Unlocking the value of EHR through analytics

Thanks to generous provider incentives included in the HITECH Act, electronic health record (EHR) adoption has grown dramatically in recent years.

When HITECH was passed in 2009, less than 10% of hospitals and 17% of physicians had adopted EHRs; today 96% of hospitals and 78% of physicians use EHRs. In addition to the financial incentives, providers have embraced EHRs in hopes of improving the quality of care, increasing patient safety, reducing medical errors and duplicate testing, and gaining overall efficiencies.

The widespread use of EHRs has enhanced the care process in several areas, thanks to such features as e-prescribing, electronic lab ordering and results reporting, and secure messaging between providers and patients. Despite these improvements, EHRs have not yet created the dramatic shift in care quality that stakeholders had once predicted. In fact, providers often blame EHRs for slowing the charting process and question their overall value. However, changes in payment models will likely spur wider use of analytics and help providers realize additional value from their EHRs.

The current landscape

Traditionally providers have been paid based on a fee-for-service model that rewarded providers for high patient throughput with little regard to the effectiveness and efficiency of care. Newer payment models, however, reward providers for delivering quality care and minimizing waste. Providers risk financial penalties if they fail to meet quality objectives tied to outcomes, patient safety, hospital readmissions and other factors, and high performance may be reflected in reduced costs, higher reimbursement rates and preferred provider status. To ensure financial health, providers must identify areas of waste, as well as efficiencies, and embrace processes that streamline care delivery while enhancing patient outcomes.

Readmissions

Consider, for example, a health system that wants to minimize re-admissions. Stakeholders could begin by evaluating follow-up care processes to ensure hand-offs between providers are well-coordinated and that newly discharged patients have ready access to affordable follow-up care. By leveraging analytics and existing EHR documentation, providers can analyze historical records in a wide variety of ways to identify trends and compare workflows and outcomes based on provider, location, diagnosis and more. Best practices can then be duplicated and less effective care routines can be altered.

This type of analysis, and the performance management processes that heavily rely on them, would not be possible without the wealth of data captured by EHRs in recent years. While clinicians may (often justifiably) complain of EHR inefficiencies and the limited bedside value of electronic charting, the reality is that, with the addition of appropriate analytics, providers now have the power to extract critical information to measure and improve the effectiveness of their care. As the industry shifts to value-based care models, such analysis is vital for ensuring quality outcomes and cost-effective care – and for maximizing provider compensation.

UTIs

Urinary tract infections (UTIs) are the most common type of healthcare-associated infection, according to the National Safety Network, and 75% of UTIs acquired in the hospital are associated with a urinary catheter. Among long-term care facilities, UTIs account for 20-30% of all infections and 30-50% of all antibiotic use; they also increase the risk of hospitalization, antibiotic resistance, and death. Organizations seeking to reduce the rate of UTIs in their facilities should leverage EHR data and analytics to identify opportunities to minimize risks.

For example, providers may want to consider ways to reduce the number of catheterizations, starting with an analysis of historical medical records. Organizations can identify best practices by isolating cases where providers were able to avoid catheterization and still achieve successful outcomes. Staff can then be retrained on these best practices to affect a decrease in catheterizations, as well as overall UTI rates.

Similarly, organizations can leverage analytics to identify cases where patients were catheterized and did not contract a UTI. By identifying best practices and retraining staff, facilities have the opportunity to cut infection rates. Armed with analytics, organizations can potentially make dramatic reductions in overall UTI rates by targeting both the rate of catheterization and the rate of infection.

The promise and the challenge

The application of analytics to EHR data holds great promise for helping organizations identify opportunities to improve the quality of care and preserve financial stability. In a value-based care world, providers must have the ability to measure their performance in order to optimize workflows and processes. Many large health systems and academic institutions already have the manpower and analytics tools in place to perform this type of analysis, but often smaller organizations lack both the required technology and the in-house expertise.

Providers who currently lack the tools required to analyze vast amounts of EHR data and extract meaningful insights should consider partnering with a third party to fill this gap. Many EHR vendors, for example, offer the technology and the technical expertise to help organizations assess their performance, recommend changes, and manage the implementation of new processes.

As providers across the healthcare spectrum continue to adopt EHRs, organizations will increase their reliance on analytics to enhance organizational performance and maximize the value of their EHR investment.

Before joining HCS in 2016 as a behavioral health specialist, Dr. Levitt spent more than 20 years working with community mental health organizations in Michigan, Colorado and New York. His responsibilities have included such diverse areas as direct services, information technology, healthcare informatics, research and evaluation, training and consulting and organizational development. Dr. Levitt holds a Ph.D. in psychiatric rehabilitation from Rutgers University. His professional and research interests focus on recovery from psychiatric disability, health behavior change and continuous quality improvement.

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