Project Strategy: The project aimed to analyze healthcare data to gain insights into patient outcomes, identify factors affecting hospital readmission rates, and recommend interventions to improve patient care. The strategy involved leveraging statistical analysis and predictive modeling to drive evidence-based decisions.
- Data Collection: Gathered patient data, including demographic information, medical history, treatments, and outcomes.
- Data Preprocessing: Cleaned and standardized data, handling missing values and outliers.
- Exploratory Data Analysis (EDA): Conducted EDA to understand trends, correlations, and patterns in patient data.
- Feature Selection: Identified relevant features affecting patient outcomes, such as age, medical conditions, procedures performed, and medication history.
- Predictive Modeling: Developed predictive models, such as logistic regression or random forest, to predict the likelihood of patient readmission.
- Feature Importance: Analyzed feature importance to understand which factors contribute most to readmission risks.
- Recommendations: Proposed evidence-based interventions, such as personalized treatment plans or post-discharge follow-up protocols, to reduce readmission rates.
Brand Strategy: Positioned the project as a proactive solution for improving patient outcomes and enhancing healthcare quality. The brand message highlighted the project’s commitment to data-driven healthcare improvements, patient well-being, and resource optimization.
- Python: Utilized for data preprocessing, analysis, and modeling using libraries like Pandas, Scikit-learn, and Statsmodels.
- Jupyter Notebook: Used for interactive data analysis, model development, and documentation.
- Tableau: Created visualizations to present patient outcomes, trends, and potential intervention strategies.
By implementing a comprehensive analytics strategy and utilizing advanced tools, the project successfully provided actionable insights to healthcare providers, allowing them to make informed decisions and improve patient care. The brand strategy emphasized the project’s focus on patient-centric care, data-driven recommendations, and healthcare optimization.