Scanning the aged to minimize missed injury: An EAST multicenter study.

BACKGROUND: Despite the high incidence of blunt trauma in older adults, there is a lack of evidence-based guidance for computed tomography (CT) imaging in this population. We aimed to identify an algorithm to guide use of a Pan-Scan (Head/C-spine/Torso) or a Selective Scan (Head/C-spine ± Torso). We hypothesized that a patient's initial history and exam could be used to guide imaging. METHODS: We prospectively studied blunt trauma patients aged 65+ at 18 Level I/II trauma centers. Patients presenting >24 h after injury or who died upon arrival were excluded. We collected history and physical elements and final injury diagnoses. Injury diagnoses were categorized into CT body regions of Head/C-spine or Torso (chest, abdomen/pelvis, and T/L spine). Using machine learning and regression modeling as well as a priori clinical algorithms based, we tested various decision rules against our dataset. Our priority was to identify a simple rule which could be applied at the bedside, maximizing sensitivity (Sens) and negative predictive value (NPV) to minimize missed injuries. RESULTS: We enrolled 5,498 patients with 3,082 injuries. Nearly half (47.1%, n = 2,587) had an injury within the defined CT body regions. No rule to guide a Pan-Scan could be identified with suitable Sens/NPV for clinical use. A clinical algorithm to identify patients for Pan-Scan, using a combination of physical exam findings and specific high-risk criteria, was identified and had a Sens of 0.94 and NPV of 0.86 This rule would have identified injuries in all but 90 patients (1.6%) and would theoretically spare 11.9% (655) of blunt trauma patients a torso CT. CONCLUSIONS: Our findings advocate for Head/Cspine CT in all geriatric patients with the addition of torso CT in the setting of positive clinical findings and high-risk criteria. Prospective validation of this rule could lead to streamlined diagnostic care of this growing trauma population. LEVEL OF EVIDENCE: Level 2, Diagnostic Tests or Criteria.

Look in the Mirror, Not Out the Window: In Favor of Internal Benchmarking.

Over the past few decades, institutions have developed complex systems to compare themselves to others with the goal of improving healthcare quality. This process of comparison to others, called external benchmarking, has become the standard approach for quality improvement. However, external benchmarking is resource intensive, may not be flexible enough to focus on problems unique to individual institutions, and may lead to complacency for institutions ranking near the top of the quality bell curve for the measured metrics. Our singular focus on external benchmarking could also divert resources from other approaches. Here, we describe how the use of internal benchmarking, in which an institution focuses on improving their own processes over time, can offer unique advantages as well as offset the limitations of external benchmarking. We advocate for investment in both internal and external benchmarking as complimentary tools to improve healthcare quality.