| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| language: |
| - en |
| tags: |
| - synthetic |
| - healthcare |
| - scheduling |
| - appointments |
| - operations |
| - tabular |
| size_categories: |
| - 10M<n<100M |
| pretty_name: Free Synthetic Clinic Appointment Scheduling (50M) |
| --- |
| |
| # Free Synthetic Clinic Appointment Scheduling (50M) |
|
|
| A free, fully synthetic dataset of **50,000,000 clinic appointment records**, built for developers and researchers working on healthcare scheduling software, no-show prediction, capacity planning, or clinic-operations analytics. |
|
|
| This dataset is **operational only** — it describes the logistics of scheduling appointments, not anything clinical. There are no symptoms, diagnoses, treatments, medications, or lab values anywhere in it. Every field is about *when* and *how* an appointment was booked and whether it happened — the kind of data a scheduling system or an operations dashboard runs on. Every value is artificially generated; no real patients, providers, or clinics are represented. |
|
|
| No-show behavior is deliberately correlated with booking lead time — appointments booked far in advance are missed more often, a well-documented operational pattern — so a no-show-prediction model trained or tested against this data has to actually learn the relationship rather than pattern-match on noise. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | appointment_id | string | Unique identifier for the appointment | |
| | patient_id | string | Synthetic patient reference (no personal attributes) | |
| | provider_id | string | Synthetic provider reference | |
| | department | string | Clinic department name (operational label only, no clinical detail) | |
| | appointment_type | string | One of: new_patient, follow_up, routine_checkup, consultation, procedure | |
| | scheduled_date | string | ISO date (`YYYY-MM-DD`) the appointment is booked for | |
| | scheduled_time | string | Time slot (`HH:MM`), clinic hours | |
| | duration_minutes | int | Scheduled slot length | |
| | booking_lead_time_days | int | Days between booking and the appointment | |
| | appointment_status | string | One of: completed, no_show, cancelled, rescheduled | |
| | wait_time_minutes | float | Actual wait past scheduled time — only populated for completed appointments, null otherwise | |
| | is_telehealth | bool | Whether the appointment is virtual | |
|
|
| ### How the correlations work |
|
|
| - **No-show rate rises with `booking_lead_time_days`** — appointments booked further out are missed more often. |
| - **`wait_time_minutes`** is only recorded for `completed` appointments — null for no-shows, cancellations, and reschedules. |
| - Certain departments (e.g. radiology, cardiology) carry longer average wait times, reflecting real operational load differences. |
| - Telehealth is more common for follow-ups and consultations than for procedures. |
| |
| ## Format |
| |
| Single Parquet file, Snappy compression, ~0.9 GB, 50,000,000 rows. |
| |
| ## Quick start |
| |
| ```python |
| import pandas as pd |
| df = pd.read_parquet("appointments_50M.parquet") |
| ``` |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("ziadatalabs/FreeSyntheticAppointmentScheduling50M") |
| ``` |
| |
| ```python |
| import duckdb |
| con = duckdb.connect() |
| con.sql("SELECT * FROM 'appointments_50M.parquet' LIMIT 10").show() |
| ``` |
| |
| ## Notes |
| |
| - Entirely operational/logistical — contains no clinical information of any kind (no symptoms, diagnoses, treatments, or lab data). |
| - `wait_time_minutes` is null for any appointment that wasn't completed — don't treat a missing wait as zero. |
| - Use `appointment_status` as your prediction target; `booking_lead_time_days` is the primary signal driving no-shows. |
| - `patient_id` and `provider_id` are bare synthetic references for grouping/joins — they carry no personal or demographic attributes. |
| |
| ## License & Usage |
| |
| Released under CC BY-NC 4.0 — free for personal, research, and educational use, with attribution. Not licensed for commercial use. |
| |
| No real people, appointments, or healthcare facilities are represented in this data. It is entirely synthetic. |
| |
| --- |
| |
| Created by Zia Data Labs. Questions or feedback: zia.data.team@protonmail.com |
| |
