Synthetic Outpatient Scheduling Dataset (2016–2025) generated with Medscheduler

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