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Статті в журналах з теми "Healthcare scheduling"

Автор: Grafiati

Опубліковано: 13 вересня 2022

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1

An, Y.-J., Y.-D. Kim, B. J. Jeong, and S.-D. Kim. "Scheduling healthcare services in a home healthcare system." Journal of the Operational Research Society 63, no. 11 (November 2012): 1589–99. http://dx.doi.org/10.1057/jors.2011.153.

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2

Ariff, Hajar, M. Ghazali Kamardan, Suliadi Sufahani, and Maselan Ali. "Review on Queueing Problem in Healthcare." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 304. http://dx.doi.org/10.14419/ijet.v7i4.30.22291.

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Анотація:

This article shows the application of queueing, simulation and scheduling used in the field of healthcare. A summary of queueing, simulation and scheduling theory used in waiting time, appointment system and patient flow are summarised in this article. Different departments in the healthcare system are also considered in this article such as emergency department, outpatient department and the pharmacy. The aim is to provide the reader a general background into queueing, simulation and scheduling in the healthcare.

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3

Restrepo, María I., Louis-Martin Rousseau, and Jonathan Vallée. "Home healthcare integrated staffing and scheduling." Omega 95 (September 2020): 102057. http://dx.doi.org/10.1016/j.omega.2019.03.015.

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4

Barg-Walkow, Laura H., and Wendy A. Rogers. "Modeling Task Scheduling in Complex Healthcare Environments: Identifying Relevant Factors." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 772–75. http://dx.doi.org/10.1177/1541931213601677.

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Multiple task coordination involves scheduling tasks, completing tasks, and integrating tasks into a workflow. Task scheduling can influence outcomes of safety, satisfaction, and efficiency when completing tasks. This is especially important in complex life-critical environments such as healthcare, which incurs many situations where there are multiple tasks and limited resources for addressing all tasks. One approach for understanding tasks coordination is the Strategic Task Overload Management (STOM) model, which is a model for task scheduling behavior. In this theoretical paper, we discuss how this model can be extended to a complex healthcare environment. There are additional considerations (e.g., time) which must be considered when applying this model to healthcare. Ultimately, understanding how emergency physicians make multiple task scheduling decisions will advance theories and models, such as STOM, which can then in turn be implemented to improve scheduling behaviors in complex healthcare environments.

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5

Lakhan, Abdullah, Ali Hassan Sodhro, Arnab Majumdar, Pattaraporn Khuwuthyakorn, and Orawit Thinnukool. "A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks." Sensors 22, no. 6 (March 19, 2022): 2379. http://dx.doi.org/10.3390/s22062379.

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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

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6

Benila S, Benila S., and Usha Bhanu N. Benila S. "Fog Managed Data Model for IoT based Healthcare Systems." 網際網路技術學刊 23, no. 2 (March 2022): 217–26. http://dx.doi.org/10.53106/160792642022032302003.

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<p>In Internet of things enabled healthcare system, sensors create vast volumes of data that are analyzed in the cloud. Transferring data from the cloud to the application takes a long time. An effective infrastructure can reduce latency and costs by processing data in real-time and close to the user devices. Fog computing can solve this issue by reducing latency by storing, processing, and analyzing patient data at the network edge. Placing the resources at fog layer and scheduling tasks is quite challenging in Fog computing. This paper proposes a Fog Managed Data Model (FMDM) with three layers namely Sensor, Fog and cloud to solve the aforementioned issue. Sensors generate patient data and that are managed and processed by Fog and cloud layers. Tasks are scheduled using a Weighted Fog Priority Job Scheduling algorithm (WFPJS) and fog nodes are allocated based on Priority based Virtual Machine Classification Algorithm (PVCA). The performance of this model is validated with static scheduling techniques with variable patient counts and network configurations. The proposed FMDM with WFPJS reduces response time, total execution cost, network usage, network latency, computational latency and energy consumption.</p> <p> </p>

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7

Zhang, Xiaojin, Shuang Ma, and Songlin Chen. "Healthcare service configuration based on project scheduling." Advanced Engineering Informatics 43 (January 2020): 101039. http://dx.doi.org/10.1016/j.aei.2020.101039.

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8

Abdalkareem, Zahraa A., Amiza Amir, Mohammed Azmi Al-Betar, Phaklen Ekhan, and Abdelaziz I. Hammouri. "Healthcare scheduling in optimization context: a review." Health and Technology 11, no. 3 (April 10, 2021): 445–69. http://dx.doi.org/10.1007/s12553-021-00547-5.

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9

Che, Haiying, Xiaolong Wang, Hong Wang, Zixing Bai, and Honglei Li. "Scheduling with Deadline Constraint of Healthcare Applications on Cloud-Based Workflow." Journal of Medical Imaging and Health Informatics 10, no. 10 (October 1, 2020): 2430–38. http://dx.doi.org/10.1166/jmihi.2020.3290.

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Cloud-based workflow technology has played an important role in the development of large scale healthcare applications with high flexibility to meet variety of healthcare process requirements. Among all the factors affecting the healthcare applications on cloud-based workflow, the tasks scheduling is the crucial one. This paper aims at the cloud-based workflow tasks scheduling with deadline constraints and its implementation in two approaches: heuristic scheduling algorithm (HSA) and meta heuristic scheduling algorithm (HSA-ACO). HSA decomposes the workflow according to its structure and divide the deadline into the level deadlines. Tasks in each level get scheduling priority according to the earliest start time under the constraint of level deadline. In another method, HSA-ACO integrates HSA with ant colony algorithm to achieve better performance. In the last part, we launch the experiment to compare HSA and HSA-ACO with algorithms like Prolis, LACO and ICPCP in three types of workflow with different scales. The experiment results show that HSA-ACO is better than the other algorithms.

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10

Iqbal, Naeem, Imran, Shabir Ahmad, Rashid Ahmad, and Do-Hyeun Kim. "A Scheduling Mechanism Based on Optimization Using IoT-Tasks Orchestration for Efficient Patient Health Monitoring." Sensors 21, no. 16 (August 11, 2021): 5430. http://dx.doi.org/10.3390/s21165430.

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Over the past years, numerous Internet of Things (IoT)-based healthcare systems have been developed to monitor patient health conditions, but these traditional systems do not adapt to constraints imposed by revolutionized IoT technology. IoT-based healthcare systems are considered mission-critical applications whose missing deadlines cause critical situations. For example, in patients with chronic diseases or other fatal diseases, a missed task could lead to fatalities. This study presents a smart patient health monitoring system (PHMS) based on an optimized scheduling mechanism using IoT-tasks orchestration architecture to monitor vital signs data of remote patients. The proposed smart PHMS consists of two core modules: a healthcare task scheduling based on optimization and optimization of healthcare services using a real-time IoT-based task orchestration architecture. First, an optimized time-constraint-aware scheduling mechanism using a real-time IoT-based task orchestration architecture is developed to generate autonomous healthcare tasks and effectively handle the deployment of emergent healthcare tasks. Second, an optimization module is developed to optimize the services of the e-Health industry based on objective functions. Furthermore, our study uses Libelium e-Health toolkit to monitors the physiological data of remote patients continuously. The experimental results reveal that an optimized scheduling mechanism reduces the tasks starvation by 14% and tasks failure by 17% compared to a conventional fair emergency first (FEF) scheduling mechanism. The performance analysis results demonstrate the effectiveness of the proposed system, and it suggests that the proposed solution can be an effective and sustainable solution towards monitoring patient’s vital signs data in the IoT-based e-Health domain.

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11

Demirbilek, Mustafa, Juergen Branke, and Arne Strauss. "Dynamically accepting and scheduling patients for home healthcare." Health Care Management Science 22, no. 1 (January 5, 2018): 140–55. http://dx.doi.org/10.1007/s10729-017-9428-0.

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12

Guo, Yang, and Yong Yao. "On Performance of Prioritized Appointment Scheduling for Healthcare." Journal of Service Science and Management 12, no. 05 (2019): 589–604. http://dx.doi.org/10.4236/jssm.2019.125040.

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13

Harris, Shannon L., Jerrold H. May, and Luis G. Vargas. "Predictive analytics model for healthcare planning and scheduling." European Journal of Operational Research 253, no. 1 (August 2016): 121–31. http://dx.doi.org/10.1016/j.ejor.2016.02.017.

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14

Aissaoui, Najla Omrane, Hejer Hachicha Khlif, and Farah Mansour Zeghal. "Integrated proactive surgery scheduling in private healthcare facilities." Computers & Industrial Engineering 148 (October 2020): 106686. http://dx.doi.org/10.1016/j.cie.2020.106686.

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15

Ala, Ali, and Feng Chen. "Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review." Journal of Healthcare Engineering 2022 (March 3, 2022): 1–16. http://dx.doi.org/10.1155/2022/5819813.

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Анотація:

This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.

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16

González-Domínguez, Jaime, Gonzalo Sánchez-Barroso, and Justo García-Sanz-Calcedo. "Scheduling of Preventive Maintenance in Healthcare Buildings Using Markov Chain." Applied Sciences 10, no. 15 (July 30, 2020): 5263. http://dx.doi.org/10.3390/app10155263.

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The optimization of maintenance in healthcare buildings reduces operating costs and contributes towards increasing the sustainability of the healthcare system. This paper proposes a tool to schedule preventive maintenance for healthcare centers using Markov chains. To this end, the authors analyzed 25 healthcare centers belonging to the three Healthcare Districts of Spain and built between 1985 and 2005. Markov chains proved useful in choosing the most suitable maintenance policies for each healthcare building without exceeding a specific degradation boundary, which enabled achieving an ideal maintenance frequency and reduced the use of resources. Markov chains have also proven useful in optimizing the periodicity of routine maintenance tasks, ensuring a suitable level of maintenance according to the frequency of the failures and reducing the cost and carbon footprint. The healthcare centers observed during the study managed to save more than 700 km of journeys, reduce emissions in its operations as a whole by 174.3 kg of CO2 per month and increase the overall efficiency of maintenance operations by 15%. This approach, therefore, renders it advisable to plan the maintenance of healthcare buildings.

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17

Paim, Rafael, Amanda Costa, Jonathan De Carvalho, and Inácio Araripe Costa Lima. "LEAN HEALTHCARE APPLICATION IN A SURGICAL PROCEDURES APPOINTMENT SCHEDULING CENTER IN A MATERNITY." Brazilian Journal of Operations & Production Management 13, no. 4 (December 30, 2016): 452. http://dx.doi.org/10.14488/bjopm.2016.v13.n4.a5.

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The study aimed to apply Lean Healthcare concepts and tools in surgical scheduling process. The intention was to structure and implement an appointment-scheduling central that was able to manage more effectively the demand for elective obstetrical surgeries in a neonatal maternity. The study used action research as a scientific method in order to solve a problem encountered in scheduling procedures involving employees in a cooperative and participatory improvement initiative. The method used was based on literature review, benchmarking visits and studies to identify good practices, and on current working condition analysis and comparison of indicators before and after the intervention. The Lean and TOC theoretical frameworks was used to improve the process, creating value for the patients and professionals, conducting root-cause analysis, identifying wastes and constrains, and proposing and implementing solutions. Using action research in the study and applying the concepts and tools was possible to reach different results as the 70% reduction in cancellations, the increase in the number of procedures scheduled in the units, and increased 97% level of appointment scheduling service among other results.

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18

Lakhan, Abdullah, Qurat-ul-ain Mastoi, Mazhar Ali Dootio, Fehaid Alqahtani, Ibrahim R. Alzahrani, Fatmah Baothman, Syed Yaseen Shah, et al. "Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network." Electronics 10, no. 16 (August 17, 2021): 1974. http://dx.doi.org/10.3390/electronics10161974.

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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

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19

Isken, Mark W. "An Implicit Tour Scheduling Model with Applications in Healthcare." Annals of Operations Research 128, no. 1-4 (April 2004): 91–109. http://dx.doi.org/10.1023/b:anor.0000019100.08333.a7.

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20

Wang, Dongyang, Kumar Muthuraman, and Douglas Morrice. "Coordinated Patient Appointment Scheduling for a Multistation Healthcare Network." Operations Research 67, no. 3 (May 2019): 599–618. http://dx.doi.org/10.1287/opre.2018.1816.

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21

Schwartz, Larry S. "Addressing Home Healthcare Staffing Challenges Through Geo-Intelligent Scheduling." Home Healthcare Now 38, no. 6 (November 2020): 307–10. http://dx.doi.org/10.1097/nhh.0000000000000913.

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22

Hiermann, Gerhard, Matthias Prandtstetter, Andrea Rendl, Jakob Puchinger, and Günther R. Raidl. "Metaheuristics for solving a multimodal home-healthcare scheduling problem." Central European Journal of Operations Research 23, no. 1 (May 29, 2013): 89–113. http://dx.doi.org/10.1007/s10100-013-0305-8.

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23

Euchi, Jalel, Salah Zidi, and Lamri Laouamer. "A new distributed optimization approach for home healthcare routing and scheduling problem." Decision Science Letters 10, no. 3 (2021): 217–30. http://dx.doi.org/10.5267/j.dsl.2021.4.003.

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Анотація:

Home health care faces new challenges day by day and it has become increasingly legitimate in the face of an aging population. Home healthcare centers are exposed to cumulative demands and academics are paying attention to the routing and scheduling matter, which is offered in literature as a Technician Routing and Scheduling Problem (TRSP) where the aim is to minimize the total cost subject to the time windows constraints to serve the patients respecting their priorities. In this paper, we develop a new distributed algorithm to resolve the home health care routing and scheduling problem (HHRSP). The principal idea of this algorithm is to apply artificial intelligence techniques in a distributed optimization method. The integration of automatic learning and search methods are applied to optimize the assignment of appointments to home caregivers. It allows us to gain time, effort, especially cost, and while complying with the problem constraints. The comparison results prove the efficacy of the recommended approach, which can offer decision support for medical executives of home health care.

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24

Adedokun, Ayoade, Oladipo Idris, and Tolulope Odujoko. "Patients’ willingness to utilize a SMS-based appointment scheduling system at a family practice unit in a developing country." Primary Health Care Research & Development 17, no. 02 (April 8, 2015): 149–56. http://dx.doi.org/10.1017/s1463423615000213.

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AimThe investigators aimed to assess the willingness of patients to utilize and pay for a proposed short message service- (SMS) based appointment scheduling service.BackgroundTelecommunication applications have been introduced to improve the delivery of healthcare services in developed countries; however, public-funded healthcare systems in developing countries like Nigeria are mostly unfamiliar with the use of such technologies for improving healthcare access.MethodsWe proposed a SMS-based (text message) appointment scheduling system to consenting subjects at an outpatients’ clinic and explored their willingness to utilize and pay for the service. Using semi-structured interview schedules, we collected information on: estimated arrival time, most important worry when seeking for healthcare services at public hospitals in the study setting, ownership of a mobile phone, willingness to utilize a SMS-based appointment for clinic visits and willingness to pay for the service. In addition, respondents were asked to suggest a tariff for the proposed system.FindingsA total of 500 consecutively recruited patients aged 16–86 (42.1±15.4) years participated; 54% (n=270) were females. Waiting time ranged from 1–7.5 h (3.9±1.1). Two overlapping themes emerged as most important worries: crowded waiting rooms and long waiting time. Ownership of mobile phones was reported by 96.4% (n=482) of subjects. Nearly all favoured the proposed appointment scheduling system (n=486, 97.2%). Majority of patients who favoured the system were willing to pay for the service (n=484, 99.6%). Suggested tariff ranged from 0.03 to 20.83 (1.53±2.11) US dollars; 89.8% (n=349) of the subjects suggested tariffs that were greater than the prevailing retail cost of the proposed service. In sum, our findings indicate that patients in this study were willing to utilize and pay for a proposed SMS-based appointment scheduling system. The findings have implications for policies aimed at improving healthcare access and delivery of healthcare services at the primary care level in developing countries like Nigeria.

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25

Riazi, Sarmad, Payam Chehrazi, Oskar Wigström, Kristofer Bengtsson, and Bengt Lennartson. "A Gossip Algorithm for Home Healthcare Scheduling and Routing Problems." IFAC Proceedings Volumes 47, no. 3 (2014): 10754–59. http://dx.doi.org/10.3182/20140824-6-za-1003.02624.

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26

Ganguly, Anirban, and Saikat Nandi. "Using Statistical Forecasting to Optimize Staff Scheduling in Healthcare Organizations." Journal of Health Management 18, no. 1 (March 2016): 172–81. http://dx.doi.org/10.1177/0972063415625575.

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27

Rinder, Maria, Gary Weckman, Diana Schwerha, Andy Snow, Peter Dreher, Namkyu Park, Helmut Paschold, and William Young. "Healthcare Scheduling by Data Mining: Literature Review and Future Directions." Journal of Healthcare Engineering 3, no. 3 (September 2012): 477–502. http://dx.doi.org/10.1260/2040-2295.3.3.477.

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28

Chen, Ping-Shun, Rex Aurelius C. Robielos, Philline Kate Vera C. Palaña, Pierre Lorenzo L. Valencia, and Gary Yu-Hsin Chen. "Scheduling Patients’ Appointments: Allocation of Healthcare Service Using Simulation Optimization." Journal of Healthcare Engineering 6, no. 2 (June 2015): 259–80. http://dx.doi.org/10.1260/2040-2295.6.2.259.

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29

Bradai, Nourchène, Lamia Chaari Fourati, and Lotfi Kamoun. "WBAN data scheduling and aggregation under WBAN/WLAN healthcare network." Ad Hoc Networks 25 (February 2015): 251–62. http://dx.doi.org/10.1016/j.adhoc.2014.10.017.

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30

Cinar, Ahmet, F. Sibel Salman, and Burcin Bozkaya. "Prioritized single nurse routing and scheduling for home healthcare services." European Journal of Operational Research 289, no. 3 (March 2021): 867–78. http://dx.doi.org/10.1016/j.ejor.2019.07.009.

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31

Cho, Jaejoon, and Sunshin An. "Optimal beacon scheduling mechanisms using cluster identifier for healthcare application." Expert Systems with Applications 36, no. 3 (April 2009): 5071–80. http://dx.doi.org/10.1016/j.eswa.2008.06.005.

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32

Al-Shujaa, Abdulmalek, MS Nabi, Qusay Al-Maatouk, Abdulaleem Zaid Al-Othmani, and NAA Rahman. "A Fingerprint Authentication for Android-Based Healthcare Appointment Scheduling System." International Journal of Current Research and Review 13, no. 12 (2021): 118–22. http://dx.doi.org/10.31782/ijcrr.2021.131227.

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33

Hashemi Doulabi, Hossein, Gilles Pesant, and Louis-Martin Rousseau. "Vehicle Routing Problems with Synchronized Visits and Stochastic Travel and Service Times: Applications in Healthcare." Transportation Science 54, no. 4 (July 2020): 1053–72. http://dx.doi.org/10.1287/trsc.2019.0956.

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Анотація:

This paper, for the first time, studies vehicle routing problems with synchronized visits (VRPS) and stochastic travel and service times. In addition to considering a home healthcare scheduling problem, we introduce an operating room scheduling problem with stochastic durations as a novel application of VRPS. We formulate VRPS with stochastic times as a two-stage stochastic integer programming model that, unlike the deterministic models in the VRPS literature, does not have any big-M constraints. This advantage comes at the cost of a large number of second-stage integer variables. We prove that the integrality constraints on second-stage variables can be relaxed, and therefore, we can apply the L-shaped algorithm and its branch-and-cut implementation to solve the problem. We enhance the model by developing valid inequalities and a lower bounding functional. We analyze the subproblems of the L-shaped algorithm and devise a specialized algorithm for them that is significantly faster than standard linear programming algorithms. Computational results show that the branch-and-cut algorithm optimally solves stochastic home healthcare scheduling instances with 15 patients and 10%–30% of synchronized visits. It also finds solutions with an average optimality gap of 3.57% for instances with 20 patients. Furthermore, the branch-and-cut algorithm optimally solves stochastic operating room scheduling problems with 20 surgeries.

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34

Mutlag, Ammar Awad, Mohd Khanapi Abd Ghani, Mazin Abed Mohammed, Abdullah Lakhan, Othman Mohd, Karrar Hameed Abdulkareem, and Begonya Garcia-Zapirain. "Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring." Sensors 21, no. 20 (October 19, 2021): 6923. http://dx.doi.org/10.3390/s21206923.

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In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.

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Lakhan, Abdullah, Mazin Abed Mohammed, Ahmed N. Rashid, Seifedine Kadry, Thammarat Panityakul, Karrar Hameed Abdulkareem, and Orawit Thinnukool. "Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System." Sensors 21, no. 12 (June 14, 2021): 4093. http://dx.doi.org/10.3390/s21124093.

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The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

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Huang, Chung-Chi, Hsiao-Man Liu, and Chung-Lin Huang. "Intelligent scheduling of execution for customized physical fitness and healthcare system." Technology and Health Care 24, s1 (December 8, 2015): S385—S392. http://dx.doi.org/10.3233/thc-151096.

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SU, Haoru, Heungwoo NAM, and Sunshin AN. "Energy-Efficient Flexible Beacon Scheduling Mechanism for Cluster-Tree Healthcare Systems." IEICE Transactions on Communications E94-B, no. 9 (2011): 2480–83. http://dx.doi.org/10.1587/transcom.e94.b.2480.

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Sherratt, Robert Simon, Balazs Janko, Terence Hui, William S. Harwin, Nilanjan Dey, Daniel Díaz-Sánchez, Jin Wang, and Fuqian Shi. "Task Scheduling to Constrain Peak Current Consumption in Wearable Healthcare Sensors." Electronics 8, no. 7 (July 15, 2019): 789. http://dx.doi.org/10.3390/electronics8070789.

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Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life, while at the same time supporting ever-increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power operation through hardware designs and intelligent methods of scheduling software tasks, all with the objective of minimizing the overall consumed electrical power. However, such an approach inevitably creates points in time where software tasks and peripherals coincide to draw large peaks of electrical current, creating short-term electrical stress for the battery and power regulators, and adding to electromagnetic interference emissions. This position paper proposes that the power profile of an embedded device using a real-time operating system (RTOS) will significantly benefit if the task scheduler is modified to be informed of the electrical current profile required for each task. This enables the task scheduler to schedule tasks that require large amounts of current to be spread over time, thus constraining the peak current that the system will draw. We propose a solution to inform the task scheduler of a tasks’ power profile, and we discuss our application scenario, which clearly benefited from the proposal.

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Marwat, Safdar Nawaz Khan, Yasir Mehmood, Farman Ullah, Ahmad Khan, Shahid Khan, Salman Ahmed, Daehan Kwak, and Afia Nazir. "Mobile Wi-Fi Based Scheduling of Cyber-Physical Systems in Healthcare." Electronics 9, no. 2 (February 2, 2020): 247. http://dx.doi.org/10.3390/electronics9020247.

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Wireless Body Area Networks (WBANs) and Wireless Local Area Networks (WLANs) have been widely regarded as solution providers for future Cyber-Physical Systems (CPS)-based ehealthcare amenities. The IEEE 802.11 standard specifies media access protocols in wireless networks, along with channel access methods. WBANs are expected to improve the existing healthcare services significantly, but several research challenges also have to be tackled for apt utilization of the technology. Guarantee of Quality-of-Service (QoS) differentiation between various health parameters, such as temperature and blood pressure, during mobility is a major challenge for the provision of ehealthcare services. The scheme proposed in this paper for the Mobile Wi-Fi based connectivity of WBANs is designed to provide QoS-based priorities for ehealthcare subscribers by altering the Contention Window (CW) for different applications of patient health monitoring. The relationship between CW and QoS is utilized to achieve efficient resource assignment. Three different health parameters, i.e., ECG (Electrocardiogram), BP (blood pressure) and temperature. are monitored using medical CPS in this work. The performance evaluation results, such as end-to-end packet delay and throughput for various data traffic classes reveal that the proposed scheme improves QoS provision.

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Aladwani, Tahani. "Scheduling IoT Healthcare Tasks in Fog Computing Based on their Importance." Procedia Computer Science 163 (2019): 560–69. http://dx.doi.org/10.1016/j.procs.2019.12.138.

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Park, KeeHyun, Insung Kim, and Joonsuu Park. "An Efficient Multi-class Message Scheduling Scheme for Healthcare IoT Systems." International Journal of Grid and Distributed Computing 11, no. 5 (May 31, 2018): 67–78. http://dx.doi.org/10.14257/ijgdc.2018.11.5.06.

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Sahoo, Prasan Kumar, and Chinmaya Kumar Dehury. "Efficient data and CPU-intensive job scheduling algorithms for healthcare cloud." Computers & Electrical Engineering 68 (May 2018): 119–39. http://dx.doi.org/10.1016/j.compeleceng.2018.04.001.

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Abdelmoneem, Randa M., Abderrahim Benslimane, and Eman Shaaban. "Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures." Computer Networks 179 (October 2020): 107348. http://dx.doi.org/10.1016/j.comnet.2020.107348.

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Neelapu, Sattva S., Sherry Adkins, Stephen M. Ansell, Joshua Brody, Mitchell S. Cairo, Jonathan W. Friedberg, Justin P. Kline, et al. "Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immunotherapy for the treatment of lymphoma." Journal for ImmunoTherapy of Cancer 8, no. 2 (December 2020): e001235. http://dx.doi.org/10.1136/jitc-2020-001235.

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The recent development and clinical implementation of novel immunotherapies for the treatment of Hodgkin and non-Hodgkin lymphoma have improved patient outcomes across subgroups. The rapid introduction of immunotherapeutic agents into the clinic, however, has presented significant questions regarding optimal treatment scheduling around existing chemotherapy/radiation options, as well as a need for improved understanding of how to properly manage patients and recognize toxicities. To address these challenges, the Society for Immunotherapy of Cancer (SITC) convened a panel of experts in lymphoma to develop a clinical practice guideline for the education of healthcare professionals on various aspects of immunotherapeutic treatment. The panel discussed subjects including treatment scheduling, immune-related adverse events (irAEs), and the integration of immunotherapy and stem cell transplant to form recommendations to guide healthcare professionals treating patients with lymphoma.

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Soomro, Zahoor A., Javed Ahmed, Raza Muhammad, Dawn Hayes, and Mahmood H. Shah. "Critical success factors in implementing an e-rostering system in a healthcare organisation." Health Services Management Research 31, no. 3 (December 15, 2017): 130–37. http://dx.doi.org/10.1177/0951484817745695.

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Effective and efficient staff scheduling has always been a challenging issue, especially in health service organisations. Both the extremes of staff shortage and overage have an adverse impact on the performance of healthcare organisations. In this case, an electronic and systematic staff scheduling (e-rostering) system is the often seen as the best solution. Unless an organisation has an effective implementation of such a system, possible cost savings, efficiency, and benefits could be minimal. This study is aimed to research key success factors for the successful effective implementation of an electronic rostering system, especially at healthcare organisations. A case study research method was used to evaluate critical success factors for effectively implementing an e-rostering system. The data were collected through interviews and observations. The findings indicate that technical support, an effective policy, leadership, clear goals and objectives, gradual change, evidence of the advantages of the new system, senior management support, and effective communication are the critical success factors in implementing an e-rostering system in healthcare organisations. Prior to this study, no such factors were grounded in the current context, so this research would help in bridging the gap towards effective implementation of an e-rostering system in the healthcare sector. This research also suggests future studies in different cultures and contexts.

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Mastoi, Qurat-ul-ain, Teh Ying Wah, Ram Gopal Raj, and Abdullah Lakhan. "A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System." Sensors 20, no. 2 (January 13, 2020): 441. http://dx.doi.org/10.3390/s20020441.

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Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

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Heale, Roberta, and Jennifer-Lynn Fournier. "Impact of Advanced Access Scheduling on Patient Care Choices and Health Behaviours in a Nurse Practitioner-Led Clinic." Diversity of Research in Health Journal 1 (June 21, 2017): 33–43. http://dx.doi.org/10.28984/drhj.v1i0.28.

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The Nurse Practitioner-Led Clinic (NPLC) is a new model of primary healthcare. The wholistic approach of nurse practitioner (NP) led care in an NPLC that implements Advanced Access scheduling has the potential to enhance timely access to care and improve health outcomes. The purpose of this study was to determine the experience of patients in one NPLC as well as their healthcare behaviours related to Advanced Access scheduling. A previously developed survey with items related to appointment access, health behaviours and satisfaction was mailed once to patients at a NPLC in northern Ontario. 535 patients replied for a response rate of 29%. A majority (85.4%) were able to access same-day appointments. Access to same-day appointments was associated with less likelihood of attending a walk-in-clinic or emergency department in addition to self-reports of improvements in lifestyle and better control of medical condition(s). Advanced access scheduling contributes to optimal patient care in an NPLC setting. The NP role in lifestyle counselling and wholistic care in the NPLC model contributes to improved self-reported health. Access to an appointment at a point of ‘readiness’ may positively contribute to lifestyle changes and overall health of patients.

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Lakhan, Abdullah, Jin Li, Tor Morten Groenli, Ali Hassan Sodhro, Nawaz Ali Zardari, Ali Shariq Imran, Orawit Thinnukool, and Pattaraporn Khuwuthyakorn. "Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud." Electronics 10, no. 22 (November 15, 2021): 2797. http://dx.doi.org/10.3390/electronics10222797.

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Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.

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Peled, Ronit, and Jerry Schenirer. "Healthcare Strategic Planning as Part of National and Regional Development in the Israeli Galilee: A Case Study of the Planning Process." Health Information Management Journal 38, no. 3 (October 2009): 43–50. http://dx.doi.org/10.1177/183335830903800307.

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This article describes a systematic process of geographic and strategic planning for healthcare services as a part of a regional development plan in the Israeli Galilee. The planning process consisted of three stages: (a) assessment of needs, demand and existing resources; (b) prioritisation of initiatives; and (c) scheduling of theoretical priorities. For many years the region has suffered from inequities and inequalities regarding the availability and accessibility of a regional healthcare system, resulting in high mortality and morbidity rates and low quality of life. The aim of the healthcare strategic plan was to suggest initiatives and actions to be taken in order to improve healthcare provision and the health and wellbeing of local residents.

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Yu, Chao, Jiming Liu, Shamim Nemati, and Guosheng Yin. "Reinforcement Learning in Healthcare: A Survey." ACM Computing Surveys 55, no. 1 (January 31, 2023): 1–36. http://dx.doi.org/10.1145/3477600.

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As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research.

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