Health Economics & Outcomes Research Data Mining

Data Mining

Data mining is the process of selecting, exploring, and modeling large amounts of transactional (administrative) and operational (Electronic Medical Record [EMR]) data for knowledge discovery. Patient information in regard to utilization, outcomes, cost, and diagnoses can be used to establish strategic direction for medical drug and device needs.

Candace Gunnarsson, EdD
Vice President, Health Economics & Outcomes Research
Dr. Gunnarsson has over 20 years of statistical and health economics experience, including data mining, statistical analyses, and economic evaluation services for pharmaceutical, diagnostic, and medical device companies. Candace’s experience in statistics and health economics has added great depth to our Health Economics & Outcomes Research team.


CTI’s Health Economics Outcomes and Research (HECOR) team uses data mining to collaborate with sponsors to plan analyses for outcomes research studies, epidemiological studies, and benchmarking by conducting research of real-world data retrospectively to investigate the clinical care pathway of patients.

We apply data mining methodologies to large-scale operational, administrative, and EMR databases to:

  • Study disease incidence, prevalence, and progression
  • Generate high quality clinical outcomes information
  • Investigate medical practices
  • Identify unmet needs
  • Analyze study device usage

We have a closed-loop process and precise approach that provide the framework and foundation to confidently set a strategy to support the goals of our sponsors.

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