Big Love for Big Data? The Remedy for Healthcare Quality Improvements
Healthcare data is nothing new, but yet, why do healthcare improvements from quantifiable data seem almost rare today? Healthcare administrators have a wealth of data accessible to them but aren't sure how much of that data is usable or even correct. With budget cuts and stretched staff, it's a dicey proposition to ask a physician to take time to collect procedural data when she could be providing patient care. What's a healthcare provider to do?
The tantalizing promise of big data is that for the first time, with an abundance of data sources and efficient analytics tools, healthcare will finally have the information necessary to plan, achieve, and measure those quantifiable program improvements -- to find success in meeting quality improvement goals that were important 10 or 20 years ago, and are absolutely essential now. Of course, too much of anything is a bad thing, and even with the best tools, you can't turn bad data into good results. Big data and the increasing requirements around using that data can be a huge ally or your worst enemy. Your choices can help determine whether the flood of data will lift your organization's boat -- or sink it.
Before you throw in the towel, we have a toolbox of systematic best practices to help you create a well-informed action plan for healthcare analytics. This report will guide you through the hairy politics involved with getting senior leadership and staff buy-in for data analytics initiatives and how to establish a governing body to take ownership of those big, audacious goals. We'll call back to the gold-standard lessons of total quality management and continuous quality improvement that were set in place decades ago and apply them to today's problems. What's more, we'll show you how to turn healthcare deadlines like ICD-10 into leverage to make the analytic improvements you need.
Applying analytics to the world of healthcare data isn't a "set it and forget it" proposition. You need a lasting solution that can react to changes in the status quo and also predict new and impending disruptive technology. By applying good data against gold-standard improvement strategies, you can enjoy a wealth of improvements for years to come -- and avoid costly analytic disasters. (S7700114)