medscheduler 0.1.8

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

medscheduler

medscheduler is a lightweight Python library for generating fully synthetic, statistically plausible outpatient appointment data. It simulates daily clinic calendars, patient cohorts, and appointment outcomes with healthcare‑aware defaults and strict validation.

Typical uses:

• Teaching and training in healthcare data science
• Prototyping dashboards, capacity planning, and scheduling models
• Reproducible experiments and benchmarks without PHI/PII risks

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Features

• Configurable clinic calendars (date ranges, working days/hours, capacity per hour)
• Patient cohort with realistic age–sex distributions
• Probabilistic scheduling: fill rate, first attendances, rebooking behavior
• Attendance outcomes with sensible defaults (attended, DNA, cancelled, unknown)
• Punctuality and check‑in time simulation
• Clear validation and informative error messages
• Minimal dependencies; optional plotting helpers

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Installation

From PyPI:

pip install medscheduler

Optional plots (Matplotlib):

pip install "medscheduler[viz]"

Requires Python 3.9 or newer.

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Quickstart

from medscheduler import AppointmentScheduler

# Instantiate with defaults (seed for reproducibility)
sched = AppointmentScheduler(seed=42)

# Generate the three core tables
slots_df, appts_df, patients_df = sched.generate()

# Optionally export to CSV
sched.to_csv(
    slots_path="slots.csv",
    patients_path="patients.csv",
    appointments_path="appointments.csv",
)

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Core concepts (overview)

• Calendar & capacity: date_ranges, working_days, working_hours, appointments_per_hour
• Demand & booking: fill_rate, booking_horizon, median_lead_time, rebook_category
• Outcomes: status_rates (attended / did not attend / cancelled / unknown)
• Demographics: age_gender_probs, bin_size, lower_cutoff, upper_cutoff, truncated
• First attendances: first_attendance (ratio)
• Punctuality: check_in_time_mean and related timing fields
• Reproducibility: seed controls the RNG

All defaults are overrideable at instantiation time.

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Outputs

generate() returns three pandas DataFrames:

• slots — canonical calendar of available appointment slots
  Columns include: slot_id, appointment_date, appointment_time, is_available, …
• appointments — scheduled visits with status and timing fields
  Columns include: appointment_id, slot_id, status, scheduling_date, check_in_time, start_time, end_time, …
• patients — synthetic cohort linked to appointments
  Columns include: patient_id, sex, age (or dob and age_group), plus any custom columns you add

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📊 Plotting Examples (optional)

If you installed the visualization extra (pip install "medscheduler[viz]"), you can generate quick diagnostic plots.
All functions return a Matplotlib Axes object. In Jupyter/Colab, plots are displayed automatically; in scripts, call plt.show().

import matplotlib.pyplot as plt
from medscheduler import AppointmentScheduler
from medscheduler.utils.plotting import (
    plot_past_slot_availability,
    plot_future_slot_availability,
    plot_monthly_appointment_distribution,
    plot_weekday_appointment_distribution,
    plot_population_pyramid,
    plot_appointments_by_status,
    plot_appointments_by_status_future,
    plot_status_distribution_last_days,
    plot_status_distribution_next_days,
    plot_scheduling_interval_distribution,
    plot_appointment_duration_distribution,
    plot_waiting_time_distribution,
    plot_arrival_time_distribution
)

# Generate synthetic data
sched = AppointmentScheduler(seed=42)
slots_df, appts_df, patients_df = sched.generate()

# Weekday distribution of appointments
ax = plot_weekday_appointment_distribution(appts_df)
plt.show()

# Monthly distribution of appointments
ax = plot_monthly_appointment_distribution(appts_df)
plt.show()

# Age–sex pyramid for patients
ax = plot_population_pyramid(appts_df)
plt.show()

Documentation & examples

A tutorial series of Jupyter notebooks (Quickstart, Core Calendar, Fill Rate & Rebooking, Status Rates, Check‑in Time, Age/Gender, Seasonality, Scenarios, Validation) will be published as project documentation.
For now, see the Quickstart above and the docstrings of AppointmentScheduler and utilities.

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Testing (for contributors)

pip install -e .[dev]
pytest -q

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License

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