Data Analyst Machine Learning

Kavya Kodadi

Healthcare Data Analyst

Location: Baltimore, USA Mail: ************@*****.*** Ph.: 667-***-****

Professional Experience:

• Experienced Healthcare Data Analyst with 4+ years of expertise in leveraging advanced data analytics, machine learning, and encryption techniques to enhance healthcare operations, ensure security, and improve patient outcomes.

• Proficient in Python, SQL, MongoDB, AWS, and cloud computing technologies, specializing in the development and deployment of predictive analytics, risk assessments, and anomaly detection models to support proactive patient care and operational efficiency.

• Expert in maintaining HIPAA compliance by implementing advanced encryption standards (AES, RSA), secure data access protocols, and privacy measures to safeguard Protected Health Information (PHI) during storage, transmission, and processing across healthcare systems.

• Skilled in data visualization and reporting using tools like Tableau, Power BI, and custom dashboards, enabling healthcare providers to make informed, data-driven decisions by translating complex health data into actionable insights and key performance indicators (KPIs).

• Collaborative in cross-functional teams to integrate Electronic Health Records (EHR), Health Information Exchanges (HIE), and Internet of Things (IoT) data into seamless, secure pipelines, proactively addressing data vulnerabilities and ensuring system integrity for better patient outcomes and compliance.

Technical Skills:

Professional Summary:

Data Analyst MedStar Health – MA Oct 2023 – Current

• Implemented advanced image processing techniques in Python to preprocess radiology images, boosting model performance in medical image analysis, leading to a 40% reduction in preprocessing time and an increase in accuracy.

• Developed end-to-end machine learning workflows, including data preprocessing, feature extraction, and model deployment on cloud platforms like AWS, reducing deployment time by 40% and improving model scalability.

• Utilized TensorFlow to construct and train deep learning models, optimizing them for detecting and classifying radiological abnormalities with a 90%+ accuracy rate in distinguishing between different types of conditions.

• Developed algorithms to analyze EHR data alongside medical imaging, combining structured patient information with radiology images to enhance diagnostic accuracy and assist in identifying critical health patterns.

• Conducted A/B testing and used performance metrics to compare multiple machines learning models, selecting the optimal algorithm for medical imaging analysis, which resulted in a 10% improvement in model performance over previous benchmarks.

• Collaborated with medical professionals to validate and enhance machine learning models for radiology imaging, ensuring compliance with HIPAA and regulatory standards, and achieving a 95% rate in model outputs with diagnoses.

• Integrated EHR data to enhance the prediction of patient outcomes, ensuring a seamless flow of patient information, improving model accuracy, and supporting clinical decision-making based on comprehensive patient records.

• Developed data visualizations using Matplotlib and Seaborn to present insights from radiology image analysis, facilitating decision-making for clinicians and improving clinical workflow efficiency.

• Carried out health outcomes analysis to evaluate healthcare interventions, identifying key factors influencing patient recovery by focusing on optimal treatment strategies and timely intervention.

• Employed healthcare analytics frameworks to analyze clinical and operational data, applying advanced analytics techniques to increase operational efficiency and reduce patient wait times, optimizing decision-making processes.

• Utilized Jira for project management and tracking, efficiently coordinating tasks across teams, managing sprint workflows, and ensuring timely delivery of machine learning models and data analysis projects.

Data Analyst NextGen Healthcare – India Aug 2021 – Jul 2022

• Developed predictive models using Python and machine learning techniques to identify high-risk patients at risk of readmission, resulting in improved patient care and reduced hospital costs.

• Extracted and manipulated healthcare data using SQL to perform feature engineering and improve the accuracy of predictive models, enabling better insights into patient outcomes.

• Integrated and standardized Electronic Health Records (EHR) data from multiple sources using MySQL, ensuring data consistency, accuracy, and compliance with industry standards like HL7 and FHIR for seamless data exchange.

• A robust healthcare analytics framework should integrate predictive models with real-time data from EHRs and IoT devices, enabling proactive decision-making. This approach enhances patient care, optimizes resource allocation, and reduces operational costs.

• Built and deployed forecasting models using TensorFlow to predict future healthcare utilization trends, optimizing resource allocation and improving cost management in healthcare organizations.

• Analyzed and processed real-time patient data from IoT devices using Spark, detecting early health deterioration signs and facilitating proactive interventions to prevent complications.

• Implemented data governance strategies for EHR systems, ensuring secure patient data storage, access control, and compliance with HIPAA and other privacy standards.

• Created interactive data visualizations with Power BI, transforming complex healthcare data into actionable insights and presenting key health metrics and trends to healthcare providers for better decision-making.

• Applied machine learning algorithms to develop predictive models for population health management, identifying at-risk populations and reducing healthcare disparities through targeted interventions and care plans.

• Implemented anomaly detection algorithms using Scikit-learn to detect fraudulent healthcare claims, enabling early identification of financial discrepancies and contributing to a reduction in fraudulent activities.

• Integrated and cleaned large-scale healthcare claims data using ETL tools like SSIS, improving the accuracy of fraud detection models and enabling better data-driven decisions for the finance team.

• Utilized Snowflake's cloud-based data warehousing to integrate and optimize large-scale healthcare data, enhancing scalability and performance.

• Leveraged machine learning algorithms and Pandas to predict the progression of chronic diseases, such as diabetes, by analyzing patient data and creating personalized models for early intervention and care management.

Client: Apollo Hospitals Location: Hyderabad, India June 2019 – July 2021

• Use historical data to predict future trends such as patient volume, disease outbreaks, or resource utilization.

• Analyze data to evaluate clinical outcomes and identify areas for improvement in patient care or clinical procedures.

• Prepare reports for healthcare organizations to ensure that they meet regulatory requirements and accreditation standards.

• Work with healthcare professionals, executives, and other stakeholders to understand their data needs and deliver actionable insights.

• Participate in research focused on understanding clinical outcomes and how they are influenced by various treatments, policies, and patient demographics.

• Provide insights to healthcare providers to help them make data-driven clinical decisions that improve patient outcomes.

Education:

M.P.S. Health Information Technology - University of Maryland Baltimore County, USA

Bachelor of Dental Surgery - Malla Reddy Institute of Dental Sciences, India

Contact this candidate