English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Patient appointments are an effective method to reduce patient waiting time. However, not all patients can make an appointment before receiving medical services. In this paper, we focus on the patient appointment scheduling problem in the presence of emergency patients. We formulate the problem as a stochastic programming (SP) model to reduce the patient waiting time and increase server utilisation. Considering the service system as a time-varying queuing system with dual-class patients, we propose two methods to evaluate the patients waiting times and the server utilisation for a given patient appointment schedule. The uniformisation method can ‘exactly’ evaluate the performance metrics with a high computation cost, while the trained machine learning models can approximate the metrics with high computing speed. Based on the proposed evaluation methods, we design a simulated annealing algorithm to solve the SP model. Numerical experiments show that the schedule computed by our heuristic algorithm can effectively improve the real-life patient appointment schedule.

Managing appointments of outpatients considering the presence of emergency patients: the combination of the analytical and data-driven approach