Challenges and Opportunities in Consolidation at High Resource Utilization: Non-monotonic Response Time Variations in n-Tier Applications

2012 ◽  
Author(s):  
Simon Malkowski ◽  
Yasuhiko Kanemasa ◽  
Hanwei Chen ◽  
Masao Yamamoto ◽  
Qingyang Wang ◽  
...  
Keyword(s):  
Response Time ◽  
Resource Utilization ◽  
High Resource ◽  
Challenges And Opportunities ◽  
Time Variations

P053 VARIATIONS IN HEALTHCARE UTILIZATION PATTERNS AMONG HIGH RISK INFLAMMATORY BOWEL DISEASE PATIENTS ENROLLED IN HIGH DEDUCTIBLE HEALTH PLANS

2020 ◽  
Vol 26 (Supplement_1) ◽  
pp. S67-S68
Author(s):  
Jeffrey Berinstein ◽  
Shirley Cohen-Mekelburg ◽  
Calen Steiner ◽  
Megan Mcleod ◽  
Mohamed Noureldin ◽  
...  
Keyword(s):  
At Risk ◽  
Resource Utilization ◽  
Time Trends ◽  
Healthcare Costs ◽  
Insurance Plan ◽  
High Resource ◽  
Patients At Risk ◽  
Inflammatory Bowel ◽  
Ed Visits ◽  
Out Of Pocket Costs

Abstract Background High-deductible health plan (HDHP) enrollment has increased rapidly over the last decade. Patients with HDHPs are incentivized to delay or avoid necessary medical care. We aimed to quantify the out-of-pocket costs of Inflammatory Bowel Disease (IBD) patients at risk for high healthcare resource utilization and to evaluate for differences in medical service utilization according to time in insurance period between HDHP and traditional health plan (THP) enrollees. Variations in healthcare utilization according to time may suggest that these patients are delaying or foregoing necessary medical care due to healthcare costs. Methods IBD patients at risk for high resource utilization (defined as recent corticosteroid and narcotic use) continuously enrolled in an HDHP or THP from 2009–2016 were identified using the Truven Health MarketScan database. Median annual financial information was calculated. Time trends in office visits, colonoscopies, emergency department (ED) visits, and hospitalizations were evaluated using additive decomposition time series analysis. Financial information and time trends were compared between the two insurance plan groups. Results Of 605,862 with a diagnosis of IBD, we identified 13,052 patients at risk for high resource utilization with continuous insurance plan enrollment. The median annual out-of-pocket costs were higher in the HDHP group (n=524) than in the THP group (n=12,458) ($1,920 vs. $1,205, p<0.001), as was the median deductible amount ($1,015 vs $289, p<0.001), without any difference in the median annual total healthcare expenses (Figure 1). Time in insurance period had a greater influence on utilization of colonoscopies, ED visits, and hospitalization in IBD patients enrolled in HDHPs compared to THPs (Figure 2). Colonoscopies peaked in the 4th quarter, ED visits peaked in the 1st quarter, and hospitalizations peaked in the 3rd and 4th quarter. Conclusion Among IBD patients at high risk for IBD-related utilization, HDHP enrollment does not change the cost of care, but shifts healthcare costs onto patients. This may be a result of HDHPs incentivizing delays with a potential for both worse disease outcomes and financial toxicity and needs to be further examined using prospective studies.

A Promising Hardware Accelerator with PAST Adder

2021 ◽  
Vol 105 ◽  
pp. 241-248
Author(s):  
Abhishek Choubey ◽  
Shruti Bhargava Choubey
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Resource Utilization ◽  
Network Inference ◽  
Hardware Accelerator ◽  
High Complexity ◽  
High Resource ◽  
The Neural Network ◽  
And Storage ◽  
Dedicated Hardware

Recent neural network research has demonstrated a significant benefit in machine learning compared to conventional algorithms based on handcrafted models and features. In regions such as video, speech and image recognition, the neural network is now widely adopted. But the high complexity of neural network inference in computation and storage poses great differences on its application. These networks are computer-intensive algorithms that currently require the execution of dedicated hardware. In this case, we point out the difficulty of Adders (MOAs) and their high-resource utilization in a CNN implementation of FPGA .to address these challenge a parallel self-time adder is implemented which mainly aims at minimizing the amount of transistors and estimating different factors for PASTA, i.e. field, power, delay.

Abstract 16763: Frailty, as Measured by the Essential Frailty Toolset, is Associated With Higher Resource Utilization in Patients Undergoing Coronary Artery Bypass Grafting

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Fady K Soliman ◽  
Lindsay Volk ◽  
Rajath Kenath ◽  
Alexis K Okoh ◽  
Joshua C Chao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Coronary Artery ◽  
Coronary Artery Bypass ◽  
Resource Utilization ◽  
Artery Bypass ◽  
Direct Costs ◽  
High Resource ◽  
Frail Patients ◽  
Multivariable Logistic Regression ◽  
Prolonged Ventilation

Introduction: Frailty is an important predictor of clinical outcomes, but its contribution to resource utilization remains understudied. This study investigates the impact of frailty on high resource utilization (HRU) in patients undergoing Coronary Artery Bypass Graft Surgery (CABG). Methods: We reviewed data on patients who underwent CABG at a single center between 04/2018 and 12/2019. A Frailty score (FS) was calculated using the Essential Frailty Toolset (EFT). Patients were divided into two groups: Frail (FS ≥ 3/5) & Non-Frail (FS <3/5). Baseline clinical characteristics and outcomes were compared in both groups. The primary outcome was HRU (post-operative length of stay > 7 days or readmission within 30-days). Secondary outcomes included operative time, prolonged ventilation, & direct procedure costs. Multivariable logistic regression was used to assess the effect of frailty on HRU. Results: The study included 740 patients of whom 18% (n=132) were frail. Compared to Non-Frail patients, Frail patients were older (66 vs. 70 yrs. P<0.001) and more likely to be high risk for operative mortality (1.3% vs. 14%, p<0.001). The incidence of HRU was 28% vs. 53%, p<0.001, in Non-Frail vs. Frail patients. Frail patients had longer operative times (272 vs. 247 mins; p<0.001), and a higher incidence of prolonged ventilation (9.9% vs. 4%; p<0.001). Median direct costs were also higher in Frail subjects ($33,434 vs. $22, 207; p<0.001). On multivariable logistic regression analysis, independent predictors of HRU were (OR: 95% C.I.) Frailty: 2.19(1.44, 3.33; p=0.003), Sex (Female): 1.66 (1.14, 2.40; p=0.008), and history of COPD: 2.32(1.53, 3.54; p<0.001). Conclusions: About one out of every five patients undergoing CABG was classified as frail by the EFT. Frailty was associated with higher direct costs and found to be an independent predictor of high resource utilization. Further attention is required to optimize outcomes and resource use in this vulnerable population.

scholarly journals Multi-criteria Checkpointing Strategies: Response-Time versus Resource Utilization

2013 ◽  
pp. 420-431 ◽  
Author(s):  
Aurelien Bouteiller ◽  
Franck Cappello ◽  
Jack Dongarra ◽  
Amina Guermouche ◽  
Thomas Hérault ◽  
...  
Keyword(s):  
Response Time ◽  
Resource Utilization

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

2020 ◽  
Vol 11 (1) ◽  
pp. 12-36
Author(s):  
Minakshi Sharma ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Qos Parameters ◽  
Load Balancing Algorithm

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

scholarly journals HIGH RESOURCE UTILIZATION FOR NON-CARDIAC HOSPITAL ADMISSIONS FOR ADULTS WITH CONGENITAL HEART DISEASE

2015 ◽  
Vol 65 (10) ◽  
pp. A543 ◽  
Author(s):  
Michael Seckeler ◽  
Ian D. Thomas ◽  
Jennifer Andrews ◽  
Omar Meziab ◽  
Elissa Heller ◽  
...  
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Resource Utilization ◽  
Congenital Heart ◽  
Hospital Admissions ◽  
High Resource

scholarly journals A fast, resource efficient and reliable rule-based system for COVID-19 symptom identification

JAMIA Open ◽  
2021 ◽  
Author(s):  
Himanshu S Sahoo ◽  
Greg M Silverman ◽  
Nicholas E Ingraham ◽  
Monica I Lupei ◽  
Michael A Puskarich ◽  
...  
Keyword(s):  
Real Time ◽  
Resource Utilization ◽  
Fine Tuning ◽  
Rule Based ◽  
Low Resource ◽  
Annotation System ◽  
High Resource ◽  
Industry Standard ◽  
Depth Analysis ◽  
Current Annotation

Abstract Objective With COVID-19 there was a need for rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from high resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution. Materials and Methods Performance, resource utilization and runtime of the rule-based gazetteer was compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP and MedTagger. Results This rule-based gazetteer was fastest, had low resource footprint and similar performance for weighted micro-average and macro-average measures of precision, recall and f1-score compared to other annotation systems. Discussion Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime. Conclusion This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of health care settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of post-acute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime and similar weighted micro-average and macro-average measures for precision, recall and f1-score compared to industry standard annotation systems. Lay Summary With COVID-19 came an unprecedented need to identify symptoms of COVID-19 patients under investigation (PUIs) in a time sensitive, resource-efficient and accurate manner. While available annotation systems perform well for smaller healthcare settings, they fail to scale in larger healthcare systems where 10,000+ clinical notes are generated a day. This study covers 3 improvements addressing key limitations of current annotation systems. (1) High resource utilization and poor scalability of existing annotation systems. The presented rule-based gazetteer is a high-throughput annotation system for processing high volume of notes, thus, providing opportunity for clinicians to make more informed time-sensitive decisions around patient care. (2) Equally important is our developed rule-based gazetteer performs similar or better than current annotation systems for symptom identification. (3) Due to minimal resource needs of the rule-based gazetteer, it could be deployed at healthcare sites lacking a robust infrastructure where industry standard annotation systems cannot be deployed because of low resource availability.

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