- Lead analytics and reporting for multiple healthcare and pharmaceutical clients—define KPIs, build recurring dashboards, and deliver executive‑ready summaries.
- Acquire, clean, and analyze EMR and claims datasets; perform data profiling, quality checks, and EDA to identify discrepancies, trends, and drivers.
- Automate data extraction and reporting workflows using SQL/Python/R, Google Sheets API, and Apps Script to reduce manual effort and improve consistency.
- Develop and validate decision‑support models (denial risk, utilization patterns, revenue impact); partner with stakeholders to interpret results and refine definitions.
- Perform validation reviews of DRGs, APCs, HCCs, and Never Events using ICD‑10‑CM/PCS, ICD‑11, CPT, and HCPCS‑II to ensure accuracy and compliant reimbursement.
- Analyze denial patterns and billing trends; create reports to guide appeals, contract evaluations, and process improvements.
- Prepare monthly/quarterly performance reporting: records reviewed, coding outcomes, savings, risk‑adjustment measures, HEDIS, and operational trends.
- Work with clinical data standards (LOINC, SNOMED CT) to support quality programs, research reporting, and clinical decision support.
- Build web‑based reporting tools and dashboards (Flask + HTML/CSS/JavaScript) so teams can access curated analytics through user‑friendly interfaces.
- Leverage multilingual communication (English, Spanish, Italian, Portuguese) to align teams and translate complex findings into clear recommendations.
Professional Summary
Data Analyst and Certified Coding Specialist (CCS, AHIMA #271762) with 10+ years of experience delivering decision‑support reporting, workflow automation, and data quality improvements in regulated, high‑accuracy environments. Proficient in SQL, Python, and R, translating complex requirements into clear metrics, dashboards, and stakeholder‑ready insights.
Hands‑on across the analytics lifecycle: data acquisition and cleaning (EMR/claims and operational datasets), EDA, trend/variance analysis, and predictive modeling to surface denial risk, utilization patterns, and revenue impact. Known for partnering closely with operations and leadership to validate definitions, QA outputs, and drive measurable process improvements.
Academic foundation: B.S. in Biochemistry (bioinformatics‑oriented). Completed Johns Hopkins University specializations in Data Science, Biostatistics, Health Informatics, and Large‑Scale Database Systems. Multilingual (English, Spanish, Italian, Portuguese) with a strong “translation mindset” for cross‑functional communication.
Selected Highlights
- Certified Coding Specialist (CCS) — AHIMA #271762; 10+ years across inpatient, surgical, ambulatory, and professional fee settings.
- Analytics + automation: build reproducible pipelines, cohorts, and decision‑support reports using SQL, Python, and R; automate data flows with Google Sheets API & Apps Script; and apply predictive modeling to anticipate denials, risk, utilization, and revenue impact.
- Healthcare domain strength (transferable): expert in ICD‑10‑CM/PCS, ICD‑11, CPT, HCPCS‑II with deep experience in DRG/APC validation, risk adjustment, and revenue integrity—bringing high-accuracy, compliance-minded analytics to any industry.
- Provide analytics, coding, and revenue‑cycle support to multiple healthcare and pharmaceutical clients, adapting to diverse operational and reporting needs.
- Extensive work with clinical data standards (LOINC, SNOMED CT) and EMR/claims data to support physicians, quality programs, and research projects.
- Creator of interactive analytics/apps (Flask + Shiny): ICD Co‑Occurrence & Predictor and Post‑Acute Care 2022 Dashboard.
- Johns Hopkins University Specializations: Biostatistics in Public Health; Health Informatics; Data Science – Statistics & Machine Learning; Data Science – Foundations using R; Large‑Scale Database Systems.
- Biochemistry (Clemson University) with genetics/bioinformatics orientation; additional clinical training via Greenville Technical College.
- Web‑based analytics delivery: build dashboards and reporting tools with Flask, HTML/JS, and Google APIs (Sheets/Calendar), turning datasets into usable, stakeholder-friendly experiences.
- Multilingual communicator — English, Spanish, Italian, and Portuguese — highly skilled at “translating” complex data into clear stories for clinical, operational, and executive audiences.
- Professional focus: accurate coding, compliant RCM, and evidence‑based insights that improve operational and clinical decisions.
Experience
- Pharmaceutical & OTC Researcher — conducted product safety and efficacy studies, supporting regulatory submissions and product development.
- GMP Compliance Oversight — ensured FDA Good Manufacturing Practice adherence across formulation, testing, and labeling processes.
- Microbiology Data Research — managed laboratory data collection and analysis to validate microbial compliance of OTC skin care products.
- Quality Assurance — collaborated with cross-functional teams to implement corrective actions, maintain audit readiness, and document compliance.
- Cosmetic-grade Formulations — contributed to development of skin care products integrating pharmaceutical standards with consumer safety requirements.
- Applied ICD‑9/ICD‑10‑PCS, CPT, and HCPCS‑II codes while ensuring regulatory compliance.
- Maintained patient records and supported medical billing and payer communications.
- Resolved billing questions with insurers and patients; supported accurate documentation workflows.
- Utilized EHR/billing software to maintain complete, accurate records.
- Reviewed diagnosis coding for completeness and accuracy across assigned care centers.
Education
Certifications, Specializations & Coursework
- Biostatistics in Public Health (Johns Hopkins University) — study design, inference, regression, and interpretation for population-level decisions.
- Health Informatics (JHU) — EHR workflows, interoperability (HL7/FHIR), clinical data standards, privacy, and governance.
- Data Science: Statistics & Machine Learning (JHU) — supervised/unsupervised ML, model validation, regularization, and reproducible analysis.
- Data Science: Foundations using R (JHU) — data wrangling (tidyverse), EDA, reporting, and pipeline automation.
- Large-Scale Database Systems (JHU) — schema design, indexing, transactions, query optimization, and scalable SQL patterns.
- Johns Hopkins Medical Office Manager — clinic operations, scheduling and patient access, compliance, coding/billing workflows, and KPI tracking.
- Data Science (JHU) — end-to-end analytics lifecycle: acquisition → cleaning → modeling → visualization → communication.
- Patient Safety (JHU) — QI frameworks (PDSA), root cause analysis, human-factors principles, and risk mitigation.
Expanded coursework & certificates (optional)
- Taking Safety and Quality Improvement Work to the Next Level (Patient Safety VII)
- Designing for Sustainment: Keeping Improvement Work on Track (Patient Safety IV)
- Foundations of Distributed Database Systems
- Implementing a Patient Safety or Quality Improvement Project (Patient Safety V)
- Reliability, Cloud Computing and Machine Learning
- Measuring the Success of a Patient Safety or Quality Improvement Project (Patient Safety VI)
- Distributed Query Optimization and Security
- Advanced Reproducibility in Cancer Informatics
- Revenue Cycle, Billing, and Coding
- Quality and Safety in Ambulatory Healthcare Management
- Multiple Regression Analysis in Public Health
- Healthcare Delivery for Medical Practice Managers
- Data and Electronic Health Records
- Planning a Patient Safety or Quality Improvement Project (Patient Safety III)
- Hypothesis Testing in Public Health
- Human Resources Management Essentials
- Setting the Stage for Success: An Eye on Safety Culture and Teamwork (Patient Safety II)
- Patient Safety and Quality Improvement: Developing a Systems View (Patient Safety I)
- Simple Regression Analysis in Public Health
- Reproducible Research
- Culminating Project in Health Informatics
- Developing Data Products
- The Outcomes and Interventions of Health Informatics
- Data Science Capstone
- Introduction to Ambulatory Healthcare Management
- The Data Science of Health Informatics
- The Social and Technical Context of Health Informatics
- Leading Change in Health Informatics
- Practical Machine Learning
- Statistical Inference
- The Data Scientist’s Toolbox
- Exploratory Data Analysis
- Getting and Cleaning Data
- R Programming
- Summary Statistics in Public Health
- Regression Models
- Advanced Methods in Machine Learning Applications
- Applied Machine Learning: Techniques and Applications