An Overview of King Hussein Cancer Center Institutional Review Board Over 12 Years (2009–2020), Successes and Challenges, Including Those Imposed by the COVID-19 Pandemic

The problem: Running an efficient institutional review board (IRB) can be challenging. The research subjects: To ensure an efficient committee, our IRB adopted several operational metrics. Methods: Analysis of retrospective data from the IRB records, database, and annual reports over 12 years. Results: The IRB roster comprises 11 members. The average medical to nonmedical member ratio is 5:6, and the male to female ratio is 4:7, which has not been consistent over the years. One thousand three hundred and twenty-four proposals were reviewed including 1077 exempt (81.3%), 126 expedited (9.5%), and 121 full board (9.2%) with a median turnaround time to approval of 4.0, 35.0, and 68.0 days, respectively. Training of the IRB members was conducted to enhance their knowledge and skills. IRB at King Hussein Cancer Center has managed to stay abreast and efficient during the COVID-19 pandemic, by working remotely. Conclusion: Running an efficient IRB mandates implementing a number of operational metrics.

A NEW LOGARITHM METHODOLOGY OF ADDITIVE WEIGHTS (LMAW) FOR MULTI-CRITERIA DECISION-MAKING: APPLICATION IN LOGISTICS

Logistics management has been playing a significant role in ensuring competitive growth of industries and nations. This study proposes a new Multi-Criteria Decision-making (MCDM) framework for evaluating operational efficiency of logistics service provider (LSP). We present a case study of comparative analysis of six leading LSPs in India using our proposed framework. We consider three operational metrics such as annual overhead expense (OE), annual fuel consumption (FC) and cost of delay (CoD, two qualitative indicators such as innovativeness (IN) which basically indicates process innovation and average customer rating (CR)and one outcome variable such as turnover (TO) as the criteria for comparative analysis. The result shows that the final ranking is a combined effect of all criteria. However, it is evident that IN largely influences the ranking. We carry out a comparative analysis of the results obtained from our proposed method with that derived by using existing established frameworks. We find that our method provides consistent results; it is more stable and does not suffer from rank reversal problem.

CONTRIBUTION OF STRATEGIC INFORMATION DISTRIBUTION TO THE DECISION MAKING PROCESSES - AN EMPIRICAL

As an organizational asset, data is one of the great sources of competitive advantage of organizations, so that its publication and storageg, aligned with the organizations' strategic and operational metrics, has become a matter of great discussion and concern among decision makers. This study aims to carry out a study about the contributions of the strategic distribution of information to decision-making processes in organizations. The methodology used, of an exploratory qualitative nature, applied the study of multiple cases, through the collection and analysis of data from semi-structured interviews with several employees of organizations that use tools for collecting, processing and disseminating information. The research shows how the information and technologies applied to its treatment collaborate with decision-making processe.

Using Monte Carlo simulations to translate military and law enforcement training results to operational metrics

There are numerous challenges comparing research initiatives due to methodological differences and scenario-specific problems. Military and law enforcement issues present an extreme variant of this challenge. Specifically, assessment and training scenarios strive for realism, but operators cannot engage one another with live rounds or induce the full spectrum of environmental stressors for obvious safety reasons. Instead, particular factors are evaluated in a given scenario via experimental statistics despite the inherent difficulty in communicating inferential statistics to the intended audience of military and law enforcement professionals. The current investigation explores how Monte Carlo simulations can use probabilistic distribution sampling to convert statistical inferences into concrete operational outcomes. Using this type of distribution sampling, statistical inferences can be translated into operational metrics such as the probability of winning a gunfight. Describing these statistical values and effect sizes in terms of survival provides a more appreciable operational metric that military and law enforcement personnel can use when evaluating the advantages of various training platforms or equipment. Several approaches are examined that each accomplish this general goal, including circumstances outside of marksmanship and lethal force decision-making.

A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network

Gear reliability assessment of vehicle transmission has been a challenging issue of determining vehicle safety in the transmission industry due to a significant amount of classification errors with high-coupling gear parameters and insufficient high-density data. In terms of the preprocessing of gear reliability assessment, this paper presents a representation generation approach based on generative adversarial networks (GAN) to advance the performance of reliability evaluation as a classification problem. First, with no need for complex modeling and massive calculations, a conditional generative adversarial net (CGAN) based model is established to generate gear representations through discovering inherent mapping between features with gear parameters and gear reliability. Instead of producing intact samples like other GAN techniques, the CGAN based model is designed to learn features of gear data. In this model, to raise the diversity of produced features, a mini-batch strategy of randomly sampling from the combination of raw and generated representations is used in the discriminator, instead of using all of the data features. Second, in order to overcome the unlabeled ability of CGAN, a Wasserstein labeling (WL) scheme is proposed to tag the created representations from our model for classification. Lastly, original and produced representations are fused to train classifiers. Experiments on real-world gear data from the industry indicate that the proposed approach outperforms other techniques on operational metrics.

Predictors of Delayed Recognition of Critical Illness in Emergency Department Patients and Its Effect on Morbidity and Mortality

Purpose: Timely recognition of critical illness is associated with improved outcomes, but is dependent on accurate triage, which is affected by system factors such as workload and staffing. We sought to first study the effect of delayed recognition on patient outcomes after controlling for system factors and then to identify potential predictors of delayed recognition. Methods: We conducted a retrospective cohort study of Emergency Department (ED) patients admitted to the Intensive Care Unit (ICU) directly from the ED or within 48 hours of ED departure. Cohort characteristics were obtained through electronic and standardized chart abstraction. Operational metrics to estimate ED workload and volume using census data were matched to patients’ ED stays. Delayed recognition of critical illness was defined as an absence of an ICU consult in the ED or declination of ICU admission by the ICU team. We employed entropy-balanced multivariate models to examine the association between delayed recognition and development of persistent organ dysfunction and/or death by hospitalization day 28 (POD+D), and multivariable regression modeling to identify factors associated with delayed recognition. Results: Increased POD+D was seen for those with delayed recognition (OR 1.82, 95% CI 1.13-2.92). When the delayed recognition was by the ICU team, the patient was 2.61 times more likely to experience POD+D compared to those for whom an ICU consult was requested and were accepted for admission. Lower initial severity of illness score (OR 0.26, 95% CI 0.12-0.53) was predictive of delayed recognition. The odds for delayed recognition decreased when ED workload is higher (OR 0.45, 95% CI 0.23-0.89) compared to times with lower ED workload. Conclusions: Increased POD+D is associated with delayed recognition. Patient and system factors such as severity of illness and ED workload influence the odds of delayed recognition of critical illness and need further exploration.