Data Quality Improvement

Monday mornings are supposed to be difficult. There’s no such thing as a “Case of the Thursdays.”  Dragging out of bed Monday morning is supposed to be hard. Some weeks it is. Just this March there was a week where it just wasn't. I’m not entirely certain I even hit the snooze button (gasp!). I had just spent my weekend, the American worker’s time for decompressing, in meetings. Lots of them. But these were not just your average meetings – they were the ImproveCareNow Learning Session meetings.

Following them, I arrived back in Cincinnati with renewed enthusiasm - a renewed enthusiasm that invariably comes from attending these sessions. See, I’m a data guy. We’re typically pretty black and white - no grey. We come to the table with preconceived notions of what’s going to work and what’s not. Clinical data managers are not usually innovators. They’re not expected to be. In many cases, they’re not allowed to be. I can tell you that they’re missing out. Fate or karma or luck or whatever steered me away from the world of highly regulated (read: robotic, monotonous, widget-producing) clinical trials work, toward ImproveCareNow where I find myself encouraged to not only be rigorous, but also innovative in my approach to nearly everything.

As ImproveCareNow’s data manager, I've seen things I never would have imagined. We've abandoned the old query-and-response method of data cleaning and replaced it with a Quality Improvement (QI) based method of data quality improvement. When I tell my old colleagues about this process, I get a range of responses from a polite you-know-better-than-that tap on the head with a smile and nod, to downright indignation. Many of my peers have expressed reservations and have been kind enough to tell me that my approach won’t work. This is how I know what we’re doing IS working.

Any time those that “know” tell me that I’m crazy, it’s time to push on. Why?  Because it works. In 3 years, we’ve been able to improve the quality of ImproveCareNow’s registry (database) in terms of completeness, accuracy and timeliness not by adding a mountain of burdensome queries and rework to the care centers’ already full plates, but by doing what we teach in this network. Becoming partners in data quality with the ImproveCareNow teams working hard at each center, by applying QI science to work and processes of our central data management team, and by applying the same QI methodologies that have enabled ImproveCareNow to raise remission rates to places we didn’t even think possible. We’re taking the quality of the data, and by extension the quality of the data-driven tools and improvement that are developed and implemented by care centers, to places we were told we couldn’t go.

I’m not naïve enough to think that anything we try is going to be a success. Quite the opposite. Part of the journey is stumbling, falling, getting back up and applying what we’ve learned. And that’s what makes it easy to drag myself out of bed on those Monday mornings and get right back to it.

Dan Jeffers, MS, CCDM is a certified clinical data manager working at Cincinnati Children’s Hospital. When he’s not coming up with innovative ways to make the data in ICN2 even better, he’s chasing his kids and chickens around his Cincinnati farm.