scholarly journals An Edge Computing Based Smart Healthcare Framework for Resource Management

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4307 ◽  
Author(s):  
Soraia Oueida ◽  
Yehia Kotb ◽  
Moayad Aloqaily ◽  
Yaser Jararweh ◽  
Thar Baker
Keyword(s):  
Resource Utilization ◽  
Waiting Time ◽  
Smart Cities ◽  
Real Life ◽  
Edge Computing ◽  
Utilization Rate ◽  
Resource Assignment ◽  
Smart Healthcare ◽  
Patient Waiting Time ◽  
Patient Waiting

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

scholarly journals Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room

2021 ◽  
Vol 03 (01) ◽  
pp. 33-45
Author(s):  
Hassan Hijry ◽  
Richard Olawoyin
Keyword(s):  
Deep Learning ◽  
Emergency Room ◽  
Waiting Time ◽  
Waiting Times ◽  
Real Life ◽  
Healthcare Sector ◽  
Medical Attention ◽  
Queue System ◽  
Patient Waiting Time ◽  
Patient Waiting

Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.

scholarly journals The Impact of Longer Consultation Time: A Simulation-Based Approach

2020 ◽  
Vol 11 (05) ◽  
pp. 857-864
Author(s):  
Abdulrahman M. Jabour
Keyword(s):  
Waiting Time ◽  
Quality Care ◽  
Working Hours ◽  
Discrete Events ◽  
Simulation Based ◽  
Patient Waiting Time ◽  
Free Environment ◽  
The Impact ◽  
Consultation Time ◽  
Patient Waiting

Abstract Background Maintaining a sufficient consultation length in primary health care (PHC) is a fundamental part of providing quality care that results in patient safety and satisfaction. Many facilities have limited capacity and increasing consultation time could result in a longer waiting time for patients and longer working hours for physicians. The use of simulation can be practical for quantifying the impact of workflow scenarios and guide the decision-making. Objective To examine the impact of increasing consultation time on patient waiting time and physician working hours. Methods Using discrete events simulation, we modeled the existing workflow and tested five different scenarios with a longer consultation time. In each scenario, we examined the impact of consultation time on patient waiting time, physician hours, and rate of staff utilization. Results At baseline scenarios (5-minute consultation time), the average waiting time was 9.87 minutes and gradually increased to 89.93 minutes in scenario five (10 minutes consultation time). However, the impact of increasing consultation time on patients waiting time did not impact all patients evenly where patients who arrive later tend to wait longer. Scenarios with a longer consultation time were more sensitive to the patients' order of arrival than those with a shorter consultation time. Conclusion By using simulation, we assessed the impact of increasing the consultation time in a risk-free environment. The increase in patients waiting time was somewhat gradual, and patients who arrive later in the day are more likely to wait longer than those who arrive earlier in the day. Increasing consultation time was more sensitive to the patients' order of arrival than those with a shorter consultation time.

Announcing Waiting Time in Emergency Departments

2019 ◽  
pp. 1-6
Author(s):  
Martin Lariviere ◽  
Sarang Deo
Keyword(s):  
Waiting Time ◽  
Emergency Departments ◽  
Waiting Times ◽  
Large Network ◽  
Regional Networks ◽  
Marketing Campaign ◽  
National Healthcare ◽  
Time Estimates ◽  
Patient Waiting Time ◽  
Patient Waiting

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?

scholarly journals Impact of value added services on patient waiting time at the ambulatory pharmacy Queen Elizabeth Hospital

2017 ◽  
Vol 15 (1) ◽  
pp. 846-846 ◽  
Author(s):  
Benjamin C. Loh ◽  
Kheng F. Wah ◽  
Carolyn A. Teo ◽  
Nadia M. Khairuddin ◽  
Fairenna B. Fairuz ◽  
...  
Keyword(s):  
Waiting Time ◽  
Value Added ◽  
Queen Elizabeth Hospital ◽  
Patient Waiting Time ◽  
Patient Waiting

scholarly journals Shortening Patient Waiting Time for mFOLFOX 6 Outpatient Chemotherapy

2008 ◽  
Vol 34 (5) ◽  
pp. 448-454 ◽  
Author(s):  
Kayoko Ohashi ◽  
Toshiya Katayama ◽  
Maki Kato ◽  
Suguru Araki ◽  
Reiko Yasuda ◽  
...  
Keyword(s):  
Waiting Time ◽  
Outpatient Chemotherapy ◽  
Patient Waiting Time ◽  
Patient Waiting

scholarly journals EVALUATION OF LINEAR ACCELERATOR UTILIZATION FOR CA MAMMAE RADIOTHERAPY AT A PRIVATE HOSPITAL

2019 ◽  
Vol 7 (2) ◽  
pp. 132
Author(s):  
Feliana Mirnawati ◽  
Sutopo Patria Jati ◽  
Johanes Sugiarto
Keyword(s):  
Waiting Time ◽  
Private Hospital ◽  
Breast Cancers ◽  
Central Java ◽  
Depth Interview ◽  
Radiation Capacity ◽  
Need To Evaluate ◽  
Patient Waiting Time ◽  
Type C ◽  
Patient Waiting

Background: Radiotherapy is an important cancer therapy in Indonesia. For hospitals which have provided radiotherapy tools for more than five years, they need to evaluate its utilization and influence on patients’ condition.Aim: This study aims to analyze the use of Linac for radiating breast cancers in one of a type-C private hospital in Central Java by using Health Technology Assessment.Method: This study is an observational and descriptive study with an in-depth interview. There were 72 medical record documents examined. Furthermore, the researchers calculated the profits from the financial feasibility of tool investment gained by the hospital. This study involved six Key Informants and four triangulation informants.Results: This study shows that in terms of effectivity aspect, one Linac can prolong patient waiting time about 2-4 weeks. Such a long waiting time may cause disease progression to increase. Meanwhile, seen from the technical characteristics, the tool is not well-maintained by the internal and external parties. It causes the tool’s performance worse. In terms of the economic aspect, the tool has lasted for 7.5 years, but it technically has been utilized for ten years. Therefore, the hospital needs to supply more radiation tools.Conclusion: The Linac utilization in a year increased, and the ca mammae patient visits were high. In addition to those aspects, the profits gained from the health services were high as well. The hospital should add radiation tools to improve the radiation capacity and decrease patient waiting time.Keywords: linac, economic evaluation, Ca Mammae.

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