Ask a healthcare practitioner his or her opinion of reporting quality measures, and you'll probably get a less-than-favorable response—or possibly an over-exaggerated eye-roll. That's because many healthcare professionals view the quality data-reporting process as a costly and burdensome waste of time—something that distracts them from the real job at hand: treating and healing patients.
In fact, according to a recent Health Affairs report detailing a survey on the costs of reporting quality metrics, "Each year US physician practices in four common specialties spend, on average, 785 hours per physician and more than $15.4 billion dealing with the reporting of quality measures." That translates to more than 12 hours of physician and staff time per physician, per week. And with most practitioners already clocking more than 40 hours a week, that kind of overtime is tough to stomach.
So, why not ditch data collection altogether? Well, even though the process of completing quality measures can be onerous, the data resulting from those efforts actually is highly valuable. When leveraged appropriately, it can improve the overall effectiveness and consistency of treatment, help shape cohesive care programs across the continuum, and inform more accurate patient diagnoses.
What's the real issue here?
The problem, then, isn't that quality measures—and the data they produce—aren't useful. And really, there's no way around them, especially with payments increasingly being tied to value and providers from all specialties increasingly being pressured to deliver higher-quality care at a lower cost. What the above-cited study really reveals is a lack of innovation and efficiency in the often-fragmented healthcare systems of today.
Barriers abound in the current quality data-collection environment. Chief among them: the proliferation of ill-aligned and constantly changing measures and the glaring lack of industry-wide data collection and reporting standards. On top of that, most of the existing technology solutions for quality measure reporting are cumbersome and do little to ease the burden on the practitioner. That all makes for an incredibly time-intensive process and data sets that are devoid of meaning due to:
1. the jumbled array of measures providers in different areas of practice are using to collect data, and
2. the lack of interoperability (i.e., connectivity) between the systems where the data is being collected and stored.
What's the solution?
To solve for these two deficiencies, the healthcare industry must embrace standardization—both in terms of quality measures and data-collection methods. And if that's not a tall enough order, the healthcare community also must unite to implement technology that supports data exchange and thus, fosters meaningful collaboration among healthcare providers across the entire continuum of care. This, in turn, puts the patient back at the center of the care delivery process by empowering all members of his or her care team to ensure he or she receives the right treatment from the right specialist at the right time. In theory, this type of system would reduce the costs associated with redundant or unnecessary care; improve the overall value of care; and arm healthcare providers and leaders with the data necessary to address healthcare needs at a population level.
Now, there's a big difference between "interoperability" and "uniformity." Achieving large-scale data-exchange doesn't necessarily mean adopting one overarching data-collection technology. In fact, requiring providers in all disciplines to use the same system would entirely defeat the purpose of the solution, as it would be impossible to create a system that's optimized for every provider's specific needs and workflows. Instead, providers must be able to report in a way that aligns with their individual treatment workflows—and then share that information in a way that preserves the integrity and meaning of the data. Otherwise, details crucial to a patient's case could be lost in translation from one specialist to the next—thus detracting from the benefits of collecting quality data in the first place. This is why many providers have started to migrate to specialty-specific EMRs—particularly ones with quality data-collection capabilities—that account for the documentation and compliance needs of their individual areas of practices.
Reporting quality measures doesn't have to be a burden. In fact, it should actually improve efficacy of care, streamline processes and procedures, and increase the value of the care being delivered. To get to that ideal, however, providers must have access to specialty-specific technology that has—or plans to have—interoperability on lock. Only then can they break free of their data-collection silos and leverage the power of data on a global scale. Until then, the majority of healthcare practitioners will continue to see quality measure reporting as a pointless—and eye-roll inducing—endeavor.
Dr. Heidi Jannenga is president and co-founder of Phoenix-based software company WebPT, the leading software solution for physical therapists, occupational therapists, and speech-language pathologists, with more than 50,000 members and 8,000 clinics as customers.
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