xpertsystems/hlt005-sample

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.

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.

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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).

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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)

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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

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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).

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Loading examples

Pandas

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

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

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

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())

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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

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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.

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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.

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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

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Companion datasets in the Healthcare vertical

• HLT-001 — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated)
• HLT-002 — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles)
• HLT-003 — Synthetic Clinical Trial Dataset (3 endpoint types + power sweep)
• HLT-004 — 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.

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Citation

If you use this dataset, please cite:

@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}
}

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Contact

• Web: https://xpertsystems.ai
• Email: 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.