Real-world evidence for surgery and medical devices

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Evidence about surgical procedures and medical devices from traditional clinical research often fails to address key questions for patients, clinicians and policymakers regarding effectiveness, tolerability and value. ‘Real-world evidence’ to fill these gaps is generated through analysis of data derived from heterogeneous patients in real-life practice settings, such as insurance claims data and electronic health records. This project will generate real-world evidence for specific surgical procedures (e.g. joint replacement) and/or devices (e.g. cardiac pacemakers) by applying cutting-edge analytics to a unique whole-of-population big data platform comprising medical and pharmaceutical claims, and emergency department, hospital inpatient and mortality datasets.

The ideal candidate will have:

  • A degree in a clinical discipline, biostatistics, epidemiology, public health, computer science, psychology or a related field
  • A strong understanding of theory and methods relating to the design of epidemiological studies and the statistical analysis of health-related data
  • Demonstrated experience in the management and analysis of large complex health datasets 
  • Excellent written and oral communication skills
Supervisory team

Centre for Big Data Research in Health

Weill Cornell Medical College, New York
Centre for Big Data Research in Health

Centre for Big Data Research in Health