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Data Benchmarks: Three Myths and the Truth Behind Them

Data benchmarks may seem like old news but they are actually an everyday novelty in healthcare. Successful organizations refresh their comparative data analyses as often as possible to stay in the know around how they’re performing. It’s high time that the common misconceptions associated with benchmarking analytics in healthcare get debunked.

Let’s go through some of these myths.

Myth #1: Benchmarking is just for comparing performance across a health system.

The Truth: Limiting benchmarks to just the health system gives a picture of performance, but it’s far from complete. By using internal data only, providers have no way to validate that their priorities are correct. To give an example, a health system may use benchmarks to uncover the fact that they’ve successfully reduced readmissions over the past year by 5 percent. But if the national trend shows a steeper decline of 8-9 percent, then the index health system isn’t keeping pace and may subsequently be subject to a readmission payment penalty, despite their progress. To fully leverage benchmarking analytics and understand performance, health systems must compare against themselves and against local, peer and national metrics. With the right partner, benchmarking analytics can help health system leaders pinpoint and prioritize opportunities. And to take it a step further, if they’re participating in a collaborative that enables health systems to share data and best practices with others, they can identify the top performers and mimic their success programs to accelerate improvement.

The Proof: More than 200 hospitals in Premier’s quality improvement collaborative share, compare and benchmark their data with the goal of reducing clinical care variation between top- and bottom-performing hospital participants. By evaluating the outcomes of top performers, as well as evidence collected about the rate of improvement that can reasonably be expected, these collaborative members have outperformed the nation in mortality, avoided payment penalties and saved $18 billion in unnecessary costs.

Myth #2: Benchmarking is only for quality improvement – not cost.

The Truth: Because cost and quality are inextricably linked in value-based payment programs, they also need equal weighting within benchmarks. In fact, cost is often the great equalizer to performance data and analytics, pinpointing areas where spending may be higher without a corresponding improvement in quality or outcomes. With this information, health system leaders have insight around discrete and specific opportunities for further investigation. Integrating cost, quality, claims, operations, supply chain and other metrics enables health systems to go beyond clinical care outcomes, get a full picture of what’s driving overall performance and fill all the cracks where variation lurks.

The Proof: Premier worked with Mercy Health, a health system with 22 acute care facilities in Ohio and Kentucky, to leverage benchmarking data and identify specific patient populations with unjustified variation in level of care and length-of-stay. The analytics compared each facility’s performance by service line, then compared each facility to top-performing peers within Premier’s robust database, which links clinical, financial and supply chain data. Leveraging and understanding data trends, as well as streamlining changes across institutions and departments that touch ICU and critical care, the health system generated nearly $7 million in savings over a two-year period – without compromising the quality of care.

Myth #3: Benchmarking is a one-time effort.

The Truth: Because performance improvement is an ongoing process, leveraging data on a regular basis (e.g., quarterly or bi-annually) enables leaders to continually assess what’s driving cost and quality outcomes, hold providers accountable and fully optimize care delivery.

The Proof: St. Luke’s University Health Network of Bethlehem, Penn., an early leader in bundled payment, had to develop trusted partnerships with post-acute care providers in order to tackle variation and improve efficiencies. St. Luke’s met quarterly with providers to review performance data, discuss opportunities and engage in two-way progress report sharing. Through these meetings, they found they needed to restructure their care model alongside their post-acute care clinicians. St. Luke’s embedded physicians and nurse practitioners within post-acute provider facilities, helping to drive interdisciplinary daily rounds, team meetings, implementation of new care pathways, and aligned treatment protocols. As a result, skilled nursing facility (SNF) length-of-stay for patients in the St. Luke’s bundled payment program was reduced by 22 percent, and SNF readmissions were reduced by 23 percent. Additionally, SNF length-of-stay for total joint replacement alone was reduced by more than 50 percent.

Forget the Myths

Don’t let these myths get in the way of building and leveraging robust, results-producing benchmarking capabilities. Using data to measure progress against local, peer and national benchmarks, healthcare organizations are proving just how important benchmarking is to their overall performance improvement efforts.

For more information about the impact that the right benchmarking analytics can have, contact us.

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