The 'Surgical Time': a myth or reality? Surgeons' prediction of operating time and its effect on theatre scheduling

2020 ◽  
Vol 44 (5) ◽  
pp. 772
Author(s):  
Raghav Goel ◽  
Harsh Kanhere ◽  
Markus Trochsler
Keyword(s):  
Prediction Models ◽  
Operating Time ◽  
Individual Characteristics ◽  
Queen Elizabeth Hospital ◽  
Start Time ◽  
Surgical Time ◽  
Preoperative Prediction ◽  
Wide Range ◽  
Time Required ◽  
Theatre List

ObjectiveIn Australia, 2.7 million surgical procedures were performed in the year 2016–17. This number is ever increasing and requires effective management of operating theatre (OT) time. Preoperative prediction of theatre time is one of the main constituents of OT scheduling, and anecdotal evidence suggests that surgeons grossly underestimate predicted surgical time. The aim of this study is to assess surgeons’ accuracy at predicting OT times across different specialties and effective theatre scheduling. MethodsA database was created with de-identified patient information from a 3-month period (late 2016). The collected data included variables such as the predicted time, actual surgery time, and type of procedure (i.e. Emergency or Elective). These data were used to make quantifiable comparisons. ResultsData were categorised into a ‘Theatre list’ and ‘Scopes list’. This was further compared as ‘Actual–Predicted’ time, which ranged from an average underestimation of each procedure by 19min (Ear Nose and Throat surgeons) to an average overprediction of 13.5min (Plastic Surgery). Urgency of procedures (i.e. Emergency and Elective procedures) did not influence prediction time for the ‘Theatre list’, but did so for the ‘Scopes list’ (P<0.001). Surgeons were poor at predicting OT times for complex operations and patients with high American Society of Anaesthesiologists grades. Overall, surgeons were fairly accurate with their OT prediction times across 1450 procedures, with an average underestimation of only 2.3 min. ConclusionsIn terms of global performance at The Queen Elizabeth Hospital institution, surgeons are fairly accurate at predicting OT times. Surgeons’ estimates should be used in planning theatre lists to avoid unnecessary over or underutilisation of resources. What is known about the topic?It is known that variables such as theatre changeover times and anaesthesia time are some of the factors that delay the scheduled start time of an OT. Furthermore, operating time depends on the personnel within the operating rooms such as the nursing staff, anaesthesiologists, team setup and day of time. Studies outside of Australia have shown that prediction models for OT times using individual characteristics and the surgeon’s estimate are effective. What does this paper add?This paper advocates for surgeons’ predicted OT time to be included in the process of theatre scheduling, which currently does not take place. It also provides analysis of a wide range of surgical specialties and assesses each professions’ ability to accurately predict the surgical time. This study encompasses a substantial number of procedures. Moreover, it compares endoscopic procedures separately to laparoscopic/open procedures. It contributes how different variables such as the urgency of procedure (Emergency/Elective), estimated length of procedure and patient comorbidities affect the prediction of OT time. What are the implications for practitioners?This will encourage hospital administrators to use surgeons’ predicted OT time in calculations for scheduling theatre lists. This will facilitate more accurate predictions of OT time and ensure that theatre lists are not over or underutilised. Moreover, surgeons will be encouraged to make OT time predictions with serious consideration, after understanding its effect on theatre scheduling and associated costs. Hence, the aim is to try to make an estimation of OT time, which is closer to the actual time required.

Microwaves: Application in Electron Microscopy

1990 ◽  
Vol 48 (3) ◽  
pp. 140-141
Author(s):  
Anthony S-Y Leong ◽  
David W Gove
Keyword(s):  
Electron Microscopy ◽  
Light Microscopy ◽  
Chemical Reactions ◽  
Electromagnetic Waves ◽  
Tissue Fixation ◽  
Primary Method ◽  
Dipolar Molecules ◽  
Wide Range ◽  
Time Required ◽  
Cryostat Sections

Microwaves (MW) are electromagnetic waves which are commonly generated at a frequency of 2.45 GHz. When dipolar molecules such as water, the polar side chains of proteins and other molecules with an uneven distribution of electrical charge are exposed to such non-ionizing radiation, they oscillate through 180° at a rate of 2,450 million cycles/s. This rapid kinetic movement results in accelerated chemical reactions and produces instantaneous heat. MWs have recently been applied to a wide range of procedures for light microscopy. MWs generated by domestic ovens have been used as a primary method of tissue fixation, it has been applied to the various stages of tissue processing as well as to a wide variety of staining procedures. This use of MWs has not only resulted in drastic reductions in the time required for tissue fixation, processing and staining, but have also produced better cytologic images in cryostat sections, and more importantly, have resulted in better preservation of cellular antigens.

TIME TO THE END OF PRIMARY CONSOLIDATION (EOP) OF SOFT CLAYEY SOILS: CONCERNING THE EFFECT OF ATTERBERG’S LIMIT AND CYCLIC LOADING HISTORY

2019 ◽  
Vol 128 (2B) ◽  
pp. 29-41
Author(s):  
Trần Thanh Nhàn
Keyword(s):  
Cyclic Loading ◽  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Clayey Soils ◽  
Loading History ◽  
Cyclic Shear ◽  
Wide Range ◽  
Primary Consolidation ◽  
Time Required

In order to observe the end of primary consolidation (EOP) of cohesive soils with and without subjecting to cyclic loading, reconstituted specimens of clayey soils at various Atterberg’s limits were used for oedometer test at different loading increments and undrained cyclic shear test followed by drainage with various cyclic shear directions and a wide range of shear strain amplitudes. The pore water pressure and settlement of the soils were measured with time and the time to EOP was then determined by different methods. It is shown from observed results that the time to EOP determined by 3-t method agrees well with the time required for full dissipation of the pore water pressure and being considerably larger than those determined by Log Time method. These observations were then further evaluated in connection with effects of the Atterberg’s limit and the cyclic loading history.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

scholarly journals Portable near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field

2021 ◽  
Vol 11 (7) ◽  
pp. 3209
Author(s):  
Karla R. Borba ◽  
Didem P. Aykas ◽  
Maria I. Milani ◽  
Luiz A. Colnago ◽  
Marcos D. Ferreira ◽  
...  
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Prediction Models ◽  
Quality Attributes ◽  
Internal Quality ◽  
Control Analysis ◽  
Tomato Quality ◽  
Wide Range ◽  
Different Types ◽  
Fresh Tomato

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.

scholarly journals Risk Factors and Prediction Models for Venous Thromboembolism in Ambulatory Patients with Lung Cancer

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 778
Author(s):  
Ann-Rong Yan ◽  
Indira Samarawickrema ◽  
Mark Naunton ◽  
Gregory M. Peterson ◽  
Desmond Yip ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Prediction Models ◽  
Poor Performance ◽  
Risk Models ◽  
Related Risk ◽  
Wide Range ◽  
Ambulatory Patients

Venous thromboembolism (VTE) is a significant cause of mortality in patients with lung cancer. Despite the availability of a wide range of anticoagulants to help prevent thrombosis, thromboprophylaxis in ambulatory patients is a challenge due to its associated risk of haemorrhage. As a result, anticoagulation is only recommended in patients with a relatively high risk of VTE. Efforts have been made to develop predictive models for VTE risk assessment in cancer patients, but the availability of a reliable predictive model for ambulate patients with lung cancer is unclear. We have analysed the latest information on this topic, with a focus on the lung cancer-related risk factors for VTE, and risk prediction models developed and validated in this group of patients. The existing risk models, such as the Khorana score, the PROTECHT score and the CONKO score, have shown poor performance in external validations, failing to identify many high-risk individuals. Some of the newly developed and updated models may be promising, but their further validation is needed.

Analysis and Reduction of the Waste in the Work Process Using Time Study Analysis: A Case Study

2014 ◽  
Vol 660 ◽  
pp. 971-975 ◽  
Author(s):  
Mohd Norzaim bin Che Ani ◽  
Siti Aisyah Binti Abdul Hamid
Keyword(s):  
Cycle Time ◽  
Lean Manufacturing ◽  
Value Added ◽  
Time Study ◽  
Study Approach ◽  
Root Cause ◽  
Wide Range ◽  
Unit Of Work ◽  
Time Required ◽  
The Impact

Time study is the process of observation which concerned with the determination of the amount of time required to perform a unit of work involves of internal, external and machine time elements. Originally, time study was first starting to be used in Europe since 1760s in manufacturing fields. It is the flexible technique in lean manufacturing and suitable for a wide range of situations. Time study approach that enable of reducing or minimizing ‘non-value added activities’ in the process cycle time which contribute to bottleneck time. The impact on improving process cycle time for organization that it was increasing the productivity and reduce cost. This project paper focusing on time study at selected processes with bottleneck time and identify the possible root cause which was contribute to high time required to perform a unit of work.

scholarly journals Infection Prevention Time Required for Construction and Design at a Large Tertiary-Care Hospital, 2019

2020 ◽  
Vol 41 (S1) ◽  
pp. s69-s70
Author(s):  
Angie Dains ◽  
Michael Edmond ◽  
Daniel Diekema ◽  
Stephanie Holley ◽  
Oluchi Abosi ◽  
...  
Keyword(s):  
Infection Control ◽  
Patient Care ◽  
Construction Projects ◽  
Small Scale ◽  
Care Hospital ◽  
Class Iii ◽  
Construction Sites ◽  
Wide Range ◽  
Time Required ◽  
Class Iv

Background: Including infection preventionists (IPs) in hospital design, construction, and renovation projects is important. According to the Joint Commission, “Infection control oversights during building design or renovations commonly result in regulatory problems, millions lost and even patient deaths.” We evaluated the number of active major construction projects at our 800-bed hospital with 6.0 IP FTEs and the IP time required for oversight. Methods: We reviewed construction records from October 2018 through October 2019. We classified projects as active if any construction occurred during the study period. We describe the types of projects: inpatient, outpatient, non–patient care, and the potential impact to patient health through infection control risk assessments (ICRA). ICRAs were classified as class I (non–patient-care area and minimal construction activity), class II (patients are not likely to be in the area and work is small scale), class III (patient care area and work requires demolition that generates dust), and class IV (any area requiring environmental precautions). We calculated the time spent visiting construction sites and in design meetings. Results: During October 2018–October 2019, there were 51 active construction projects with an average of 15 active sites per week. These sites included a wide range of projects from a new bone marrow transplant unit, labor and delivery expansion and renovation, space conversion to an inpatient unit to a project for multiple air handler replacements. All 51 projects were classified as class III or class IV. We visited, on average, 4 construction sites each week for 30 minutes per site, leaving 11 sites unobserved due to time constraints. We spent an average of 120 minutes weekly, but 450 minutes would have been required to observe all 15 sites. Yearly, the required hours to observe these active construction sites once weekly would be 390 hours. In addition to the observational hours, 124 hours were spent in design meetings alone, not considering the preparation time and follow-up required for these meetings. Conclusions: In a large academic medical center, IPs had time available to visit only a quarter of active projects on an ongoing basis. Increasing dedicated IP time in construction projects is essential to mitigating infection control risks in large hospitals.Funding: NoneDisclosures: None

scholarly journals Prediction Models for the Estimation of Soil Moisture Content

2011 ◽  
Author(s):  
Swathi Gorthi ◽  
Huifang Dou
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Prediction Models ◽  
Recursive Partitioning ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Modeling Tools ◽  
Wide Range

This paper provides a survey on different kinds of prediction models developed for the estimation of soil moisture content of an area, using empirical information including meteorological and remotely sensed data. The different models employed extend over a wide range of machine learning techniques starting from Basic Linear Regression models through models based on Bayesian framework, Decision tree learning and Recursive partitioning, to the modern non-linear statistical data modeling tools like Artificial Neural Networks. The fundamental mathematical backgrounds, pros and cons, prediction results and efficiencies of all the models are discussed.

scholarly journals Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associate cytokine storm

2021 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Naorem Leimarembi Devi ◽  
Gajendra P. S. Raghava
Keyword(s):  
Prediction Models ◽  
Validation Dataset ◽  
Cytokine Storm ◽  
Hybrid Features ◽  
Stat3 Signaling ◽  
Stat3 Signaling Pathway ◽  
Wide Range ◽  
Proinflammatory Responses ◽  
Highly Correlated ◽  
Approved Drugs

Abstract It has been shown in the past that levels of cytokines, including interleukin 6 (IL6), is highly correlated with the disease severity of COVID-19 patients. IL6 mediated activation of STAT3 is responsible to proliferate proinflammatory responses that leads to promotion of cytokine storm. Thus, STAT3 inhibitors may play a crucial role in managing pathogenesis of COVID-19. This paper describes a method developed for predicting inhibitors against the IL6-mediated STAT3 signaling pathway. The dataset used for training, testing, and evaluation of models contains small-molecule based 1564 STAT3 inhibitors and 1671 non-inhibitors. Analysis of data indicates that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. In order to build models, we generate a wide range of descriptors for each chemical compound. Firstly, we developed models using 2-D and 3-D descriptors and achieved maximum AUC 0.84 and 0.73, respectively. Secondly, fingerprints (FP) are used to build prediction models and achieved 0.86 AUC and accuracy of 78.70% on validation dataset. Finally, models were developed using hybrid features or descriptors, achieve a maximum of 0.87 AUC on the validation dataset. We used our best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen, and Perindopril) that can be used to manage COVID-19 associated cytokine storm. A webserver “STAT3In” (https://webs.iiitd.edu.in/raghava/stat3in/ ) has been developed to predict and design STAT3 inhibitors.

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