Priority Classes Research Articles

Do emergency medicine health care workers rate triage level of chest pain differently based upon appearance in simulated patients?

There seems to be evidence of gender and ethnic bias in the early management of acute coronary syndrome. However, whether these differences are related to less severe severity assessment or to less intensive management despite the same severity assessment has not yet been established. To show whether viewing an image with characters of different gender appearance or ethnic background changes the prioritization decision in the emergency triage area. The responders were offered a standardized clinical case in an emergency triage area. The associated image was randomized among eight standardized images of people presenting with chest pain and differing in gender and ethnic appearance (White, Black, North African and southeast Asian appearance). Each person was asked to respond to a single clinical case, in which the priority level [from 1 (requiring immediate treatment) to 5 (able to wait up to 2 h)] was assessed visually. Priority classes 1 and 2 for vital emergencies and classes 3-5 for nonvital emergencies were grouped together for analysis. Among the 1563 respondents [mean age, 36 ± 10 years; 867 (55%) women], 777 (50%) were emergency physicians, 180 (11%) emergency medicine residents and 606 (39%) nurses. The priority levels for all responses were 1-5 : 180 (11%), 686 (44%), 539 (34%), 131 (9%) and 27 (2%). There was a higher reported priority in male compared to female [62% vs. 49%, difference 13% (95% confidence interval; CI 8-18%)]. Compared to White people, there was a lower reported priority for Black simulated patients [47% vs. 58%, difference -11% (95% CI -18% to -4%)] but not people of southeast Asian [55% vs. 58%, difference -3% (95% CI -10-5%)] and North African [61% vs. 58%, difference 3% (95% CI -4-10%)] appearance. In this study, the visualization of simulated patients with different characteristics modified the prioritization decision. Compared to White patients, Black patients were less likely to receive emergency treatment. The same was true for women compared with men.

Software bug severity and priority prediction using SMOTE and intuitionistic fuzzy similarity measure

A software bug tracking system receives several bug reports in a rapid manner during the maintenance of software. In order to fix the important and urgent bugs, the triager has to assign severity and priority to individual bugs on time. However, there are a lot of uncertainties in the bug reports due to bias, noise, and abnormal data. At the same time, the presence of common terms in multiple severity and priority classes creates confusion in the mind of the triager. Furthermore, machine learning and deep learning approaches generally belong to discriminative learning with a clear-cut outcome. Instances of software bug reports are textual in nature. As a result, these are fuzzy and cannot be classified with a clear-cut outcome. To overcome the above problems, in this paper, an Intuitionistic Fuzzy Similarity Measure (IFSM) based severity prediction technique (IFSMSP) and priority prediction technique (IFSMPP) are proposed for predicting the severity and priority of a new bug by using already labeled bugs. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the severity and priority label of software bugs. Then the severity-term dictionary or priority-term dictionary is created by extracting the most frequent terms from the bug summary using text mining and Natural Language Processing (NLP). Then the data is represented using an intuitionistic fuzzy set (IFS) by calculating the membership, non-membership, and hesitancy degrees. Then 15 different IFSM techniques are investigated for predicting the severity and priority of software bugs. Experiments are carried out on large software bug repositories (Eclipse, Mozilla, Apache, and NetBeans) with a 10-fold cross-validation technique. IFSMSP outperformed other state-of-the-art priority models by obtaining an accuracy of 92.3%, 90.6%, 91.9%, and 91.2%, and IFSMPP outperformed other state-of-the-art models by obtaining an accuracy of 93.2%, 91.9%, 92.7%, and 92.3% on the Eclipse, Mozilla, Apache, and NetBeans software bug repositories, respectively.

Comparative Analysis of MCDA Techniques for Identifying Erosion-Prone Areas in the Burhanpur Watershed in Central India for the Purposes of Sustainable Watershed Management

The degradation of land and increasing water scarcity are existing challenges for agricultural sustainability, necessitating the implementation of improved soil-conservation practices at the watershed scale. The identification and selection of critical/prone areas based on erosion-governing criteria is essential and helps in the execution of the management process for determining priority. This study prioritizes erosion-prone sub-watersheds (alternatives) based on morphometric parameters (multiple criteria) via five Multi-Criteria Decision Analysis (MCDA) approaches, i.e., AHP: Analytical Hierarchy Process; TOPSIS: Technique for Order of Preference by Similarity to Ideal Solution; VIKOR: VIseKriterijumska Optimizacija I Kompromisno Resenje; SAW: Simple Additive Weighting; and CF: Compound Factor. Based on their priority score, 19 sub-watersheds were classified into four priority classes: low priority (0–0.25), moderate priority (0.25–0.50), high priority (0.50–0.75), and very high priority (0.75–1). The results revealed that about 8.34–30.15% area of the Burhanpur watershed is critically prone to erosion, followed by 23.38–52.05% area classed as high priority, 7.47–49.99% area classed as moderate priority, and 10.33–18.28% area classed as low priority. Additionally, four indices—percentage of changes (∆P), intensity of changes (∆I), the Spearman rank correlation coefficient test (SCCT), and the Kendall tau correlation coefficient test (KTCCT)—were employed to compare the models. This study confirms the efficacy of morphometric parameters for prioritizing sub-watersheds to preserve soil and the environment, particularly in areas for which limited information is available.

Approximation Method for a Non-preemptive Multiserver Queue with Quasi-Poisson Arrivals

We consider a non-preemptive multiserver queue with multiple priority classes. We assume distinct exponentially distributed service times and separate quasi-Poisson arrival processes with a predefined maximum number of requests that can be present in the system for each class. We present an approximation method to obtain the steady-state probabilities for the number of requests of each class in our system. In our method, the priority levels (classes) are solved “nearly separately,” linked only by certain conditional probabilities determined approximately from the solution of other priority levels. Several numerical examples illustrate the accuracy of our approximate solution. The proposed approach significantly reduces the complexity of the problem while featuring generally good accuracy.

Open access as an intervention to improve outpatient waiting list

Abstract Background Waiting list management is a critical aspect of health care facilities, representing the excess of demand that could be guaranteed in a specific time. The effective implementation of policies to govern waiting lists requires the adoption of adequate organizational booking structures to contain the risk of underuse of care services and maximize their ability to ensure compliance with the maximum waiting time for each priority class. Methods The study analyzes the Open Access model adopted by the Azienda Ospedaliero-Universitaria Pisana (AOUP). The model involves booking within 3 days and daily sliding of services monitored by a Regional masterplan, with supply evenly distributed throughout the week. Since Open Access model was implemented in June 2020, the study analyzes the effect of this between June 1, 2019 and May 31, 2021, regarding specialist visits and diagnostic tests. Results In the study time, the percentage of all guaranteed services within the national timeframe raised significantly from 84.02% to 93.56%. For example, rheumatology visit that went from 68% to 99%. Speaking of waiting time, allergology visit went from 96 days to 8 days and the pneumology visit from 82 days to 4 days. Visits that were already starting from times in line with priority also benefited from the methodology, such as the vascular surgery visit that went from 2 to 0 days. Conclusions Therefore, proper governance of waiting lists is crucial in the healthcare system since timely access to healthcare services is an essential right for citizens and has a significant impact on the management of healthcare services. However, there are no one-size-fits-all solutions for managing waiting lists, but complex actions need to be implemented. The importance of digital tools for waiting list management is emphasized and adequate control structures are needed to detect any ‘anomalies’ and verify the proper functioning of the system of access to care. Key messages • Efficient waitlist management safeguards citizens’ rights to access to care and proper governance. • Control structures are crucial for effective management as a universal solution is unattainable.

ARTICLE WOODY SPECIES STRUCTURE AND REGENERATION STATUS IN MEHAL WONZ - ZEGO FOREST, N/ SHEWA ZONE, AMHARA REGIONAL STATE, ETHIOPIA

This study was conducted on Mehalwonze- Zego forest in North Shewa zone, Amhara Regional State, Ethiopia, A Transect sampling method was used to collect vegetation data. Sixty seven(67) plots of 20m × 20m were laied each at 300m intervals and five subplots of 2m × 2m, one in the center and four in the corner of the main plots, were laid for seedling and sapling inventory. Vegetation parameters such as DBH >2.5cm, H ≥2.5m of as well as seedling and sapling density with five sub plots of 2m × 2m. A total of 55 woody species belonging to 51 genera and 38 families were identified. The density of seedlings, saplings and matured woody plants in all of the plots were 1302, 1517 and 905 individuals, respectively. Importance Value Index (IVI) of each species were calculated, based on this value the conservation priority classes was done which indicated that there are woody species that require urgent conservation. The total basal area of the forest was 67.96 m2/ha. DBH and height class distribution of woody spcies confirms that the forest is under a secondary stage of development. Based on the results of regeneration assessment, conservation priority should be given for species with poor regeneration status.

Optimizing city-level centralized wastewater management system using machine learning and spatial network analysis

Access to proper sanitation is a basic human right and a crucial component of public health. Wastewater treatment plants (WWTP) play a critical role in ensuring sanitation access by preventing water pollution. However, in Bandung, one of Indonesia’s strategic cities, only 63% of the population has access to proper sanitation. Therefore, this study aims to identify priority areas for developing a centralized WWTP system using models that consider wastewater production and the availability of sanitation access on a building scale. The novelty of this study lies in integrating wastewater production models and sanitation access availability to prioritize developing WWTP systems at the building level and developing a pipeline model to connect WWTP candidate points with priority building targets. We also analyzed the suitability of optimal WWTP locations to support the development of wastewater treatment systems and determine wastewater pipeline routes using the optimal path method in priority areas. To model wastewater production, we used the number of residents per building and building area as the basis. The availability of sanitation access was modeled by considering the percentage of access per district, identifying urban slums, and analyzing proximity to existing wastewater pipes and rivers. After the modeling process, we classified nine development priority classes. Our results revealed that there is approximately 7.38 km2 of priority area for developing a wastewater treatment system in Bandung which comprises 67,135 priority buildings and 178 potential sites for WWTP. These priority areas are mostly located in the districts of Bojongloa Kaler, Astanaanyar, Batununggal, Kiaracondong, Cibeunying Kidul, Coblong, and Sukajadi. This study’s findings provide valuable insights for policymakers and urban planners to prioritize developing WWTP systems in areas that need them most. It also highlights the importance of considering sanitation access availability and wastewater production models when planning and developing WWTP systems.

Transient Solution of Two –Class priority Queuing System with Balking and Catastrophes

Transient Solution of Two –Class priority Queuing System with Balking and Catastrophes

Analytics-driven complaint prioritisation via deep learning and multicriteria decision-making

Complaint analysis is an essential business analytics application because complaints have a strong influence on customer satisfaction (CSAT). However, the process of categorising and prioritising complaints manually can be extremely time consuming for large companies. In this paper, we propose a framework for automatic complaint labelling and prioritisation using text analytics and operational research techniques. The labelling step of the training set is performed using a simple weighting approach from the multiple-criteria decision-making (MCDM) literature, while transformer-based deep learning (DL) techniques are used for text classification. We define two priority classes, namely, urgent complaints and other claims, and develop a system for automatic complaint categorisation. Our experimental results show that excellent predictive performance can be achieved with state-of-the-art text classification models. In particular, BETO, a bidirectional encoder representations from transformers (BERT) model trained on a large Spanish corpus, reaches an accuracy (ACCU) and area under the curve (AUC) of 92.1% and 0.9785, respectively. This positive result translates into a successful complaint prioritisation scheme, which improves CSAT and reduces the churn rate.

Efficient and accurate Lyapunov function-based truncation technique for multi-dimensional Markov chains with applications to discriminatory processor sharing and priority queues

Online service providers aim to satisfy the tail performance requirements of customers through Service-Level Objectives (SLOs). One approach to ensure tail performance requirements is to model the service as a Markov chain and obtain its steady-state probability distribution. However, obtaining the distribution can be challenging, if not impossible, for certain types of Markov chains, such as those with multi-dimensional or infinite state-space and state-dependent transitions. Examples include M/M/1 with Discriminatory Processor Sharing (DPS) and preemptive M/M/c with multiple priority classes and customer abandonment.To address this fundamental problem, we propose a Lyapunov function-based state-space truncation technique that leverages moments or bounds on moments of the state variables. This technique allows us to obtain tight truncation bounds while ensuring arbitrary probability mass guarantees for the truncated chain. We highlight the efficacy of our technique for multi-dimensional DPS and M/M/c priority queue with abandonment, demonstrating a substantial reduction in state space (up to 74%) compared to existing approaches. Additionally, we present three practical use cases that highlight the applicability of our truncation technique by analyzing the performance of the DPS system.

Congestion games with priority-based scheduling

We reconsider atomic and non-atomic affine congestion games under the assumption that players are partitioned into p priority classes and resources schedule their users according to a priority-based policy, breaking ties uniformly at random. We derive tight bounds on both the price of anarchy and the price of stability as a function of p, revealing an interesting separation between the general case of p≥2 and the priority-free scenario of p=1. In fact, while in absence of priorities the worst-case prices of anarchy and stability of non-atomic games are lower than their counterparts in atomic ones, the two classes share the same bounds when p≥2. Moreover, while the worst-case price of stability is lower than the worst-case price of anarchy in atomic games with no priorities, their values become equal when p≥2. Said differently, the presence of priorities simultaneously irons out any combinatorial difference between atomic and non-atomic requests and among different pure Nash equilibria to produce a unique representative worst-case situation. Notably, our results keep holding even under singleton strategies. Besides being of independent interest, priority-based scheduling shares tight connections with online load balancing and finds a natural application within the theory of coordination mechanisms and cost-sharing policies for congestion games. Under this perspective, a number of possible research directions also arise.

Analytical Study of Two Serial Channels with Priority and Reneging

Most of the studies of queuing theory, which are useful in our daily life has been investigated by many researchers. The present research is the study of pre-emptive priority queuing system consisting two serial channels in stochastic environment. The impatient behavior of customer’s will be discussed with exponential service distribution and Poisson arrivals. Higher priority customers have pre-emptive priority over the low priority customers. The G.F. technique is used to derive the performance measures of high & low priority queues and assuming FCFS discipline in busy schedule of higher priority class. Also evaluate queue behavior graphically and discussed a special case at the end which shows utilization of channels.

Capacity allocation in service systems with preferred delivery times and multiple customer classes

Motivated by operational problems in click-and-collect systems, such as curbside pickup programs, we study a joint admission control and capacity allocation problem. We consider systems where customers have preferred service delivery times and can be of different priority classes. The service provider can reject customers upon arrival or serve jobs via overtime when service capacity is insufficient. The service provider’s goal is to find the minimum-cost admission and capacity allocation policy to dynamically decide when to serve and whom to serve. We model this problem as a Markov Decision Process and present structural results to partially characterize suboptimal solutions. We then develop a linear programming-based exact solution method using these results. We also present a problem-specific approximation method using a new state aggregation rule to address computational challenges faced due to large state and action spaces. Finally, we develop heuristic policies for large instances based on the behavior of optimal policies in small problems. We evaluate our methods through extensive computational experiments where we vary the service capacity, arrivals, associated service costs, customer segmentation, and order patterns. Our solution methods perform significantly better than several benchmarks in managing the tradeoff between the computation time and solution quality.

3D MODELS CITYGML-BASED COMBINED WITH TECHNICAL DECISION SUPPORT SYSTEM FOR THE SETTING UP OF DIGITAL CONSERVATION PLANS OF HISTORIC DISTRICTS

Abstract. The setting up of recovery plans for historic districts requires a multi-level and multi-thematic process for their analysis and diagnosis to determine classes of priorities and interventions for buildings at the district scale of relevance. Traditional tools and protocols have already highlighted operative complexity and expensive activities, affecting the organicity and effectiveness in interpreting data. On the other hand, recent scientific and practical activities based on the use of parametric Digital Models and Informative Systems have highlighted their advantages in standardizing complex issues and knowledge. Recent work by the authors has defined the structured organization of technical knowledge for the creation of a digital recovery plan using Informative Parametric Models, based on descriptors, and primary and secondary factors. These aim at converting properties and information in qualitative and quantitative data, and then structuring dependencies on descriptors and primary factors, according to thematic taxonomies, existent ontologies for the geometric and semantic representation of urban and architectural entities, thematic normative and established approaches for the recovery of cultural and landscape heritage. Thus, the present work shows a workflow for the semi-automatic setting up of intervention classes for architectures in historic districts in Italy. It is structured on CityGML-based models, coherently implemented with a Technical Decision-Support System (T-DSS). Specifically, the T-DSS is determined considering the relations among thematic normative: UNI 11182, UNI/CEN TS 17385:2019, and Italian Consolidated Law on Building. The workflow is finally tested in the historic district of Ascoli Satriano, in the Apulia Region (South of Italy).

Setting wait time targets in a multi‐priority patient setting

In current clinical practice, priority‐specific wait time targets are usually determined based on the consensus of medical specialists and health care administrators. The rationale behind this approach considers clinical urgency but it does not consider the efficient use of clinical resources and the patient volume associated with each priority class. The approach we present here aims to determine wait time targets in a systematic fashion that both respects clinically acceptable maximum recommended wait times and considers clinic size and demand distribution across patient classes. First, we discuss the performance of several advance patient scheduling policies in the literature in terms of average wait times and overtime and select one for illustrative purposes. Second, we simulate the chosen policy given a demand distribution and a fixed system capacity and approximate (using regression and neural networks) the average wait time for each priority class and the use of overtime as a function of potential wait time targets. Finally, using a parameterized cost function, we formulate forward and inverse mathematical problems to determine when the implicit unit wait time costs drop to zero as wait time target values increase. Using illustrative examples with two patient classes, and a practical application with four patient classes, we demonstrate the potential managerial benefits of the proposed approach in terms of improved clinic efficiency and reduced wait times. This approach ensures that wait times are set to the minimum value that still achieves the maximal resource efficiency ensuring that patients wait for service is not extended unnecessarily.

Geographic Virtual Pooling of Hospital Resources: Data-Driven Trade-off Between Waiting and Traveling

Problem definition: Patient-level data from 72 magnetic resonance imaging (MRI) hospitals in Ontario, Canada from 2013 to 2017 show that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the patient fraction exceeding (FET) target for MRI services through geographic virtual resource-sharing while limiting incremental driving time. We present a data-driven method to solve the geographic pooling problem of partitioning 72 hospitals with heterogeneous patients with different wait time targets located in a two-dimensional region into a set of clusters. Methodology/results: We propose an “augmented-priority rule,” which is a sequencing rule that balances the patient’s initial priority class and the number of days until her wait time target. We then use neural networks to predict patient arrival and service times. We combine this predicted information and the sequencing rule to implement “advance scheduling,” which informs the patient of her treatment day and location when requesting an MRI scan. We then optimize the number of geographic resource pools among the 72 hospitals using genetic algorithms. Our resource-pooling model lowers the FET from 66% to 36% while constraining the average incremental travel time below three hours. In addition, our model shows that only 10 additional scanners are needed to achieve 10% FET, whereas 50 additional scanners would be needed without resource sharing. Over 70% of the hospitals are not worse off financially. Each individual hospital, measured over at least two weeks, achieves a higher machine utilization and a lower FET. Managerial implications: Our paper provides a practical, data-driven geographical resource-sharing model that hospitals can readily implement. Our method achieves a near-optimal solution with low computational complexity. Using smart data-driven scheduling, a little extra capacity placed at the right location is all we need to achieve the desired FET under geographic resource-sharing. Funding: This paper is supported by the following grant: Canadian Institutes of Health Research (CIHR) [Grant CIHR-950-231935]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1225 .

Assessment of soil erosion using WSA and SPR techniques for Giri watershed, Himachal Pradesh, NW Himalaya, India

A watershed is the result of several geomorphic processes such as weathering, erosion, degradation and aggradation which are influenced by several factors viz. tectonics, lithology, climate, landslides and mass wasting processes etc. In a tropical climate, watersheds contribute a significant amount of eroded material which is reflecting the impact of lithology, precipitation, tectonics, relief and anthropogenic activities. In the Himalayan region besides significant heterogeneity in lithology, stratigraphy, structure and tectonics, it is observed that variability is exhibited in climatic conditions over a small region. These factors contribute to the development of geomorphic landforms and are best studied in watersheds or river basins. In the present study, Giri Watershed (GW) is assessed to contemplate susceptibility to erosion for 66 sub-watersheds using geomorphic parameters. The prioritization of subwatersheds has been done using Weighted Sum Analysis (WSA) and Sediment Production Rate (SPR) methods. The quantitative analysis of subwatersheds is categorized into different priority classes viz. very high, high, moderate, low and very low, among which 27 subwatersheds have very high to high susceptibility to erosion.

Negative externality on service level across priority classes: Evidence from a radiology workflow platform

AbstractWe study the potential negative impact of imbalanced compensation schemes on firm performance. We analyze data from a radiology workflow platform that connects off‐site radiologists with hospitals. These radiologists select tasks from a common pool, while service level is defined by priority‐specific turnaround time targets. However, imbalances between pay and workload of different tasks could result in higher priority tasks with low pay‐to‐workload ratio receiving poorer service. We investigate this hypothesis, showing turnaround time is decreasing in pay‐to‐workload for lower priority tasks, whereas it is increasing in workload for high‐priority tasks. Crucially, we find evidence of an externality effect: Having many economically attractive tasks with low priority can lead to longer turnaround times for higher priority tasks, increasing their likelihood of delay, thus partially defeating the purpose of the priority classes.

The new timing in acute care surgery (new TACS) classification: a WSES Delphi consensus study

BackgroundTimely access to the operating room for emergency general surgery (EGS) indications remains a challenge across the globe, largely driven by operating room availability and staffing constraints. The “timing in acute care surgery” (TACS) classification was previously published to introduce a new tool to triage the timely and appropriate access of EGS patients to the operating room. However, the clinical and operational effectiveness of the TACS classification has not been investigated in subsequent validation studies. This study aimed to improve the TACS classification and provide further consensus around the appropriate use of the new TACS classification through a standardized Delphi approach with international experts.MethodsThis is a validation study of the new TACS by a selected international panel of experts using the Delphi method. The TACS questionnaire was designed as a web-based survey. The consensus agreement level was established to be ≥ 75%. The collective consensus agreement was defined as the sum of the percentage of the highest Likert scale levels (4–5) out of all participants. Surgical emergency diseases and correlated clinical scenarios were defined for each of the proposed classes. Subsequent rounds were carried out until a definitive level of consensus was reached. Frequencies and percentages were calculated to determine the degree of agreement for each surgical disease.ResultsFour polling rounds were carried out. The new TACS classification provides 6 colour-code classes correlated to a precise timing to surgery, defined scenarios and surgical condition. The WHITE colour-code class was introduced to rapidly (within a week) reschedule cancelled or postponed surgical procedures. Haemodynamic stability is the main tool to stratify patients for immediate surgery or not in the presence of sepsis/septic shock. Fifty-one surgical diseases were included in the different colour-code classes of priority.ConclusionThe new TACS classification is a comprehensive, simple, clear and reproducible triage system which can be used to assess the severity of the patient and the surgical disease, to reduce the time to access to the operating room, and to manage the emergency surgical patients within a “safe” timeframe. By including well-defined surgical diseases in the different colour-code classes of priority, validated through a Delphi consensus, the new TACS improves communication among surgeons, between surgeons and anaesthesiologists and decreases conflicts and waste and waiting time in accessing the operating room for emergency surgical patients.Graphical

Sound event triage: detecting sound events considering priority of classes

We propose a new task for sound event detection (SED): sound event triage (SET). The goal of SET is to detect an arbitrary number of high-priority event classes while allowing misdetections of low-priority event classes where the priority is given for each event class. In conventional methods of SED for targeting a specific sound event class, it is only possible to give priority to a single event class. Moreover, the level of priority is not adjustable, i.e, the conventional methods can use only types of target event class such as one-hot vector, as inputs. To flexibly control much information on the target event, the proposed SET exploits not only types of target sound but also the extent to which each target sound is detected with priority. To implement the detection of events with priority, we propose class-weighted training, in which loss functions and the network are stochastically weighted by the priority parameter of each class. As this is the first paper on SET, we particularly introduce an implementation of single target SET, which is a subtask of SET. The results of the experiments using the URBAN–SED dataset show that the proposed method of single target SET outperforms the conventional SED method by 8.70, 6.66, and 6.09 percentage points for “air_conditioner,” “car_horn,” and “street_music,” respectively, in terms of the intersection-based F-score. For the average score of classes, the proposed methods increase the intersection-based F-score by up to 3.37 percentage points compared with the conventional SED and other target-class-conditioned models.