Analytics and Data Science

Translating Data Into Action

The DCRI pushes beyond what’s most apparent. By tapping the experience of Duke faculty statisticians and the real-world insights of practicing physicians, we find the question that most effectively probes the challenge in your research and the fullest potential in data it generates. The result: meaningful decisions reached more quickly and efficiently, evidence that has more immediate impact on patient care and datasets with value beyond the immediate analyses.

Insights from the Team

group photo

Where Do We Go From Here?

Utilizing rigorous, best-in-class tools and mathematical algorithms, our renowned biostatisticians and data scientists can tailor methods to the question. We find patterns within datasets that lead to stronger and more actionable insights. This approach of framing the right question to provide context and focus and applying optimal methods marks our work in:

Dam Data: Health Systems, Machines, And Learning

Machine learning allows practitioners to reach beyond their own experiences and access the sum of experiences of all patients in the health system.  It mimics how physicians think about treatment options, but at a scale that can only be achieved with computing.

But machine learning is not sufficient on its own.  Clinical expertise, study design, and deep understanding of the data to be analyzed are as critical as the machine learning methods to be applied.

Can Randomized Pragmatic Trials Fulfill Their Promise?

Michael Pencina, PhD and Eric Peterson, MD, and co-authors from McKinsey & Company, describe the steps that researchers, the private sector and regulators can take to design and implement clinical trials that are better, faster, and cheaper using real world evidence.

Illustrating Informed Presence Bias in EHRs

EHR data have a number of appealing characteristics, but they also pose a number of analytic challenges. How a patient interacts with a health system can influence which data are recorded in the EHR. Benjamin Goldstein, PhD, and his colleagues explore the implications.

Three Risks to Avoid in the Rush for Health Care Big Data

How can we ensure that big data in health care will be the rich resource we imagine? In this commentary for STAT, Michael Pencina, PhD, likens the clamor to collect more and larger datasets to the 19th century Gold Rush and describes three risks that data scientists and data consumers must avoid.

Our Partners

Duke-Margolis brings together capabilities that generate and analyze evidence across the spectrum of policy to practice, supporting the triple aim of health care – improving the experience of care, the health of populations and reducing the per capita cost.

Learn about Duke-Margolis' proposal for regulatory use of real-world evidence.

 

The Duke Department of Population Health Sciences engages faculty members from a variety of disciplines — including epidemiology, health services research and policy, health economics, health measurement and behavior, and implementation science — who work to answer complex questions about the drivers of health in large populations.

 

The Forge is Duke University’s center for health data science. Based in the Duke University School of Medicine and led by Vice Chancellor for Health Data Science Dr. Robert M. Califf,  the Forge is part of a community spanning the university, the Duke health system, and beyond that is using data science to develop actionable insights and improve health outcomes.

Learn more about how the Forge and the DCRI collaborate on the Forge blog.