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Utilizing Benchmarking to Reduce Adverse Outcomes

      The advent of large, multicenter datasets has allowed institutions to benchmark their health care performance, but successful application of these data to improve care remains a challenge to many institutions. Although big data allows for sophisticated statistical modeling of adverse outcomes, effective quality improvement (QI) requires understanding of the local care environment with input from health care providers, administration, process improvement experts, and other stakeholders.
      • Minami CA
      • et al.
      Process improvement in surgery.
      In this issue, Tignanelli et al. describe their experience leveraging benchmarked data from the American College of Surgeons Trauma Quality Improvement Project (ACS TQIP).
      • Tignanelli CJ
      • et al.
      Implementation of a prophylactic anticoagulation guideline for patients with traumatic brain injury.
      They found higher than expected venous thromboembolism (VTE) rates after traumatic brain injury (TBI), and rates of VTE prophylaxis that were lower than the national benchmark. By implementing a multidisciplinary protocol to improve the administration of VTE prophylaxis in these patients, they decreased their VTE rate and improved patient outcomes.
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