Demonstrated expertise in working with real-world healthcare data from various sources, including MarketScan and Optum, while a member of the Forecasting and Disease Cohorts teamUsed SAS statistical programming to manipulate large healthcare databases and produce comprehensive reportsLeveraged training and experience in using the OMOP common data model while comparing drug concepts in OMOP versus MedispanBuilt patient cohorts and assessed healthcare events to create data for the drug explorers used by epidemiologists Created intricate analysis data sets from diverse data sources, with keen attention to outliers and errors to maintain data quality and integrityFamiliarity with Databricks and R, indicating the willingness to learn about and explore new technologiesProvided peer review to identify and rectify coding errors in the team's outputs, enhancing code efficiency, reliability, and accuracy of reportsOrchestrated the workflow and prioritization of biostatistical programming tasks for real-world data analysis, ensuring efficient project progression and adherence to deadlines.

Developed research protocols, performed literature reviews, and participated in client meetings with pharmaceutical companies and physiciansPerformed a patient cohort analysis of resource utilization before and after a diagnosis of IBSConducted statistical analysis of real-world EMR data to determine which patients were more likely to have a successful kidney transplantAnalyzed hospital discharge data from a state database with millions of records to quantify charges for initial and/or repeat catheter ablations for atrial fibrillation over a three-year periodEvaluated the relationship between BMI and healthcare costs in a commercial and Medicare population using hospital claimsAnalyzed a cohort of obese patients from a state database comparing claims costs for those receiving an anti-obesity medication versus patients who did notCreated propensity scores and developed a GLM with mixed effects to assess the effect of an IBS test on healthcare resource utilization using real world evidenceLed the efforts to identify and secure healthcare claims and hospital discharges to use as a data source for performing health economics and outcomes research

Aggregated data from multiple sources to analyze medical claims by CPT codes to develop an algorithm to help improve processor productivity and business performancePerformed time series statistical analysis of medical claims to understand trends in medical costs, resolved claim volume, auto adjudication, membership, and aging inventoryWorked with team members to ensure quality of data used in monitoring claims appeals to understand the incoming amount and those held in inventoryMonitored claims submitted by physicians and hospitals during the transition from ICD9 to ICD10Performed root cause analysis of disturbances in medical and dental claims flow to aid actuariesConducted financial analysis of the rate of claims pended for additional information, denied, or rejectedCreated code using regular expressions to perform text mining of unstructured comments made by processors in the special investigation unitTechnologies Used: SAS (Multiple Linear Regression, Time Trend Modeling), R, Python, SQL Server, SSIS/SSAS Server Business Intelligence, Microsoft Access, Microsoft Excel

Built statistical models using logistic regression to predict customer acquisition and retentionPerformed customer segmentation to identify patterns in customer service phone calls using cluster analysisParticipated in the design and analysis of champion/challenger email and direct mail marketing campaignsPerformed text mining on unstructured call center data to identify and analyze named entities, keywords, and sentimentUsed speech analytics platform to query, analyze, and report on call center quality and efficiencyAnalyzed effectiveness of call messaging for debt collection and overall accuracy of modelsPerformed ETL of internal and external data in SQL Server database including billing, payments, Experian demographics, Nielsen consumer data, credit scores, and web trafficWorked with client services to develop customer relationships, deliver projects on time, and ensure customer satisfactionWrote white papers to be used in marketing efforts and reviewed request for proposalsIdentified tasks and created time estimates for future projectsTechnologies Used: SAS (Logistic Regression, K-Means Clustering, Chi-Squared test), R, JMP, SQL Server, SSIS/SSAS Server Business Intelligence, Microsoft Access, Microsoft Excel, Nexidia

Responsible for data migration of health, dental, pharmacy and Medicare data into a transactional application that interfaces with a claims processing systemPerformed root cause analysis of data consistencies and created reports for business usersIdentified bottlenecks in current methods of manually unstructured data from health insurance documents and created system to lower labor costs by automating processDesigned and implemented a system to extract health insurance services and benefits using statistical analysis and machine learningMet with business leaders to gather data requirementsPartnered with project managers and business analysts to create project charter, project scope, business requirements and project plans for health exchange dataLed team of analysts in performing ETL, data mapping, data conversion/migration, data validation, data mining, data governance and creation of metadataResponsible for project planning, resource allocation, meeting project deadlines, and creating system requirements documentationTechnologies Used: SQL Server, SSIS/SSAS Server Business Intelligence Studio (Decision Trees, Association Rules, Logistic Regression), PowerPivot, XML, Visio, Microsoft Access, Microsoft Excel, Microsoft Project