medscheduler 0.2.1

Synthetic outpatient scheduling dataset generator (slots, patients, appointments).

Medscheduler

Synthetic Outpatient Appointment Data Generator

medscheduler creates realistic, privacy-safe outpatient datasets — including appointment calendars, patient demographics, and visit outcomes — suitable for education, analytics, and research in healthcare operations.

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Features

• End-to-end simulation
  • Generates slots, patients, and appointments tables.
  • Reproduces booking, cancellation, and rebooking dynamics.
  • Simulates punctuality and in-clinic timing (arrival, start, end, waiting time, duration).
• Realistic defaults
  • Parameters reflect NHS England outpatient activity (2023–24) and peer-reviewed literature.
• Configurable
  • Calendar structure (days, hours, slot density), fill rate, booking horizon, lead time, attendance outcomes, rebooking intensity, demographics, and randomness.
• Reproducible
  • Controlled via seed and noise.
• Lightweight
  • Minimal scientific-Python dependencies; plotting utilities are optional.

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Installation

pip install medscheduler

Requires Python 3.9 or newer.

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Quickstart

from medscheduler import AppointmentScheduler

# Initialize the scheduler
sched = AppointmentScheduler(
    seed=42,
    date_ranges=[("2024-01-01", "2024-12-31")],
    working_days=[0, 1, 2, 3, 4],  # Monday–Friday
    appointments_per_hour=4,       # 15-minute slots
    fill_rate=0.9
)

# Run the full pipeline
slots_df, appointments_df, patients_df = sched.generate()

# Export CSV files
sched.to_csv(
    slots_path="slots.csv",
    appointments_path="appointments.csv",
    patients_path="patients.csv",
)

Outputs:

Table	Description
slots.csv	Calendar capacity (one row per slot).
patients.csv	Synthetic patient registry (demographics).
appointments.csv	Central table combining patient, slot, timing, and outcome data.

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Core Concepts

Calendar and Capacity

• date_ranges and ref_date delimit the simulation window and separate past from future.
• working_days, working_hours, and appointments_per_hour define slot structure and density.

Booking Dynamics

• fill_rate controls overall utilization.
• booking_horizon and median_lead_time shape how far ahead and how early patients book.
• rebook_category (min, med, max) defines the probability of rebooking cancellations.

Attendance and Flow

• status_rates determines attended / cancelled / did not attend / unknown proportions.
• visits_per_year and first_attendance regulate repeat visits and the share of new patients.

Demographics

• age_gender_probs, bin_size, lower_cutoff, upper_cutoff, truncated control the cohort, derived from NHS distributions by default.

Timing

• check_in_time_mean controls early/late arrivals.
• Durations follow a Beta(1.48, 3.6) model (mean ≈ 17 minutes).

Randomness

• seed ensures reproducibility; noise introduces controlled variability.

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API Surface (selected)

• AppointmentScheduler.generate() — full pipeline: slots → appointments → patients
• AppointmentScheduler.generate_slots()
• AppointmentScheduler.generate_appointments()
• AppointmentScheduler.assign_actual_times()
• AppointmentScheduler.generate_patients()
• AppointmentScheduler.assign_patients()
• AppointmentScheduler.add_custom_column()
• AppointmentScheduler.to_csv()

See the full API in the documentation.

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Plotting Utilities

Module: medscheduler.utils.plotting

• summarize_slots(df, scheduler, ...) — summary metrics for calendar and availability.
• plot_population_pyramid(df, ...) — age–sex pyramid.
• plot_past_slot_availability(slots_df, ...) — availability before ref_date (Y/Q/M/W auto-aggregation).
• plot_future_slot_availability(slots_df, ...) — availability on/after ref_date (D/W/M).
• plot_monthly_appointment_distribution(df) — appointments by month (%).
• plot_weekday_appointment_distribution(df) — appointments by weekday (%).
• plot_status_distribution_last_days(df, scheduler, days_back=30, ...) — daily status counts last N days.
• plot_status_distribution_next_days(df, scheduler, days_ahead=30, ...) — daily status counts next N days.
• plot_appointments_by_status(df, scheduler, ...) — past appointments by status (%).
• plot_appointments_by_status_future(df, scheduler, ...) — future appointments by status (%).
• plot_scheduling_interval_distribution(df, interval_col="scheduling_interval", ...) — lead-time distribution.
• plot_appointment_duration_distribution(df, ...) — consultation duration distribution (attended only).
• plot_waiting_time_distribution(df, ...) — waiting time distribution (attended only).
• plot_arrival_time_distribution(df, ...) — arrival offset distribution vs scheduled time.
• plot_first_attendance_distribution(df, scheduler, ...) — first vs. returning attendance ratio.
• plot_custom_column_distribution(df, column_name, ...) — categorical distribution for user-added columns.
• plot_patients_visits(df, scheduler, ...) — distribution of patient visit frequency.

All functions return a matplotlib.axes.Axes and follow a consistent, publication-grade styling.

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Visualization Gallery

Below are examples of the default visualization set produced by medscheduler.utils.plotting.

See the complete gallery at https://medscheduler.readthedocs.io/en/latest/visualization.

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Repository Structure

medscheduler/
├─ src/medscheduler/
│  ├─ __init__.py
│  ├─ constants.py
│  ├─ scheduler.py
│  └─ utils/
│     ├─ plotting.py
│     └─ reference_data_utils.py
├─ tests/
│  └─ test_scheduler.py
├─ docs/
│  ├─ _static/logo.png
│  └─ _static/visuals/...
├─ README.md
├─ LICENSE
└─ pyproject.toml

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Testing

pytest -q

The test suite covers constructor validation and generation logic. Target coverage ≥ 80%.

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Documentation

Comprehensive documentation (User Guide, API Reference, Examples, and Visualization gallery) is available at:

• https://medscheduler.readthedocs.io

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References

• Buttz, L. (2004). How to use scheduling data to improve efficiency. Family Practice Management, 11(7), 27–29. PMID: 15315285.
• Cerruti, B., Garavaldi, D., & Lerario, A. (2023). Patient's punctuality in an outpatient clinic: the role of age, medical branch and geographical factors. BMC Health Services Research, 23(1), 1385. https://doi.org/10.1186/s12913-023-10379-w
• Ellis, D. A., & Jenkins, R. (2012). Weekday affects attendance rate for medical appointments: Large-scale data analysis and implications. PLoS ONE, 7(12), e51365. https://doi.org/10.1371/journal.pone.0051365
• Grande, D., Zuo, J. X., Venkat, R., Chen, X., Ward, K. R., Seymour, J. W., & Mitra, N. (2018). Differences in Primary Care Appointment Availability and Wait Times by Neighborhood Characteristics: a Mystery Shopper Study. Journal of General Internal Medicine, 33(9), 1441–1443. https://doi.org/10.1007/s11606-018-4407-9
• NHS Digital. Provisional Monthly Hospital Episode Statistics for Admitted Patient Care, Outpatient and Accident and Emergency Data. https://digital.nhs.uk/data-and-information/publications/statistical/provisional-monthly-hospital-episode-statistics-for-admitted-patient-care-outpatient-and-accident-and-emergency-data/april-2025---may-2025
• NHS England (2024). Hospital Outpatient Activity 2023–24: Summary Reports 1–3. https://files.digital.nhs.uk/34/18846B/hosp-epis-stat-outp-rep-tabs-2023-24-tab.xlsx
• Rao, A., Shi, Z., Ray, K. N., Mehrotra, A., & Ganguli, I. (2019). National Trends in Primary Care Visit Use and Practice Capabilities, 2008–2015. Annals of Family Medicine, 17(6), 538–544. https://doi.org/10.1370/afm.2474
• Tai-Seale, M., McGuire, T. G., & Zhang, W. (2007). Time allocation in primary care office visits. Health Services Research, 42(5), 1871–1894. https://doi.org/10.1111/j.1475-6773.2006.00689.x
• Faker library documentation. https://faker.readthedocs.io/

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License

This project is released under the MIT License. See LICENSE for details.

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Citation

If this library is helpful in your work, please cite:

Carolina González Galtier. medscheduler: A synthetic outpatient appointment simulator, 2025.
GitHub: https://github.com/carogaltier/medscheduler