xpertsystems/hlt010-sample

HLT-010 — Synthetic Hospital Resource Usage Dataset (Sample Preview)

A free, schema-identical preview of the full HLT-010 commercial product from XpertSystems.ai.

A fully synthetic hospital operations dataset combining operating room schedules, staffing/workforce records, biomedical equipment utilization, daily capacity metrics, and facility master data across mixed facility types (academic / large community / medium community / critical access). Calibrated to AHA Annual Survey 2023, AORN benchmarks, NSI nursing data, ECRI Institute equipment data, and CMS Conditions of Participation.

⚠️ PRIVACY & SYNTHETIC NATURE Every record in this dataset is 100% synthetic. No real patient data, no PHI, no real facility identifiers, no real surgeon or staff NPIs. Population-level distributions match published AHA / AORN / NSI / ECRI benchmark sources but the facilities and operational events are computationally generated.

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What's in this sample

File	Rows	Cols	Description
facilities.csv	3	11	Facility master — type, teaching status, trauma level, bed count, OR suites, PACU bays, region
hospital_resources.csv	42	41	Daily capacity + financial + quality KPIs per facility (14 days × 3 facilities)
or_schedule.csv	~4,200	18	One row per surgical case — 22 case types, scheduled vs actual timing, cancellations, block ownership
staffing.csv	~13,500	11	One row per staff-shift — 12 perioperative roles, OT/float/agency flags, staff-to-patient ratios
equipment.csv	~17,500	14	One row per equipment-day — 18 equipment classes, utilization, downtime, maintenance schedule, repair cost

Total: ~3.9 MB across 6 files.

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Schema highlights

facilities.csv (11 columns) — facility master

facility_id, facility_type (academic / large / medium / small), teaching_status (Major Teaching / Minor Teaching / Non-Teaching), trauma_level (Level I-IV), bed_count, icu_beds, or_suite_count, pacu_bays, state, region (Northeast / Midwest / South / West), daily_or_capacity

hospital_resources.csv (41 columns) — daily operational KPIs

Identity & temporal: facility_id, census_date, day_of_week, is_weekend

Bed capacity: total_beds, occupied_beds, occupancy_rate, icu_beds_x, icu_occupied, icu_occupancy_rate, pacu_bays_x, pacu_patients, pacu_utilization_rate

ED throughput: ed_boarding_hours, diversion_flag, diversion_hours, capacity_breach_flag, surge_day_flag

OR financial & operational: or_utilization_rate, surgical_cases_scheduled, or_revenue_usd, or_cost_per_min_usd, total_or_minutes, contribution_margin_usd, block_release_efficiency

Quality & safety: staffing_adequacy_score, operational_efficiency_index, surgical_site_infection_flag, near_miss_flag, consent_timeout_completed, equipment_safety_check_flag

or_schedule.csv (18 columns) — per-case scheduling

case_id, facility_id, case_date, or_id, case_type (22 types: Orthopedic, Cardiac, General Surgery, Neurosurgery, OB/GYN, Urology, ENT, Plastic Surgery, Vascular, Thoracic, Transplant, Trauma, Ophthalmology, Colorectal, Bariatric, Endoscopy, Interventional Radiology, Gynecologic Oncology, Pediatric Surgery, Spinal, Hand Surgery, Robotic Assisted), surgeon_id, is_emergency, scheduled_start_min, actual_start_min, start_delay_min, first_case_ontime_flag, scheduled_duration_min, actual_duration_min, turnover_time_min, cancellation_flag, cancellation_reason, block_owner, add_on_flag

staffing.csv (11 columns) — daily shift records

shift_id, facility_id, shift_date, staff_id, staff_role (12 roles: Surgeon, Anesthesiologist, CRNA, Scrub Tech, RN Circulator, PA/NP, Resident, Pharmacist, Radiology Tech, Biomedical Tech, Environmental Services, Unit Coordinator), shift_type (Day / Evening / Night), hours_worked, overtime_flag, float_pool_flag, agency_flag, staff_to_patient_ratio

equipment.csv (14 columns) — daily equipment utilization

asset_id, facility_id, record_date, equipment_class (18 classes including Anesthesia Machine, Patient Monitor, Infusion Pump, Electrosurgical Unit, Sterilization Autoclave, CT Scanner, MRI Scanner, C-Arm Fluoroscopy, Endoscope Processor, Intraoperative MRI, ECMO Circuit, CRRT Machine, Cardiac Cath Lab Equipment, Defibrillator, Ventilator, Robotic Surgical System, Imaging Workstation, Hybrid OR Imaging), equipment_age_yrs, utilization_rate, in_service_hours, downtime_hours, unplanned_downtime_flag, downtime_cause (Hardware Failure / Software Error / Power Surge / User Error / Calibration Failure / Component Wear / Connectivity Issue / Sensor Malfunction), last_maintenance_date, next_maintenance_due, failure_flag, repair_cost_usd

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Calibration source story

The full HLT-010 generator anchors all distributions to authoritative hospital operations references:

• AHA Annual Survey 2023 (American Hospital Association) — OR utilization (78.4%), case cancellations (8.2%), bed occupancy (81.2%), ED boarding (3.2hr), revenue per case (~$18,400)
• AORN Benchmarks (Association of periOperative Registered Nurses) — first-case on-time start (82%), OR turnover (28 ± 8 min), surgical tech vacancy (22.8%)
• NSI Nursing Solutions 2023 — RN vacancy rate (15.6%), turnover patterns
• ECRI Institute — Equipment unplanned downtime (~4.2%), age-related failure curves
• CMS Conditions of Participation — ICU occupancy target max 85%, staffing-to-patient ratios
• IHI (Institute for Healthcare Improvement) — Operational efficiency benchmarks, surge capacity

Sample-scale validation scorecard

Metric	Observed	Target	Tolerance	Status	Source
OR utilization rate	71.3%	70%	±10%	✅ PASS	AHA 2023
Case cancellation rate	7.6%	8%	±3%	✅ PASS	AHA 2023
First-case on-time rate	84.5%	82%	±8%	✅ PASS	AORN Benchmarks
OR turnover (min)	27.4	28.0	±4.0	✅ PASS	AORN
Bed occupancy rate	81.7%	78%	±10%	✅ PASS	AHA 2023
ED boarding hours (mean)	3.28	3.2	±1.2	✅ PASS	AHA 2023
ICU occupancy (under CMS max)	80.7%	≤85%	—	✅ PASS	CMS CoP
Equipment downtime rate	5.1%	4.8%	±1.8%	✅ PASS	ECRI Institute
Case type diversity	22	22	±2	✅ PASS	AORN surgical taxonomy
Staff role diversity	12	12	—	✅ PASS	AORN team composition

Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).

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Loading examples

Pandas — explore the operational data

import pandas as pd

facilities = pd.read_csv("facilities.csv")
capacity = pd.read_csv("hospital_resources.csv", parse_dates=["census_date"])
ors = pd.read_csv("or_schedule.csv", parse_dates=["case_date"])
staffing = pd.read_csv("staffing.csv", parse_dates=["shift_date"])
equipment = pd.read_csv("equipment.csv", parse_dates=["record_date"])

# OR utilization by facility type
print(capacity.merge(facilities, on="facility_id")
        .groupby("facility_type")["or_utilization_rate"]
        .agg(["mean", "std", "min", "max"]).round(3))

# Case type mix
print(ors["case_type"].value_counts(normalize=True).head(10).round(3))

# Cancellation reasons
print(ors.loc[ors["cancellation_flag"] == True, "cancellation_reason"]
        .value_counts())

Hugging Face Datasets

from datasets import load_dataset

ds = load_dataset("xpertsystems/hlt010-sample", data_files={
    "facilities":         "facilities.csv",
    "hospital_resources": "hospital_resources.csv",
    "or_schedule":        "or_schedule.csv",
    "staffing":           "staffing.csv",
    "equipment":          "equipment.csv",
})
print(ds)

OR utilization forecasting baseline

import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import train_test_split

cap = pd.read_csv("hospital_resources.csv", parse_dates=["census_date"])
cap["month"] = cap["census_date"].dt.month
cap["dayofweek_num"] = cap["census_date"].dt.dayofweek

features = ["bed_count", "or_suite_count", "is_weekend", "dayofweek_num",
            "month", "occupancy_rate", "icu_occupancy_rate",
            "surgical_cases_scheduled"]
X = cap[features].fillna(0)
y = cap["or_utilization_rate"]
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.3, random_state=42)
m = GradientBoostingRegressor(random_state=42).fit(Xtr, ytr)
print(f"OR utilization R²: {m.score(Xte, yte):.3f}")

Equipment maintenance prediction

import pandas as pd

eq = pd.read_csv("equipment.csv", parse_dates=["record_date"])

# Downtime rate by equipment age
eq["age_bucket"] = pd.cut(eq["equipment_age_yrs"],
                          [0, 3, 6, 10, 15],
                          labels=["0-3yr", "3-6yr", "6-10yr", "10-15yr"])
print(eq.groupby("age_bucket")["unplanned_downtime_flag"].mean().round(3))

# Repair cost distribution
print(eq.loc[eq["repair_cost_usd"] > 0, "repair_cost_usd"].describe())

Staffing analysis

import pandas as pd

staff = pd.read_csv("staffing.csv")

# Overtime rate by role
print(staff.groupby("staff_role")["overtime_flag"].mean().sort_values(ascending=False))

# Agency staff reliance
print(staff.groupby(["facility_id", "staff_role"])["agency_flag"]
        .mean().unstack().round(3))

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Suggested use cases

• OR utilization forecasting — predict next-day OR utilization from facility characteristics + recent operational patterns
• Surgical case cancellation prediction — classify cancellation risk to enable proactive intervention
• Block schedule optimization — analyze block release efficiency and underutilized blocks
• Equipment failure prediction — predict unplanned_downtime_flag from age + utilization + maintenance history
• Maintenance scheduling optimization — risk-adjusted preventive maintenance interval modeling
• Staffing-to-acuity matching — analyze staff_to_patient_ratio × acuity patterns for nurse scheduling
• Overtime / agency cost modeling — predict overtime hours and agency staffing needs
• Bed capacity surge prediction — predict surge_day_flag and diversion_flag from upstream factors
• ED boarding root cause analysis — relate ed_boarding_hours to ICU occupancy and discharge patterns
• Quality & safety event modeling — predict near-miss / SSI / consent timeout events from staffing + acuity
• Financial contribution margin modeling — analyze contribution margin drivers across facility types
• Hospital ML pretraining — pretrain operational forecasting models before fine-tuning on real EHR/EMR data
• Operations research education — perioperative scheduling, queueing theory, capacity planning coursework

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Sample vs. full product

Aspect	This sample	Full HLT-010 product
Facilities	3 (mixed)	50+ (default) up to 500+
Time window	14 days	365+ days (multi-year configurable)
Facility types	Mixed (3)	Mixed / academic-only / community-only / critical_access
Output format	CSV	CSV / Parquet / JSON
Schema	identical	identical
Calibration	identical	identical
License	CC-BY-NC-4.0	Commercial license

The full product unlocks:

• Up to 500+ facilities for system-wide operations modeling
• Multi-year longitudinal windows for trend analysis and intervention impact studies
• Configurable facility mix for targeted segmentation (academic-only / community-only / CAH)
• Parquet output for production data pipelines
• Commercial use rights

Contact us for the full product.

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Limitations & honest disclosures

• Sample is preview-only. 3 facilities × 14 days × ~35K operational records is enough to demonstrate schema and calibration, but is not statistically sufficient for facility-level capacity planning models or season-aware forecasting. Use the full product (50+ facilities × 365 days) for serious work.
• Sample includes 3 facility types (academic + large + medium), not all 4. The critical_access facility type is not represented at n=3 due to random sampling. The full product reliably covers all 4 types.
• OR utilization runs slightly below the headline AHA target. Sample mean is ~70% vs AHA 78.4% pure target. This is partly because mixed facility_mix includes community facilities (which average lower OR utilization) and partly small-N effects at 3 facilities × 14 days. The full product hits the AHA target at scale.
• Equipment downtime runs slightly elevated (5.1% vs ECRI 4.2%). The generator's age-based failure_multiplier produces realistic but somewhat-higher-than-target downtime for aging assets. Reflects real-world equipment fleet aging — production hospitals with younger fleets see lower rates.
• PACU utilization clips at 1.0. The generator caps PACU utilization at 100% rather than allowing over-capacity. At busy academic centers, real PACU congestion exceeds capacity (queue forms) — this is hidden by the cap.
• Staff IDs are synthetic random integers. No real NPIs, no real practitioner identifiers. Surgeon IDs are equally synthetic.
• Equipment IDs are synthetic identifiers, not real GUDID device IDs.
• Block-schedule data is daily-aggregated, not minute-level. The full product can be extended with minute-level block scheduling for highly-detailed OR room optimization.
• No real ICD-10 / CPT case data joins. Case types are categorical groupings (Orthopedic, Cardiac, etc.) — the full ICD-10/CPT/MS-DRG joins are in the companion HLT-005 hospital admission dataset.
• Synthetic, not derived from real hospital operations data. Distributions match published AHA/AORN/NSI/ECRI references but do NOT reflect any specific real hospital.

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Ethical use guidance

This dataset is designed for:

• Hospital operations analytics methodology development
• OR scheduling and capacity planning research
• Equipment maintenance prediction ML
• Nursing workforce analytics
• ED throughput optimization research
• Healthcare AI pretraining for operational forecasting
• Educational use in hospital operations management and operations research

This dataset is not appropriate for:

• Making real staffing decisions about real personnel
• Real surgeon performance evaluation
• Real equipment retirement/procurement decisions without validation
• Discriminatory analyses targeting protected demographic groups
• Hospital quality scoring or pay-for-performance modeling without real-data validation

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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 (5K admissions + bed utilization)
• HLT-006 — Synthetic Medical Imaging Dataset (1K studies + COCO annotations + reports)
• HLT-007 — Synthetic Drug Response Dataset (3K patient-treatments × 25 drug classes + PGx + PK)
• HLT-008 — Synthetic Healthcare Claims Dataset (500 members + 30K claims + fraud labels)
• HLT-009 — Synthetic Continuous Vital Sign Monitoring Dataset (25 ICU episodes + alarms)
• HLT-010 — Synthetic Hospital Resource Usage Dataset (you are here)

Use HLT-001 through HLT-010 together for the complete healthcare data stack: clinical (population/EHR/trials/progression) + operational (admissions/imaging/pharma/claims/monitoring/resources) — 10 datasets covering every major workflow in the modern hospital.

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Citation

If you use this dataset, please cite:

@dataset{xpertsystems_hlt010_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HLT-010 Synthetic Hospital Resource Usage Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hlt010-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.