جستجوی مقالات مرتبط با کلیدواژه "scheduling" در نشریات گروه "پزشکی"

There has been increasing concern over opioid-related harms across the world. In Australia in 2018, codeine-containing products were up-scheduled from over-the-counter access at pharmacies, to requiring a prescription. The drug regulator’s decision to up-schedule was contentious and widely debated, due to the potentially large impact on consumers and healthcare professionals. This study aimed to analyse influences on the codeine up-scheduling policy.

This retrospective policy analysis used the Advocacy Coalition Framework (ACF) to understand how policy actors with shared beliefs formed adversarial coalitions to shape policy. Data were drawn from documents (regulator policy documents, public submissions, news reports, organisational media releases and position statements) and semistructured interviews with 15 key policy actors. Codes were generated relating to policy processes and actor beliefs; broad themes included the role of health professionals, perceptions of opioids, impact on consumers, and the role of government in healthcare.

Two coalitions in this policy subsystem were identified: (1) supportive [with respect to the up-scheduling], and (2) opposing. The key evident beliefs of the supportive coalition were that the harms of codeine outweighed the benefits, and that government regulation was the best pathway for protecting consumers. The opposing coalition believed that the benefits of codeine accessible through pharmacists outweighed any harms, and consumers should manage their health without any more intervention than necessary. The policy decision reflected the influence of the supportive coalition, and this analysis highlighted the importance of their public health framing of the issue, the acceptability of their experts and supporting evidence, and the perceived legitimacy of the up-scheduling process. 

Understanding these coalitions, their beliefs, and how they are translated through existing policy processes and institutions provides insight for those interested in influencing future health policy. Specific lessons include the importance of strategic frames and advocacy, and engagement with formal policy processes.

Nursing care contributes to the safety and the quality of care of patients. During the COVID‑19 pandemic, nurses became frontline care providers.

A qualitative study was conducted using an online focus group discussion of eight nurse committee members from six hospitals. After the data were collected, the study continued with inductive thematic analysis. The data were organized and extracted to identify meaningful statements and formulate meanings. Inductive thematic analysis was used, resulting in three themes and six subthemes.

The themes related to managing the nursing workforce, schedules, rosters, shifts, goals of re‑design staffing, and the nurse‑patient ratio.

The management of the nursing staffing was modified to protect nurses during the COVID‑19 pandemic. The nurse manager redesigned workforce planning to ensure a safe environment for nurses.

The pharmaceutical supply chain should provide medicines in the right quantity, with the acceptable quality, to the right place and customers, at the right time and with optimum cost. Pharmaceutical companies, the most important player in the pharmaceutical supply chain, have a crucial role. An integrated approach in production planning and scheduling in pharmaceutical companies typically optimizes several consecutive stages in the pharmaceutical supply chain because these companies, are both linked to suppliers and distributors/end users. Hospitals are the most crucial end-user among the end-users because of the necessity of a massive amount of drugs to render service to a large number of patients in hospitals. Intravenous fluids and irrigation solutions help patients maintain the optimal internal temperature and increase comfort in hospitals during procedures like surgery or recovery. In this work, simultaneous production planning and scheduling in a real-world application, a production line of intravenous fluids and irrigation solutions at Darou Pakhsh Pharmaceutical Manufacturing Company is addressed.

A novel mixed-integer linear programming model is formulated for multi-period simultaneous production planning and scheduling. Since the problem is NP-hard in the strong sense, a memetic algorithm is proposed that reduces the computational effort of the problem. The chromosome representation is based on a permutation matrix, and a new algorithm is developed to construct a complete schedule from the permutation matrix through the planning horizon.

40 problems were investigated, containing 36 randomly generated instances and four real problems according to the data within the last two years. The generated instances were divided into small-sized, medium-sized, and large-sized instances. Among the 36 instances, 22 instances were optimally solved by both the exact method and the proposed memetic algorithm. The average gap for small-sized, medium-sized, and large-sized instances are respectively 0.00%, -1.15%, and -51.38%, indicating as the size of instances grows, the gap becomes considerable. The exact method could not reach an optimal solution for four real instances. The running time for real instances is expanded to 8 hours. The results revealed that the proposed memetic algorithm significantly outperformed the exact method in obtaining better solutions for real instances.

The computational results showed that the proposed memetic algorithm obtained optimal solutions on all the instances solved optimally by the exact method. It outperformed the exact method in other problems. This outperformance becomes more evident as the size of instances grows.

Low-energy consumption is a vital concern in E-learning due to high-volume processing and the fact that mobile technologies are usually battery-operated devices.

The method is simulated by developing a discrete-event simulation in C#. The validation of the proposed method is performed on generated task sets as used in similar work. The characteristic of randomly produced tasks is similar to the well-known techniques of task generation in mixed-criticality (MC) systems.

The simulation results show that energy consumption can be improved up to 23% in comparison to similar approaches. The most important factor for this satisfaction was the reservation times of critical tasks to further reduce the processor frequency.

The internet of thing (IoT) is poised to be one of the most disruptive technologies in E-learning environment. The IoT is a kind of MC system that integrates multiple things with different criticalities into the same platform. Mobile technologies provide education to people through mobile devices. These devices are usually battery-operated and owing to high-volume processing, Low-energy consumption becomes a vital concern in E-learning. Therefore, this paper was discussed about the MC system in general. Finally, the paper was proposed a scheduling technique to minimize the energy consumption of E-learning devices that use the IoT.