If there is a constant in healthcare quality reporting, it’s change.
Since Q-Centrix started partnering with hospitals just over a decade ago – a mere blip on the quality movement timeline – we have witnessed countless changes in clinical data reporting. Participation in quality programs has shifted from being largely voluntary to mandatory; penalties have replaced rewards, and value-based care is taking the place of fee-for-service – to name just a few!
Because of all this, data reporting for clinical performance improvement is growing – so much that it’s outpacing the ability to perform it. Hospitals that can’t keep up risk seeing penalties and care costs rise and their clinicians forced to spend valuable time on preparing data instead of patient care.
Fortunately, technologies that are more capable than ever of automating traditionally manual data-management tasks are emerging. Yes, I’m talking about Artificial Intelligence! While the thought of AI may conjure up science fiction visions of robots and futuristic-looking devices, what it really excels at is taking over tedious and mundane tasks from humans. Take, for example, Natural Language Processing: NLP is a form of machine learning designed to understand human language and information patterns versus requiring data to be stored in a structured manner. Since 80% of data in electronic health records can be unstructured, leveraging this technology in clinical quality reporting is an obvious fit – and is why our team at Q-Centrix is now using it in our clinical data registry solution.
While we love technology (a lot) at Q-Centrix, we understand that it alone cannot address all of today’s clinical quality reporting challenges – nor can exclusively depending on clinical expertise.
In other words, NLP would be useless in clinical quality reporting without the clinical experts familiar with a facility’s workflow and existing technologies. Like stagehands working behind the scenes, these folks perform the necessary audits and adjustments to get the most out of the technology.
The reality is each hospital faces a unique set of clinical data reporting demands. For some, the right technology and/or process improvement might be enough to meet their needs. But for many, purchasing a single piece of software or niche solution may lead to an accumulation of multiple technologies that simply don’t work well together. So, let’s take this idea a step further …
By using technology to augment the clinical intelligence of people, the abstraction process can evolve to be more efficient, accurate and scalable.
Outsourcing clinical data management can provide hospitals with both the technology and necessary staff-equivalent hours to meet modern quality reporting demands. The most innovative solution providers are using, or at least testing, automated technologies like NLP in their processes. Just as important is ensuring a facility can shift and scale as its clinical quality reporting needs change over time. For example, at Q-Centrix, we have a team of more than 1,200 nurse-educated clinical data experts. Acting as virtual extensions of hospital quality departments, these individuals ease data management burdens and assess and redirect resources as needed.
If the quality program updates planned by the federal government are any indication, we can expect an uptick in additions and removals of measures in upcoming years. Constant changes in reporting requirements are inevitable. Likely fueling the trend is the increase in insurers offering value-based care program options and greater participation in clinical registries … Not to mention the response to existing and emerging health crises – like widespread opioid addiction and antibiotic-immune superbugs.
Looking at the big picture, we firmly believe the organizations that can most easily adapt to all these constantly shifting factors have the best opportunity to get, and stay, ahead of their clinical reporting demands.