Duke University School of Medicine
Like sitting on a hill watching a storm roll in, the feeling in the air is palpable: we are on the cusp of a data revolution in healthcare. As hospitals instantiate electronic health records across the country, institutions sequence hundreds of genomes a day, major companies secure personal health information and support computational power for data analysis pipelines, and researchers generate expansive repositories of disease data, medicine is becoming more precise and delivering new diagnoses to patients who have had few answers. Yet there are still sticking points that significantly delay the conversion between data generation and the actionable answers that researchers, doctors, and patients can use.
As a research scientist doing data-intensive work that cuts across the clinical, basic, and translational sciences, I will discuss examples from my work on rare disease genetics where my team faces data workflow challenges. These challenges generally stem from cross-discipline differences in metrics for productivity and success, competing regulations and philosophies on data handling and sharing, and a hodge-podge of technology support stacks. While the challenges are real, our will is stronger. Thanks to a common commitment to improved health for every individual, the brakes are coming off precision medicine at Duke. I invite you to join the discussion and share your own experiences so that we can get there together.