Hi, I’m Rachael Rosen.

Data scientist with a clinical foundation applying real-world analytics, machine learning, and behavioral insights to improve care systems, products, and user experiences.

What I do

I work at the intersection of health, data, and user insight. I translate complex healthcare datasets into actionable analytics that drive decisions across product, policy, and care delivery. My expertise includes predictive modeling, health equity analytics, and patient-centered outcomes research using claims, EHR, and survey-based data.

In my PhD research, I led multi-year studies using survival analysis, machine learning, and risk stratification to predict hospital readmissions and mortality following major surgery. I also contributed to the development and validation of outcome measures through mixed-methods research, including qualitative interviews and user-centered study design.

My work combines clinical experience, research leadership, and technical fluency. I specialize in building analytics workflows, cohort logic, and stakeholder-ready outputs that improve real-world healthcare performance and product strategy.

Clinical + Research Expertise

My background in both CLinical care & research enables me to:

  1. Identify gaps in care delivery, patient trajectories, and system workflows using both clinical insight and data analytics.
  2. Apply user-centered methods across studies, including qualitative interviews and real-world data analysis, to align research with lived experience.
  3. Translate findings from complex datasets into actionable product strategies, care improvements, and policy recommendations.
  4. Collaborate across disciplines to advance health innovations.
  5. Communicate technical insights clearly and effectively to stakeholders with varying levels of domain knowledge, from clinicians to product teams.

Core Skills

Data Science & Analytics

  • Predictive modeling and risk stratification using machine learning (RF, SVM, regression)
  • Longitudinal and claims-based analysis using Medicare and EHR data
  • Social determinants of health modeling and disparities research
  • Data visualization and insight reporting for mixed technical audiences

Research Methods & Study Design

  • Health outcomes research and healthcare policy evaluation
  • Mixed methods research: qualitative interviews, focus groups, and survey-based studies
  • Outcome measure development and psychometric validation
  • A/B testing frameworks and pilot study design

User-Centered & Collaborative Work

  • Human-centered research design and participatory methods
  • Cross-functional collaboration with clinicians, engineers, and data scientists
  • Wearable technology evaluation and real-world outcomes tracking
  • Translating research into clinical, product, and policy recommendations

Educational background

PhD in Rehabilitation Science

University of Washington

Master of Prosthetics & Orthotics

University of Washington

Bachelor of Science in Kinesiology

Western Washington University

Current work

I apply data science and health services research to improve care delivery, guide clinical decisions, and reduce disparities in rehabilitative healthcare. My current work focuses on real-world data analytics, predictive modeling, and the development of decision-support tools for complex patient populations.

My dissertation, Post-acute outcomes among Medicare beneficiaries after lower limb amputation: Patterns of care, readmission, and mortality, used Medicare claims and machine learning to model patient outcomes and predict hospital readmission and mortality after amputation. The goal: identify risk early, inform care coordination, and influence reimbursement policy.

My recent work includes:

  • Mixed methods research addressing health inequities among people with disabilities, integrating qualitative and quantitive data for stronger insight.
  • Development of novel outcome measures to assess fall risk and community mobility among lower limb prosthesis users.
  • Predictive analytics using claims and EHR-linked data to quantify real-world care trajectories, identify high-risk subgroups, and identify systemic barriers to recovery.

Let’s Connect

Seeking roles in data science, UX research, or health analytics where I can translate complex healthcare data into actionable insights that improve decision-making, product design, and care delivery.

Contact me