[Applying Multiple Strategies to Enhance the Completion Rate of Critical Care in COVID-19 Patients].

BACKGROUND & PROBLEMS: Taiwan entered the community transmission stage of COVID-19 in May 2021, with numbers of locally confirmed cases and critical cases increasing sharply. Medical institutions deployed special units to treat patients. In our hospital, a special COVID-19 intensive care units staffed with nursing personnel across various specialties was established. The rate of COVID-19 critical care completion among nurses in this unit was 79.1%. The reasons for non-completion were found to include limited intensive care standards for COVID-19; inadequate training, teaching aids, and practice manuals; and the overwhelming amount of new COVID-19-related information and updates. PURPOSE: The aim of this project was to increase the team's COVID-19 critical care completion rate from 79.1% to 93.5%. RESOLUTIONS: Multiple strategies were implemented, including: (1) providing online education and training, (2) establishing a platform for sharing COVID-19-related updates, (3) creating a QR-code accessible COVID-19 reference database, (4) creating a COVID-19 practice manual, and (5) providing simulation training sessions on wearing personal protective equipment during critical care. RESULTS: The critical-care completion rate for patients with COVID-19 infection increased significantly in this unit from 79.1% to 98.2%, which exceeded the project goal. CONCLUSIONS: Implementing a multi-strategy intervention that includes both online and simulation training may be effective in improving the critical care completion rate for patients with COVID-19 infection.

Hybrid proximal linearized algorithm for the split DC program in infinite-dimensional real Hilbert spaces.

To be the best of our knowledge, the convergence theorem for the DC program and split DC program are proposed in finite-dimensional real Hilbert spaces or Euclidean spaces. In this paper, to study the split DC program, we give a hybrid proximal linearized algorithm and propose related convergence theorems in the settings of finite- and infinite-dimensional real Hilbert spaces, respectively.

An algorithm for the split-feasibility problems with application to the split-equality problem.

In this paper, we study the split-feasibility problem in Hilbert spaces by using the projected reflected gradient algorithm. As applications, we study the convex linear inverse problem and the split-equality problem in Hilbert spaces, and we give new algorithms for these problems. Finally, numerical results are given for our main results.