I am proficient in Python and R, which are essential for data manipulation, statistical analysis, and machine learning. I also use SQL for database querying and Tableau for data visualization. Additionally, I have experience with healthcare-specific tools like Epic Clarity and HL7 for handling electronic health records (EHR) data. My expertise in these tools allows me to efficiently analyze large datasets, derive insights, and present findings to stakeholders in a clear and actionable manner.

I prioritize data privacy by implementing strict access controls, anonymizing patient data, and using encryption for data storage and transmission. I stay updated on HIPAA regulations and ensure all analyses comply with these standards. Additionally, I work closely with legal and compliance teams to conduct regular audits and risk assessments. By adhering to these practices, I ensure that sensitive healthcare data is handled securely and ethically, minimizing the risk of breaches or non-compliance.

In a recent project, I developed a predictive model to identify patients at high risk of readmission within 30 days of discharge. Using EHR data, I applied logistic regression and random forest algorithms to predict readmission likelihood. The model achieved an accuracy of 85%, enabling the hospital to implement targeted interventions for high-risk patients. This reduced readmission rates by 15%, improving patient outcomes and lowering healthcare costs. The project highlighted the power of machine learning in driving data-driven decision-making in healthcare.

I address missing data by first understanding its nature—whether it’s missing at random or systematically. For random missingness, I use imputation techniques like mean, median, or predictive modeling. For systematic missingness, I analyze the underlying cause and adjust the analysis accordingly. I also document the extent of missing data and its potential impact on results. By applying these methods, I ensure the integrity and reliability of my analyses while maintaining transparency about data limitations.

I tailor my communication to the audience by simplifying technical jargon and using visual aids like charts, graphs, and dashboards. I focus on actionable insights and their implications for decision-making, rather than overwhelming stakeholders with technical details. For example, I might present a predictive model’s impact on patient outcomes using a clear narrative and visualizations. This approach ensures that stakeholders understand the value of the analysis and can make informed decisions to improve healthcare delivery.