Add TsFile (converted from xpertsystems/hlt010-sample)

.gitattributes

@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+equipment.tsfile filter=lfs diff=lfs merge=lfs -text
+or_schedule.tsfile filter=lfs diff=lfs merge=lfs -text
+staffing.tsfile filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series-forecasting
+language:
+- en
+tags:
+- tsfile
+- timeseries
+- synthetic
+- healthcare
+- hospital-operations
+- operating-room
+- or-utilization
+- surgical-scheduling
+- staffing
+- workforce
+- nursing-shortage
+- equipment
+- biomedical
+- capacity-planning
+- bed-management
+- ed-boarding
+- aha
+- aorn
+- nsi
+- ecri
+- cms-conditions-of-participation
+- perioperative
+- case-cancellation
+- first-case-ontime
+- or-turnover
+- rn-vacancy
+- icu-occupancy
+- time-series
+- format:tsfile
+pretty_name: HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview) (TsFile)
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: equipment
+    path: equipment.tsfile
+  - split: facilities
+    path: facilities.tsfile
+  - split: hospital_resources
+    path: hospital_resources.tsfile
+  - split: or_schedule
+    path: or_schedule.tsfile
+  - split: staffing
+    path: staffing.tsfile
+---
+
+# HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview) (TsFile)
+
+Apache TsFile version of [`xpertsystems/hlt010-sample`](https://huggingface.co/datasets/xpertsystems/hlt010-sample).
+
+## Overview
+
+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.
+
+- **Tables:** 5 TsFile table(s), 35,255 rows in total.
+  - `equipment`: 17,461 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:01.238000 (UTC)
+  - `facilities`: 3 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.002000 (UTC)
+  - `hospital_resources`: 42 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.002000 (UTC)
+  - `or_schedule`: 4,226 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.189000 (UTC)
+  - `staffing`: 13,523 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.605000 (UTC)
+
+## Schema (TsFile structure)
+
+### `equipment.tsfile`
+
+- **Time** (INT64, milliseconds) — the source `record_date` column.
+- **TAG**: none — the table is stored as a single device.
+- **FIELD** (13): `asset_id` (string), `facility_id` (string), `equipment_class` (string), `equipment_age_yrs` (double), `utilization_rate` (double), `in_service_hours` (double), `downtime_hours` (double), `unplanned_downtime_flag` (int64), `downtime_cause` (string), `last_maintenance_date` (string), `next_maintenance_due` (string), `failure_flag` (int64), `repair_cost_usd` (double)
+
+### `facilities.tsfile`
+
+- **Time** (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
+- **TAG**: none — the table is stored as a single device.
+- **FIELD** (11): `facility_id` (string), `facility_type` (string), `teaching_status` (int64), `trauma_level` (string), `bed_count` (int64), `icu_beds` (int64), `or_suite_count` (int64), `pacu_bays` (int64), `state` (string), `region` (string), `daily_or_capacity` (double)
+
+### `hospital_resources.tsfile`
+
+- **Time** (INT64, milliseconds) — the source `census_date` column.
+- **TAG**: none — the table is stored as a single device.
+- **FIELD** (40): `facility_id` (string), `day_of_week` (string), `is_weekend` (int64), `total_beds` (int64), `occupied_beds` (int64), `occupancy_rate` (double), `icu_beds_x` (int64), `icu_occupied` (int64), `icu_occupancy_rate` (double), `pacu_bays_x` (int64), `pacu_patients` (int64), `pacu_utilization_rate` (double), `ed_boarding_hours` (double), `diversion_flag` (int64), `diversion_hours` (double), `capacity_breach_flag` (int64), `surge_day_flag` (int64), `or_utilization_rate` (double), `surgical_cases_scheduled` (int64), `or_revenue_usd` (double), `or_cost_per_min_usd` (double), `total_or_minutes` (int64), `contribution_margin_usd` (double), `block_release_efficiency` (double), `staffing_adequacy_score` (double), `operational_efficiency_index` (double), `surgical_site_infection_flag` (int64), `near_miss_flag` (int64), `consent_timeout_completed` (int64), `equipment_safety_check_flag` (int64), `facility_type` (string), `teaching_status` (int64), `trauma_level` (string), `bed_count` (int64), `icu_beds_y` (int64), `or_suite_count` (int64), `pacu_bays_y` (int64), `state` (string), `region` (string), `daily_or_capacity` (double)
+
+### `or_schedule.tsfile`
+
+- **Time** (INT64, milliseconds) — the source `case_date` column.
+- **TAG**: none — the table is stored as a single device.
+- **FIELD** (17): `case_id` (string), `facility_id` (string), `or_id` (string), `case_type` (string), `surgeon_id` (string), `is_emergency` (int64), `scheduled_start_min` (int64), `actual_start_min` (double), `start_delay_min` (double), `first_case_ontime_flag` (string), `scheduled_duration_min` (int64), `actual_duration_min` (double), `turnover_time_min` (double), `cancellation_flag` (int64), `cancellation_reason` (string), `block_owner` (string), `add_on_flag` (int64)
+
+### `staffing.tsfile`
+
+- **Time** (INT64, milliseconds) — the source `shift_date` column.
+- **TAG**: none — the table is stored as a single device.
+- **FIELD** (10): `shift_id` (string), `facility_id` (string), `staff_id` (string), `staff_role` (string), `shift_type` (string), `hours_worked` (int64), `overtime_flag` (int64), `float_pool_flag` (int64), `agency_flag` (int64), `staff_to_patient_ratio` (double)
+
+## Conversion notes
+
+- One TsFile per source CSV (5 tables), each stored as a single device: the facility id, asset id, staff id and surgeon id stay ordinary FIELDs.
+- Time = the daily reporting date of each table (`record_date`, `census_date`, `case_date`, `shift_date`) as INT64 milliseconds at 00:00 UTC. Several rows share a day, so they are ordered by a 1 ms step in stable source order; the calendar day is unaffected.
+- `facilities` is a 3-row facility master with no date column, so its Time is a synthetic row index. No column is dropped.
+
+## Usage
+
+Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
+
+```python
+from pathlib import Path
+from tsfile import TsFileReader
+
+path = Path("equipment.tsfile")
+with TsFileReader(str(path)) as reader:
+    schemas = reader.get_all_table_schemas()
+    print("tables:", list(schemas))
+    table_name = next(iter(schemas))
+    table = schemas[table_name]
+    columns = [column.get_column_name() for column in table.get_columns()]
+    print("columns:", columns)
+    field_names = [
+        column.get_column_name()
+        for column in table.get_columns()
+        if column.get_column_name() not in {"Time", "time"}
+    ]
+    if field_names:
+        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
+            batch = result.read_arrow_batch()
+            if batch is not None:
+                print(batch.to_pandas().head())
+```
+
+## Source & license
+
+- Original dataset: [`xpertsystems/hlt010-sample`](https://huggingface.co/datasets/xpertsystems/hlt010-sample)
+- Source card title / publisher: HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview)
+- License: cc-by-nc-4.0 (as declared by the original dataset).

equipment.tsfile

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b1c861ba0facf834f5c15204b12b6542499eadcea412327e85701202cb393666
+size 476189

or_schedule.tsfile

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38e79bc2204d8441ef436030be912d6989e53e3bc74e81605f986dd350e3a82d
+size 101112

source_dataset_card.md

@@ -0,0 +1,339 @@
+---
+license: cc-by-nc-4.0
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series-forecasting
+language:
+- en
+tags:
+- synthetic
+- healthcare
+- hospital-operations
+- operating-room
+- or-utilization
+- surgical-scheduling
+- staffing
+- workforce
+- nursing-shortage
+- equipment
+- biomedical
+- capacity-planning
+- bed-management
+- ed-boarding
+- aha
+- aorn
+- nsi
+- ecri
+- cms-conditions-of-participation
+- perioperative
+- case-cancellation
+- first-case-ontime
+- or-turnover
+- rn-vacancy
+- icu-occupancy
+pretty_name: HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview)
+size_categories:
+- 10K<n<100K
+---
+
+# HLT-010 — Synthetic Hospital Resource Usage Dataset (Sample Preview)
+
+**A free, schema-identical preview of the full HLT-010 commercial product from [XpertSystems.ai](https://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.
+
+---
+
+## 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.
+
+---
+
+## 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`
+
+---
+
+## 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).**
+
+---
+
+## Loading examples
+
+### Pandas — explore the operational data
+
+```python
+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
+
+```python
+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
+
+```python
+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
+
+```python
+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
+
+```python
+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))
+```
+
+---
+
+## 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
+
+---
+
+## 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.**
+
+---
+
+## 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.
+
+---
+
+## 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
+
+---
+
+## 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](https://huggingface.co/datasets/xpertsystems/hlt005-sample) — Synthetic Hospital Admission Dataset (5K admissions + bed utilization)
+- [HLT-006](https://huggingface.co/datasets/xpertsystems/hlt006-sample) — Synthetic Medical Imaging Dataset (1K studies + COCO annotations + reports)
+- [HLT-007](https://huggingface.co/datasets/xpertsystems/hlt007-sample) — Synthetic Drug Response Dataset (3K patient-treatments × 25 drug classes + PGx + PK)
+- [HLT-008](https://huggingface.co/datasets/xpertsystems/hlt008-sample) — Synthetic Healthcare Claims Dataset (500 members + 30K claims + fraud labels)
+- [HLT-009](https://huggingface.co/datasets/xpertsystems/hlt009-sample) — 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.
+
+---
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@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}
+}
+```
+
+---
+
+## 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.

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