A framework for early-stage sustainability assessment of innovation projects enabled by weighted sum multi-criteria decision analysis in the presence of uncertainty.

A two-level hierarchical framework for early-stage sustainability assessment (FESSA) amongst a set of alternatives applicable from the earliest stages of process or product development is introduced, and its use in combination with an improved method weighted-sum method multi-criteria decision analysis (WSM-MCDA) in the presence of uncertainty is presented through application to a case study based upon a real-world decision scenario from speciality polymer manufacture. The approach taken addresses the challenge faced by those responsible for innovation management in the manufacturing process industries to make simultaneously timely and rational decisions early in the innovation cycle when knowledge gaps and uncertainty about the options tend to be at their highest. The Computed Uncertainty Range Evaluations (CURE) WSM-MCDA method provides better discrimination than the existing Multiple Attribute Range Evaluations (MARE) method without the computational burden of generating heuristic outcome distributions via Monte-Carlo simulation.

This paper introduces a framework that teams can use to think systematically about the wide range of criteria which go into deciding whether a proposed innovation enhances sustainability or not and shows how an improved method for multiple-criteria decision analysis can be used to put it into practice with an example drawn from the speciality chemicals industry. Innovation in the manufacturing process industries requires decisions to be made. In individual projects, scientists and technical managers must decide which technology, materials, and equipment to use. Equally, those responsible for directing a portfolio of projects must choose which projects to prioritise. In either case, early decision making is desirable to avoid sinking time and money into dead-end projects, and to identify what further work is needed for projects with a future. The earlier you decide however, the harder it can be to obtain firm evidence (e.g. conclusive experimental data, fully validated costings, or life cycle impacts) upon which to base your decision. The growing societal expectation that sustainability criteria are factored into such decisions merely adds to the challenges faced by the decision maker. Decisions must be made upon the evidence that is available combined with the informed judgement of those with knowledge of the system under consideration. This is best approached as a facilitated, team-based activity where assertions, assumptions and interpolations or extrapolations from the limited data can be tested and challenged. A sound decision-making process needs a suitable computational method for turning this complex qualitative and semi-quantitative assessment into a clear output indicator of potential success or failure for the options under consideration. The method described in this paper addresses this need but, just as importantly, the methodology ensures that the thought process behind whatever decision is indicated is clearly and transparently documented for future reference.

A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region.

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site.

Clinical Impact of an AI Decision Support System for Detection of Intracranial Hemorrhage in CT Scans.

This study aimed to evaluate the predictive value and clinical impact of a clinically implemented artificial neural network software model. The software detects intracranial hemorrhage (ICH) from head computed tomography (CT) scans and artificial intelligence (AI)-identified positive cases are then annotated in the work list for early radiologist evaluation. The index test was AI detection by the program Zebra Medical Vision-HealthICH+. Radiologist-confirmed ICH was the reference standard. The study compared whether time benefits from using the AI model led to faster escalation of patient care or surgery within the first 24 h. A total of 2,306 patients were evaluated by the software, and 288 AI-positive cases were included. The AI tool had a positive predictive value of 0.823. There was, however, no significant time reduction when comparing the patients who required escalation of care and those who did not. There was also no significant time reduction in those who required acute surgery compared with those who did not. Among the individual patients with reduced time delay, no cases with evident clinical benefit were identified. Although the clinically implemented AI-based decision support system showed adequate predictive value in identifying ICH, there was no significant clinical benefit for the patients in our setting. While AI-assisted detection of ICH shows great promise from a technical perspective, there remains a need to evaluate the clinical impact and perform external validation across different settings.

Sutton's Law: A Lesson in Decision-Analysis from the Past and Present.

In 1960, Dr. William Dock, visiting professor at Yale, discussed the case of a young girl with an unknown liver disease. Dock recommended biopsy, invoking bank robber Willie Sutton's words "that's where the money is." Drs. Petersdorf and Beeson, in attendance that day, included the following in their 1961 publication on fever of unexplained origin, "We are indebted to Dr. William Dock for the term Sutton's Law. It recommends proceeding immediately to the diagnostic test most likely to provide a diagnosis, and deplores the tendency to carry out a battery of 'routine' examinations in conventional sequence." Thereafter, Sutton's Law became an acclaimed aphorism advocating pursuit of tissue, because of its specificity for achieving diagnostic certainty. However, its popularity was fleeting, as formal medical decision-analysis (FMDA) became the standard. In the 1940s and 1950s, pioneers in the field laid the groundwork, and by the 1980s, clinician researchers had introduced the science into everyday clinical practice. The original version of Sutton's Law neglected the reality that FMDA is more than specificity, tissue, and absolute certainty. The newer version encourages clinicians to employ and prioritize their "routine" interview questions, physical examination assessments, and laboratory tests that provide clarity to differentiate between disease and no disease, to influence favorably the patient's management, and to discard those evaluations more likely to provide misleading results. Dock, Petersdorf, and Beeson may not have spoken the language of FMDA, but they were adept at applying its principles. Without them, and the unknown medical student who made the diagnosis of schistosomiasis, there would be no Sutton's Law. For many, it is an obsolete and apocryphal aphorism valuable solely for touting the importance of specificity in tissue diagnosis. For others, it has evolved, remaining relevant as an authentic lesson in decision-analysis, past and present.

Are Hazard Assessment Methods in the Assessment of Chemical Alternatives Suitable for REACH?

The assessment of chemical alternatives for hazardous substances is an important prerequisite for avoiding regrettable substitution, and several methods have been developed in the past to perform such a hazard assessment for chemical alternatives. We investigate here whether GreenScreen, Cradle to Cradle, multiple-criteria decision analysis (MCDA), the Pollution Prevention Options Analysis System, the U.S. EPA Safer Choice Standard and Criteria, and the GHS column model 2020 from IFA use similar criteria for the evaluation of substances as Article 57 of the European chemicals regulation, REACH, and how suitable these methods are for assessing per- and polyfluoroalkyl substances. MCDA and GreenScreen were analyzed in detail using two different data sets. The results of the assessments show that none of the investigated hazard assessment methods use the same criteria as described in Article 57 of REACH. It was also not possible to parametrize multi-attribute value theory (MAVT), a commonly used MCDA method, to align with Article 57 of REACH by using the relatively simple objective hierarchy that has been proposed in previous publications. There is therefore an urgent need for a modified/new method that can be used in the future to assess organic substances that are used within the European Economic Area.

An integrated environmental and economic assessment for the disposal of food waste from grocery retail stores towards resource recovery.

Food waste gives rise to many environmental problems. A large amount of food waste is produced by grocery retail stores. It is therefore important to apply efficient food waste treatment technologies with minimal environmental impact and investigate the optimal approach for food waste collection, transportation, and treatment. In the present study, a life cycle impact assessment (LCIA) was conducted to analyze different food waste disposal scenarios, including incineration, landfilling, composting, anaerobic digestion, and bioconversion. The impacts of the five scenarios on the environmental, economic, and social aspects were assessed. The results suggested that the landfilling scenario has the lowest net cost for the treatment of food waste, followed by the incineration scenario. The bioconversion treatment cost has the most significant positive effect on the net cost of the bioconversion scenario, and both the price and yield of compost have a significant negative effect on the net cost. The rankings of the five scenarios are the same under both weight determination methods, with the bioconversion scenario performing the best, followed by the composting scenario. The results of this study can help improve the disposal of food waste in grocery retail stores in the framework of sustainability and the circular economy.

Multiple-Criteria Decision Analysis for Assessments of Chemical Alternatives (MCDA-ACA).

A comprehensive assessment of chemical alternatives (ACA) is necessary to avoid regrettable substitution. In a preceding study, an analysis of six hazard assessment methods found that none of them are fully aligned with the hazard assessment criteria of Article 57 of the European REACH regulation, indicating a need for a method better reflecting hazard assessment schemes in European chemical regulations. This paper presents a multiple-criteria decision analysis (MCDA) method for the ACA that takes the criteria of Article 57 of REACH into account. Investigated and presented are objective hierarchies, the aggregation of objectives, the curvature of the value functions, weights, and the introduction of a classification threshold. The MCDA-ACA method allows for the aggregation of hazards in such a way that poor performance in one hazard cannot be compensated for by good performance in another hazard. The method parameters were developed and tested using two data sets with the aim to classify chemical alternatives into acceptable (nonregrettable) and unacceptable (regrettable) alternatives according to the regulations set in Europe. The flexibility of the general method was explored by adapting the method to align with two hazard assessment schemes, Article 57 of REACH and GreenScreen. The results show that MCDA-ACA is so flexible and transparent that it can easily be adapted to various hazard assessment schemes.

Development of an multicriteria decision analysis framework for rare disease reimbursement prioritization in Malaysia.

OBJECTIVES: Rare diseases (RD)-related policies have received significant attention due to the pressing medical requirements associated with these medical conditions and the substantial impact and treatments they may have on healthcare budgets. Nevertheless, policymakers frequently encounter difficulties in managing issues concerning resource allocation and prioritization within this population. Realizing the need to address such problems, this study was conducted to develop a framework based on the multicriteria decision analysis to improve RD reimbursement prioritization in Malaysia. METHODS: Primarily, a scoping review was performed to identify the methods and criteria used for the reimbursement of RD treatment, followed by strategic stakeholder engagement and a deliberative process on determining the best approach for the framework, including criteria identification, elicitation of weights, and a pilot assessment using the framework. RESULTS: The findings reflected the priorities and perspectives of the stakeholders, which identified eight key criteria and their associated weights, namely effectiveness (19.6 percent), disease severity (15.6 percent), safety (14.2 percent), access to treatment (12.6 percent), economic consideration (12.2 percent), type of therapeutic treatment (11.5 percent), availability of alternatives (8.3 percent), and population group (6 percent). CONCLUSIONS: In summary, the developed framework was well-accepted by the Rare Disease Committee, which will be applied as part of the committee deliberation for transparent and equitable decision making on fund allocation and reimbursement of orphan and RD treatment in Malaysia.

Embedding Water-Energy-Food nexus and circularity assessment for organization benchmarking: A case study for dairy farms.

The Water-Energy-Food (WEF) nexus approach is increasingly being used for supporting a transition to sustainable development, with initiatives involving the concept of circular economy (CE). In the agricultural sector in particular, assessing this nexus is crucial to ensure food security, control the consumption of key resources such as water and energy, as well as measure atmospheric emissions linked to climate change. This manuscript aims to propose a novel approach by coupling the WEF nexus with a circularity indicator, seeking to capture in a single index (the WEF+CEi) both performances in a sample of companies. The novel approach is applied to 30 dairy farms located in Galicia (NW Spain) to benchmark them in a holistic manner. To do this, the WEF nexus of each farm was represented through the following indicators: carbon footprint, water footprint, energy footprint, and food productivity. In addition, the percentage of circularity for each farm, and for the agro-industrial cooperative was measured thanks to the application of a circularity tool in percentage terms. Finally, the WEF+CEi indicator was obtained using the multicriteria mathematical tool of Data Envelopment Analysis (DEA). The results show that without considering the agro-industrial cooperative, the system is 51 % circular. On the other hand, considering the farms and the cooperative, the system goes up to 80 % of circularity. Finally, the proposed approach can support decision-making and provide insights for producers and stakeholders in the area.

What Constitutes High Risk for Venous Thromboembolism? Comparing Approaches to Determining an Appropriate Threshold.

Background: Guidelines recommend pharmacological venous thromboembolism (VTE) prophylaxis only for high-risk patients, but the probability of VTE considered "high-risk" is not specified. Our objective was to define an appropriate probability threshold (or range) for VTE risk stratification and corresponding prophylaxis in medical inpatients. Methods: Patients were adults admitted to any of 10 Cleveland Clinic Health System hospitals between December 2020 and August 2021 (N = 41,036). Hospital medicine physicians and internal medicine residents from included hospitals were surveyed between June and November 2023 (N = 214). We compared five approaches to determining a threshold: decision analysis, maximizing the sensitivity and specificity of a logistic regression model, deriving a probability from a point-based model, surveying physicians' understanding of VTE risk, and deriving a probability from physician behavior. For each approach, we determined the probability threshold above which a patient would be considered high-risk for VTE. We applied each threshold to the Cleveland Clinic VTE risk assessment model (CCM) and calculated the percentage of the 41,036 patients in our cohort who would be considered eligible for prophylaxis due to their high-risk status. We compared these hypothetical prophylaxis rates with physicians' observed prophylaxis rates. Results: The different approaches yielded thresholds ranging from 0.3% to 5.4%, corresponding inversely with hypothetical prophylaxis rates of 0.2% to 75%. Multiple thresholds clustered between 0.52% to 0.55%, suggesting an average hypothetical prophylaxis rate of approximately 30%, whereas physicians' observed prophylaxis rates ranged from 48% to 76%. Conclusions: Multiple approaches to determining a probability threshold for VTE prophylaxis converged to suggest an optimal threshold of approximately 0.5%. Other approaches yielded extreme thresholds that are unrealistic for clinical practice. Physicians prescribed prophylaxis much more frequently than the suggested rate of 30%, indicating opportunity to reduce unnecessary prophylaxis. To aid in these efforts, guidelines should explicitly quantify high-risk.

Comparing flow and pulmonary artery growth post-patent ductus arteriosus stenting in patients with ductal-dependent pulmonary flow using 4D magnetic resonance imaging.

Aims: The 4D magnetic resonance imaging (4D-flow MRI) provides a qualitative and quantitative assessment of cardiovascular structures and processes. 4D-flow MRI was used to study pulmonary flow in post-patent ductus arteriosus (PDA) stent insertion in duct-dependent pulmonary flow neonates at baseline (PDA stent insertion) and after 6 months, and also, to evaluate the effect of flow dynamics on the growth of pulmonary arteries (PAs). Methods and results: This prospective observational study included neonates with ductus arteriosus-dependent pulmonary circulation who underwent ductal stenting between June 2021 and November 2022. Cardiac 4D-flow MRI and magnetic resonance angiography were conducted in two phases; after the deployment of the PDA stent during the neonatal period and after 6 months from stent deployment. Eight neonates were recruited, but only five completed both scans. A total of 10 PAs were evaluated during each phase. The median left PA (LPA) and right PA (RPA) diameters and indexed flow for LPA and RPA were evaluated. The growth rate of LPA was observed to be lower than that of RPA (percentage diameter increase: 74 vs. 153%). LPA Z-score was lower than RPA. Indexed flow in both LPA and RPA showed a reduction in the 6-month scan, which was consistent with reduced stent patency. Conclusion: 4D-flow cardiac MRI showed different growth rates and reduced flow between LPA and RPA post-PDA stent. These insights can aid in future management decisions.

GIS-based multi-criteria decision analysis model for utility water demand: The case of Lodwar Municipality, Turkana County, Kenya.

Water scarcity is a global issue resulting from rapid urbanization, increasing population growth, industrial development and expansion of human activities over time and space. Water shortage affects every continent and is listed as one of the largest global risks hence the need for proper management of water resources. Municipalities and cities worldwide are struggling to maintain a steady supply of water to meet the increasing water demand. The study used Geographic Information System (GIS) techniques and Multi-Criteria Decision Analysis (MCDA) to develop a decision support model that can be applied to improve the utility water demand management for the Lodwar Municipality in Turkana Kenya. The data comprised remotely sensed data, population density, spatial plans, utility infrastructure maps and metered water connections data. The AHP pairwise comparison matrix was applied to assign weights for the 8 criteria influencing water demand in the area. The population density, proximity to water network facilities and land use criteria were equivalent to 30 %, 25 %, and 23 % respectively whereas 22 % of other criteria were dependent on each other. The analysis of satellite images showed the expansion of built-up areas and emerging human activities in regions towards the South and Western of Lodwar Town. The resulting model outcome identified the potential demand priority sites within the region of which some are underserved. The model efficiency was assessed through the application of statistical indicators as well as graphical and map presentations. Consequently, the addition of more input variables affecting demand is likely to improve the results over changing aspects within the zones. Municipality water utility managers and decision-makers can therefore employ the model information to highlight suitable areas for network expansion as well as infrastructure management planning within the municipality. This method offers an alternative hybrid technique for mapping potential utility water demand in rural municipalities with inadequate data.

Method "Monte Carlo" in healthcare.

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.

Reducing mental health emergency visits: population-level strategies from participatory modelling.

BACKGROUND: Emergency departments (EDs) are often the front door for urgent mental health care, especially when demand exceeds capacity. Long waits in EDs exert strain on hospital resources and worsen distress for individuals experiencing a mental health crisis. We used as a test case the Australian Capital Territory (ACT), with a population surge of over 27% across 2011-2021 and a lagging increase in mental health care capacity, to evaluate population-based approaches to reduce mental health-related ED presentations. METHODS: We developed a system dynamics model for the ACT region using a participatory approach involving local stakeholders, including health planners, health providers and young people with lived experience of mental health disorders. Outcomes were projected over 2023-2032 for youth (aged 15-24) and for the general population. RESULTS: Improving the overall mental health care system through strategies such as doubling the annual capacity growth rate of mental health services or leveraging digital technologies for triage and care coordination is projected to decrease youth mental health-related ED visits by 4.3% and 4.8% respectively. Implementation of mobile crisis response teams (consisting of a mental health nurse accompanying police or ambulance officers) is projected to reduce youth mental health-related ED visits by 10.2% by de-escalating some emergency situations and directly transferring selected individuals to community mental health centres. Other effective interventions include limiting re-presentations to ED by screening for suicide risk and following up with calls post-discharge (6.4% reduction), and limiting presentations of frequent users of ED by providing psychosocial education to families of people with schizophrenia (5.1% reduction). Finally, combining these five approaches is projected to reduce youth mental health-related ED presentations by 26.6% by the end of 2032. CONCLUSIONS: Policies to decrease youth mental health-related ED presentations should not be limited to increasing mental health care capacity, but also include structural reforms.

Cost Analysis of Early vs Delayed Outpatient Repair of Inguinal Hernias in Premature Infants.

INTRODUCTION: Premature infants treated for inguinal hernias after hospital discharge require overnight post-operative observation for apnea monitoring until 50-60 weeks adjusted gestational age (AGA). This study aimed to compare costs associated with early (at time of diagnosis) versus delayed (at AGA not requiring overnight observation) repair of inguinal hernia in premature infants. METHODS: Costs were estimated using the average hospital charges at a single institution for three scenarios: 1) delayed repair 2) early repair requiring overnight observation, and 3) incarcerated inguinal hernia reduced but requiring delayed repair at 48 h. A decision analysis model was used to estimate the cost for premature infants undergoing delayed repair of inguinal hernia while considering the risk of incarceration and associated costs. The base model used 50 weeks AGA for delayed repair and an incarceration rate of 0.5%/week. Sensitivity analyses varied incarceration rate from 0.1 to 4%/week and delayed repair to 55 and 60 weeks AGA. RESULTS: In the base model, delayed repair incurred lower estimated costs than early repair at all time points of diagnosis. In sensitivity analyses, estimated cost for delayed repair only rose above the estimated cost for early repair when estimated incarceration risk reached 3%/week with repair at 60 weeks AGA (if repair before 38 weeks AGA) or 4%/week with repair at 55 weeks AGA (if repair before 39 weeks AGA). CONCLUSIONS: Using solely cost as a deciding factor, repair of premature inguinal hernias diagnosed as an outpatient should be delayed until overnight observation is no longer necessary. TYPE OF STUDY: Decision Analysis model. LEVEL OF EVIDENCE: III.

Public deliberation on health gain measures.

Researchers and decision-makers use health gain measures to assess the value of health interventions. However, our current understanding of how these measures are understandable and accessible to the community is limited. This study examined a diverse group of stakeholders' attitudes and preferences for 9 commonly used health gain measures. We recruited 20 stakeholders, including patients, caregivers, pharmacists, allied health professionals, and citizens. We conducted 2 in-person deliberative meetings in which participants learned, discussed, deliberated on, and ranked 9 health gain measures. The final ranking conducted after unified deliberation showed the quality-adjusted life year (QALY) as the top-ranked measure, followed by the clinical benefit rating method used by the U.S. Preventive Services Task Force, and multicriteria decision analysis (MCDA). We identified 3 themes during deliberations: the importance of using patient values in population-based health gain measures, examining complementary measures together, and choosing measures that are intuitive and easy to understand. Future policymaking should consider incorporating the QALY, clinical benefit rating, and MCDA into prioritization decisions.

Value contribution of etranacogene dezaparvovec gene therapy in moderately severe and severe haemophilia B through multi-criteria decision analysis.

INTRODUCTION: The value of gene therapies for haemophilia needs to be assessed holistically. AIM: To determine the value of etranacogene dezaparvovec (ED) compared to current extended half-life (EHL) recombinant factors (rFIX), using multi-criteria decision analysis (MCDA). METHOD: MCDA EVIDEM methodology adapted to orphan drugs was used, with nine quantitative criteria and four contextual criteria. The MCDA framework was rated by 28 multidisciplinary experts. Descriptive statistics were performed for quantitative and qualitative criteria. RESULTS: Haemophilia B (HB) was considered a severe disease (mean ± SD: 4.3 ± 0.7) with some unmet needs (mean ± SD 3.3 ± 0.9). Experts found ED more effective (mean ± SD 2.0 ± 2.3) and provide better quality of life (QoL) (mean ± SD: 1.8 ± 1.5) than the comparative HB treatments but with safety uncertainties (mean ± SD -1.2 ± 1.8). ED could lead to medical cost and non-medical cost savings over time (mean ± SD: 1.6 ± 2.0 and 2.0 ± 1.5, respectively). The quality of the evidence was high (mean ± SD: 3.9 ± 0.9). ED was considered aligned with the priorities of the National Health System (NHS) and the specific interests of patients. ED's value contribution was 0.45 (+1 = highest value). CONCLUSIONS: ED brings added value in the treatment of moderately severe and severe HB (sHB) compared to current EHL rFIX, addressing the severity of the disease and increasing efficacy and patients' QoL especially related to the single dose and low bleeding rate. Concerns about long-term safety need to be addressed.

A participatory multi-criteria decision analysis framework reveals transition potential towards non-grid wastewater management.

Many public environmental decisions are wicked problems due to high complexity and uncertainty. We test a participatory value-based framework based on multi-criteria decision analysis (MCDA) to tackle such problems. Our framework addresses two important gaps identified in reviews of MCDA applications to environmental problems: including stakeholders and treating uncertainty. We applied our framework in two complex real-world cases concerning a paradigm shift in the wastewater sector; the transition from centralized wastewater systems to decentralized non-grid systems. Non-grid systems may solve some problems of centralized systems by reducing costs, increasing flexibility, and addressing growing demands on environmental issues, especially in rural areas. But non-grid systems have rarely been implemented in OECD countries, because it is unclear whether a transition is recommendable, and whether stakeholders would accept this shift. This problem allows addressing several fundamental research questions. As theoretical contribution, we found that stakeholder participation in MCDA is necessary, because different preferences of stakeholders can lead to different best-performing options in the assessments. Compared to the typical integrated assessment (IA) approach that excludes stakeholders' preferences, the MCDA process led to clearer outcomes. Results indicate that including the uncertainty of predicted consequences of options with Monte Carlo simulation helped discriminate between options and identify best-performing options. Challenging the uncertainty of elicited stakeholder preferences with sensitivity analyses, we found that best-performing options were especially sensitive to the MCDA aggregation model. Despite the high uncertainty, it was possible to suggest robust consensus options that would perform reasonably well for all stakeholders. As practical contribution, results indicated that a transition from the centralized to decentralized non-grid systems seems feasible. Most stakeholders assigned highest weights to environmental protection objectives in decision-making workshops. These stakeholder preferences implemented in MCDA led to a generally better assessment of innovative non-grid systems, especially when including urine source separation. Stakeholders perceived the MCDA process as beneficial and found results plausible. We conclude that the proposed participatory value-based framework is rigorous, but still feasible in practice. The framework is certainly transferable to any context and is open to testing and refinement in various applications to wicked decision problems.

Improving the academic resilience of hospital nursing interns through a hybrid multi-criteria decision analysis model.

Purpose: To identify the main variables affecting the academic adaptability of hospital nursing interns and key areas for improvement in preparing for future unpredictable epidemics. Methods: The importance of academic resilience-related variables for all nursing interns was analyzed using the random forest method, and key variables were further identified. An importance-performance analysis was used to identify the key improvement gaps regarding the academic resilience of nursing interns in the case hospital. Results: The random forest showed that five items related to cooperation, motivation, confidence, communication, and difficulty with coping were the main variables impacting the academic resilience of nursing interns. Moreover, the importance-performance analysis revealed that three items regarding options examination, communication, and confidence were the key improvement areas for participating nursing interns in the case hospital. Conclusions: For the prevention and control of future unpredictable pandemics, hospital nursing departments can strengthen the link between interns, nurses, and physicians and promote their cooperation and communication during clinical practice. At the same time, an application can be created considering the results of this study and combined with machine learning methods for more in-depth research. These will improve the academic resilience of nursing interns during the routine management of pandemics within hospitals.