Better information leads to better decisions. It stands to reason that evidence-based guidelines help providers formulate smarter, more effective treatment plans that return individuals to health quickly and safely.
Lower costs follow suit when unnecessary variations in care are eliminated. But the benefits of an evidence-based approach don't end there. When organizations consistently use evidence-based treatment guidelines, they are better able to track and compare clinical and financial performance against benchmarks to guide ongoing quality improvement.
In order to demonstrate improvement, organizations require data gathered about patients, their treatments and their outcomes in conjunction with advanced analytics to guide decision making. The best practices at the best cost can be identified and implemented, and then assessed on an ongoing basis.1 To apply benchmarking successfully, organizations need to adopt processes for establishing baselines, gathering and aggregating data, and comparing data against baselines as well as other objective benchmarks. This provides the framework for coordinated, data-driven action to identify and correct what isn't working well and optimize what is, resulting in reduced costs, lower risk and better clinical outcomes.
Benchmarking also serves as a tool for motivating providers and others across the enterprise to engage in improvement work and to understand where their performance falls in comparison to others.2 For example, using evidence-based readmission reduction models, San Francisco Bay-area hospitals and their post-acute partners collaborated to reduce hospital readmissions by 20 percent, preventing more than 4,000 readmissions.3
Assessing performance over time
Establishing baselines using data currently available, such as how long it takes on average for patients to return to health for a wide range of diagnoses, is an important first step to benchmarking success. With baseline data in hand, organizations can more thoroughly analyze the effectiveness of treatment protocols over time. It's key to identify metrics that strike a balance between being meaningful to patient outcomes as well as controllable.4 When evidence-based clinical guidelines and other resources, such as physiological duration tables, are integrated directly into the electronic health record system (EHR), data needed to support ongoing outcomes improvement can be gathered as a natural part of the care process.
With a framework in place and data gathering on track, it's possible to objectively measure performance. As an example, an organization can review how it performs for repetitive strain injuries. Average durations for how long patients took to recover can be benchmarked against an average dataset of millions of cases. Comparisons can also be made against the physiological recovery times or "optimum" duration. By analyzing data at increasingly granular levels, specific areas where performance meets, exceeds or falls below benchmarks come to light.
The next step is for the organization to analyze actual treatment patterns against the clinical guidelines in place. In the example of repetitive strain injuries, the organization might review patient cases for carpel tunnel syndrome to see how long patients took to recover and if treatment variations exist. This analysis may offer insight into the specific conditions where care may have strayed from standardized treatment protocols, where resources were used inefficiently or where patients failed to comply with treatment guidance. This analysis can quickly reveal where processes are working well and where inefficiencies linger. Both aspects are important and allow organizations to continuously fine-tune best practices, apply them enterprise-wide, and correct problem areas.
Implementing targeted improvements
With a data-driven understanding of clinical performance, healthcare administrators can identify clear targets for improvement initiatives going forward. For example, an organization may need to adjust specific treatment protocols in order to deliver more consistent and effective outcomes. Safely and appropriately decreasing durations based on the optimum treatment plan tailored to a patient directly reduces costs and aids the best use of resources. Targeting specific cohorts of patients or specific conditions for proactive intervention and management can also improve outcomes. In addition, opportunities can be identified to offer further education and training to help providers understand and adhere to evidence-based protocols and actively engage patients in their recoveries.
As the industry continues its shift to value-based care models as a means to drive out waste, consistent use of evidence-based treatment guidelines, benchmarking and analytics can deliver actionable insights to improve clinical outcomes and financial performance. Ultimately, patients – and the providers, payers and employers responsible for their care – succeed when all parties have a clear understanding of what's needed to achieve their shared goals of managing costs while returning patients to healthy living.
1 Benchmarking: A Method for Continuous Quality Improvement in Health. Healthcare Policy. U.S. National Library of Medicine, National Institutes of Health. May 2012.
2 Practice Facilitation Handbook. Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services.
3 “Providers Collaborate to Reduce Readmissions,” AHC Media, May 1, 2014.
4 “The Burdens of Benchmarking,” Becker’s Hospital Review, October 1, 2014.
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