The premise is straightforward enough. When healthcare providers have access to complete and accurate information, patients receive better medical care. EHRs can provide that information, improving the physician’s ability to diagnose diseases, reducing or preventing medical errors, and ultimately improving patient outcomes.
According to a survey from the College of Healthcare Information Management Executives (CHIME), 80 percent of respondents believe the clinical analytics used in their health systems are more important now than they were last year. Of that group, 87 percent said they used clinical data analytics for quality reporting and that doing so has enabled them to compete successfully against other providers.
Whether they work in a large health system or a small physicians’ practice, providers can learn from analyzing data for care trends, resulting in significant cost savings. This is especially true for preventive care. For example, a practice’s EHR might capture data that can be used for predictive analytics, such as identifying patients at risk for sepsis or other complications.
Smaller practices are not as inclined to mine their EHR data to gauge their performance or update care delivery processes. Most are collecting and analyzing data mainly for required reporting purposes, and usually they’re gathering information with the help of automated features in their EHRs.
Smaller offices face financial and time constraints that larger practices and health systems do not. But even if a practice commits to beefing up its analytical strength, physicians might not know what to look for or where to find it. Even the most inquisitive practitioner doesn’t have time to compare software packages, develop analysis best practices, or discover the tools to squeeze value out of a rapidly accumulating pile of data. He or she has patients for which to care.
It’s little wonder that many smaller practices don’t have an analytics strategy and aren’t looking for ways to leverage analytics to improve their practice.
This is unfortunate, because providers who are leveraging analytics technology are transforming bits of data into information they can use to improve care quality metrics and patient outcomes. In larger healthcare systems, analytics has been one of the fastest-growing segments of the provider IT budget for several years. Regardless of the regime change in Washington, investment in ACO, clinical care, and quality analytics will likely continue well beyond 2017. Hot areas of analytics investment include provider and care team performance, as well as referral patterns and other financial analytics areas.
For analytics to be an economical part of a small practice, these capabilities must be included in the EHR system. So to be helpful to smaller practices, analytics bundled with EHRs must be capable of carrying out the tasks these practices need.
What to measure and how
The question of what to measure has been answered, more or less, by federal regulations, Medicare, and other external forces. Smaller practices are collecting data on the designated quality measures for meaningful use, according to Health Affairs, but not much else. With value-based reimbursement on the rise, however, practices will be under increasing pressure to prove they are providing high-quality care to patients.
The question of how to measure data for best results has not been answered as completely. Practices that want to take a deep data dive must decide whether they’d be better off trying to create a modest repository confined to their own offices and go after the “skinny data” they have on hand or tap into a larger health system’s enterprise data warehouse, containing an entire universe of potential healthcare information to target their biggest challenges. Smaller might be easier to execute; bigger might provide better comparisons to other practices.
Getting to the point at which EHR data can be analyzed effectively isn’t easy, but a practice has to start somewhere. Many organizations start by examining their own clinical data, working to get EHR data and patient outcomes data into an analytics-friendly format before attacking the problems associated with operational data analytics and revenue cycle management. Since there are still no industrywide data standards, don’t be surprised if you encounter trouble when combining disparate data sources.
Physicians may see the potential of analytics, but many are dealing with a reality that isn’t as promising. A report from Peer60 quantifies that vexation. Sources of frustration are interoperability issues, difficulties defining what data must be collected, and cultural resistance. Storing and effectively managing voluminous data can be a headache as well. Some practices will discover that their existing infrastructure simply cannot support the influx of new information.
It can be an overwhelming chore for a small office to design and implement data management strategies, but EHR vendors and third-party firms are working to provide highly interoperable data capture and analytics components. The numbers already show the rewards that small practices can gain if they partner with these vendors and their colleagues in larger organizations.
Last Updated on January 10, 2017