Unity’s HIPAA Compliant Remote Patient Monitoring solution, integrating seamlessly with Electronic Health Record (EHR) systems and streamlining RPM monthly billing processes. Collaborated with leading FDA-approved device manufacturers to deliver cutting-edge capabilities. Leveraged expertise in Center for Medicare & Medicaid Services (CMS) guidelines to align with the Final Rule on Remote Patient Monitoring Billing Codes, enabling Physician Practices to receive monthly reimbursements for remote monitoring of vital physiological parameters such as weight, blood glucose, blood pressure, heart rhythm, ECG, and pulse.

❖ Designed, developed, and implemented complex data workflows using Apache Airflow to automate ETL processes from SQL Server database to perform Stored Procedure optimizing for seamless data pipeline execution to automate repetitive tasks, improve overall operational efficiency and reduce errors by streamlining task dependencies.
❖ Managed data integration from multiple health kits, facilitating bi-directional data flow with iOS (Apple Health) and Android (Google Fit) platforms.
❖ Spearheaded the development of a Unity Mobile Application for real-time remote monitoring. Utilized Python to extract vitals from Apple Health or Google Fit into the Unity App, enhancing monitoring capabilities.
❖ Implemented a suite of stored procedures to automate data retrieval from patient remote monitoring systems, ensuring efficient data ingestion into our SQL Server.
❖ Integrated patient vitals seamlessly into the Epic platform, utilizing Unity-verified APIs to load live results such as weight, blood glucose, blood pressure, heart rhythm, ECG, and pulse onto Epic for comprehensive patient monitoring.
❖ Conducted Risk Analysis on Patient Vital Data using Machine Learning techniques, employing clustering mechanisms to predict dominant affected patient groups.
❖ Developed insightful Power BI dashboards for data visualization, enhancing data presentation and facilitating report creation for informed decision-making.
❖ Proficient in Python-based environments, with expertise in data analytics, data wrangling, and Excel data extraction. Skilled in Pandas, NumPy, matplotlib, and SciPy for advanced data analysis and visualization.

Value-Based Care

❖ Designed and implemented cloud-based solutions using Azure services.
❖ Working on an automation system using Python to generate lists of non-compliant patients based on Clinical, Functional, and Operational checks defined by Value-Based Care (VBC) Guidelines, and display them on Provider Electronic Health Records (EHR).
❖ Implement a Unified Data Systems (UDS) reporting system using Databricks (Spark SQL) and Power BI to create reports for Healthcare clients focusing on clinical compliance.
❖ Handle sensitive Electronic Health Record (EHR) data using APIs, retrieve results in JSON format, and ensure secure handling and compliance with data privacy regulations.
❖ Created and managed HDFS clusters and optimized data storage and retrieval.
❖ Implement Stored Procedure on SQL Server to automate tasks for fetching data from a database.
❖ Worked on performance tuning of long-running Spark Jobs using Data Frames, Spark-SQL, Memory tuning, Executors tuning, Spark YARN.
❖ Automating the whole integration between Patient Providers Engagement using chatbot. Handling, email notifications, changing data capture, slowly changing dimensions, etc.
❖ Data and Analytics to Reduce Utilization, Improve Quality of Care and Optimize Financial Outcomes.
❖ Strong experience in querying against data warehouses, data lakes with advanced SQL functions like window functions, procedures and creating views.
Environment: Azure, Databricks, Spark, SQL Server, Power BI, Python, Visual Studio code, OLAP, API’s, JSON.

Patient Engagement {Foster Patient Outreach}

Patient Chatbot framework to promote health literacy, and enhance overall patient satisfaction. The patient engagement initiatives focused on creating a personalized and interactive experience for patients, empowering them to take an active role in managing their health and well-being.

❖ Created and implemented a robust Patient Chatbot using Unity, incorporating advanced features for scheduling appointments, conducting mental health screenings, and managing remote patient monitoring.
❖ Integrated APIs from Electronic Health Record (EHR) systems such as EPIC and Allscripts to manage and process patient data, ensuring seamless communication between the chatbot and healthcare systems.
❖ Implemented automated alert systems using Python within the chatbot to notify healthcare providers and patients of critical threshold values in patient data, enabling timely interventions and monitoring.
❖ Utilized Azure cloud and CI/CD and orchestration for scalable and efficient deployment of the chatbot, optimizing resource utilization and ensuring high availability.
❖ Managed project tasks and communication channels using Jira for task tracking and MS Teams for real-time collaboration, facilitating cross-functional teamwork and project delivery efficiency.
Environment: Python, Visual Studio code, MS Outlook, Azure, Node, API’s, JSON.

Successfully implemented projects (Patient Engagement, Remote Patient Monitoring, UDS Reporting) through the entire development cycle, driving down storage costs by 25%, increasing customer satisfaction by 20%, streamlining integration and profiling processes by 40%, and more.

❖ Handled creation of data models design in Star Schema, Snowflake schema.
❖ Expert in implementing Spark ML with data stored in HDFS and processed over PySpark.
❖ Maintained Data Integration ecosystem from source to Snowflake with Airflow Orchestration.
❖ Maintained documentation related to difficulties, complexities, and anomalies that may arise during the ETL/ELT process in Databricks.
❖ Utilized Databricks notebooks for an interactive and collaborative environment to develop and execute a comprehensive predictive Risk analysis framework on clinical data.
❖ Proficient in creating data pipelines to handle data integration entirely as part of the data ecosystem.
❖ Expert in statistical programming languages like R, Python with ML implementation.
❖ Employed MLflow for end-to-end Machine learning lifecycle management, including experiment tracking, model versioning, and deployment, ensuring reproducibility and scalability of the project.
❖ Applied advanced data preprocessing techniques, including standard scaling and PCA, to prepare the dataset for Clustering analysis.
❖ Conducted Agglomerative and K-means clustering to identify patterns and trends in patient data based on Diagnosis, Lab results, Immunizations, and Screening tests, enhancing the understanding of patient risk profiles.
❖ Automated the creation of a dashboard using Python scripts to visualize trends in vitals and lab tests by clustering for every ICD, facilitating data-driven decision-making for healthcare providers.
❖ Achieved significant improvements in the accuracy and efficiency of risk prediction models through iterative experimentation and model evaluation, contributing to more informed patient care.

❖ Designing and developing various Machine learning frameworks Python and R to forecast student performance, achieving an accuracy rate of 84% and contributing to a robust predictive framework.
❖ Conducted data cleaning, preprocessing, and feature engineering to ensure data integrity, facilitating accurate analysis and insights generation.
❖ Using Agile methodology to develop a project when working on a team.
❖ Developing and maintaining SQL scripts and stored procedures for automating repetitive data analysis tasks.
❖ Clean and transform data using SAS procedures and data manipulation techniques.
❖ Built dashboards using Power BI, Tableau to visualize core student KPI’s and helped Students to identify potential.
❖ Generated good story telling dashboards for different use cases like Student performance, Subject’s Analysis.
❖ Developed Tableau workbooks from multiple data sources using data blending and interactive views, trends, and drilldowns.
❖ Implemented techniques like forward selection, backward elimination, and stepwise approach for selection of most significant independent variables.
❖ Performed Feature selection, dimensionality reduction methods to figure out significant variables.
❖ Involving requirements gathering and source data analysis and identified Student’s data migration and for developing the data warehouse.
❖ Performing data analysis and maintenance on information stored in MySQL, MongoDB.
❖ Conducting Data analysis including acquisition, cleansing, transformation, modeling, visualization, documentation, and presentation of results.
❖ Created complex Excel formulas and functions to perform data transformations, calculations, and analysis.