Perceptions of Collaboration and Satisfaction with Collaboration Regarding Care Decision-Making in Intensive Care Units in Japan: A National Survey

Multi-professional collaboration is being promoted worldwide as a response to the need for sophisticated medical care and for catering to patients’ diverse needs. Patients in Intensive Care Units (ICUs) are seriously ill, and their families may be at risk depending on the patient’s situation. Considering these characteristics of patients and families, there is a strong need for multi-professional collaboration within ICUs. The purpose of this descriptive study was to examine recognition and other factors related to collaboration and satisfaction involving care decisions in Japanese ICUs. A mail survey about collaboration of activities and systems was sent to physicians, clinical engineers, and nurses working in ICUs in Japan, 387 consented to participate in this study. Results showed that satisfaction scores were generally high among the three aforementioned professions, but collaboration scores on deciding care for patients showed significant differences (p < 0.05). The total collaboration score was the highest among physicians (36.7 ± 6.7 points), followed by nurses (32.8 ± 7.4 points), and CEs (32.8 ± 7.4 points). The factors that commonly affected collaboration scores were the satisfaction score and the ability to collaborate with other professionals and set team medical care as a goal. Moreover, it is worth noting that the degree of difficulty in collaboration negatively affected this factor. On the other hand, other factors differed among the three professions, suggesting that the purpose and need for collaboration differ depending on the profession.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

Clinical decision making on the use of physical restraint in intensive care units

Physical restraint is a common nursing intervention in intensive care units and nurses often use it to ensure patients'safety and to prevent unexpected accidents.However,existing literature indicated that the use of physical restraint is a complex one because of inadequate rationales,the negative physical and emotional effects on patients,but the lack of perceived alternatives.This paper is aimed to interpret the clinical decision-making theories related to the use of physical restraint in intensive care units in order to facilitate our understanding on the use of physical restraint and to evaluate the quality of decisions made by nurses.By reviewing the literature,intuition and heuristics are the main decision-making strategies related to the use of physical restraint in intensive care units because the rapid and reflexive nature of intuition and heuristics allow nurses to have a rapid response to urgent and emergent cases.However,it is problematic if nurses simply count their decision-making on experience rather than incorporate research evidence into clinical practice because of inadequate evidence to support the use of physical restraint.Besides that,such a rapid response may lead nurses to make decisions without adequate assessment and thinking and therefore biases and errors may be generated.Therefore,despite the importance of intuition and heuristics in decision-making in acute settings on the use of physical restraint,it is recommended that nurses should incorporate research evidence with their experience to make decisions and adequate assessment before implementing physical restraint is also necessary.

Optimal Selection of Hybrid Renewable Energy System Using Multi-Criteria Decision-Making Algorithms

Several models of multi-criteria decision-making(MCDM)have identified the optimal alternative electrical energy sources to supply certain load in an isolated region in Al-Minya City,Egypt.The load demand consists of water pumping system with a water desalination unit.Various options containing three different power sources:only DG,PV-B system,and hybrid PV-DG-B,two different sizes of reverse osmosis(RO)units;RO-250 and RO-500,two strategies of energy management;load following(LF)and cycle charging(CC),and two sizes of DG;5 and 10 kW were taken into account.Eight attributes,including operating cost,renewable fraction,initial cost,the cost of energy,excess energy,unmet load,breakeven grid extension distance,and the amount of CO_(2),were used during the evaluation process.To estimate these parameters,HOMER®software was employed to perform both the simulation and optimization process.Four different weight estimation methods were considered;no priority of criteria,based on a pairwise comparisons matrix of the criteria,CRITIC-method,and entropy-based method.The main findings(output results)confirmed that the optimal option for the case study was hybrid PV-DG-B with the following specification:5 kW DG,RO-500,and load following control strategy.Under this condition,the annual operating cost and initial costs were$5546 and$161022,respectively,whereas the cost of energy was 0.077$/kWh.The excess energy and unmet loads were 40998 and 2371 kWh,respectively.The breakeven grid extension distance and the amount of CO_(2) were 3.31 km and 5171 kg per year,respectively.Compared with DG only,the amount of CO_(2) has been sharply reduced by 113939 kg per year.

Vigilance in Nurses Working in Intensive Care Units

Background and Aim: Vigilance is an essential element in intensive care nursing. This study was conducted to determine nursing vigilance in nurses working in the intensive care units of educational and medical centers in Ardabil, Iran. Methods: This was a cross-sectional descriptive-analytical study with 192 ICU nurses as the participants. The data were obtained through questionnaires regarding demographic characteristics and nursing vigilance. SPSS software version 24 was used for the statistical analysis. Results: The mean total vigilance score was 3.86 ± 0.23 of 5. The mean scores of the timely diagnosis of changes, pattern recognition, and clinical decision-making subscales were 4.07 ± 0.26, 04.04 ± 0.41, and 3.44 ± 0.25, respectively. No significant relationships were observed between the total or subscale vigilance scores and other demographic characteristics. Conclusion: We assessed ICU nurses’ vigilance behaviors and found that their mean vigilance score was higher than the expected average, indicating our participants had a high level of clinical vigilance. These results suggest a need for effective educational interventions to boost clinical decision-making skills in ICU nurses, especially younger nurses.

Development of a Decision Aid for Family Surrogate Decision Makers of Critically Ill Patients Requiring Renal Replacement Therapy in ICU:A User-Centered Design for Rapid Prototyping

Objectives Renal replacement therapy(RRT)is increasingly adopted for critically ill patients diagnosed with acute kidney injury,but the optimal time for initiation remains unclear and prognosis is uncertain,leading to medical complexity,ethical conflicts,and decision dilemmas in intensive care unit(ICU)settings.This study aimed to develop a decision aid(DA)for the family surrogate of critically ill patients to support their engagement in shared decision-making process with clinicians.Methods Development of DA employed a systematic process with user-centered design(UCD)principle,which included:(i)competitive analysis:searched,screened,and assessed the existing DAs to gather insights for design strategies,developmental techniques,and functionalities;(ii)user needs assessment:interviewed family surrogates in our hospital to explore target user group's decision-making experience and identify their unmet needs;(iii)evidence syntheses:integrate latest clinical evidence and pertinent information to inform the content development of DA.Results The competitive analysis included 16 relevant DAs,from which we derived valuable insights using existing resources.User decision needs were explored among a cohort of 15 family surrogates,revealing four thematic issues in decision-making,including stuck into dilemmas,sense of uncertainty,limited capacity,and delayed decision confirmation.A total of 27 articles were included for evidence syntheses.Relevant decision making knowledge on disease and treatment,as delineated in the literature sourced from decision support system or clinical guidelines,were formatted as the foundational knowledge base.Twenty-one items of evidence were extracted and integrated into the content panels of benefits and risks of RRT,possible outcomes,and reasons to choose.The DA was drafted into a web-based phototype using the elements of UCD.This platform could guide users in their preparation of decision-making through a sequential four-step process:identifying treatment options,weighing the benefits and risks,clarifying personal preferences and values,and formulating a schedule for formal shared decision-making with clinicians.Conclusions We developed a rapid prototype of DA tailored for family surrogate decision makers of critically ill patients in need of RRT in ICU setting.Future studies are needed to evaluate its usability,feasibility,and clinical effects of this intervention.

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.

Application of Transformed Two-Stage Network DEA to Strategic Design of Biofuel Supply Chain Network

This paper proposes an innovative procedure for designing efficient biomass-biofuel logistics networks(BBLNs).This procedure is based on the two-stage network data envelopment analysis(TSN-DEA)models that have been developed to provide specific process guidance for the managers to improve the efficiency of the decision-making unit(DMU)with the TSN process.The crucial issue of the TSN-DEA is that the overall efficiency score depends on the DMUs under evaluation.Thus,the rankings for the DMUs generated by the TSN-DEA model are inconsistent.As a result,the TSN-DEA-based ranking methods are limited.The TSN-DEA’s inconsistency frequently makes it difficult for decision-makers to select the top-rated DMUs.We develop the transformed TSN(T-TSN)DEA method by applying the multi-criteria DEA model to overcome this issue.The proposed method transforms the DMUs with any number of inputs,intermediate measures,and outputs in the TSN process,through the multi-objective programming model with a minimax objective approach,into the DMUs with two inputs and one output in the single-stage network(SSN)process.Then,the well-known DEA methods for the SSN,such as the cross-efficiency and super-efficiency DEA methods,can be applied to evaluate and rank the transformed DMUs more consistently.We exhibit the applicability of the proposed approach for the BBLN design problem.A case study of South Carolina in the USA demonstrates that the proposed method performs well in identifying efficient BBLN schemes more consistently than the traditional TSN-DEA.

Application of Integrated Multiple Criteria Data Envelopment Analysis to Humanitarian Logistics Network Design

This paper considers a humanitarian logistics network(HTLN)design problem,including the emergency relief facilities(ERFs)location-allocation decision for the efficient distribution of emergency supplies from the ERFs to the affected areas.A goal programming(GP)approach is applied to consider the multiple objectives simultaneously.Solving the GP model with a given weight assigned to each goal yields a single HTLN scheme,so there will be various schemes available by solving the GP with multiple values of the weights.For evaluating these schemes and identifying the most efficient one,we apply the data envelopment analysis(DEA)methods considering each scheme as a decision-making unit(DMU).Since the classical DEA(C-DEA)intrinsically aims to identify efficient DMUs and the efficient frontier,the use of C-DEA may not lead to a full ranking in many situations.There are several independent evaluation approaches to increasing discriminating power.Among them,this study integrates the multiple criteria DEA(MC-DEA)with the following three DEA methods,(i)stratification DEA(S^DEA),(ii)cross-efficiency DEA(CE-DEA),and(iii)super-efficiency DEA(SE-DEA),to make the most use of each method's strengths.Through a case study of designing the HTLN system for South Carolina,the procedure of implementing the integrated multiple criteria DEA(IMC-DEA)method is demonstrated.It is observed that the IMCDEA method performs well in terms of designing the HTLN system and would help the decision-makers consider more efficient options and make a final decision.

Efficiency measurement of fertilizer-manufacturing organizations usingFuzzy data envelopment analysis

The aim of the paper is to benchmark the performance of the Indian fertilizermanufacturing organizations based on the ranking of efficiencies using a fuzzy data envelopment analysis(FDEA).FDEA has been used to find the relative efficiency and ranking of the fertilizer-manufacturing organizations.The last few years’data have been converted into the fuzzy inputs and outputs as minimum,mean,and maximum values,respectively.The performance of the fertilizer manufacturing organizations is based on the output maximization model of DEA.The frontier organizations set the benchmark for the lagging organizations for further improvement in the performance.This method can also be used to incorporate the data of the several years for multiple inputs and outputs instead of consideration of data of only one year.The proposed approach in this study may help organizations to improve its efficiency to fulfill its goal.

Data Envelopment Analysis for Measuring Scientific Research Efficiency of Universities

In this paper, the DEA method-data envelopment analysis is used to evaluate the relative efficiency of the scientific research of universities and colleges. Three models: OCR, CCGSS and CCWH are chosen at the same time. Compared with the traditional comprehensive evaluating methods, the DEA method is more reasonable and accurate.