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Automation is transforming healthcare by reducing manual workload, minimizing calculation errors, and allowing professionals to focus on patient care. Routine calculations, such as determining Body Mass Index (BMI) or medication dosages, are performed countless times each day. Automating these processes not only saves significant time but also ensures consistency and accuracy across large sets of patient records.


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import pandas as pd

def calculate_bmi(weight_kg, height_cm):
    height_m = height_cm / 100
    return weight_kg / (height_m ** 2)

# Example list of patient records
patients = [
    {'name': 'Alice', 'weight_kg': 68, 'height_cm': 165},
    {'name': 'Bob', 'weight_kg': 82, 'height_cm': 175},
    {'name': 'Charlie', 'weight_kg': 90, 'height_cm': 180}
]

# Calculate BMI for each patient and add to record
for patient in patients:
    patient['BMI'] = round(calculate_bmi(patient['weight_kg'], patient['height_cm']), 2)

print(patients)

The Body Mass Index (BMI) is a widely used measurement to assess whether a person has a healthy body weight for their height. The formula for BMI is: weight in kilograms divided by the square of height in meters. By defining a function that performs this calculation and applying it to each patient record, you can efficiently process large datasets without manual repetition. This method ensures every patient's BMI is calculated accurately and consistently, regardless of the dataset's size.


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import pandas as pd

# Create DataFrame from patient data
df = pd.DataFrame([
    {'name': 'Alice', 'weight_kg': 68, 'height_cm': 165},
    {'name': 'Bob', 'weight_kg': 82, 'height_cm': 175},
    {'name': 'Charlie', 'weight_kg': 90, 'height_cm': 180}
])

def calculate_bmi(weight_kg, height_cm):
    height_m = height_cm / 100
    return weight_kg / (height_m ** 2)

# Add a new 'BMI' column
df['BMI'] = df.apply(lambda row: round(calculate_bmi(row['weight_kg'], row['height_cm']), 2), axis=1)

print(df)

1. What is the formula for calculating BMI?

2. How does automation benefit healthcare professionals?

3. Fill in the blank: To apply a function to a DataFrame column, use `df['col'].___(func)`.

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Sektion 3. Kapitel 1

Automating Routine Healthcare Calculations

1. What is the formula for calculating BMI?

2. How does automation benefit healthcare professionals?

3. Fill in the blank: To apply a function to a DataFrame column, use df['col'].___(func).

3. Fill in the blank: To apply a function to a DataFrame column, use `df['col'].___(func)`.