Learn Generating Automated Patient Reports | Healthcare Automation and Reporting

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Automated patient reporting is a powerful way to make healthcare data more actionable for clinicians and patients. By generating summary reports, you can distill complex medical information into concise, easy-to-understand formats. These reports typically include key statistics, trends, and clear highlights of any values that may need attention. Automation ensures that summaries are consistent, timely, and less prone to human error, supporting better clinical decisions and patient engagement.


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import pandas as pd

# Example patient lab results DataFrame
data = {
    "Test": ["Hemoglobin", "WBC", "Platelets", "Glucose"],
    "Value": [13.2, 11.5, 250, 180],
    "Unit": ["g/dL", "10^3/uL", "10^3/uL", "mg/dL"],
    "Normal Low": [12.0, 4.0, 150, 70],
    "Normal High": [16.0, 10.5, 400, 140]
}
df = pd.DataFrame(data)

# Identify abnormal values
def flag_abnormal(row):
    if row["Value"] < row["Normal Low"]:
        return "Low"
    elif row["Value"] > row["Normal High"]:
        return "High"
    else:
        return ""

df["Abnormal"] = df.apply(flag_abnormal, axis=1)

# Display summary with abnormal values highlighted
summary = df[["Test", "Value", "Unit", "Abnormal"]]
print(summary)

This script loads a patient's lab results into a DataFrame, then checks each result against its normal range. The function flag_abnormal compares each lab value to its respective low and high thresholds. If a value falls below the normal range, it is marked as "Low"; if it exceeds the upper limit, it is marked as "High". Normal values remain unmarked. By adding an "Abnormal" column, the script makes it easy for clinicians to quickly scan the summary and focus on results that may require further action or explanation. This formatting reduces the risk of missing critical findings and improves communication with patients.


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# Export the summary report to a CSV file
summary.to_csv("patient_lab_summary.csv", index=False)
print("Summary exported to patient_lab_summary.csv")

1. Why is it important to highlight abnormal lab results in reports?

2. What Python library is commonly used to export DataFrames to CSV?

3. Fill in the blank: To save a DataFrame to a CSV file, use df.____('filename.csv').

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