Medication Adherence Analysis
Medication adherence refers to how closely patients follow their prescribed medication regimens. Good adherence means patients take their medications as directed, while poor adherence can lead to worse health outcomes, increased hospitalizations, and higher healthcare costs. For pharmacists, analyzing medication adherence data helps identify patients at risk of non-adherence, allowing for targeted interventions to improve health outcomes.
12345678910import pandas as pd # Create a DataFrame with patient adherence data data = { "patient_id": [101, 102, 103, 104, 105], "total_prescribed_doses": [30, 30, 30, 30, 30], "doses_taken": [28, 24, 30, 18, 26] } df = pd.DataFrame(data) print(df)
To assess medication adherence, calculate the adherence percentage for each patient using the formula: (doses taken / total prescribed doses) * 100. By adding this calculation as a new column in your DataFrame, you can quickly identify patients whose adherence falls below a critical threshold, such as 80%. These patients may require additional support or counseling to improve their adherence and overall treatment outcomes.
123456# Calculate adherence percentage and add as a new column df["adherence_percent"] = (df["doses_taken"] / df["total_prescribed_doses"]) * 100 # Filter patients with adherence below 80% at_risk = df[df["adherence_percent"] < 80] print(at_risk[["patient_id", "adherence_percent"]])
1. How is medication adherence percentage calculated?
2. Why is it important to identify patients with low adherence?
3. Which pandas operation can be used to add a calculated column to a DataFrame?
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Medication adherence refers to how closely patients follow their prescribed medication regimens. Good adherence means patients take their medications as directed, while poor adherence can lead to worse health outcomes, increased hospitalizations, and higher healthcare costs. For pharmacists, analyzing medication adherence data helps identify patients at risk of non-adherence, allowing for targeted interventions to improve health outcomes.
12345678910import pandas as pd # Create a DataFrame with patient adherence data data = { "patient_id": [101, 102, 103, 104, 105], "total_prescribed_doses": [30, 30, 30, 30, 30], "doses_taken": [28, 24, 30, 18, 26] } df = pd.DataFrame(data) print(df)
To assess medication adherence, calculate the adherence percentage for each patient using the formula: (doses taken / total prescribed doses) * 100. By adding this calculation as a new column in your DataFrame, you can quickly identify patients whose adherence falls below a critical threshold, such as 80%. These patients may require additional support or counseling to improve their adherence and overall treatment outcomes.
123456# Calculate adherence percentage and add as a new column df["adherence_percent"] = (df["doses_taken"] / df["total_prescribed_doses"]) * 100 # Filter patients with adherence below 80% at_risk = df[df["adherence_percent"] < 80] print(at_risk[["patient_id", "adherence_percent"]])
1. How is medication adherence percentage calculated?
2. Why is it important to identify patients with low adherence?
3. Which pandas operation can be used to add a calculated column to a DataFrame?
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