Predicting Patient Length of Stay Using Artificial Intelligence to Assist Health Care Professionals in Resource Planning and Scheduling Decisions

Artificial Intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today’s healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research’s findings have practical and theoretical implications in AI and HSC management.

Designing a locating-routing three-echelon supply chain network under uncertainty

PurposeIn the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.Design/methodology/approachBy adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.FindingsAccording to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.Research limitations/implicationsSince this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.Practical implicationsThe suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.Originality/valueAccording to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

REA: Resource Exergy Analysis - A basic guideline for application focusing on energy systems evaluation

This publication provides a basic guideline to the application of Resource Exergy Analysis (REA) with a focus on energy systems evaluation. REA is a proven application of exergy analysis to the field of technology comparison.REA aims to help decision makers to obtain an indicator in addition to GHG emissions, that is grounded in science, namely Resource Consumption.Even if an energy system uses GHG-free energy increased Resource Consumption likely increases the need for fossil fuels and thus GHG emissions of the global economy. Resource Consumption can replace the less comprehensive Primary Energy Consumption as an indictor and reduce the risk of suboptimal decisions.Evaluating energy systems using REA is key to ensure that climate targets are reached in time.

The multi-dimensional space of the futures of work

Purpose The authors present nine dimensions to provide structure for the many Futures of Work (FoW). This is done to advance a more sociotechnical and nuanced approach to the FoW, which is too-often articulated as singular and unidimensional. Futurists emphasize they do not predict the future, but rather, build a number of possible futures – in plural – often in the form of scenarios constructed based on key dimensions. Such scenarios help decision-makers consider alternative actions by providing structured frames for careful analyses. It is useful that the dimensions be dichotomous. Here, the authors focus specifically on the futures of knowledge work.Design/methodology/approach Building from a sustained review of the FoW literature, from a variety of disciplines, this study derives the nine dimensions.Findings The nine FoW dimensions are: Locus of Place, Locus of Decision-making, Structure of Work, Technologies’ Roles, Work–Life, Worker Expectations, Leadership Model, Firm’s Value Creation and Labor Market Structure. Use of the dimensions is illustrated by constructing sample scenarios.Originality/value While FoW is multi-dimensional, most FoW writing has focused on one or two dimensions, often highlighting positive or negative possibilities. Empirical papers, by their nature, are focused on just one dimension that is supported by data. However, future-oriented policy reports tend are more often multi-faceted analyses and serve here as the model for what we present.

Designing a knowledge-based system (KBS) to study consumer purchase intention: the impact of digital influencers in Pakistan

PurposeThe purpose of this study is to present a complete framework that defines the link between choices and decision criteria based on existing research on digital influencers (DIs) connected to consumer purchase intentions. The primary goal of this article is to assess the effect of DIs on customer purchase intentions via the creation of an integrated knowledge-based system (KBS).Design/methodology/approachThe suggested KBS is based on the fuzzy analytic hierarchy process (AHP), which creates a link between DI elements and their overall effect on consumer purchase intentions.Findings With the help of a KBS, the performance of DIs may be evaluated. It demonstrates the link between choices connected to factors and decision criteria of various variables, demonstrating the beneficial effect of DIs in molding customer purchase intentions in the organic skincare industry.Practical implicationsThe proposed KBS would aid marketing managers and decision makers in assessing the effect of DIs on customer purchase intentions. This research would also give decision makers with extensive information on influencer marketing and crucial elements that have a significant effect on customer purchase intentions.Originality/valueThis is the first research to employ the fuzzy AHP methodology and KBS in relation to influencers' effect. No prior research has targeted the organic skincare industry to assess the effect of Internet influencers on consumer purchase intentions. Furthermore, the KBS offers a holistic and complete way to studying influencers' effect on cost per impression (CPI) by establishing a linkage between choices and decision criteria.

Solving Patient Allocation Problem during an Epidemic Dengue Fever Outbreak by Mathematical Modelling

Dengue fever is a mosquito-borne disease that has rapidly spread throughout the last few decades. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns. However, appropriate mechanisms of response are indispensable to face the consequent events when an outbreak takes place. This study applied single and multiple objective linear programming models to optimize the allocation of patients and additional resources during an epidemic dengue fever outbreak, minimizing the summation of the distance travelled by all patients. An empirical study was set in Ciudad del Este, Paraguay. Data provided by a privately run health insurance cooperative was used to verify the applicability of the models in this study. The results can be used by analysts and decision makers to solve patient allocation problems for providing essential medical care during an epidemic dengue fever outbreak.

Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Integrated sustainability policy assessment – an agent-based ecological-economic model

The paper proposes an agent-based evolutionary ecological-economic model that captures the link between the economy and the ecosystem in a more inclusive way than standard economic optimization models do. We argue that an evolutionary approach is required to understand the integrated dynamics of both systems, i.e. micro–macro feedbacks. In the paper, we illustrate that claim by analyzing the non-triviality of finding a sustainability policy mix as a use case for such a coupled system. The model has three characteristics distinguishing it from traditional environmental and resource economic models: (1) it implements a multi-dimensional link between the economic and the ecological system, considering side effects of production, and thus combines the analyses of environmental and resource economics; (2) following literature from biology, it uses a discrete time approach for the biological resource allowing for the whole range of stability regimes instead of artificially stabilizing the system, and (3) it links this resource system to an evolving, agent-based economy (on the basis of a Nelson-Winter model) with bounded rational decision makers instead of the standard optimization model. The policy case illustrates the relevance of the proposed integrated assessment as it delivers some surprising results on the effects of combined and consecutively introduced policies that would go unnoticed in standard models.

Data Analytics for COVID-19 Pandemic

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, the pandemic situation has begun to undergo positive changes with the joint efforts of various countries and world organizations. However, pressures such as the COVID-19 mutations and the sharp rise in confirmed cases have brought uncertainties to the prevention and control of the pandemic. The overall situation is still severe and complex. Based on the multi-dimensional spatial-temporal COVID-19 data collected by the open-source NetEase News (NEN) website and a real-time dynamic website, it is to explore the characteristics of the pandemic data, visualize the development trend, and analyze the spread of the pandemic in this paper. Moreover, it is to provide a rule basis for the prevention and control of the COVID-19 pandemic by constructing the decision tree model. From the results, some suggestions are provided for decision-makers.

Coproduce Conservation Technology With Conservation Decision Makers and Practitioners to Increase Its Impact

Addressing the biodiversity crisis will mean developing and adopting new resources and methods that effectively improve public conservation efforts. Technologies have a long track record of increasing the efficiency of carrying out time-consuming tasks or even making new feats possible, and if applied thoughtfully, can serve as a key means of strengthening conservation outcomes. Yet technology development sometimes proceeds without clear mechanisms for application and scaling, or key adopters like government agencies are not able to use the technologies. To overcome these discrepancies, we recommend the use of a coproduction model of conservation technology development that starts from detailed knowledge of conservation laws, regulations, policies, and their implementation; identifies choke points in those processes amenable to technological solutions; and then develops those solutions while integrating existing users and needs. To illustrate the model, we describe three tools recently developed to help improve the efficiency and effectiveness of implementing the U.S. Endangered Species Act. We also highlight several outstanding questions and challenges that the broad conservation technology and policy communities may help address.