Better Data Leads to Better Decision-Making in Healthcare
The amount of global data is growing so rapidly that it is now measured in zettabytes, which are massive in size. One zettabyte is equivalent to a trillion gigabytes.
But most of these data are messy and hard to make sense of. For example, health data come from many different places, including laboratories and hospitals. They also take diverse forms: think about interpreting written case notes or X-ray images. Errors and inaccuracies make it harder to use the data to derive insights about health.
Extracting nuggets of knowledge from massive health datasets is complicated, particularly when the accuracy and completeness of the data is substandard. New insights are harder to find when the data contain a lot of “noise” than when they are error-free.
Dr. Lisa Lix’s research aims to measure the quality of health data and develop methods to improve it. As Canada Research Chair in Methods for Electronic Health Data Quality, she uses computer simulation and real-world examples to provide complementary approaches to tackling the challenges of working with health data. She and her research team also work with a variety of data types, including both structured sources (that are easily organized into new measures) and unstructured text data.
Better quality data can lead to better decision-making. Ultimately, Lix’s research will lead to improved patient care and better health outcomes for people around the world.