Datasets:

xpertsystems
/

hlt005-sample

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.

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

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())

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 — 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.

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}
}

Contact

Web: https://xpertsystems.ai
Email: pradeep@xpertsystems.ai
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Sample License: CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) Full product License: Commercial — please contact for pricing.