Search

Practitioners aim to improve healthcare systems and clinical care through a variety of activities as part of a learning healthcare system. Yet the distinction between projects requiring Research Ethics Board (REB) approval or not is becoming increasingly blurred, making it difficult for researchers and others to classify projects and then navigate the required compliance pathway appropriately. To address this challenge, the Provincial Health Services Authority (PHSA) of British Columbia (BC) created a decision tool called the "PHSA Project Sorter Tool" to serve its diverse community while also meeting the unique needs of the BC regulatory and policy environment. The goal of the tool was to standardize and clarify organizational project review and ensure project leads were referred to the appropriate review body or service provider within the PHSA in the most efficient manner possible. In this paper, we describe the ethics needs assessment that was conducted to inform the tool and the results of our ongoing evaluation of the tool since it was launched in January, 2020. Our project shows that this simple tool can reduce burdens on staff and provide clarity to users by standardizing processes and terms and directing users to appropriate internal resources. [ABSTRACT FROM AUTHOR]

This work introduces ALMERIA, a decision-support tool for drug discovery. It estimates compound similarities and predicts activity, considering conformation variability. The methodology spans from data preparation to model selection and optimization. Implemented using scalable software, it handles large data volumes swiftly. Experiments were conducted on a distributed computer cluster using the DUD-E database. Models were evaluated on different data partitions to assess generalization ability with new compounds. The tool demonstrates excellent performance in molecular activity prediction (ROC AUC: 0.99, 0.96, 0.87), indicating good generalization properties of the chosen data representation and modelling. Molecular conformation sensitivity is also evaluated. [ABSTRACT FROM AUTHOR]

Companies are increasingly focusing on sustainable business practices. Internal and external stakeholders' expectations manifest in legal requirements, national and international standards, and market and customer expectations, among other things, must be considered. In addition to profit maximization, which is the usual target for corporate management, management must consider environmental sustainability aspects such as resource efficiency, greenhouse gas intensity, and a company's emissions behavior. In addition, social aspects related to the company's employees, the immediate urban environment, the situation in the supply chain, and effects on the market environment must increasingly be considered. Specifically, companies are faced with the challenge of dealing with conflicting objectives regarding the various aspects of sustainability and, if necessary, weighing them up against each other. These trade-offs must be made against the company's socio-economic and ecological environment, corporate strategy, and sustainability goals. This paper provides an overview of current approaches and research gaps on this topic through a literature review. It highlights the lack of methods and frameworks to specifically deal with trade-offs and conflicts between goals. [ABSTRACT FROM AUTHOR]

For students with severe or complex challenging behavior, incorporating hypothesis testing as a component of functional behavior assessment (FBA) is often warranted. Several hypothesis testing strategies (i.e., functional analysis, antecedent analysis, concurrent operant analysis) can confirm whether and how features of a student's environment impact their behavior to then inform effective intervention. Yet practitioners have limited guidance on how to select and individualize best-fit strategies for a given student and context. We developed a decision tool for behavior specialists and classroom teachers to collaboratively plan and implement individualized hypothesis testing strategies for students whose initial FBA was inconclusive. We piloted this tool with 12 practitioner teams and students, aiming to (a) identify which assessment strategies were indicated based on practitioner responses; (b) evaluate whether indicated assessments produced conclusive results; and (c) explore practitioner perceptions of the individualized assessment process. The most commonly indicated hypothesis testing strategy was functional analysis. Across teams, one or more hypothesis was successfully confirmed on the first or second assessment iteration. The assessment process was perceived positively by practitioners. Yet they reported feeling ill-equipped to complete the process independently, highlighting important next steps for training and technical assistance work. [ABSTRACT FROM AUTHOR]

Chronic limb-threatening ischemia (CLTI) causes significant morbidity with profound negative effects on health-related quality of life. As the prevalence of peripheral artery disease and diabetes continue to rise in our aging population, the public health impact of CLTI has escalated. Patient-reported outcome measures (PROMs) have become common and important measures for clinical evaluation in both clinical care and research. PROMs are important for the measurement of clinical effectiveness and cost effectiveness and for shared decision-making on treatment options. However, the PROMs used to describe the experience of patients with CLTI are heterogeneous, incomplete, and lack specific applicability to the underlying disease processes and diverse populations. For example, certain PROMs exist for patients with extremity wounds, and other PROMs exist for patients with pain, and still others exist for patients with vascular disease. Despite this multiplicity of tools, no single PROM encompasses all of the components necessary to describe the experiences of patients with CLTI. This significant unmet need is evident from both published reports and contemporary large-scale clinical trials in the field. In this systematic review, we review the current use of PROMs for patients with CLTI in clinical practice and in research trials and highlight the gaps that need to be addressed to develop a unifying PROM instrument for CLTI.

Abstract Background Demonstrating safety and efficacy of new medical treatments requires clinical trials but clinical trials are costly and may not provide value proportionate to their costs. As most health systems have limited resources, it is therefore important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. Methods A search of the academic and grey literature and stakeholder consultation was undertaken to identify a set of criteria to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included criteria and to estimate the partial-value function for each underlying metric. The set of criteria and their respective weights were applied to the results of six different clinical trials to calculate their value. Results Seven criteria were identified: ‘unmet need’, ‘size of target population’, ‘eligible participants can access the trial’, ‘patient outcomes’, ‘total trial cost’, ‘academic impact’ and ‘use of trial results’. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an ‘Unmet Need’ was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved ‘Patient Outcomes’ with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. Conclusion We confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway.

Background: Demonstrating safety and efficacy of new medical treatments requires clinical trials but clinical trials are costly and may not provide value proportionate to their costs. As most health systems have limited resources, it is therefore important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. Methods: A search of the academic and grey literature and stakeholder consultation was undertaken to identify a set of criteria to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included criteria and to estimate the partial-value function for each underlying metric. The set of criteria and their respective weights were applied to the results of six different clinical trials to calculate their value. Results: Seven criteria were identified: 'unmet need', 'size of target population', 'eligible participants can access the trial', 'patient outcomes', 'total trial cost', 'academic impact' and 'use of trial results'. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an 'Unmet Need' was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved 'Patient Outcomes' with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. Conclusion: We confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway. [ABSTRACT FROM AUTHOR]

Botanic gardens play an increasingly important role in the conservation of global biodiversity. However, although botanical gardens periodically report the results of introducing certain species of native flora, they rarely attempt to summarize existing knowledge to make general recommendations regarding ex situ collections. The aim of this study was to analyze the many years of experience of the Tashkent Botanical Garden in creating and maintaining living collections of threatened species of Uzbekistan (the majority of which are endemic to the country or Central Asia) in order to identify species whose cultivation ex situ is advisable, and whose cultivation will not result in meaningful conservation. Careful analysis of the species introduction history revealed that a simple dichotomy of the introduction results (success/failure) appears to be an oversimplification. In terms of the cultivation success, the introduced plant species can rather be classified into three categories: success, failure, and dubious success. For many species whose introduction was earlier considered successful, the introduction success is questionable and further efforts to conserve these species ex situ should be abandoned. A decision tree and classification of threatened perennials for possible ex situ introduction are proposed and the species in TBG collections are tabulated according to the latter. Species considered unsuitable for ex situ conservation are recommended for quasi in situ conservation. Both approaches, ex situ and quasi in situ, should be intensively used as a part of an integral conservation strategy for preserving plant biodiversity.

This paper addresses the optimal allocation of water among competing stakeholders during a finite planning horizon. We focus on those water systems where there are two levels of decision making organized according to a hierarchical framework. At the upper level, a central authority allocates water to demand points having regard to environmental and sustainability issues as well as balancing water users' supply/demand. At the lower level of the hierarchy, demand point managers allocate water to users prioritizing economic strategies. On the other hand, when it comes to allocating limited resources that affect public welfare, the authority in charge can also use different political instruments such as fees to influence the decisions made at those levels of decision making that are not directly within its competence. We propose a multiobjective multifollower bilevel optimization problem that aims to fulfill the central authority goals while including the reaction of the demand point managers in terms of optimization problems as constraints. Using the well‐known Karush–Kuhn–Tucker approach, we transform the bilevel model into an equivalent multiobjective mixed‐integer single‐level model for which we provide tight big‐M values. For the purpose of showing the versatility of the model, extensive computational experiments on a set of instances have been carried out. The results show that the optimization problem can be solved to optimality in small computing times using off‐the‐shelf mixed‐integer solvers even for complex water systems and long planning periods. In addition, they illustrate the effect of imposing fees on the achievement of the central authority's objectives. [ABSTRACT FROM AUTHOR]

Botanic gardens play an increasingly important role in the conservation of global biodiversity. However, although botanical gardens periodically report the results of introducing certain species of native flora, they rarely attempt to summarize existing knowledge to make general recommendations regarding ex situ collections. The aim of this study was to analyze the many years of experience of the Tashkent Botanical Garden in creating and maintaining living collections of threatened species of Uzbekistan (the majority of which are endemic to the country or Central Asia) in order to identify species whose cultivation ex situ is advisable, and whose cultivation will not result in meaningful conservation. Careful analysis of the species introduction history revealed that a simple dichotomy of the introduction results (success/failure) appears to be an oversimplification. In terms of the cultivation success, the introduced plant species can rather be classified into three categories: success, failure, and dubious success. For many species whose introduction was earlier considered successful, the introduction success is questionable and further efforts to conserve these species ex situ should be abandoned. A decision tree and classification of threatened perennials for possible ex situ introduction are proposed and the species in TBG collections are tabulated according to the latter. Species considered unsuitable for ex situ conservation are recommended for quasi in situ conservation. Both approaches, ex situ and quasi in situ, should be intensively used as a part of an integral conservation strategy for preserving plant biodiversity. [ABSTRACT FROM AUTHOR]

Ex situ ('off‐site') management refers to keeping species in artificial conditions away from their natural habitat and includes captive breeding facilities, botanical gardens and seed banks. There is scope for ex situ programmes to be more commonly used for supplementing or establishing wild populations. However, undertaking ex situ management comes with risks, costs and uncertainties, which must be assessed in the context of available in situ ('on‐site') management options. The PACES (Planning and Assessment for Conservation through Ex situ management) tool tailors the principles of structured decision‐making to the specific problem of assessing and comparing ex situ and in situ management options. We applied the PACES tool to the mahogany glider (Petaurus gracilis), a threatened arboreal marsupial endemic to north Queensland, Australia. Through an expert elicitation process, we predicted the likely benefits of an ex situ and two in situ management options, as compared to a baseline 'do‐nothing' scenario. The 'in situ plus' alternative (where extra resources are dedicated to in situ management) was predicted to result in the largest population increase according to the participants' best estimates. However, this benefit came at a much larger cost than the ex situ alternative, and without the benefit of an ex situ insurance population. The PACES tool assessment allowed the Mahogany Glider Recovery Team to document and plan the financial costs, risks and benefits of potential future management options for the mahogany glider, laying a transparent basis for future assessment and decision‐making. [ABSTRACT FROM AUTHOR]

These article introduces five questions to consider before using relative motion with patients: • What is the primary purpose of the orthosis? • How much Metacarpal Phalangeal Joint differential angle is needed? • How many fingers should be included in the orthosis? • Is a rigid or flexible orthosis material required? • What are the person-centered preferences/priorities to consider? We introduce a five-question decision-making tool for using relative motion in practice. The tool considers the primary aim of the relative motion orthosis, the amount of relative motion required and the number of fingers that should be included. The tool also helps clincians consider the person-specific characteristics that will impact the use of the orthoses, as well as, a variety of materials that could be used to fabricate the relative motion orthosis. Clinical examples demonstrate the use of the decision tool and illustrate the varied use of relative motion orthoses. [ABSTRACT FROM AUTHOR]

Abstract Background A systematic review (SR) helps us make sense of a body of research while minimizing bias and is routinely conducted to evaluate intervention effects in a health technology assessment (HTA). In addition to the traditional de novo SR, which combines the results of multiple primary studies, there are alternative review types that use systematic methods and leverage existing SRs, namely updates of SRs and overviews of SRs. This paper shares guidance that can be used to select the most appropriate review type to conduct when evaluating intervention effects in an HTA, with a goal to leverage existing SRs and reduce research waste where possible. Process We identified key factors and considerations that can inform the process of deciding to conduct one review type over the others to answer a research question and organized them into guidance comprising a summary and a corresponding flowchart. This work consisted of three steps. First, a guidance document was drafted by methodologists from two Canadian HTA agencies based on their experience. Next, the draft guidance was supplemented with a literature review. Lastly, broader feedback from HTA researchers across Canada was sought and incorporated into the final guidance. Insights Nine key factors and six considerations were identified to help reviewers select the most appropriate review type to conduct. These fell into one of two categories: the evidentiary needs of the planned review (i.e., to understand the scope, objective, and analytic approach required for the review) and the state of the existing literature (i.e., to know the available literature in terms of its relevance, quality, comprehensiveness, currency, and findings). The accompanying flowchart, which can be used as a decision tool, demonstrates the interdependency between many of the key factors and considerations and aims to balance the potential benefits and challenges of leveraging existing SRs instead of primary study reports. Conclusions Selecting the most appropriate review type to conduct when evaluating intervention effects in an HTA requires a myriad of factors to be considered. We hope this guidance adds clarity to the many competing considerations when deciding which review type to conduct and facilitates that decision-making process.

Process system engineering approaches have a considerably broader reach, which is one of the benefits for decision-makers. Making a decision, however, has many drawbacks. It includes biased decisions, timeconsuming analyses, and an unpredictable future. A decision-making integration framework based on hybrid process network synthesis and machine learning was presented in this study. The municipal solid waste management case study uses to demonstrate the applicability decision-making framework. The focus of this paper is to facilitate equipment selection for municipal solid waste management. P-graph was used to generate the 160 possible structures. Then, using the WEKA software, the data from the feasible structure would be processed and evaluated using the chosen algorithm. The J48 is the best model for equipment selection using an 80:20 ratio train and test learning technique in WEKA. The kappa statistics J48 algorithm function for the training and testing dataset is 0.9722 and 1. The mean absolute error and root mean square error are 0.0042 and 0.0354. The decision-making integration framework represents by a graphical user interface in MATLAB. The focus of user interface for selection of waste conversion technologies. As a result, the model can be used to determine the best municipal solid waste conversion technology.

Background: Clinicians' decision thresholds for initiating antibiotics in patients with suspected sepsis have not been quantified. We aimed to define an average threshold of infection likelihood at which clinicians initiate antibiotics when treating a patient with suspected infection and to evaluate the influence of severity of illness and clinician-related factors on the threshold. Design: This was a prospective survey of 153 clinicians responding to 8 clinical vignettes constructed from real-world data from 3 health care systems in the United States. We treated each hour in the vignette as a decision to treat or not treat with antibiotics and assigned an infection probability to each hour using a previously developed infection prediction model. We then estimated decision thresholds using regression models based on the timing of antibiotic initiation. We compared thresholds across categories of severity of illness and clinician-related factors. Results: Overall, the treatment threshold occurred at a 69% probability of infection, but the threshold varied significantly across severity of illness categories—when patients had high severity of illness, the treatment threshold occurred at a 55% probability of infection; when patients had intermediate severity, the threshold for antibiotic initiation occurred at an infection probability of 69%, and the threshold was 84% when patients had low severity of illness (P < 0.001 for group differences). Thresholds differed significantly across specialty, highest among infectious disease and lowest among emergency medicine clinicians and across years of experience, decreasing with increasing years of experience. Conclusions: The threshold infection probability above which physicians choose to initiate antibiotics in suspected sepsis depends on illness severity as well as clinician factors. Implications: Incorporating these context-dependent thresholds into discriminating and well-calibrated models will inform the development of future sepsis clinical decision support systems. Clinician-related differences in treatment thresholds suggests potential unwarranted variation and opportunities for performance improvement. Highlights: Decision making about antibiotic initiation in suspected sepsis occurs under uncertainty, and little is known about clinicians' thresholds for treatment. In this prospective study, 153 clinicians from 3 health care systems reviewed 8 real-world clinical vignettes representing patients with sepsis and indicated the time that they would initiate antibiotics. Using a model-based approach, we estimated decision thresholds and found that thresholds differed significantly across illness severity categories and by clinician specialty and years of experience. [ABSTRACT FROM AUTHOR]

The greater concern toward depleting oil resources and environmental restrictions has attracted a great interest of the scientific community to employ vegetable oils in high-temperature applications. Vegetable oils are environmentally friendly, renewable, non-hazardous, abundant and less flammable in comparison with their mineral counterparts. Moreover, the vegetable oils possess comparable thermophysical properties to that of other thermal oils used in solar applications. Owing to the associated advantages, researchers have studied the applicability of numerous vegetable oils for solar applications. However, a major challenge lies into adjudge the performance of given vegetable oil with respect to the other vegetable oil for a given solar application as heat transfer fluids with due consideration to economic as well as time aspect during the early phases of project implementation. The present work, therefore, proposes to address the aforementioned challenge of performance evaluation of vegetable oils employed in solar power plants through an interval-valued fuzzy information decision-making tool integrated with TOmada de Decisao Interativa Multicriterio (TODIM) method. A comparative analysis and sensitivity analysis have been carried out to validate the obtained ranking results. The proposed decision-making methodology not only relives the decision-making process with unreasonable data but also takes into consideration the inevitable psychological behavior of the experts toward risk. Therefore, the proposed decision-making framework can be used with confidence in making decisions within a sustainable technology framework. [ABSTRACT FROM AUTHOR]

Background: For certain communicable disease outbreaks, mass prophylaxis of uninfected individuals can curtail new infections. When an outbreak emerges, decision makers could benefit from methods to quickly determine whether mass prophylaxis is cost-effective. We consider 2 approaches: a simple decision model and machine learning meta-models. The motivating example is plague in Madagascar. Methods: We use a susceptible-exposed-infectious-removed (SEIR) epidemic model to derive a decision rule based on the fraction of the population infected, effective reproduction ratio, infection fatality rate, quality-adjusted life-year loss associated with death, prophylaxis effectiveness and cost, time horizon, and willingness-to-pay threshold. We also develop machine learning meta-models of a detailed model of plague in Madagascar using logistic regression, random forest, and neural network models. In numerical experiments, we compare results using the decision rule and the meta-models to results obtained using the simulation model. We vary the initial fraction of the population infected, the effective reproduction ratio, the intervention start date and duration, and the cost of prophylaxis. Limitations: We assume homogeneous mixing and no negative side effects due to antibiotic prophylaxis. Results: The simple decision rule matched the SEIR model outcome in 85.4% of scenarios. Using data for a 2017 plague outbreak in Madagascar, the decision rule correctly indicated that mass prophylaxis was not cost-effective. The meta-models were significantly more accurate, with an accuracy of 92.8% for logistic regression, 95.8% for the neural network model, and 96.9% for the random forest model. Conclusions: A simple decision rule using minimal information about an outbreak can accurately evaluate the cost-effectiveness of mass prophylaxis for outbreak mitigation. Meta-models of a complex disease simulation can achieve higher accuracy but with greater computational and data requirements and less interpretability. Highlights: We use a susceptible-exposed-infectious-removed model and net monetary benefit to derive a simple decision rule to evaluate the cost-effectiveness of mass prophylaxis. We use the example of plague in Madagascar to compare the performance of the analytically derived decision rule to that of machine learning meta-models trained on a stochastic dynamic transmission model. We assess the accuracy of each approach for different combinations of disease dynamics and intervention scenarios. The machine learning meta-models are more accurate predictors of mass prophylaxis cost-effectiveness. However, the simple decision rule is also accurate and may be a preferred substitute in low-resource settings. [ABSTRACT FROM AUTHOR]

Transportation networks are sized to efficiently achieve some set of service objectives. Under particular circumstances, such as the COVID-19 pandemic, the demand for transportation can significantly change, both qualitatively and quantitatively, resulting in an over-capacitated and less efficient network. In this paper, we address this issue by proposing a framework for resizing the network to efficiently cope with the new demand. The framework includes a model to determine an optimal transportation sub-network that guarantees the following: (i) the minimal access time from any node of the urban network to the new sub-network has not excessively increased compared to that of the original transportation network; (ii) the delay induced on any itinerary by the removal of nodes from the original transportation network has not excessively increased; and (iii) the number of removed nodes from the transportation network is within a preset known factor. A solution is optimal if it induces a minimal global delay. We modelled this problem as a Mixed Integer Linear Program and applied it to the public bus transportation network of Lyon, France, in a case study. In order to respond to operational issues, the framework also includes a decision tool that helps the network planners to decide which bus lines to close and which ones to leave open according to specific trade-off preferences. The results on real data in Lyon show that the optimal sub-network from the MILP model can be used to feed the decision tool, leading to operational scenarios for network planners.

ObjectivesWith vector-borne diseases emerging across the globe, precipitated by climate change and other anthropogenic changes, it is critical for public health authorities to have well-designed surveillance strategies in place. Sentinel surveillance has been proposed as a cost-effective approach to surveillance in this context. However, spatial design of sentinel surveillance system has important impacts on surveillance outcomes, and careful selection of sentinel unit locations is therefore an essential component of planning.MethodsA review of the available literature, based on the realist approach, was used to identify key decision issues for sentinel surveillance planning. Outcomes of the review were used to develop a decision tool, which was subsequently validated by experts in the field.ResultsThe resulting decision tool provides a list of criteria which can be used to select sentinel unit locations. We illustrate its application using the case example of designing a national sentinel surveillance system for Lyme disease in Canada.ConclusionsThe decision tool provides researchers and public health authorities with a systematic, evidence-based approach for planning the spatial design of sentinel surveillance systems, taking into account the aims of the surveillance system and disease and/or context-specific considerations.

Background: A systematic review (SR) helps us make sense of a body of research while minimizing bias and is routinely conducted to evaluate intervention effects in a health technology assessment (HTA). In addition to the traditional de novo SR, which combines the results of multiple primary studies, there are alternative review types that use systematic methods and leverage existing SRs, namely updates of SRs and overviews of SRs. This paper shares guidance that can be used to select the most appropriate review type to conduct when evaluating intervention effects in an HTA, with a goal to leverage existing SRs and reduce research waste where possible. Process: We identified key factors and considerations that can inform the process of deciding to conduct one review type over the others to answer a research question and organized them into guidance comprising a summary and a corresponding flowchart. This work consisted of three steps. First, a guidance document was drafted by methodologists from two Canadian HTA agencies based on their experience. Next, the draft guidance was supplemented with a literature review. Lastly, broader feedback from HTA researchers across Canada was sought and incorporated into the final guidance. Insights: Nine key factors and six considerations were identified to help reviewers select the most appropriate review type to conduct. These fell into one of two categories: the evidentiary needs of the planned review (i.e., to understand the scope, objective, and analytic approach required for the review) and the state of the existing literature (i.e., to know the available literature in terms of its relevance, quality, comprehensiveness, currency, and findings). The accompanying flowchart, which can be used as a decision tool, demonstrates the interdependency between many of the key factors and considerations and aims to balance the potential benefits and challenges of leveraging existing SRs instead of primary study reports. Conclusions: Selecting the most appropriate review type to conduct when evaluating intervention effects in an HTA requires a myriad of factors to be considered. We hope this guidance adds clarity to the many competing considerations when deciding which review type to conduct and facilitates that decision-making process. [ABSTRACT FROM AUTHOR]

In the drive toward faster patient access to treatments, health technology assessment (HTA) agencies and payers are increasingly faced with reliance on evidence based on surrogate endpoints, increasing decision uncertainty. Despite the development of a small number of evaluation frameworks, there remains no consensus on the detailed methodology for handling surrogate endpoints in HTA practice. This research overviews the methods and findings of four empirical studies undertaken as part of COMED (Pushing the Boundaries of Cost and Outcome Analysis of Medical Technologies) program work package 2 with the aim of analyzing international HTA practice of the handling and considerations around the use of surrogate endpoint evidence. We have synthesized the findings of these empirical studies, in context of wider contemporary body of methodological and policy‐related literature on surrogate endpoints, to develop a web‐based decision tool to support HTA agencies and payers when faced with surrogate endpoint evidence. Our decision tool is intended for use by HTA agencies and their decision‐making committees together with the wider community of HTA stakeholders (including clinicians, patient groups, and healthcare manufacturers). Having developed this tool, we will monitor its use and we welcome feedback on its utility. [ABSTRACT FROM AUTHOR]

Participatory decision tools enable stakeholders to reconcile conflicting natural resources management objectives. Fisheries targeting highly productive species can have profound impacts on co-occurring bycatch species with low fecundity and other life history traits that make them vulnerable to anthropogenic sources of mortality. This study developed a decision tool for integrated bycatch management for data-limited to data-rich fisheries, improving upon current piecemeal approaches. First, through a systematic literature review, participants compile a comprehensive database of methods to mitigate the catch and fishing mortality of threatened bycatch species. These mitigation methods are then categorized into tiers of a sequential mitigation hierarchy, where interventions that avoid capture are considered before those that minimize catchability, followed by methods that minimize fishing mortality, before approaches that offset residual impacts. The methods are also assembled within an evidence hierarchy, where findings from meta-analytic modelling studies are more robust and generalizable than from individual studies. The decision tool enables stakeholders to evaluate alternative bycatch management strategies' efficacy at meeting specific and measurable objectives for mitigating the catch and mortality of bycatch and for costs from multispecies conflicts, economic viability, practicality and safety, while accounting for the fishery-specific feasibility of compliance monitoring of alternative bycatch management measures. Ongoing adaptation of the bycatch management framework addresses findings from performance assessments, updated evidence, new mitigation methods and changes to governance systems. The proposed decision tool therefore enables stakeholders to develop bycatch management frameworks that provide precautionary protection for the most vulnerable populations with acceptable tradeoffs. [ABSTRACT FROM AUTHOR]

Enterprises now face a global, dynamic, and unpredictable economic environment. In response to quick changes of the demand, the production needs to have the ability to adapt rapidly to meet the production requirements. The Reconfigurable Manufacturing System (RMS) paradigm enables such capabilities. However, defining design and reconfiguration rules is highly challenging, as it requires a broad knowledge encompassing the inclusion of technological, production, and economic metrics, as well as an understanding of a reconfiguration strategy, determining the necessary reconfiguration frequency of the system. For now, no global methodology taking all those aspects into account has been proposed. This article presents an original framework for the design, evaluation, and reconfiguration of the Reconfigurable Production System (RPS). New metrics to measure reconfigurability are defined. The design approach consists in three main steps which are individually developed. The selection of the appropriated production system is based on the comparison of reconfigurability and productivity indicators. Finally, the reconfiguration strategy is presented. The methodology is applied on a case study from the automotive industry. [ABSTRACT FROM AUTHOR]

Nowadays, the imbalance between supply and demand increases the volatility of many raw materials for both production and consumption. In this regard, stock management inventory would be necessary in order to limit losses and increase margins. It this perspective, we propose through this article, the development of a model combining time series, machine learning algorithm and combinatorial optimization in order to identify the opportunities to buy stock at a lower cost and to sale a portion of the unused stock to generate additional profits for the organization. In this model, we use machine learning models and time series to predict stock prices and demand forecast of stock to ensure the production and then integrate them into a combinatorial optimization algorithm to help making the best decision (Buy, Sell or Hold), and the exact quantities to buy or to sell in order to maximize earnings without risks. [ABSTRACT FROM AUTHOR]

Purpose: Computed tomography (CT) is currently the standard for evaluation of intra-abdominal injury (IAI) after BAT. Pediatric patients receiving CT scans based on adult clinical protocols are potentially exposed to unnecessary radiation. The purpose of this study is to determine the rate of CT scans before and after implementation of a pediatric BAT decision tool. Methods: We adapted and implemented an evidence-based decision tool for pediatric BAT based on five clinical variables. We reviewed patient charts 18 months pre- and post-implementation. Demographics and outcomes were compared using Chi-square and Fisher's exact test, accordingly. Results: The pre and post-implementation groups were uniform when comparing age, sex, mechanism, and Injury Severity Score. The decision tool was utilized in 85% of patients post-implementation. Fewer CT scans were obtained in the post-implementation group (28 vs. 21%, p = 0.215) with no missed injuries or late diagnoses. Conclusion: Implementation of a pediatric BAT decision tool decreased CT usage and radiation exposure without an obvious compromise to patient care. This experience supports the utilization of these tools for the assessment of IAI after BAT and have resulted in more selective use of CT during pediatric BAT in our program. [ABSTRACT FROM AUTHOR]

Ligand Based Virtual Screening methods are used to screen molecule databases to select the most promising compounds for a query. This is performed by decision-makers based on the information of the descriptors, which are usually processed individually. This methodology leads to a lack of information and hard post-processing dependent on the expert's knowledge that can end up in the discarding of promising compounds. Consequently, in this work, we propose a new multi-objective methodology called MultiPharm-DT where several descriptors are considered simultaneously and whose results are offered to the decision-maker without effort on their part and without relying on their expertise. [ABSTRACT FROM AUTHOR]

Suppressing pest populations below economically-damaging levels is an important element of sustainable peanut (Arachis hypogaea L.) production. Peanut farmers and their advisors often approach pest management with similar goals regardless of where they are located. Anticipating pest outbreaks using field history and monitoring pest populations are fundamental to protecting yield and financial investment. Microsoft Excel was used to develop individual risk indices for pests, a composite assessment of risk, and costs of risk mitigation practices for peanut in Argentina, Ghana, India, Malawi, and North Carolina (NC) in the United States (US). Depending on pests and resources available to manage pests, risk tools vary considerably, especially in the context of other crops that are grown in sequence with peanut, cultivars, and chemical inputs. In Argentina, India, and the US where more tools (e.g. mechanization and pesticides) are available, risk indices for a wide array of economically important pests were developed with the assumption that reducing risk to those pests likely will impact peanut yield in a positive manner. In Ghana and Malawi where fewer management tools are available, risks to yield and aflatoxin contamination are presented without risk indices for individual pests. The Microsoft Excel platform can be updated as new and additional information on effectiveness of management practices becomes apparent. Tools can be developed using this platform that are appropriate for their geography, environment, cropping systems, and pest complexes and management inputs that are available. In this article we present examples for the risk tool for each country. [ABSTRACT FROM AUTHOR]

Peanut (Arachis hypogaea L.) growers and their advisors need to address a wide range of biotic and abiotic stresses to maximize yield and financial return. Mitigating risk to yield and financial investment requires knowledge of interactions among pests and strategies to manage pests, including chemical inputs, crop rotation, cultivar selection, field pest history, planting pattern and population, planting date, and tillage systems. Using Microsoft Excel, a comprehensive peanut risk tool was developed to assist growers and advisors in identifying and selecting production strategies to minimize risk to yield based on empirical data and practical experience while providing cost estimates of production practices. Initially, the risk tool was developed for North Carolina (USA) peanut production. However, the current platform is designed to facilitate the development of similar tools for other USA peanut regions, peanut production systems in other countries, and with the capability to develop risk tools for other crops. This article discusses components of the risk management tool developed for North Carolina peanut production. Benefits of the risk tool to practitioners, extension services, genetics and breeding programs, and formal classroom instruction will be discussed. One goal of this paper is to provide an example of how the Microsoft Excel framework used for peanut in North Carolina can be used for peanut in other regions of the USA and other countries. [ABSTRACT FROM AUTHOR]

Abstract Background In 2019, the Patient Autonomy Act went into effect, allowing Taiwanese citizens to establish legal advance decisions. In an effort to secure a more realistic and accurate perception of situations, a virtual reality video was developed by the palliative care team of Chi-Mei hospital in southern Taiwan for citizens to use before advance care planning. This study explores the change in participants’ preference and certainty regarding end-of-life decisions after using this tool. Methods Participants were at least 20 years old and capable of reading and understanding the information provided in the written handout with information about the legal process of making an advance decision. They completed pre-test questionnaires, viewed a six-minute 360-degree virtual reality video on a portable headset, and then completed a post-test questionnaire about their preference on the five medical options—CPR, life-sustaining treatments, antibiotics, blood transfusion, and artificial nutrition and hydration—followed by feedback on the helpfulness of the virtual reality. The control group included 40 participants who only read the handout and completed pre-test and post-test questionnaires. Results After viewing the virtual reality video, preference for not using CPR, life-sustaining treatment, antibiotics, blood transfusion, and artificial nutrition and hydration increased significantly in the virtual reality intervention group. Uncertainty regarding the five medical options mentioned above significantly decreased. The intervention was generally recognized by participants for its help in making decisions. Discussion The decrease in the number of participants who could not make decisions indicates that the virtual reality video may be helpful for users in making end-of-life decision. According to feedback, the virtual reality video helped equip users with better understanding of medical scenarios, and that it is a good decision tool for advance care planning. Conclusion This is the first study since the Patient Autonomy Act has been passed that explores the effectiveness of using a virtual reality video as a decision tool in advance care planning and reveals decreased preference of CPR, life sustaining treatment, antibiotics, blood transfusion, and artificial nutrition and hydration after intervention. This decision aid proved to be an effective tool for clarifying their end-of-life care preferences.

Glen S Hazlewood,1– 3 Deborah A Marshall,1– 3 Claire EH Barber,1– 3 Linda C Li,3 Cheryl Barnabe,1– 3 Vivian Bykerk,4,5 Peter Tugwell,6 Pauline M Hull,7 Nick Bansback3,8 1Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada; 2McCaig Institute of Bone and Joint Health, University of Calgary, Calgary, Canada; 3Arthritis Research Canada, Vancouver, BC, Canada; 4Weill Cornell Medical College, Cornell University, New York, NY, USA; 5Department of Rheumatology, Hospital for Special Surgery, New York, NY, USA; 6Department of Medicine, Department of Epidemiology and Community Medicine, Canada Research Chair, University of Ottawa, Institute of Population Health, Ottawa, Canada; 7University of Calgary, Calgary, Canada; 8Faculty of Medicine, School of Population and Public Health, University of British Columbia, Vancouver, CanadaCorrespondence: Glen S HazlewoodDepartments of Medicine and Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, 3AA10, Calgary AB T2N 4Z6, CanadaTel +1 403 220-5903Fax +1 403 210-3899Email gshazlew@ucalgary.caPurpose: To evaluate, in a proof-of-concept study, a decision aid that incorporates hypothetical choices in the form of a discrete-choice experiment (DCE), to help patients with early rheumatoid arthritis (RA) understand their values and nudge them towards a value-centric decision between methotrexate and triple therapy (a combination of methotrexate, sulphasalazine and hydroxychloroquine).Patients and Methods: In the decision aid, patients completed a series of 6 DCE choice tasks. Based on the patient’s pattern of responses, we calculated his/her probability of choosing each treatment, using data from a prior DCE. Following pilot testing, we conducted a cross-sectional study to determine the agreement between the predicted and final stated preference, as a measure of value concordance. Secondary outcomes including time to completion and usability were also evaluated.Results: Pilot testing was completed with 10 patients and adjustments were made. We then recruited 29 patients to complete the survey: median age 57, 55% female. The patients were all taking treatment and had well-controlled disease. The predicted treatment agreed with the final treatment chosen by the patient 21/29 times (72%), similar to the expected agreement from the mean of the predicted probabilities (68%). Triple therapy was the predicted treatment 24/29 times (83%) and chosen 20/29 (69%) times. Half of the patients (51%) agreed that completing the choice questions helped them to understand their preferences (38% neutral, 10% disagreed). The tool took an average of 15 minutes to complete, and median usability scores were 55 (system usability scale) indicating “OK” usability.Conclusion: Using a DCE as a value-clarification task within a decision aid is feasible, with promising potential to help nudge patients towards a value-centric decision. Usability testing suggests further modifications are needed prior to implementation, perhaps by having the DCE exercises as an “add-on” to a simpler decision aid.Keywords: conjoint analysis, decision tool, value concordance, methotrexate

Radiology referral quality impacts patient care, yet factors influencing quality are poorly understood. This study assessed the quality of computed tomography (CT) referrals, identified associated characteristics, and evaluated the ESR-iGuide clinical decision support tool's ability to optimize referrals. A retrospective review analyzed 300 consecutive CT referrals from an acute care hospital. Referral quality was evaluated on a 5-point scale by three expert reviewers (inter-rater reliability κ = 0.763-0.97). The ESR-iGuide tool provided appropriateness scores and estimated radiation exposure levels for the actual referred exams and recommended exams. Scores were compared between actual and recommended exams. Associations between ESR-iGuide scores and referral characteristics, including the specialty of the ordering physician (surgical vs. non-surgical), were explored. Of the referrals, 67.1% were rated as appropriate. The most common exams were head and abdomen/pelvis CTs. The ESR-iGuide deemed 70% of the actual referrals "usually appropriate" and found that the recommended exams had lower estimated radiation exposure compared to the actual exams. Logistic regression analysis showed that non-surgical physicians were more likely to order inappropriate exams compared to surgical physicians. Over one-third of the referrals showed suboptimal quality in the unstructured system. The ESR-iGuide clinical decision support tool identified opportunities to optimize appropriateness and reduce radiation exposure. Implementation of such a tool warrants consideration to improve communication and maximize patient care quality., (© 2024. The Author(s).)

The sea lamprey (Petromyzon marinus) is an invasive species in the Laurentian Great Lakes which requires control to protect valuable fisheries. The Sea Lamprey Control Program (program) uses chemical methods (lampricides) each year to treat infested streams, but limited resources only allow control agents to target 100–125 streams annually. To provide the most cost-effective use of these resources, the program began using the Empiric Stream Treatment Ranking (ESTR) system in 2000 as a decision support tool for selecting streams for treatment. Following a comprehensive review of larval assessment techniques in 2002, the program transitioned from a labor-intensive quantitative technique to a more rapid catch-per-unit-effort based approach. Staff resources were re-allocated to lampricide control, allowing for treatment of more streams annually. Beginning in 2009, stream selection shifted to cost-per-kill of large larvae (>100 mm) and incorporated expert opinion along with targeted treatment strategies. This resulted in a greater number of treatments on an annual basis overall as well as increased treatment frequency for many individual streams. The average adult sea lamprey index decreased by 26% and lake trout wounding rates decreased by 21% across the Great Lakes basin during 2010 to 2017 when compared to 2000 to 2009. Recommended improvements to the stream selection model include: leveraging historic data and expert knowledge to identify streams for treatment, refining the accuracy of the ESTR decision tool, incorporating supplemental and alternative control methods into the treatment selection process, and identifying areas to improve larval assessment techniques that guide and evaluate lampricide control efforts. [ABSTRACT FROM AUTHOR]

Introduction: Acute heart failure (HF) exacerbation is a serious and common condition seen in the Emergency Department (ED) that has significant morbidity and mortality. There are multiple clinical decision tools that Emergency Physicians (EPs) can use to reach an appropriate evidence-based disposition for these patients.Objective: This narrative review is an evidence-based discussion of clinical decision-making tools aimed to assist EPs risk stratify patients with AHF and determine disposition.Discussion: Risk stratification in patients with AHF exacerbation presenting to the ED is paramount in reaching an appropriate disposition decision. High risk features include hypotension, hypoxemia, elevated brain natriuretic peptide (BNP) and/or troponin, elevated creatinine, and hyponatremia. Patients who require continuous vasoactive infusions, respiratory support, or are initially treatment-resistant generally require intensive care unit admission. In most instances, new-onset AHF patients should be admitted for further evaluation. Other AHF patients in the ED can be risk stratified with the Ottawa HF Risk Score (OHFRS), the Multiple Estimation of Risk Based on Spanish Emergency Department Score (MEESSI), or the Emergency HF Mortality Risk Grade (EHFMRG). These tools take various factors into account such as mode of arrival to the ED, vital signs, laboratory values like troponin and pro-BNP, and clinical course. If used appropriately, these scores can predict patients at low risk for adverse outcomes.Conclusion: This article discusses evidence-based disposition of patients in acute decompensated HF presenting to the ED. Knowledge of these factors and risk tools can assist emergency clinicians in determining appropriate disposition of patients with HF. [ABSTRACT FROM AUTHOR]

Aim: An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. Patients & methods: An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performance was assessed using dates of death in patients' electronic health records. Results: For patients scored as highest risk for 30-day mortality, the event rate was 4.9% (vs 0.7% in patients scored as low risk; a 7.4-times greater risk). Conclusion: The development and validation of a decision tool to accurately identify patients with cancer who are at risk for short-term mortality is feasible. [ABSTRACT FROM AUTHOR]

Background: Atraumatic subarachnoid hemorrhage (SAH) is a deadly condition that most commonly presents as acute, severe headache. Controversy exists concerning evaluation of SAH based on the time from onset of symptoms, specifically if the headache occurred > 6 h prior to patient presentation.Clinical Question: Do patients undergoing evaluation for atraumatic SAH who have a negative computed tomography (CT) scan of the head obtained more than 6 h after symptom onset require a subsequent lumbar puncture to rule out the diagnosis?Evidence Review: Studies retrieved included a retrospective cohort study, two prospective cohort studies, and a case-control study. These studies provide estimates of the diagnostic accuracy of head CT imaging obtained > 6 h from symptom onset and diagnostic test characteristics of subsequent lumbar puncture.Conclusion: The probability of SAH above which emergency clinicians should perform a lumbar puncture is 1.0%. This threshold is essentially the same as the estimated probability of SAH in patients with a negative head CT obtained more than 6 h from symptom onset. Emergency physicians might reasonably decide to either perform or forego this procedure. Consequently, we contend that the decision whether to perform lumbar puncture in these instances is an excellent candidate for shared decision-making. [ABSTRACT FROM AUTHOR]

The application of selective dry cow therapy is one of the measures currently suggested to reduce the use of antibiotics in dairy herds. However, the application of selective dry cow therapy will have a profound impact on Italian dairy herds, very likely affecting both milk yield and quality. Identifying cows to be treated at drying off is crucial for farmers and health authorities, therefore it is necessary the definition of a consistent and certified procedure. This article reports the results of a study aiming to identify which SCC threshold would be the most appropriate to identify cows to be treated and the potential consequences of different selection protocols on udder health after calving under field condition. Last milk test record before drying off and the average of lactation milk test records were considered on a database including 45,682 cow from 709 herd. Five different threshold were considered (50,000; 100,000; 150,000; 200,000; and 250,000 cells/mL). The statistical analysis of the database and a rational evaluation of the results suggest to define thresholds of 100,000 cells/mL for primiparous cows and 200,000 cells/mL for pluriparous cows measured either before drying-off or as the average of all the milk tests of the lactation. The criteria proposed will be useful to manage herd health and, specifically, dry-cows in an efficient and sustainable way, decreasing the use of antimicrobials without increasing the risk of affecting milk yield and quality after calving.Highlights The definition of a consistent and approved procedure to identify cow to be treated in a selective dry cow therapy approach is crucial. SCC from milk test records are a convenient, accurate and certified method. SCC values obtained before drying off or calculated as the average of lactation records can be used. The thresholds of 100,000 cells for primiparous cows and of 200,000 cells for pluriparous cows are suggested as an efficient and sustainable decision tool.

Abstract Background Overviews of reviews of healthcare interventions (overviews) integrate information from multiple systematic reviews (SRs) to provide a single synthesis of relevant evidence for decision-making. Overviews may identify multiple SRs that examine the same intervention for the same condition and include some, but not all, of the same primary studies. Different researchers use different approaches to manage these “overlapping SRs,” but each approach has advantages and disadvantages. This study aimed to develop an evidence-based decision tool to help researchers make informed inclusion decisions when conducting overviews of healthcare interventions. Methods We used a two-stage process to develop the decision tool. First, we conducted a multiple case study to obtain empirical evidence upon which the tool is based. We systematically conducted seven overviews five times each, making five different decisions about which SRs to include in the overviews, for a total of 35 overviews; we then examined the impact of the five inclusion decisions on the overviews’ comprehensiveness and challenges, within and across the seven overview cases. Second, we used a structured, iterative process to transform the evidence obtained from the multiple case study into an empirically based decision tool with accompanying descriptive text. Results The resulting decision tool contains four questions: (1) Do Cochrane SRs likely examine all relevant intervention comparisons and available data? (2) Do the Cochrane SRs overlap? (3) Do the non-Cochrane SRs overlap? (4) Are researchers prepared and able to avoid double-counting outcome data from overlapping SRs, by ensuring that each primary study’s outcome data are extracted from overlapping SRs only once? Guidance is provided to help researchers answer each question, and empirical evidence is provided regarding the advantages, disadvantages, and potential trade-offs of the different inclusion decisions. Conclusions This evidence-based decision tool is designed to provide researchers with the knowledge and means to make informed inclusion decisions in overviews. The tool can provide practical guidance and support for overview authors by helping them consider questions that could affect the comprehensiveness and complexity of their overviews. We hope this tool will be a useful resource for researchers conducting overviews, and we welcome discussion, testing, and refinement of the proposed tool.

Annual outbreaks of seasonal influenza cause a substantial health burden. The aim of this study was to compare patient demographic/clinical data in two influenza patient groups presenting to hospital; those requiring O2 or critical care admission and those requiring less intensive treatment. The study was conducted from 1 December 2017 until 1 April 2019 at a district general hospital in East London. Patient demographic and clinical information was collected for all patients who had tested influenza positive by near‐patient testing. χ2 test was used for categorical variables to see if there were significant differences for those admitted and the Wilcoxon rank‐sum test to compare the length of inpatient stay. Of 127 patients, 56 (44.1%) required oxygen or critical care. There were significant increases in National Early Warning Score (NEWS) observations (P %3C.001), Charlson comorbidity index (P =.049), length of inpatient stay (P %3C.001), and a strong association with increasing age (P =.066) when the more intensive treatment group was compared with the less intensive treatment group. A total of 13 (18.3%) of 71 patients not requiring oxygen or critical care were not admitted to the hospital. Following rapid influenza testing, NEWS scores, comorbidities, and age should be incorporated into a decision tool in Accident and Emergency to aid hospital admission or discharge decisions. [ABSTRACT FROM AUTHOR]

The Dredged Material Decision Tool (DMDT) was developed by the United States Environmental Protection Agency (USEPA) to allow project managers, stakeholders, and communities to quantify environmental, economic, and social considerations of using dredged material for beneficial purposes. Dredged material may be disposed in a confined disposal facility (CDF); however, this option is unfavorable because of the finite capacity problems these facilities pose. A more sustainable option is to use dredged materials beneficially such as construction material, for habitat restoration, or for brownfield remediation projects. This study demonstrates the applicability of the DMDT to three relevant candidate projects: (1) Dog Beach, Greenwood, and Lee Street Beaches (Evanston, IL, USA); (2) New York-New Jersey Harbor (New York/New Jersey); and (3) Poplar Island (Chesapeake Bay). The DMDT requires the project information and then completion of worksheets with each criteria (biophysical environment, economic, governance, social, and built environment) ranked, weighed, and scored. The DMDT is applied for all potential alternatives and the results are then analyzed to select the best beneficial reuse alternative. It was found that for the beaches in Evanston, the most beneficial option was on-beach placement with hydraulic dredging. The best option for the New Jersey Harbor was found to be using for brownfield and landfill remediation. The best option for Poplar Island was the lateral and vertical expansion of 50% uplands and 50% wetlands. Overall, DMDT is found to be a valuable tool to facilitate the evaluation of multi-criteria based on the project-specific data and help select the best beneficial use alternative for the dredged material.

The paper focuses on factors affecting the economics of milk production based on modelling future effects of present managerial decisions. We evaluate a sample Slovak dairy farms from the economic and performance indicator datasets of the National Agricultural and Food Centre – Research institute for animal production Nitra (NPPC-RIAP), referring to semi-intensive dairy farm and typical Holstein dairy farm. The EkonMOD milk decision support tool for dairy farm managers will be used to run various scenarios in heifer replacement strategies. If the average age at first calving (AFC) is reduced from 30 down to 24 months, the dairy could expect additional heifers for potential sale, growth, or culling pressure on the lactating herd. This approach means that in the first two years’ heifer development is emphasized, expenses in feed and management are decreased by 47,520 € per year, and 53,750 € worth of heifers are sold, bringing the total potential income for those two years to 101,270 €. The reduction of AFC in semi-intensive dairy farm in the what-if scenario 1 reduced the number of heifers needed for replacement from 290 to 269 heifers, also having the positive impact on the profitability resulting from these interrelations. In the what-if scenario 2 the AFC remained the same as in what-if scenario 1, meaning that the number of replacements needed was without any change. In the last–different sample farm case scenario presented in this paper, we considered the Holstein dairy farm with 500 productive dairy cows. The reduction in the AFC from 26 to 24 months reduced the number of feeding days in the heifer category by 12 410 feeding days, resulting to the reduction in total costs of at least 22 000 €, in this case.