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Objective: To report the results of a nationwide critical-care course for non-intensivists to increase staff capacity of intensive care units (ICU) during the COVID-19 pandemic in Argentina. Methods: Three academic organizations, with special funding from 55 private companies, developed a short virtual course comprised of web-based videos, virtual tutorials, and a forum chat. Each state assigned scholarships to non-ICU staff from public hospitals. Students received active follow-up for the completion of the course and took a survey upon course completion. Results: After four months, there were 10,123 students registered from 661 hospitals in 328 cities. Of these, 67.8% passed the course, 29.1% were still ongoing and 3.1% were inactive. Most students were female (74.2%) with a median of 37 years old (IQR 31-44). The group was composed of 56.5% nurses, 36.2% physicians, and 7.4% physiotherapists, of whom 48.3% did not have any experience in critical care. Mean overall satisfaction was 4.4/5 (SD 0.9), and 90.7% considered they were able to apply the contents to their practice. Conclusions: This course was effective for rapid training of non-ICU personnel. The assignment strategy, the educational techniques, and the close follow-up led to low dropout and high success rates and satisfaction.

Summary Age expectancy has significantly increased over the last 50 years, as well as some age-related health conditions such as hip fractures. The development of hip fracture registries has shown enhanced patient outcomes through quality improvement strategies. The development of the Argentinian Hip Fracture Registry is going in the same direction. Introduction Age expectancy has increased worldwide in the last 50 years, with the population over 64 growing from 4.9 to 9.1%. As fractures are an important problem in this age group, specific approaches such as hip fracture registries (HFR) are needed. Our aim is to communicate the Argentinian HFR (AHFR) development resulting from an alliance between Fundación Trauma, Fundación Navarro Viola, and the Argentinian Network of Hip Fracture in the elderly. Methods Between October 2020 and May 2021, an iterative consensus process involving 5 specialty-focused meetings and 8 general meetings with more than 20 specialists was conducted. This process comprised inclusion criteria definitions, dataset proposals, website deployment with data protection and user validation, the definition of hospital-adjusted registry levels, implementation planning, and sustainability strategies. Results By June 2021, we were able to (1) outline data fields, including epidemiological, clinical, and functional dimensions for the pre-admission, hospitalization, discharge, and follow-up stages; (2) define three levels: basic (53 fields), intermediate (85), and advanced (99); (3) identify 21 benchmarking indicators; and (4) make a correlation scheme among fracture classifications. Simultaneously, we launched a fundraising campaign to implement the AHFR in 30 centers, having completed 18. Conclusion AHFR development was based on four pillars: (1) representativeness and support, (2) solid definitions from onset, (3) committed teams, and (4) stable funding. This tool may contribute to the design of evidence-based health policies to improve patient outcomes, and we hope this experience will help other LMICs to develop their own tailored-to-their-needs registries.

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durante el año 2018, considerados básicos para detectar oportunidades de mejora en los procesos de atención. Materiales y métodos: Estudio retrospectivo observacional analizando la calidad de 48 campos de 4.489 hechos ingresados en el Registro de Trauma (RT) de Fundación Trauma (FT) en 13 hospitales durante el año 2018. Los datos fueron distribuidos y analizados en 6 categorías: datos del hecho; atención prehospitalaria; datos del paciente; ingreso al hospital y proceso de atención; signos vitales; comorbilidades y lesión; e índices y scores de trauma. Resultados: Al analizar los 48 campos en su conjunto, se encontró un promedio de completitud de 64%. De los 13 hospitales analizados, el que contaba con mejor calidad de los datos presentaba un promedio de completitud de 92%; y el de peor calidad 58%. Considerando la media de completitud, las categorías se distribuyen en el siguiente orden: (1) datos del paciente, 97.5%; (2) índices y scores, 71.2%; (3) hecho, 68.6%; (4) signos vitales, comorbilidades y lesión, 66.5%; (5) ingreso y proceso de la atención, 60%; y (6) atención prehospitalaria, 38,6%. Conclusión: La distribución de datos completos presenta una amplia variación entre las categorías analizadas, teniendo en un extremo los datos del paciente y las lesiones, y en el otro los procedimientos y las complicaciones. En términos generales, podemos decir que, si bien se cuenta con información para trabajar en la implementación y monitoreo de estrategias de mejora de la calidad, la calidad de la información para el desarrollo de scores y estrategias de mejora se presenta como un desafío en sí mismo. En este sentido, es necesario contar con estrategias específicas orientadas a mejorar la calidad de la información de las historias clínicas.