Effect of trauma quality improvement initiatives on outcomes and costs at community hospitals: A scoping review

McIver, Reba; Erdogan, Mete; Parker, Robin; Evans, Allyson; Green, Robert; Gomez, David; Johnston, Tyler

doi:10.1016/j.injury.2024.111492

Cited by 1 publication

(2 citation statements)

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“…This can be conducted for trauma systems at all levels, even though data quality is a known barrier to this in LMICs. 24,34,35…”

Section: Discussionmentioning

confidence: 99%

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Effects of trauma quality improvement program implementation on mortality: A nonrandomized controlled trial

Berg,

David,

Bakhshi

et al. 2024

Preprint

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Importance: Trauma causes over four million deaths annually, the majority of these in low− and middle−income countries. Implementing trauma quality improvement programs may improve outcomes, and though extensively used, high−quality evidence of their effectiveness is scarce. Objective: To assess if implementing a trauma quality improvement program using on audit filters improves trauma patients outcomes. Design: A controlled interrupted time-series study, the Trauma Audit Filter Trial (TAFT). Setting: A prospective, multi−center study across four tertiary care hospitals in urban India. Participants: Adult patients admitted to participating hospitals with a history of trauma, defined as having any of the external causes listed in block V01-Y36, chapter XX of the International Classification of Disease version 10 as reason for admission. Intervention: In the intervention arm, comprised of two hospitals, we implemented a trauma quality improvement program using audit filters after an observation period of one year. The remaining two hospitals, the control arm, continued baseline data collection during the entire study period, 2017 − 2022, with no intervention made. Main Outcomes: All−cause mortality (in−hospital and at 30 days). For time series analysis, we applied segmented regression with a generalized additive model (GAM) to assess the effect on in−hospital mortality. We performed secondary analysis applying difference−in−differences analysis with linear regression to assess the effect on in−hospital and 30−day mortality. The study was not adequately powered to do time series analysis on 30−day mortality. Results: We included 10143 patients, median age 35 (IQR 26 − 50), 83% men. Using time series analysis, we observed a significant reduction in in-hospital mortality (32% vs 24%; OR 0.57, 95% CI 0.41−0.79, p<0.001) in the intervention arm, with no significant change in the control arm. Using difference-in-differences analysis we found a significant reduction in 30-day mortality (39% vs 26%; −0.15 95% CI -0.19 to −0.11, p<0.001) and in-hospital mortality (32% vs 24%; −0.11 95% CI− 0.15 to −0.07, p<0.001). However, external factors such as the opening of a dedicated trauma center at one intervention hospital and the COVID−19 pandemic may have influenced these results. Conclusion: Implementing a trauma quality improvement program using audit filters may reduce mortality. More research is needed to confirm these findings across different settings and to understand what makes these programs efficient, useful and sustainable in terms of improving outcomes. Trial registration: Trauma Audit Filter Trial, ClinicalTrials.gov IDNCT03235388, https://clinicaltrials.gov/study/NCT03235388

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“…This can be conducted for trauma systems at all levels, even though data quality is a known barrier to this in LMICs. 24,34,35…”

Section: Discussionmentioning

confidence: 99%

“…This can be conducted at all levels of trauma systems, even though data quality is a known barrier to this in LMICs. 24,34,35 Our study has several limitations. Firstly, the introduction of a new trauma center likely influenced patient outcomes, posing a significant confounder.…”

mentioning

confidence: 95%