The utilization of data in medicine is not a new concept. It has been the backbone of innovation within the industry for decades. However, utilizing data to measure quality of care delivery has historically been a challenge.
As discussed by David Lansky of Health Affairs, the United States healthcare system has long struggled to define and measure quality to assist with healthcare improvement.
“The current retrospective, transactional system for measuring and rewarding improvement is ineffective, expensive, burdensome, no longer credible, and does not measure health or the outcomes of health care, ”said Lansky. “And 10 years since the implementation of “meaningful use” and the launch of the Center for Medicare and Medicaid Innovation (the Innovation Center) alternative payment models, we still don’t know if our innovations have led to better health or improved value.”
Key to modernizing quality measurement is understanding our current shortcomings and what is required to push the industry forward.
At the organizational level, hospitals, health systems, and all other provider organizations have turned their attention to improvement – ensuring the resources they are extending and the services they are providing are beneficial for patients and for the organization as whole. However, this hasn’t always been a focus, and today organizations are consistently coming up against ancient systems or processes that inhibit their ability to truly measure and improve care.
Visit any healthcare organization today, even those with departments dedicated to quality improvement, and you will find groups of people, tasked with projects to improve a specific issue, who are simultaneously trying to find a solution to the problem while also trying to define how bad the problem actually is. They come up against inabilities to pull data from their systems or find that data hasn’t been collected at all on the topic. And how does the saying go? “You can’t improve what you can’t measure.” It’s a backwards process that is time-consuming and often in effective.
Similarly, at the industry level, Lansky points out that we still define quality based on “administrative claims data, office and hospital-based care modalities, and a transactional view of healthcare payment and delivery.” Additionally, Lansky discusses how our reliance on process measures is a barrier to our industry improving outcomes our patients expect us to. Outcomes such as “improvements in function, reduced symptom burden, overall quality of life, and longevity.”
Our healthcare system needs to adjust its approach to quality measurement – and it must begin at the source. The industry needs to change course in methodology and leverage modern data platforms that collect and analyze patient-reported outcome measures (PROMs).
As Lansky defines, these data structures should:
And, as Lansky highlights, these platforms are “far more than supercharged electronic health records.” The data platforms must be built for this purpose, rather than simply being added-on functionality.
Since its founding, PatientIQ has been driven by the points Lansky defines. Not only does PatientIQ recognize the industry-wide need to define quality more easily and effectively, but it also strives to be a catalyst for improvement. The PatientIQ platform is built to collect patient-reported outcomes and then provide real-time, actionable insights that allow healthcare organizations and individual providers to know exactly where and how they can improve – and measure the effectiveness of their improvement initiatives overtime.
Modernized quality measurement is no longer something to imagine – it’s something to realize – and it starts with PatientIQ.
The fastest growing network for collaborating on clinical research and improving patient outcomes.
