Learn Scheduling and Reminders with Python | Healthcare Automation and Reporting

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Scheduling is a critical aspect of healthcare operations, directly impacting both patient outcomes and the efficiency of medical staff. Missed appointments can lead to delayed care, increased costs, and poorer health results for patients. By automating scheduling and reminder processes with Python, you can help reduce no-shows and ensure that patients receive timely care. Automation streamlines communication, allowing healthcare professionals to focus more on patient care and less on administrative tasks.


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import pandas as pd
from datetime import datetime, timedelta

# Create a DataFrame with sample appointments
appointments = pd.DataFrame({
    "patient_name": ["Alice Smith", "Bob Jones", "Carol White"],
    "date": [
        datetime.today() + timedelta(days=1),
        datetime.today() + timedelta(days=3),
        datetime.today() + timedelta(days=10)
    ],
    "time": ["10:00 AM", "2:00 PM", "9:00 AM"]
})

# Simulate sending reminders for appointments within the next 7 days
today = datetime.today()
seven_days = today + timedelta(days=7)

for idx, row in appointments.iterrows():
    if today <= row["date"] <= seven_days:
        print(f"Reminder: {row['patient_name']}, you have an appointment on {row['date'].strftime('%Y-%m-%d')} at {row['time']}.")

This script first creates a list of upcoming appointments using a pandas DataFrame. It then checks each appointment to see if it falls within the next seven days. For each qualifying appointment, it prints a reminder message with the patient's name, appointment date, and time. This simulates how an automated system could send reminders, helping patients remember their appointments and reducing the likelihood of missed visits. The date comparison ensures that only relevant reminders are generated, and the message formatting makes the reminder clear and actionable for the patient.


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import pandas as pd
from datetime import datetime, timedelta

# Sample appointment data
appointments = pd.DataFrame({
    "patient_name": ["Alice Smith", "Bob Jones", "Carol White"],
    "date": [
        datetime.today() + timedelta(days=1),
        datetime.today() + timedelta(days=3),
        datetime.today() + timedelta(days=10)
    ]
})

# Filter appointments for the next week
today = pd.Timestamp.today()
next_week = today + pd.Timedelta(days=7)
upcoming = appointments[(appointments["date"] >= today) & (appointments["date"] <= next_week)]

print(upcoming)

1. How can automation improve appointment adherence in healthcare?

2. What pandas function is useful for filtering rows based on date criteria?

3. Fill in the blank: To filter rows where 'date' is after today, use df[df['date'] ____ pd.Timestamp.today()].

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Section 3. Chapter 3