---
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