README.md

metadata

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.

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:

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
• 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).