About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Readmission Risk Assessment
- Language – English
- Category – Predictive Healthcare
- Prompt Title – AI Prompt for Predicting Patient Readmission Risk Based on Medical History
Prompt Details
**Prompt Type:** Dynamic
**Target AI Platform:** All
**Niche:** Readmission Risk Assessment
**Purpose:** Predictive Healthcare
**Prompt Structure:**
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## Patient Readmission Risk Prediction
**Objective:** Predict the likelihood of readmission within 30 days for the patient described below. Provide a risk score (low, medium, or high) and a detailed rationale explaining the factors influencing the prediction.
**Input Data:**
* **Patient Demographics:** {age}, {gender}, {race/ethnicity}, {zip code}
* **Medical History:** {list of diagnoses using ICD-10 codes}, {list of procedures using ICD-10-PCS codes}, {dates of diagnoses and procedures}, {medication history (including names, dosages, and start/end dates)}, {allergies}
* **Hospitalization Details (for the index admission):** {admission date}, {discharge date}, {primary diagnosis (ICD-10 code)}, {secondary diagnoses (ICD-10 codes)}, {procedures performed (ICD-10-PCS codes)}, {length of stay}, {discharge disposition (e.g., home, skilled nursing facility)}
* **Social Determinants of Health (Optional, but highly recommended):** {living situation}, {support system}, {access to transportation}, {socioeconomic status indicators (e.g., insurance type, employment status)}
* **Laboratory Results (Optional):** {relevant lab values with dates and units}
* **Vital Signs (Optional):** {vital signs with dates and times}
**Output Requirements:**
1. **Risk Score:** Classify the patient’s readmission risk as “Low,” “Medium,” or “High.”
2. **Rationale:** Provide a detailed explanation of the factors contributing to the predicted risk score. This explanation should clearly link specific input data elements (e.g., diagnoses, medications, social factors) to their influence on the prediction. Quantify the impact of these factors whenever possible. For example, specify if a particular diagnosis increases the risk by a certain percentage.
3. **Key Factors:** List the 3-5 most influential factors driving the prediction.
4. **Mitigation Strategies (Optional):** Suggest specific, actionable interventions that could reduce the patient’s risk of readmission. These could include medication adjustments, follow-up appointments, care coordination strategies, or referrals to social services.
**Important Considerations:**
* **Data Format:** Ensure the input data is provided in a structured and consistent format. Using standardized codes (e.g., ICD-10, ICD-10-PCS) for diagnoses and procedures is essential for accurate interpretation.
* **Missing Data:** If certain data elements are unavailable, explicitly state this in the prompt. The AI should be able to handle missing data gracefully and still provide a prediction, although the accuracy might be affected.
* **Bias Awareness:** Be aware of potential biases in the data and the AI model. Consider factors like race, ethnicity, and socioeconomic status and how they might influence the prediction. Strive for equitable and unbiased risk assessments.
* **Explainability:** Prioritize explainability and transparency in the AI’s output. The rationale should be easily understandable by healthcare professionals and provide actionable insights for patient care.
**Example:**
**(Replace the placeholders with actual patient data)**
**Patient Demographics:** {age: 75}, {gender: Female}, {race/ethnicity: White}, {zip code: 90210}
… (rest of the input data)
**Expected Output (Illustrative):**
1. **Risk Score:** High
2. **Rationale:** The patient’s advanced age (75), recent hospitalization for heart failure (I50.9), history of diabetes (E11.9), and current use of multiple medications (including insulin and diuretics) significantly increase her risk of readmission. Her limited social support and lack of access to reliable transportation further elevate the risk. The heart failure diagnosis contributes approximately 40% to the overall risk, while the diabetes adds another 25%.
3. **Key Factors:** Heart failure, diabetes, polypharmacy, limited social support, lack of transportation.
4. **Mitigation Strategies:** Ensure medication reconciliation and adherence counseling upon discharge. Schedule a follow-up appointment with a cardiologist and primary care physician within one week. Arrange for home healthcare services to assist with medication management and monitor vital signs. Connect the patient with community resources to address transportation challenges and provide social support.
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This dynamic prompt structure allows for customization based on individual patient data while providing clear instructions and expectations for the AI. By adhering to best practices in prompt engineering, this approach enhances the accuracy, reliability, and interpretability of AI-driven readmission risk predictions, ultimately contributing to improved patient outcomes.