SAHRASH FATIMA

Experienced health informatics professional with over 3 years of diverse experience in healthcare, academia, pharmaceuticals, and research . Proficient in R, SQL, Python, REDCap, OnCore, and Tableau/Power BI to develop data-driven insights, manage data, and create impactful visualizations. Passionate about optimizing business processes, effective data management, and driving innovation through the power of data analytics.

Technical Skills:

Python, SQL, R, RapidMiner, GitHub, Postman, Tableau, PowerBI, Excel, Camunda, ETL pipelines, HL7-FHIR, RedCap, OnCore, Micromedex, OpenEMR, RELMA, GoStar, Wrike, Zoom, BPML diagram, Usecase Diagram, DHI2, Qualtrics Survey, WCMS, Wordpress, Excel, Teams, Outlook

Soft Skills:

Problem solving, Good Oral and Written Communication skills, Proficient Presentation skills, Collaborative, Quick learner, Leadership qualities, Critical thinking, Innovative. 

• CITI (Biomedical Research, Stage 1), CSA (Campus Security Authority), FERPA (Family Education Rights Protection Act), Good Clinical Practice (GCP) Certification, RedCap, Power BI, OnCore

IUI – Introduction to Informatics Course Project: Hemodialysis Frequency Prediction

• Developed a model predicting hemodialysis frequency based on patient characteristics, comorbidities, and lab values.
• Utilized Python, MySQL, and Kaggle datasets for data collection and preprocessing.
• Applied machine learning algorithms and statistical tools to analyze trends and visualize data.

Thanks! Here’s the updated version with RStudio included:

IUI – Applied Statistical Methods for Biomedical Informatics

Course Project: Parkinson’s Disease Diagnosis Prediction

• Analyzed a Kaggle dataset of 2,105 patients to identify key predictors of Parkinson’s Disease using RStudio for statistical tests and logistic regression modeling.
• Performed data preprocessing, including normalization and categorical encoding, to support non-parametric and multivariate analysis.
• Visualized trends with boxplots, violin plots, and heatmaps, revealing UPDRS and MoCA as the strongest diagnostic indicators (AUC = 0.8261).

IUI – Health Informatics Standards and Terminologies

Course Project: FHIR-Based ETL Pipeline for EHR Interoperability

• Built an ETL pipeline using Python and FHIR APIs to extract, transform, and load patient, condition, observation, and procedure data between OpenEMR and a Primary Care EHR system.
• Integrated SNOMED-CT and LOINC codes to enhance clinical data granularity and ensure semantic interoperability; implemented OAuth 2.0 authentication and robust error handling.
• Visualized patient distribution using OpenEMR data to support healthcare planning; emphasized secure, structured data exchange aligned with FHIR standards.