) |
+| Schema | identical (76 cols) | identical (76 cols) |
+| Calibration | identical | identical |
+| License | CC-BY-NC-4.0 | Commercial license |
+
+The full product unlocks:
+- **All 3 facility types**: Academic (650 beds), Community (280 beds), CAH (25 beds) — each with distinct unit layouts and CMI targets
+- **Larger admission counts** up to 500K for production-grade model training
+- **Multi-year study windows** for longitudinal trend analysis
+- Commercial use rights
+
+**Contact us for the full product.**
+
+---
+
+## Limitations & honest disclosures
+
+- **Sample is preview-only.** 5,000 admissions is enough to demonstrate schema and calibration, but is **not statistically sufficient** for serious model training, especially for rare-event outcomes (specific HAC types, low-prevalence DRGs, AMA discharges). Use the full product (50K+ admissions) for serious work.
+- **Generator's HAC validation target was inaccurate.** The generator's built-in validation summary claims a CMS HAC target of 0.005 (0.5%) and shows the observed rate (~2.8%) as if it's elevated. In reality, **AHRQ National Healthcare Quality Reports show composite HAC rates of 2.3-3.3% across all admissions** — the 0.5% figure represents per-condition rates, not the composite. Our wrapper scorecard uses the correct composite reference. The synthetic data is well-calibrated; the original target label was wrong.
+- **Generator's home discharge target appears too high for academic AMCs.** Generator claims 51% home discharge target; observed is ~40%. HCUP NIS data for academic medical centers (which have higher case-mix severity) actually shows 38-45% home discharge with the balance going to Home Health Services, SNF, and Inpatient Rehab. The synthetic data is realistic for academic centers; the 51% target may be calibrated to community hospitals.
+- **CMI runs ~14% below academic target (1.44 vs 1.65 target).** This reflects a slight under-weighting of MCC patients in the DRG sampling. For exact CMI calibration, the full product can be tuned via MCC rate parameters.
+- **Single facility type in this sample.** Only academic AMC is included; full product supports community + CAH for cross-facility comparative analysis.
+- **MRN is synthetic random integer.** No SSA / SSN / real patient identifiers. The `mrn_synthetic` column exists for join-key purposes only.
+- **No ICD-10 detail codes.** This sample uses MS-DRG codes (~25 groups); full ICD-10-CM diagnosis detail is in the companion HLT-002 EHR dataset.
+- **No physician / nurse identifiers.** Care team attribution is not in this sample (provider productivity analysis requires the full product with team-level extensions).
+- **Bed utilization is sampled from a subset of admissions.** The bed_utilization.csv aggregates daily census patterns; individual ADT events are derived from a sample of admissions for tractability. For full ADT event logs, contact us.
+- **Race/ethnicity, payer, and SDOH categories follow CMS/CDC public reporting conventions.** Use for equity research with appropriate care.
+
+---
+
+## Ethical use guidance
+
+This dataset is designed for:
+- Hospital operations analytics development
+- Readmission / mortality / HAC risk modeling research
+- Bed utilization / capacity planning ML
+- Educational use in health services research
+- Synthetic data validation methodology research
+- ETL pipeline testing for inpatient claims data
+
+This dataset is **not appropriate for**:
+- Making decisions about real individual patients
+- Insurance underwriting, pricing, or claim adjudication
+- Hospital quality scoring or pay-for-performance modeling without real-data validation
+- Training models that produce clinical recommendations without separate validation
+- Discriminatory analyses targeting protected demographic groups
+
+---
+
+## Companion datasets in the Healthcare vertical
+
+- [HLT-001](https://huggingface.co/datasets/xpertsystems/hlt001-sample) — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated)
+- [HLT-002](https://huggingface.co/datasets/xpertsystems/hlt002-sample) — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles)
+- [HLT-003](https://huggingface.co/datasets/xpertsystems/hlt003-sample) — Synthetic Clinical Trial Dataset (3 endpoint types + power sweep)
+- [HLT-004](https://huggingface.co/datasets/xpertsystems/hlt004-sample) — Synthetic Disease Progression Dataset (NSCLC + Heart Failure longitudinal)
+- **HLT-005** — Synthetic Hospital Admission Dataset (you are here)
+
+Use **HLT-001 through HLT-005 together** for the full healthcare data stack: population → EHR encounters → clinical trials → disease progression → inpatient admissions.
+
+---
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{xpertsystems_hlt005_sample_2026,
+  author       = {XpertSystems.ai},
+  title        = {HLT-005 Synthetic Hospital Admission Dataset (Sample Preview)},
+  year         = 2026,
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/xpertsystems/hlt005-sample}
+}
+```
+
+---
+
+## Contact
+
+- **Web:** [https://xpertsystems.ai](https://xpertsystems.ai)
+- **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai)
+- **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more
+
+**Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0)
+**Full product License:** Commercial — please contact for pricing.