IJPDS International Journal of Population Data Science

Throughout the COVID-19 pandemic, governments and healthcare institutions used rate of infection and death rates to help identify how best to tackle the pandemic and distribute services. But this approach was not entirely equitable, particularly amongst marginalised communities, and it shone a spotlight on healthcare disparities that already exist. Governments had no option but to confirm the fact that we need to find more equitable pandemic support services.

In Learning Health Systems (LHS), strong partnerships exist between clinical operations and research.  Health-system data are continuously collected, analyzed and used to help make informed, rapid improvements to health processes and practices, which could certainly help provide more equitable pandemic support. They have the capability to quickly create healthcare service approaches that match various populations’ needs, and in an affordable way. And, with Learning Health Systems, data are seen as valuable starting points researchers use to inform healthcare practice and future studies, making this a more innovative and targeted approach to conducting healthcare research.

However, even innovations have certain limitations. The quality and level of healthcare data collected and analysed throughout healthcare are very inconsistent. There are many gaps data around community demographics, patient views of healthcare quality and the social determinants of health and non-medical factors that can impact individuals’ health. These gaps can prevent us from gaining a thorough understanding of healthcare systems and often results in leaving marginalised populations uncounted for and at the edges of improvement efforts. Researchers often see data gaps as a sign that more data are needed rather than an opportunity to critically analyse and continuously learn from the mass of data that already exists. This ongoing cycle of data collection and analysis leaves one to wonder if healthcare has a data problem, or a learning problem?

Dr. Nakia Lee-Foon and Dr. Robert Reid from Trillium Health Partners in Canada, discuss the ways that healthcare data are often used and how Learning Health Systems can shake up this cycle. In their commentary ‘Everybody’s talking about equity, but is anyone really listening?: The Case for Better Data-Driven Learning in Health Systems’ published in the International Journal of Population Data Science, they explain how Learning Health Systems can turn data into learning opportunities that impact healthcare practice and future research in real-time, and provide tips that readers can use to make learning from data just as important as the data itself.

Dr. Nakia Lee-Foon added that “Moving beyond the traditional view of data as merely numbers is vital to addressing some of the major healthcare issues of our time. This commentary sheds light on Learning Health Systems and the ways it can promote context driven collection and analysis to help develop a healthcare system that better matches its users’ needs in a timely fashion.”

 

Click here to read the full open access article

Dr. Nakia Lee-Foon, Learning Health Systems Research Associate, Trillium Health Partners

Lee-Foon, N. and Reid, R. . (2023) “Everybody’s talking about equity, but is anyone really listening?: The Case for Better Data-Driven Learning in Health Systems”, International Journal of Population Data Science, 8(1). doi: 10.23889/ijpds.v8i1.2125.