1. Hospital Readmission Prediction

Goal: Predict if a patient will be readmitted within 30 days to reduce costs and improve care.

Data Needed:

• Patient demographics
• Diagnosis codes
• Previous admissions
• Treatment details, lab results
• Discharge summaries (Dataset: Diabetes 130-US hospitals dataset)

Steps:

1. Data cleaning (handle missing values, encode categorical features).
2. Feature engineering (count of past admissions, length of last stay).
3. Train-test split.
4. Apply classification models (Logistic Regression, XGBoost).
5. Evaluate using ROC-AUC, precision/recall.
6. Deploy dashboard showing predicted readmission risk.

Tools: Python (Scikit-learn, Pandas), Power BI.

Deliverable:

• Readmission risk scoring dashboard + intervention suggestions.

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2. No-Show Appointment Predictor

Goal: Predict whether a patient will show up for their appointment to improve scheduling.

Data Needed:

• Appointment ID, date/time
• Booking lead time
• Patient demographics
• Previous no-shows
• SMS reminder status (Dataset: Medical Appointment No Shows)

Steps:

1. Clean date/time columns, calculate days between booking and appointment.
2. Encode categorical variables (e.g., gender).
3. Train models (Random Forest, Gradient Boosting).
4. Feature importance to find main no-show reasons.

Tools: Python, Tableau for final visualization.

Deliverable:

• No-show prediction dashboard with patient flagging system.

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3. Health Insurance Claim Fraud Detection

Goal: Detect suspicious claims to prevent financial loss.

Data Needed:

• Claim ID
• Patient details
• Diagnosis and procedure codes
• Claim amount, service dates
• Historical fraud flags (Dataset: Healthcare Claims Fraud)

Steps:

1. Clean and standardize ICD codes.
2. Identify anomalies using isolation forests / clustering.
3. Build supervised fraud classifier (if labels exist).
4. Visualize high-risk claims.

Tools: Python (Scikit-learn, PyCaret), Power BI.

Deliverable:

• Fraud risk detection dashboard with claim details.

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4. Disease Outbreak Mapping

Goal: Map and track spread of diseases geographically.

Data Needed:

• Case ID
• Location (lat/long or district)
• Disease type
• Date reported (Dataset: WHO or CDC outbreak datasets)

Steps:

1. Aggregate cases by location & date.
2. Create time-series and geospatial layers.
3. Map hotspots and trend lines.

Tools: Python (Folium, GeoPandas), Tableau / Power BI maps.

Deliverable:

• Interactive outbreak map with time slider to show spread.

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5. Patient Satisfaction Survey Analysis

Goal: Analyze survey feedback to improve healthcare services.

Data Needed:

• Survey ID
• Patient demographics
• Ratings for doctors, facilities, wait times
• Comments (text feedback)

Steps:

1. Clean numerical ratings, normalize scale.
2. Text sentiment analysis on comments.
3. Correlate satisfaction with demographics.
4. Identify key improvement areas.

Tools: Python (NLTK, VADER), Power BI for KPIs.

Deliverable:

• Dashboard with satisfaction score trends and sentiment insights.

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6. Length of Stay Forecast

Goal: Predict how long a patient will stay in hospital for resource planning.

Data Needed:

• Admission diagnosis
• Age, gender
• Treatment procedures
• Historical stay durations

Steps:

1. Convert stay duration into numeric target variable.
2. Train regression models (XGBoost, Random Forest).
3. Analyze most influential features.

Tools: Python, Tableau.

Deliverable:

• LOS prediction dashboard with patient-specific estimates.

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7. Doctor Performance Evaluation

Goal: Evaluate doctors based on treatment outcomes, patient feedback, and productivity.

Data Needed:

• Doctor ID
• Number of patients treated
• Patient recovery rates
• Average treatment cost
• Feedback ratings

Steps:

1. Aggregate doctor-level KPIs.
2. Benchmark against department averages.
3. Rank doctors by performance index.

Tools: Power BI, SQL.

Deliverable:

• Performance dashboard with filters by department and time.

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8. Drug Usage Trend Analysis

Goal: Analyze usage patterns of specific drugs over time.

Data Needed:

• Prescription ID
• Drug name, category
• Prescription date
• Quantity dispensed

Steps:

1. Aggregate prescriptions by month/year.
2. Identify seasonal spikes.
3. Compare with treatment outcomes.

Tools: Power BI, Python (Matplotlib).

Deliverable:

• Trend charts + drug usage forecast.

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9. EHR Data Cleaning and Profiling

Goal: Prepare messy Electronic Health Record data for analysis.

Data Needed:

• Raw EHR extract
• Patient IDs, visits, labs, treatments

Steps:

1. Standardize formats (dates, codes).
2. Remove duplicates, fix null values.
3. Create data dictionary & profiling report.

Tools: Python (Pandas-Profiling, Great Expectations).

Deliverable:

• Cleaned dataset + profiling summary PDF.

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10. Medical Cost Analysis

Goal: Analyze hospital billing to find cost drivers and optimize expenses.

Data Needed:

• Patient ID
• Services used
• Costs per service
• Insurance coverage

Steps:

1. Aggregate costs by service type.
2. Identify high-cost patients and treatments.
3. Compare insured vs uninsured costs.

Tools: Power BI, SQL.

Deliverable:

• Cost breakdown dashboard with optimization suggestions.