Server Workloads Research Articles

Surveillance of surgical site infections using artificial intelligence

Abstract Background Surgical site infection (SSI) is a major health problem associated with high morbidity and mortality, the consequences of which can be very serious for the patient and costly for the organisation. Surveillance systems provide insight into the incidence of SSI, the analysis of which enables improvement plans to be established to reduce it. Traditionally, surveillance systems have been based on manual medical record review for evidence of the presence of surgical infection. Objectives To create and validate a global SSI diagnostic algorithm and to optimise the SSI workload of Preventive Medicine services. Methods The study population included patients undergoing surgical procedures of rectal surgery, colon surgery, knee replacement surgery, hip replacement surgery, coronary bypass and valve surgery, at the Hospital Universitario Vinalopó in Elche (n = 1240). Data mining was used to collect clinical, microbiological and postoperative follow-up values from the electronic medical record. Machine Learning techniques were used to train (n = 1054) and validate the model (n = 186). Time spent on SSI surveillance of the preventive medicine service was measured. Results Model performance after validation was: sensitivity of 0.83 specificity of 0.87, accuracy of 0.87, an F1 score of 0.45, and an area under the curve of 0.87. The time spent by preventive medicine staff in identifying patients with surgical site infection decreased from 168 person-hours per procedure per quarter to 34 person-hours, a 77% reduction in the workload allocated to this task. Conclusions This project not only improves surveillance efficiency, but also optimises the workload in Preventive Medicine services very significantly. Key messages • This project improves the efficiency of surgical site infection surveillance and significantly optimises the workload in preventive medicine departments. • It makes it possible to extend surveillance to new surgical procedures and to work more intensively on preventive measures.

Does the Third Sputum Exam has an Added Value in the Diagnosis of Pulmonary Tuberculosis?

Abstract Introduction/Objective Introduction: Current practice of examining three sputum for the diagnosis of pulmonary tuberculosis (PTB) has tended to overburden the already scarce human resources in all-purpose laboratories, and to increase the turn- around- time for patients seeking treatment. Therefore, assessing the added value of the third sputum in the diagnosis of PTB in resource-limited country with less functional External Quality Assurance (EQA) system is critical. Methods/Case Report Materials and methods: Data on patients diagnosed with smear-positive PTB during 2012- 2016 was collected retrospectively from laboratory register of nine hospitals in Eritrea. The data was extracted and analyzed for incremental yield of three sputum samples. Results (if a Case Study enter NA) Results: Data from 2,484 microscopically confirmed acid-fast bacilli (AFB) smear positive PTB patients were reviewed. AFB was detected in the first sputum in 87.5% of patients, and 99.1% for the combined first and second sputum specimens. The incremental yield of the third sputum specimen as a diagnostic value was only 0.9%. No significant variation was detected by region, residence, gender or HIV. Background information Number of cases Percent Region Maekel 649 26.10% Anseba 293 11.80% Gash-Barka 815 32.80% Debub 297 12.00% Northern Red Sea 337 13.60% Southern Red Sea 93 3.70% Sex Male 1,251 50.40% Female 1,233 49.60% Residence Urban 1,296 52.20% Rural 1,188 47.80% Age group <15 35 1.40% 15 + 2,449 98.60% Total 2,484 100 Conclusion Conclusion: The added value of the third sputum is so minimal that its omission will reduce patient visits, workload and cost for patients and health services. Therefore, even countries like Eritrea, with limited EQA, should consider limiting the number of sputum examinations to improve quality.

Analysis Implementation of Medication Timeline (ME TIME) onthe workload of clinical pharmacy services in inpatient care:A randomized controlled trial

Analysis Implementation of Medication Timeline (ME TIME) onthe workload of clinical pharmacy services in inpatient care:A randomized controlled trial

Optimisasi Web Service REST API Menggunakan Load Balancer dan Cache dengan Algoritma Round Robin (Studi Kasus: Madani Infosphere)

In today's digital era, the use of REST API Web Services is increasing, becoming a major foundation in web application development due to its simplicity and scalability. However, increased demand and data complexity often lead to a decrease in Web Service performance, especially in terms of response time and service availability. This research focuses on the performance optimisation of Madani Infosphere Web Service, an information platform developed by the non-profit organisation MADANI Berkelanjutan. This platform previously used a single server architecture that was prone to performance issues. To overcome this, this research applies load balancer technology with a round robin algorithm to distribute the workload evenly across multiple servers and uses a cache to store frequently accessed data. The results show that the use of load balancer with round robin algorithm is effective in overcoming potential server overload, making Web Service performance more stable and responsive. In addition, the cache implementation successfully reduces server workload and speeds up response time. Thus, this optimisation can improve the efficiency and availability of Madani Infosphere Web Services.

Improving preparation in the emergency trauma room: the development and impact of real-time data transfer and dashboard visualization system.

This study examines, with clinical end users, the features of a visualization system in transmitting real-time patient data from the ambulance to the emergency trauma room (ETR) to determine if the real-time data provides the basis for more informed and timely interventions in the ETR before and after patient arrival. We conducted a qualitative in-depth interview study with 32 physicians in six German and Swiss hospitals. A visualization system was developed as prototype to display the transfer of patient data, and it serves as a basis for evaluation by the participating physicians. The prototype demonstrated the potential benefits of improving workflow within the ETR by providing critical patient information in real-time. Physicians highlighted the importance of features such as the ABCDE scheme and vital signs that directly impact patient care. Configurable and mobile versions of the prototype were suggested to meet the specific needs of each clinic or specialist, allowing for the transfer of only essential information. The results highlight on the one hand the potential need for adaptable interfaces in medical communication technologies that balance efficiency with minimizing additional workload for emergency medical services and show that the use of pre-notification systems in communication between ambulance and hospital can be supportive. Further research is recommended to assess practical application and support in clinical practice, including a re-evaluation of the enhanced prototype by professionals.

WP2.1 - New Sarcoma Hub and Spoke Service Can Timely Exclude Malignancy and Effectively Cut Patient Waiting Time for Removal of Benign Lesions

Abstract Aims To assess efficiency of the newly established Sarcoma Hub and Spoke Service (SHSS) through assessment of the length of duration on the Sarcoma 2 week-wait pathway (2WW) and time to surgery for patients already cleared from suspicions of malignancies within one Trust’s Spoke Service. Methods Patients referred to our SHSS on the 2WW who have been cleared from suspicions of malignancies and subsequently underwent removal of lumps/lesions between July 2022 and December 2023 at our Trust were identified from the Hospital Information System. Data is collected on first appointment, time of surgery, as well as time from exclusion of malignancy (Off 2WW) to surgery. Results We identified 119 patients referred to 2WW within the 18-month period, and 90 having had surgery after being taken off 2WW. The median duration from their first appointment to Off 2WW was 37 days (Q1=21d, Q3=61d). The median time from Off 2WW to surgical excision was 132 days (Q1=81d, Q3=202d). Conclusion Given how the ‘traditional’ Sarcoma Pathway at tertiary centres discharges patients cleared from suspicion of malignancies back to GP for them to then refer on on routine pathways to local surgical services, the new SHSS model can investigate and timely exclude malignancies. It is also able to effectively cut waiting time to surgery within the same unit for patients as shown through the acceptable surgery waiting time despite backlog from the recent pandemic and industrial actions. This can also mean decreased workload for both GPs and local General Surgery services.

The Workload of the Italian Military Health Service During the First World War: A Quantitative Assessment

The article proposes a quantitative evaluation of the workload of the Italian military health service during the First World War. In the Great War, there were unprecedented medical and health care issues. This was a result of the size of armies; the poor living conditions of the soldiers; and the introduction of new weapons. This article reconstructs some quantitative aspects of the Italian military health service in the Great War in terms of both supply (e.g. the number of health structures and medical personnel) and demand (e.g. the soldiers who were injured and who became ill during the conflict).

Staffing and Workload in Primary Care Facilities of Selected Geographically Isolated and Disadvantaged Communities in the Philippines.

Staffing shortages and health inequities are persistent barriers in the Philippines toward achieving universal health care. To ensure an adequate and responsive health workforce, there is a need to evaluate the Human Resources for Health (HRH) status across health facilities, particularly those in underserved communities. Hence, this study aims to determine the staffing requirements and workload pressure among primary care facilities in selected geographically isolated and disadvantaged areas (GIDAs) in the Philippines. The study utilized the workload indicators of staffing need (WISN) methodology from the World Health Organization to determine the staffing and workload situation among three health worker cadres (physicians, nurses, and midwives) in the study sites. Particularly, six primary care facilities (four rural health units and two community hospitals) located in Surigao del Norte were involved in the study. WISN-related data (health service statistics, available working time, and health professionals' workload components) were collected through records review, focus group discussions, and key informant interviews. The WISN software was used to analyze the staffing levels and workload pressure in the selected facilities. A total of 40 health workers, including physicians (n = 5, 13%), nurses (n = 21, 52%), and midwives (n = 14, 35%) participated in the study. The findings noted varying levels of staffing and workload pressures among the three cadres in selected primary care facilities, which were influenced by several factors. Particularly, health facilities with additional human resources obtained from deployment programs indicated adequate staffing and low to normal workload pressures. However, further analysis revealed potential HRH maldistribution and reliance on the temporary nature of the staff augmentation program in delivering primary care services, which need to be addressed to optimize health workforce planning. Service workload may also have been impacted by the temporary closure of health facilities due to disasters. Among the few cadres that reported staffing shortage and high workload pressure, these were due to higher service demands, increased task delegation, and inadequate service coordination. Hence, context-specific challenges and situational factors in GIDAs need to be considered when determining the staffing and workload requirements. There is a need to improve the capacities of health facilities and local government units (LGUs) to engage in evidence-based HRH planning through the WISN methodology. Doing so could improve staffing and workload distribution among health care facilities in the country. Moreover, interorganizational collaboration (DOH, LGUs, and health facilities) should be strengthened to improve delineation and prevent duplication/omission of health services, rationalize HRH distribution and augmentation, and streamline the priority health services based on the local contextual factors.

Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers

Cooling system energy consumption occupies approximately 40% of the total energy consumption in data centers, and efficient control is crucial for improving cooling efficiency and reducing the operational costs of data centers. This paper introduced a novel economic model predictive control (EMPC) strategy based on a linear parameter-varying state-space model. The primary objective was to maintain the thermal environment of data centers while minimizing the energy consumption of the cooling system. Therein, a linear parameter-varying state-space model was developed based on the parameter identification method to precisely predict the temperature field of the rack-based cooling data center in real-time. The energy management and thermal regulation performance of the EMPC and the proportion integration differentiation (PID) controller were comparatively analyzed through simulation experiments. Moreover, the impacts of the server workload level and optimization time domain on the performance of EMPC were comprehensively investigated. The results show that, the proposed EMPC was able to minimize the energy consumption by optimizing the supplied cold air temperature and airflow rate simultaneously. In comparison with the PID controller, EMPC not only achieves a 9.7% energy saving by preventing server over-cooling but also diminishes server temperature fluctuations, promoting the safe and stable operation of the server. The research shows that the EMPC reveals excellent performance along with all server workload levels. In addition, the length of the prediction horizon has a notable impact and three minutes is suggested for the rack-based cooling system.

Describing values in tension and neoliberalism at a mission-driven, private university: a qualitative case study of faculty service workload

ABSTRACT Faculty workload tension exists between espoused service and social justice-oriented university missions and neoliberal academic values of scholarship. The tension has significant consequences for attracting, advancing, and retaining faculty from minoritized racial, ethnic, and gender groups. A case study qualitative approach was conducted to investigate perspectives on the valuing of faculty service at a private university with a clearly articulated service and social justice-oriented mission. Interviews were conducted first with department chairs (n = 26). Themes from these interviews were then shared with faculty (n = 153) and leadership (i.e. deans and provosts) via a web-based survey for comments on refining themes. Final analysis yielded a meta-theme (Values in tension), four themes (The paradox of faculty service, we are stretched thin, workload nimbleness and confusion, and inequity in burden and advancement) with diverging perspectives based on race and gender of participants. Implications are drawn for university leadership and culture related to faculty workload and advancement.

Taming latency at the edge: A user-aware service placement approach

Modern network and computing infrastructures are tasked with addressing the stringent demands of today’s applications. A pivotal concern is the minimization of latency experienced by end-users accessing services. While emerging network architectures provide a conducive setting for adept orchestration of microservices in terms of reliability, self-healing and resiliency, assimilating the awareness of the latency perceived by the user into placement decisions remains an unresolved problem. Current research addresses the problem of minimizing inter-service latency without any guarantee to the level of latency from the end-user to the cluster. In this research, we introduce an architectural approach for scheduling service workloads within a given cluster, prioritizing placement on the node that offers the lowest perceived latency to the end-user. To validate the proposed approach, we propose an implementation on Kubernetes (K8s), currently one of the most used workload orchestration platforms. Experimental results show that our approach effectively reduces the latency experienced by the end-user in a finite time without degrading the quality of service. We study the performance of the proposed approach analyzing different parameters with a particular focus on the size of the cluster and the number of replica pods involved to measure the latency. We provide insights on possible trade-offs between computational costs and convergence time.

Pediatric anesthesia in Mongolia and Thailand.

In Mongolia, pediatric anesthesia has advanced during the past 25 years through expanded, standardized education programs and international collaboration. Pediatric anesthesia is a recognized specialty, covering all surgical services, including cardiac and transplant, using physicians and nurses. The pediatric anesthesia fellowship is 6 months after 2 years of residency; pediatric nurse anesthesia training is 6 months. As a Low- and Middle-Income Country (LMIC) with low population density and extreme weather, the challenges include insufficient equipment, supplies, and clinician numbers, matching few clinicians to many varied patient locations, and covering surgical emergencies over distance and weather. In Thailand, education and training in pediatric anesthesia remain a focus: Pediatric anesthesia is an official subspecialty, the fellowship is accredited, using a competency-based curriculum with milestones of Direct Observation of Procedural Skills and Entrusted Professional Activities. The Bangkok Anesthesia Regional Training Center (BARTC)-Pediatrics, jointly sponsored by the World Federation of Societies of Anesthesiologists (WFSA) and the Society for Pediatric Anesthesia (SPA), have expanded training to anesthesiologists worldwide. Challenges include difficulty balancing service workload and education, as well as attracting pediatric anesthesia fellows due to the strong private sector job market.

A deep reinforcement approach for computation offloading in MEC dynamic networks

In this study, we investigate the challenges associated with dynamic time slot server selection in mobile edge computing (MEC) systems. This study considers the fluctuating nature of user access at edge servers and the various factors that influence server workload, including offloading policies, offloading ratios, users’ transmission power, and the servers’ reserved capacity. To streamline the process of selecting edge servers with an eye on long-term optimization, we cast the problem as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL)-based algorithm as a solution. Our approach involves learning the selection strategy by analyzing the performance of server selections in previous iterations. Simulation outcomes show that our DRL-based algorithm surpasses benchmarks, delivering minimal average latency.

How did the Covid-19 pandemic affect wellbeing of staff working in child and family teams?

During the height of the Covid-19 pandemic, child and family health teams were required to adapt and prioritise services, which were increasing in demand. This investigation aimed to understand how the changes in service delivery and workload, as a consequence of the pandemic, impacted the wellbeing of staff in these teams. Using two datasets, a quantitative secondary analysis of anonymised routine patient data and a qualitative primary interpretive analysis of data, collected via focus groups and an interview with staff, were conducted. The quantitative data confirmed that universal contacts have increased dramatically since the pandemic and all tiers have increased in duration. Three themes were identified in the qualitative analysis as having the greatest impact on staff wellbeing: redeployment, increased workload and remote working. Staff need supportive opportunities to share their experiences to identify specific dimensions of stresses felt individually during times of pressure on services.

AI-Enabled Spatial-Temporal Mobility Awareness Service Migration for Connected Vehicles

In the future 6 G intelligent transportation system, the edge server will bring great convenience to the timely computing service for connected vehicles. To guarantee the quality of service, the time-critical services need to be migrated according to the future location of the vehicle. However, predicting vehicle mobility is challenging due to the time-varying of road traffic and the complex mobility patterns of vehicles. To address this issue, a spatial-temporal awareness proactive service migration strategy is proposed in this paper. First, a spatial-temporal neural network is designed to obtain accurate mobility by using gated recurrent units and graph convolutional layers extracting features from spatial road traffic and multi-time scales driving data. Then a proactive migration method is proposed to guarantee the reliability of services and reduce energy consumption. Considering the reliability of services and the real-time workload of servers, the migration problem is modeled as a multi-objective optimization problem, and the Lyapunov optimization method is utilized to obtain utility-optimal migration decisions. Extensive simulations based on real-world datasets are performed to validate the performance of the proposed method. The results show that the proposed method achieved 6% higher prediction accuracy, 10% lower dropping rate, and 10% lower energy consumption compared to state-of-the-art methods.

Service Improvements and Workload Increases: Royal College of Radiologists (RCR) Re-Audit of Curative Intent Radiotherapy for Non-Small Cell Lung Cancer (NSCLC)

AimsThe Royal College of Radiologists (RCR) audit of radical radiotherapy (RR) for patients with non-small cell lung cancer (NSCLC) in 2013 concluded that there was under-treatment compared to international comparators and marked variability between cancer networks. Elderly patients were less likely to receive guideline recommended treatments. Access to technological developments was low. Various national and local interventions have since taken place. This study aims to re-assess national practice. Materials and methodsRadiotherapy departments completed one questionnaire for each patient started on RR for 4 weeks in January 2023. ResultsNinety-three percent of centres returned data on 295 patients. RR has increased 70% since 2013 but patients on average wait 20% longer to start treatment (p = 0.02). Staging investigations were often outside a desirable timeframe (79% of PET/CT scans). Advanced planning techniques are used more frequently: 4-dimensional planning increased from 33% to 90% (P < 0.001), cone beam imaging from 67% to 97% (p < 0.001) and colleague led peer review increased from 41% to 73% (P < 0.001). ConclusionThere have been significant improvements in care. There has been a considerable increase in clinical oncology workload with evidence of stress on the system that requires additional resourcing.

Learning-driven hybrid scaling for multi-type services in cloud

In order to deal with the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different completion delays of different scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdrawal of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. For making a proactive decision, the ensemble learning model with the structure improved deep network is designed to predict the future workload. On the basis of the predicted results and the scaling time of different types of services, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space, which integrates horizontal and vertical scaling actions. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior compared to state-of-the-art approaches.

Efficient ASIC Architecture for Low Latency Classic McEliece Decoding

Post-quantum cryptography addresses the increasing threat that quantum computing poses to modern communication systems. Among the available “quantum-resistant” systems, the Classic McEliece key encapsulation mechanism (KEM) is positioned as a conservative choice with strong security guarantees. Building upon the code-based Niederreiter cryptosystem, this KEM enables high performance encapsulation and decapsulation and is thus ideally suited for applications such as the acceleration of server workloads. However, until now, no ASIC architecture is available for low latency computation of Classic McEliece operations. Therefore, the present work targets the design, implementation and optimization of a tailored ASIC architecture for low latency Classic McEliece decoding. An efficient ASIC design is proposed, which was implemented and manufactured in a 22 nm FDSOI CMOS technology node. We also introduce a novel inversionless architecture for the computation of error-locator polynomials as well as a systolic array for combined syndrome computation and polynomial evaluation. With these approaches, the associated optimized architecture improves the latency of computing error-locator polynomials by 47% and the overall decoding latency by 27% compared to a state-of-the-art reference, while requiring only 25% of the area.

Pengaruh Kualitas Pelayanan dan Beban Kerja Dokter Gigi terhadap Kecemasan Pasien di Klinik Gigi I-Dental Care Jakarta Selama Masa Pandemi Covid-19 Tahun 2022-2023

This study aims to explain the influence of service quality and workload on patient anxiety at the I-Dental Care dental clinic in South Jakarta during the Covid-19 pandemic. The method used in this research is a descriptive method. The approach used is a quantitative approach. In this study, the author took samples using the saturated sample method or research census, namely taking all patients who visited the I-Dental Care clinic for one month. The research subjects in this study were 105 patients and dentists at the I-Dental Care Clinic. The data analysis techniques used are validity tests, reliability tests, descriptive analysis and inferential statistical data analysis. Research results show that service quality influences patient anxiety at the I-Dental Care Clinic during the Covid-19 pandemic. Workload influences patient anxiety at the I-Dental Care Clinic. Service Quality and Workload Influence Patient Anxiety at the I-Dental Care Clinic during the Covid-19 pandemic.

An evaluation of a service expansion to include patients with heart failure with preserved ejection fraction

Background/Aims The Central London Community Healthcare Trust West Hertfordshire heart failure service expanded in 2020 to include patients with heart failure with preserved ejection fraction, in addition to the patients with heart failure with reduced ejection fraction. The patient population was predicted to double, requiring staff and service adjustments; this warranted an evaluation to determine if care targets were maintained. This study aimed to evaluate the impact of service expansion on service referral rates, length of stay in the service and clinical workload. Methods A retrospective quantitative evaluation of the service data from October 2020 to April 2021 was undertaken to compare referral rates, length of stay in the service and key workload metrics between patients with heart failure with reduced ejection fraction and those with heart failure with preserved ejection fraction. All referrals to the service with a new diagnosis of heart failure (confirmed by echocardiogram or magnetic resonance imaging) were considered for evaluation. Of 250 eligible referrals, 81 were selected for inclusion using a random sampling method. Data were analysed using Chi square test, Fisher’s exact test or the Wilcoxon signed rank test; a P value of &lt;0.05 indicated statistical significance. Results The participants with heart failure with preserved ejection fraction had a median length of stay in the service of 17 weeks. The participants with heart failure with reduced ejection fraction had a significantly longer stay of 35.57 weeks (P&lt;0.001) compared to a pre-expansion length of stay of approximately 17 weeks. Workload was proportional between the two cohorts. Patients with heart failure with preserved ejection fraction were more likely to be reviewed in multidisciplinary teams or by the consultant community clinic. This group was less likely to attend clinic, with 96.4% of face-to-face reviews taking place at home. Telephone reviews occurred at a similar frequency for both cohorts, comprising 50% of follow ups. The heart failure with reduced ejection fraction cohort required more alterations in medication and medication titration, generating additional follow ups. Conclusions The service expansion to include patients with heart failure with preserved ejection fraction has had a significant impact on workload, leading to a reduction in the quality of care for those with heart failure with reduced ejection fraction.