The benefits (or detriments) of adapting to demand disruptions in a hospital pharmacy with supply chain disruptions [0.03%]
供应链中断情况下医院药房适应需求干扰的利与弊
Lauren L Czerniak,Mariel S Lavieri,Mark S Daskin et al.
Lauren L Czerniak et al.
Supply chain disruptions and demand disruptions make it challenging for hospital pharmacy managers to determine how much inventory to have on-hand. Having insufficient inventory leads to drug shortages, while having excess inventory leads t...
Robin Buter,Arthur Nazarian,Hendrik Koffijberg et al.
Robin Buter et al.
Volunteer responder systems (VRS) alert and guide nearby lay rescuers towards the location of an emergency. An application of such a system is to out-of-hospital cardiac arrests, where early cardiopulmonary resuscitation (CPR) and defibrill...
A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework [0.03%]
一项关于“自动出院”急诊患者的调查：一种优化的可解释人工智能框架
Abdulaziz Ahmed,Khalid Y Aram,Salih Tutun et al.
Abdulaziz Ahmed et al.
The issue of left against medical advice (LAMA) patients is common in today's emergency departments (EDs). This issue represents a medico-legal risk and may result in potential readmission, mortality, or revenue loss. Thus, understanding th...
A systematic literature review of predicting patient discharges using statistical methods and machine learning [0.03%]
基于统计方法和机器学习的患者出院预测的系统文献回顾
Mahsa Pahlevani,Majid Taghavi,Peter Vanberkel
Mahsa Pahlevani
Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest o...
A novel approach to forecast surgery durations using machine learning techniques [0.03%]
基于机器学习技术的手术时长预测新方法研究
Marco Caserta,Antonio García Romero
Marco Caserta
This study presents a methodology for predicting the duration of surgical procedures using Machine Learning (ML). The methodology incorporates a new set of predictors emphasizing the significance of surgical team dynamics and composition, i...
Enhancing affordability and profit in a non-cooperative, coordinated, hypothetical pediatric vaccine market via sequential optimization [0.03%]
通过顺序优化增强假想儿童疫苗市场的可负担性和利润（非合作协调市场）
Bruno Alves-Maciel,Ruben A Proano
Bruno Alves-Maciel
This study considers a hypothetical global pediatric vaccine market where multiple coordinating entities make optimal procurement decisions on behalf of countries with different purchasing power. Each entity aims to improve affordability fo...
Editorial: management science for pandemic prevention, preparedness, and response [0.03%]
编者按：管理科学与大流行病的预防、预凈和应对措施
Hrayer Aprahamian,Vedat Verter,Manaf Zargoush
Hrayer Aprahamian
Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel [0.03%]
基于模拟的多目标优化利用神经网络元模型管理急诊科低危患者
Marco Boresta,Tommaso Giovannelli,Massimo Roma
Marco Boresta
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can tre...
Examining chronic kidney disease screening frequency among diabetics: a POMDP approach [0.03%]
糖尿病患者慢性肾病筛查频率的研究：一种POMDP方法
Chou-Chun Wu,Yiwen Cao,Sze-Chuan Suen et al.
Chou-Chun Wu et al.
Forty percent of diabetics will develop chronic kidney disease (CKD) in their lifetimes. However, as many as 50% of these CKD cases may go undiagnosed. We developed screening recommendations stratified by age and previous test history for i...
Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning [0.03%]
可解释机器学习的考虑不满因素的门诊患者候诊时间预测模型
Jongkyung Shin,Donggi Augustine Lee,Juram Kim et al.
Jongkyung Shin et al.
Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected wai...