Big data in healthcare: Better care, better health

The utilization of big data analytics in healthcare is still in its infancy – partly due to the fact that healthcare data are complex, spread among multiple stakeholders, structured and unstructured, and siloed. Nonetheless, translational data analytics in nursing has the potential to significantly and rapidly impact health and healthcare in the United States, where our spending on healthcare is disproportionate to the health outcomes we achieve. A report by the McKinsey Global Institute estimates that the U.S. could realize over $300 billion in value for healthcare if we leverage big data.

In order to achieve a better return on our investment of healthcare dollars, we need to move towards providing the right intervention to the right patient, at the right time. Use of translational data analytics and predictive modeling will also help facilitate identification of individuals at high-risk for developing certain conditions, leading to early preventive, tailored interventions that can substantially reduce healthcare costs. These improvements could ultimately benefit the patient, healthcare provider, payer and healthcare system administration.

I also see translational data analytics helping us reduce the 17-year timeline that it takes for widespread adoption of effective treatments and interventions into practice. All too often, our research findings go no further than peer-reviewed journals and professional conference presentations. As researchers, we need new and more powerful mechanisms to help us more broadly translate those findings for clinically and cost-effective interventions and treatments into clinical practice.

Already we are seeing a shift in how healthcare is provided, from a treatment-based model to a health promotion and prevention model using a more personalized approach grounded in the best available evidence. Additionally, hospitals and other healthcare systems are incentivized to keep patients well and reduce unnecessary healthcare costs. And there are vast amounts of data (e.g., electronic health records, genomic, imaging, laboratory, behavioral) generated throughout the healthcare systems that we can integrate and analyze in increasingly complex and sophisticated ways to better inform healthcare decision-making.

Thus, there is an enormous opportunity to apply translational data analytics to healthcare data, and this exciting initiative being undertaken by The Ohio State University places us in a prime position to fill significant gaps in the science.

 

Discussion

Add a Comment

To add a comment you must be signed in.

SIGN INREGISTER
Share this page
Suggested Articles
To be translational, data analytics must consider data context and meaning

Data analytics can be computationally powerful and sophisticated but not be translational or have much leverage in the real world. This is a cautionary note about the dangers of big...

Big data in healthcare: Better care, better health

The utilization of big data analytics in healthcare is still in its infancy – partly due to the fact that healthcare data are complex, spread among multiple stakeholders, structured and...

From Theory to Practice: Generating Value from Big Data

The past several years have seen a growing focus on big data, data science, and data analytics that spans a spectrum—from fundamental issues related to the management of large-scale and...

Vuolo awarded Fulbright U.S. Scholar Grant

TDAI affiliate Michael Vuolo, associate professor of sociology, is the recipient of a prestigious 2018-2019 Fulbright-Schuman European Union Affairs Program award, which is designed to strengthen relationships between the U.S....

Kui Xie awarded TDA seed grant

TDA congratulates Kui Xie, PhD, who received the first-ever TDA@OhioState seed grant for $24,970 for his proposal Using Learning Analytics in Examining Students’ Learning Habits in Online Nursing Classes. Xie...