​Objectives To assess the impact of lower socioeconomic status on the outcome of major torso trauma patients under the single-payer system by the National Health Insurance (NHI) in Taiwan. ​Design A nationwide, retrospective cohort study. ​Setting An observational study from the NHI Research Database (NHIRD), involving all the insurees in the NHI. ​Participants Patients with major torso trauma (injury severity score ≥16) from 2003 to 2013 in Taiwan were included. International Classification of Disease, Ninth Revision, Clinical Modification codes were used to identify trauma patients. A total of 64 721 patients were initially identified in the NHIRD. After applying the exclusion criteria, 20 009 patients were included in our statistical analysis. ​Primary and secondary outcome measures The primary outcome measure was in-hospital mortality, and we analysed patients with different income levels and geographic regions. Multiple logistic regression was used to control for confounding variables. ​Results In univariate analysis, geographic disparities and low-income level were both risk factors for in-hospital mortality for patients with major torso trauma (p=0.002 and <0.001, respectively). However, in multivariate analysis, only a low-income level remained an independent risk factor for increased in-hospital mortality (p<0.001). ​Conclusion Even with the NHI, wealth inequity still led to different outcomes for major torso trauma in Taiwan. Health policies must focus on this vulnerable group to eliminate inequality in trauma care.

Objectives COVID-19 has changed the epidemiology of trauma. However, Taiwan is a country with a low COVID-19 threat, and people’s daily lives have remained mostly unchanged during this period. The purpose of this study is to investigate whether the trend of trauma incidence and the service of trauma care is affected by the relatively minor COVID-19 pandemic in Taiwan. Design A single-institute, retrograde cohort study. Setting An observational study based on the trauma registry of Chang Gung Memorial Hospital (CGMH). Participants Trauma patients presented to the emergency department of CGMH in the period of 1 January to 30 June 2020 (week 1 to week 26) were designated as the COVID-19 group, with 1980 patients in total. Patients of the same period in 2015–2019 were designated as the pre-COVID-19 group, with 10 334 patients overall. Primary and secondary outcome measures The primary outcome is the incidence of trauma admission. Differences in trauma mechanism, severity, location and outcome were also compared in both groups. Results A decrease in trauma incidence during March and April 2020 was noticed. Significant change (p<0.001) in trauma mechanisms was discovered, with decreased burn (5.8% vs 3.6%) and assault (4.8% vs 1.2%), and increased transport accidents (43.2% vs 47.2%) and suicide (0.2% vs 1.0%) in the COVID-19 cohort. A shift in injury locations was also found with a 5% decrement of workplace injuries (19.8% vs 14.8%, p<0.001). Conclusion The limited COVID-19 outbreak in Taiwan has led to a decreased incidence of trauma patients, and the reduction is mostly attributed to the decline in workplace injuries.

(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as “how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting”. We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84–0.96) to 0.95 (0.93–0.97), the sensitivity from 0.97 (0.89–1.00) to 0.97 (0.94–0.99), and the specificity from 0.84 (0.71–0.93) to 0.93(0.990–0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers.