DOI: 10.5281/zenodo.17466783
title: Synthetic Outpatient Scheduling Dataset (2016–2025) generated with Medscheduler

description: Description

This dataset simulates a realistic outpatient appointment scheduling system, generated using the open-source Python library Medscheduler (v0.2.2).It is entirely synthetic — no real patient information is included — yet designed to reproduce statistically realistic appointment behaviors and patient demographics observed in real healthcare systems.

The dataset covers a 10-year simulation period (2016–2025) and was generated using default library parameters with a fixed random seed for reproducibility.The configuration models a standard outpatient clinic operating Monday to Friday, from 8:00 AM to 6:00 PM, with 15-minute appointment intervals and approximately 90% calendar utilization.

Purpose

This dataset aims to provide a reproducible, realistic, and safe-to-use data resource for professionals and learners working in health analytics, software engineering, or data science.

Intended uses include:





Learning — understanding healthcare data structures, relational modeling, and analytics workflows.




Prototyping — developing and testing dashboards, ML pipelines, or scheduling algorithms.




Research & Teaching — simulating outpatient operations without handling sensitive patient data.




Portfolio Building — demonstrating data manipulation, visualization, and reproducible modeling skills in a healthcare context.



Dataset Structure

The dataset contains three main tables:

1. Slots Table (slots.csv)








Column
Description




slot_id
Unique identifier for each available time slot.


appointment_date
Date of the slot.


appointment_time
Scheduled time in 15-minute intervals.


is_available
Boolean flag indicating if the slot is free or booked.


weekday
Day of the week (0=Monday, …, 6=Sunday).


month
Month number of the year (1–12).






2. Patients Table (patients.csv)








Column
Description




patient_id
Unique identifier for each synthetic patient.


name
Synthetic name generated using the Faker library.


sex
Patient gender (“Male”, “Female”, “Non-binary”).


dob
Date of birth.


age
Age at the reference date (2025-12-01).


age_group
Age category (5-year bins, 15–90).


visits_per_year
Simulated mean annual visit frequency (≈1.2).






3. Appointments Table (appointments.csv)








Column
Description




appointment_id
Unique identifier for each appointment.


slot_id
References the corresponding slot.


patient_id
References the patient.


scheduling_date
Date when the appointment was booked.


appointment_date
Date of the actual visit.


scheduling_interval
Days between booking and visit (median ≈10).


status
Appointment outcome: “attended”, “cancelled”, “did not attend”, or “unknown”.


check_in_time
Actual patient arrival time (mean ≈10 minutes early).


start_time, end_time
Simulated consultation times.


appointment_duration
Duration in minutes (mean ≈17, median ≈16).


waiting_time
Waiting time in minutes.


age, sex, age_group
Patient information replicated for convenience.






Key Simulation Parameters








Parameter
Default Value
Source / Description




Working Days
Monday–Friday
Typical outpatient operation


Working Hours
08:00–18:00
Continuous 10-hour day


Appointments per Hour
4
15-minute slots


Fill Rate
0.9
Approx. 90% of slots filled


Booking Horizon
30 days
Forward window for future bookings


Median Lead Time
10 days
Typical booking delay


Check-in Offset
−10 min
Patients arrive early on average


Rebooking Intensity
“medium”
~50% of cancellations are rescheduled


Visits per Year
1.2
Average outpatient visit frequency


Age Range
15–90 years
Truncated at realistic limits






Evidence and Calibration

MedScheduler’s defaults are calibrated using open NHS datasets and published studies:





Ellis & Jenkins (2012) – weekday attendance patterns.




NHS Hospital Outpatient Activity (2023–24) – monthly outpatient activity weights.




Grande et al. (2018) – lead times and booking behaviors.




Tai-Seale et al. (2007) – consultation duration averages.




Cerruti et al. (2023) – punctuality and arrival offsets.



All probabilistic models (attendance, timing, seasonality) are implemented as parameterized stochastic distributions, with optional control via random seed and noise factor for reproducibility.

Reference Date and Simulation Window





Date range: January 1st, 2016 – December 31st, 2025




Reference date: December 1st, 2025(Appointments before this date are considered historical, while those after are upcoming.)



Licensing and Reuse

This dataset is released under the Creative Commons Attribution 4.0 (CC BY 4.0) license.You are free to use, share, and adapt it for any purpose, provided proper attribution is given.

Generator: Medscheduler v0.2.2Repository: https://github.com/carogaltier/medscheduler

Suggested Citation

González Galtier, C. (2025). Synthetic Outpatient Scheduling Dataset (2016–2025) generated with Medscheduler [Data set]. Zenodo.https://doi.org/10.5281/zenodo.17466783

Keywords

synthetic healthcare data, appointment scheduling, outpatient simulation, healthcare analytics, reproducible data, data generation, no-show prediction, operational modeling, Medscheduler