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.

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.

Design of a multiple criteria decision analysis framework for prioritizing high-impact health technologies in a regional health service.

OBJECTIVES: This study aims to develop a framework for establishing priorities in the regional health service of Murcia, Spain, to facilitate the creation of a comprehensive multiple criteria decision analysis (MCDA) framework. This framework will aid in decision-making processes related to the assessment, reimbursement, and utilization of high-impact health technologies. METHOD: Based on the results of a review of existing frameworks for MCDA of health technologies, a set of criteria was proposed to be used in the context of evaluating high-impact health technologies. Key stakeholders within regional healthcare services, including clinical leaders and management personnel, participated in a focus group (n = 11) to discuss the proposed criteria and select the final fifteen. To elicit the weights of the criteria, two surveys were administered, one to a small sample of healthcare professionals (n = 35) and another to a larger representative sample of the general population (n = 494). RESULTS: The responses obtained from health professionals in the weighting procedure exhibited greater consistency compared to those provided by the general public. The criteria more highly weighted were "Need for intervention" and "Intervention outcomes." The weights finally assigned to each item in the multicriteria framework were derived as the equal-weighted sum of the mean weights from the two samples. CONCLUSIONS: A multi-attribute function capable of generating a composite measure (multicriteria) to assess the value of high-impact health interventions has been developed. Furthermore, it is recommended to pilot this procedure in a specific decision context to evaluate the efficacy, feasibility, usefulness, and reliability of the proposed tool.

Prospective cohort study for assessment of integrated care with a triple aim approach: hospital at home as use case.

BACKGROUND: Applicability of comprehensive assessment of integrated care services in real world settings is an unmet need. To this end, a Triple Aim evaluation of Hospital at Home (HaH), as use case, was done. As ancillary aim, we explored use of the approach for monitoring the impact of adoption of integrated care at health system level in Catalonia (Spain). METHODS: Prospective cohort study over one year period, 2017-2018, comparing hospital avoidance (HaH-HA) with conventional hospitalization (UC) using propensity score matching. Participants were after the first episode directly admitted to HaH-HA or the corresponding control group. Triple Aim assessment using multiple criteria decision analysis (MCDA) was done. Moreover, applicability of a Triple Aim approach at health system level was explored using registry data. RESULTS: HaH-HA depicted lower: i) Emergency Room Department (ER) visits (p < .001), ii) Unplanned re-admissions (p = .012); and iii) costs (p < .001) than UC. The weighted aggregation of the standardized values of each of the eight outcomes, weighted by the opinions of the stakeholder groups considered in the MCDA: i) enjoyment of life; ii) resilience; iii) physical functioning; iv) continuity of care; v) psychological wellbeing; (vi) social relationships & participation; (vii) person-centeredness; and (viii) costs, indicated better performance of HaH-HA than UC (p < .05). Actionable factors for Triple Aim assessment of the health system with a population-health approach were identified. CONCLUSIONS: We confirmed health value generation of HaH-HA. The study identified actionable factors to enhance applicability of Triple Aim assessment at health system level for monitoring the impact of adoption of integrated care. REGISTRATION: ClinicalTrials.gov (26/04/2017; NCT03130283).

The Group Decision-Making Using Pythagorean Fuzzy Entropy and the Complex Proportional Assessment.

The Pythagorean fuzzy sets conveniently capture unreliable, ambiguous, and uncertain information, especially in problems involving multiple and opposing criteria. Pythagorean fuzzy sets are one of the popular generalizations of the intuitionistic fuzzy sets. They are instrumental in expressing and managing hesitant under uncertain environments, so they have been involved extensively in a diversity of scientific fields. This paper proposes a new Pythagorean entropy for Multi-Criteria Decision-Analysis (MCDA) problems. The entropy measures the fuzziness of two fuzzy sets and has an influential position in fuzzy functions. The more comprehensive the entropy, the more inadequate the ambiguity, so the decision-making established on entropy is beneficial. The COmplex PRoportional ASsessment (COPRAS) method is used to tackle uncertainty issues in MCDA and considers the singularity of one alternative over the rest of them. This can be enforced to maximize and minimize relevant criteria in an assessment where multiple opposing criteria are considered. Using the Pythagorean sets, we represent a decisional problem solution by using the COPRAS approach and the new Entropy measure.

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach.

People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts' opinions. Later, the confidence level is measured using a novel Weighted Kendall's W for the optimal number of the clusters, and the threshold is checked. Finally, the conventional problem is solved using the Group Weighted Ordinal Priority Approach (GWOPA) model in multiple attributes decision making (MADM), and the performance of the projects is determined. The validity of the proposed approach is confirmed through a comparative analysis. Also, a real-world case is solved, and the performance of some healthcare construction projects in China is gauged with a comprehensive sensitivity analysis.

Supporting contaminated sites management with Multiple Criteria Decision Analysis: Demonstration of a regulation-consistent approach.

This study proposes a set of key decision-making features of the contaminated site remediation process to assist in selecting the most appropriate decision support method(s). Using a case study consistent with the requirements of the U.S. regulation for contaminated sites management, this article shows that suitable Multiple Criteria Decision Analysis methods can be selected based on a dynamic and evolving problem structuring. The selected methods belong to the family of PROMETHEE methods and can provide ranking recommendations of the considered alternatives using variable structures of the criteria, evaluation of the alternatives and exploitation of the preference model. It was found that in order to support a quick and up-to-date application of powerful decision support techniques in the process of remediation of contaminated sites, decision analysts and stakeholders should interact and co-develop the process. This research also displays how such interactions can guarantee a transparent and traceable decision recommendation so that stakeholders can better understand why some alternatives perform comprehensively better than others when a multitude of inputs is used in the decision-making process.

Improving Learner-Computer Interaction through Intelligent Learning Material Delivery Using Instructional Design Modeling.

This paper describes an innovative and sophisticated approach for improving learner-computer interaction in the tutoring of Java programming through the delivery of adequate learning material to learners. To achieve this, an instructional theory and intelligent techniques are combined, namely the Component Display Theory along with content-based filtering and multiple-criteria decision analysis, with the intention of providing personalized learning material and thus, improving student interaction. Until now, the majority of the research efforts mainly focus on adapting the presentation of learning material based on students' characteristics. As such, there is free space for researching issues like delivering the appropriate type of learning material, in order to maintain the pedagogical affordance of the educational software. The blending of instructional design theories and sophisticated techniques can offer a more personalized and adaptive learning experience to learners of computer programming. The paper presents a fully operating intelligent educational software. It merges pedagogical and technological approaches for sophisticated learning material delivery to students. Moreover, it was used by undergraduate university students to learn Java programming for a semester during the COVID-19 lockdown. The findings of the evaluation showed that the presented way for delivering the Java learning material surpassed other approaches incorporating merely instructional models or intelligent tools, in terms of satisfaction and knowledge acquisition.

New approach for selecting a suitable recycling collection program for recovered paper and pulp recyclables using AHP-TOPSIS techniques.

In the past decade, the use of multiple-criteria decision analysis technology has dramatically increased in solving complex real-world problems in solid waste management. Likewise, many municipalities have paid attention to finding feasible solutions for disposal and recycling of solid waste due to the increase in waste generation rates worldwide. Therefore, policy-makers must determine which recycling program to be implemented among various recycling program options. In this paper, a new approach to select a recycling program for recovered paper and pulp recyclables was proposed using analytic hierarchy process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) techniques. A set of essential parameters of the decision-making system were identified, and a numerical case to illustrate the procedure was conducted. Our findings show very encouraging results to use a combined model between AHP and TOPSIS to select a suitable recycling program for different recovered recyclable materials.

ICER Value Framework 2020 Update: Recommendations on the Aggregation of Benefits and Contextual Considerations.

The Institute for Clinical and Economic Review (ICER) in the United States recently published a 2020 update to its value assessment framework. We are commenting on the method by which the benefits of health interventions are integrated, relating to contextual considerations and other factors relevant to an intervention's value. We start by discussing the theoretical foundations of decision analysis and its extension to multiple criteria decision analysis (MCDA). Then we provide a detailed, evidence-based response to some of the claims made by ICER with regard to the use of MCDA methods and stakeholder engagement. Finally, we provide a number of recommendations on the use of quantitative decision analysis and decision conferencing that could be of relevance to the ICER methodology. Overall, we agree that some of the proposed changes by ICER are moving in the right direction toward improving transparency in the value assessment process, but these changes are probably inadequate. We advocate that more serious attention should be paid to the use of quantitative decision analysis together with decision conferencing for the construction of value preferences via group processes for the integration of an intervention's various benefit components.

Integrating nanoparticle quantification and statistical design of experiments for efficient HIV-1 virus-like particle production in High Five cells.

The nature of enveloped virus-like particles (VLPs) has triggered high interest in their application to different research fields, including vaccine development. The baculovirus expression vector system (BEVS) has been used as an efficient platform for obtaining large amounts of these complex nanoparticles. To date, most of the studies dealing with VLP production by recombinant baculovirus infection utilize indirect detection or quantification techniques that hinder the appropriate characterization of the process and product. Here, we propose the application of cutting-edge quantification methodologies in combination with advanced statistical designs to exploit the full potential of the High Five/BEVS as a platform to produce HIV-1 Gag VLPs. The synergies between CCI, MOI, and TOH were studied using a response surface methodology approach on four different response functions: baculovirus infection, VLP production, VLP assembly, and VLP productivity. TOH and MOI proved to be the major influencing factors in contrast with previous reported data. Interestingly, a remarkable competition between Gag VLP production and non-assembled Gag was detected. Also, the use of nanoparticle tracking analysis and flow virometry revealed the existence of remarkable quantities of extracellular vesicles. The different responses of the study were combined to determine two global optimum conditions, one aiming to maximize the VLP titer (quantity) and the second aiming to find a compromise between VLP yield and the ratio of assembled VLPs (quality). This study provides a valuable approach to optimize VLP production and demonstrates that the High Five/BEVS can support mass production of Gag VLPs and potentially other complex nanoparticles.

Implicit decision framing as an unrecognized source of confusion in endangered species classification.

Legal classification of species requires scientific and values-based components, and how those components interact depends on how people frame the decision. Is classification a negotiation of trade-offs, a decision on how to allocate conservation efforts, or simply a comparison of the biological status of a species to a legal standard? The answers to problem-framing questions such as these influence decision making in species classifications. In our experience, however, decision makers, staff biologists, and stakeholders often have differing perspectives of the decision problem and assume different framings. In addition to differences between individuals, in some cases it appears individuals themselves are unclear about the decision process, which contributes to regulatory paralysis, litigation, and a loss of trust by agency staff and the public. We present 5 framings: putting species in the right bin, doing right by the species over time, saving the most species on a limited budget, weighing extinction risk against other objectives, and strategic classification to advance conservation. These framings are inspired by elements observed in current classification practices. Putting species in the right bin entails comparing a scientific status assessment with policy thresholds and accounting for potential misclassification costs. Doing right by the species adds a time dimension to the classification decision, and saving the most species on a limited budget classifies a suite of species simultaneously. Weighing extinction risk against other objectives would weigh ecological or socioeconomic concerns in classification decisions, and strategic classification to advance conservation would make negotiation a component of classification. We view these framings as a means to generate thought, discussion, and movement toward selection and application of explicit classification framings. Being explicit about the decision framing could lead decision makers toward more efficient and defensible decisions, reduce internal confusion and external conflict, and support better collaboration between scientists and policy makers.

The Probabilistic Efficiency Frontier: A Framework for Cost-Effectiveness Analysis in Germany Put into Practice for Hepatitis C Treatment Options.

BACKGROUND: The German Institute for Quality and Efficiency in Health Care (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen) adapted the efficiency frontier (EF) approach to conform to statutory provisions on cost-effectiveness analysis of health technologies. EF serves as a framework for evaluating cost-effectiveness and indirectly for pricing and reimbursement decisions. OBJECTIVES: To calculate an EF on the basis of single multidimensional benefit by taking patient preferences and uncertainty into account; to evaluate whether EF is useful to inform decision makers about cost-effectiveness of new therapies; and to find whether a treatment is efficient at given prices demonstrated through a case study on chronic hepatitis C. METHODS: A single multidimensional benefit was calculated by linear additive aggregation of multiple patient-relevant end points. End points were identified and weighted by patients in a previous discrete-choice experiment (DCE). Aggregation of overall benefit was ascertained using preferences and clinical data. Monte-Carlo simulation was applied. Uncertainty was addressed by price acceptability curve (PAC) and net monetary benefit (NMB). RESULTS: The case study illustrates that progress in benefit and efficiency of hepatitis C virus treatments could be depicted very well with the EF. On the basis of cost, effect, and preference data, the latest generations of interferon-free treatments are shown to yield a positive NMB and be efficient at current prices. CONCLUSIONS: EF was implemented taking uncertainty into account. For the first time, a DCE was used with the EF. The study shows how DCEs in combination with EF, PAC, and NMB can contribute important information in the course of reimbursement and pricing decisions.

Multiple criteria decision analysis in the context of health technology assessment: a simulation exercise on metastatic colorectal cancer with multiple stakeholders in the English setting.

BACKGROUND: Multiple criteria decision analysis (MCDA) has appeared as a methodology to address limitations of economic evaluation in health technology assessment (HTA), however there are limited empirical evidence from real world applications. The aim of this study is to test in practice a recently developed MCDA methodological framework known as Advance Value Framework (AVF) through a proof-of-concept case study engaging multiple stakeholders. METHODS: A multi-attribute value theory methodological process was adopted involving problem structuring, model building, model assessment and model appraisal phases. A facilitated decision analysis modelling approach was used as part of a decision conference with thirteen participants. An expanded scope of the National Institute for Health and Care Excellence (NICE) remit acted as the study setting with the use of supplementary value concerns. Second-line biological treatments were evaluated for metastatic colorectal cancer (mCRC) patients having received prior chemotherapy, including cetuximab monotherapy, panitumumab monotherapy and aflibercept in combination with FOLFIRI chemotherapy. Initially 18 criteria attributes were considered spanning four value domains relating to therapeutic impact, safety profile, innovation level and socioeconomic impact. RESULTS: Nine criteria attributes were finally included. Cetuximab scored the highest overall weighted preference value score of 45.7 out of 100, followed by panitumumab with 42.3, and aflibercept plus FOLFIRI with 14.4. The relative weights of the two most important criteria (overall survival and Grade 4 adverse events) added up to more than the relative weight of all other criteria together (52.1%). Main methodological limitation was the lack of comparative clinical effects across treatments and challenges included the selection of "lower" and "higher" reference levels on criteria attributes, eliciting preferences across attributes where participants had less experience, and ensuring that all attributes possess the right decision theory properties. CONCLUSIONS: This first application of AVF produced transparent rankings for three mCRC treatments based on their value, by assessing an explicit set of evaluation criteria while allowing for the elicitation and construction of participants' value preferences and their trade-offs. It proved it can aid the evaluation process and value communication of the alternative treatments for the group participants. Further research is needed to optimise its use as part of policy-making.

Multiple Criteria Decision Analysis for Health Care Decision Making--An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force.

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting, objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making and a set of techniques, known under the collective heading multiple criteria decision analysis (MCDA), are useful for this purpose. MCDA methods are widely used in other sectors, and recently there has been an increase in health care applications. In 2014, ISPOR established an MCDA Emerging Good Practices Task Force. It was charged with establishing a common definition for MCDA in health care decision making and developing good practice guidelines for conducting MCDA to aid health care decision making. This initial ISPOR MCDA task force report provides an introduction to MCDA - it defines MCDA; provides examples of its use in different kinds of decision making in health care (including benefit risk analysis, health technology assessment, resource allocation, portfolio decision analysis, shared patient clinician decision making and prioritizing patients' access to services); provides an overview of the principal methods of MCDA; and describes the key steps involved. Upon reviewing this report, readers should have a solid overview of MCDA methods and their potential for supporting health care decision making.

Multiple Criteria Decision Analysis for Health Care Decision Making--Emerging Good Practices: Report 2 of the ISPOR MCDA Emerging Good Practices Task Force.

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making. A set of techniques, known under the collective heading, multiple criteria decision analysis (MCDA), are useful for this purpose. In 2014, ISPOR established an Emerging Good Practices Task Force. The task force's first report defined MCDA, provided examples of its use in health care, described the key steps, and provided an overview of the principal methods of MCDA. This second task force report provides emerging good-practice guidance on the implementation of MCDA to support health care decisions. The report includes: a checklist to support the design, implementation and review of an MCDA; guidance to support the implementation of the checklist; the order in which the steps should be implemented; illustrates how to incorporate budget constraints into an MCDA; provides an overview of the skills and resources, including available software, required to implement MCDA; and future research directions.

TESTING MULTI-CRITERIA DECISION ANALYSIS FOR MORE TRANSPARENT RESOURCE-ALLOCATION DECISION MAKING IN COLOMBIA.

OBJECTIVES: In 2012, Colombia experienced an important institutional transformation after the establishment of the Health Technology Assessment Institute (IETS), the disbandment of the Regulatory Commission for Health and the reassignment of reimbursement decision-making powers to the Ministry of Health and Social Protection (MoHSP). These dynamic changes provided the opportunity to test Multi-Criteria Decision Analysis (MCDA) for systematic and more transparent resource-allocation decision-making. METHODS: During 2012 and 2013, the MCDA framework Evidence and Value: Impact on Decision Making (EVIDEM) was tested in Colombia. This consisted of a preparatory stage in which the investigators conducted literature searches and produced HTA reports for four interventions of interest, followed by a panel session with decision makers. This method was contrasted with a current approach used in Colombia for updating the publicly financed benefits package (POS), where narrative health technology assessment (HTA) reports are presented alongside comprehensive budget impact analyses (BIAs). RESULTS: Disease severity, size of population, and efficacy ranked at the top among fifteen preselected relevant criteria. MCDA estimates of technologies of interest ranged between 71 to 90 percent of maximum value. The ranking of technologies was sensitive to the methods used. Participants considered that a two-step approach including an MCDA template, complemented by a detailed BIA would be the best approach to assist decision-making in this context. Participants agreed that systematic priority setting should take place in Colombia. CONCLUSIONS: This work may serve as the basis to the MoHSP on its interest of setting up a systematic and more transparent process for resource-allocation decision-making.

Two approaches to incorporate clinical data uncertainty into multiple criteria decision analysis for benefit-risk assessment of medicinal products.

BACKGROUND: The Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions (PrOACT-URL) framework and multiple criteria decision analysis (MCDA) have been recommended by the European Medicines Agency for structured benefit-risk assessment of medicinal products undergoing regulatory review. OBJECTIVE: The objective of this article was to provide solutions to incorporate the uncertainty from clinical data into the MCDA model when evaluating the overall benefit-risk profiles among different treatment options. METHODS: Two statistical approaches, the δ-method approach and the Monte-Carlo approach, were proposed to construct the confidence interval of the overall benefit-risk score from the MCDA model as well as other probabilistic measures for comparing the benefit-risk profiles between treatment options. Both approaches can incorporate the correlation structure between clinical parameters (criteria) in the MCDA model and are straightforward to implement. RESULTS: The two proposed approaches were applied to a case study to evaluate the benefit-risk profile of an add-on therapy for rheumatoid arthritis (drug X) relative to placebo. It demonstrated a straightforward way to quantify the impact of the uncertainty from clinical data to the benefit-risk assessment and enabled statistical inference on evaluating the overall benefit-risk profiles among different treatment options. CONCLUSIONS: The δ-method approach provides a closed form to quantify the variability of the overall benefit-risk score in the MCDA model, whereas the Monte-Carlo approach is more computationally intensive but can yield its true sampling distribution for statistical inference. The obtained confidence intervals and other probabilistic measures from the two approaches enhance the benefit-risk decision making of medicinal products.

Efficiency evaluation of 28 health systems by MCDA and DEA.

BACKGROUND: Policymakers, who are constantly discussing growing health expenditures, should know whether the health system is efficient. We can provide them with such information through international health system efficiency evaluations. The main objectives of this study are: (a) to evaluate the efficiency of health systems in 28 developed countries by multiple-criteria decision analysis (MCDA) and data envelopment analysis (DEA) and (b) to identify reasonable benchmark countries for the Czech Republic, for which we collect information on the relative importance of health system inputs and outputs. METHODS: We used MCDA and DEA to evaluate the efficiency of the health systems of 28 developed countries. The models included four health system inputs (health expenditure as a relative share of GDP, the number of physicians, nurses, and hospital beds) and three health system outputs (life expectancy at birth, healthy life expectancy, and infant mortality rate). The sample covers 27 OECD countries and Russia, which is also included in the OECD database. To determine the input and output weights, we used a questionnaire sent to health policy experts in the Czech Republic. RESULTS: We obtained subjective information on the relative importance of the health system inputs and outputs from 27 Czech health policy experts. We evaluated health system efficiency using four MCDA and two DEA models. According to the MCDA models, Turkey, Poland, and Israel were found to have efficient health systems. The Czech Republic ranked 16th, 19th, 15th, and 17th. The benchmark countries for the Czech Republic's health system were Israel, Estonia, Luxembourg, Italy, the UK, Spain, Slovenia, and Canada. The DEA model with the constant returns to scale identified four technically efficient health systems: Turkey, the UK, Canada, and Sweden. The Czech Republic was found to be one of the worst-performing health systems. The DEA model with the variable returns to scale identified 15 technically efficient health systems. We found that efficiency results are quite robust. With two exceptions, the Spearman rank correlations between each pair of models were statistically significant at the 0.05 level. CONCLUSIONS: During the model formulation, we investigated the pitfalls of efficiency measurement in health care and used several practical solutions. We consider MCDA and DEA, above all, as exploratory methods, not methods providing definitive answers.

A multiple criteria decision analysis to establish the use cases and candidate point of care tests to enter into a platform trial of multiple in vitro diagnostic point of care tests in the prehospital environment.

Background: There are increasing demands on Emergency Medical Services. More efficient treatment pathways are required to support conveyance decision making and patient referral in prehospital care. Point of Care testing is increasingly available and utilised across the NHS to support optimal ways of working. We aimed to design and conduct a Multiple Criteria Decision Analysis to prioritise in vitro point of care tests and use cases for inclusion in a platform trial of in vitro point of care testing in UK Emergency Medical Services. Methods: We designed a Multiple Criteria Decision Analysis that included systematic scoping reviews stakeholder recruitment, two stakeholder surveys and two stakeholder workshops to scope the use cases, explore criteria and map use cases, evaluate the criteria and measure the use cases against the criteria. Results: We recruited 32 stakeholders. We developed a scoring matrix with 4 criteria for scoring the use cases and 8 criteria for scoring the point of care tests and applied weighting determined from survey results. Use cases were scored by the stakeholders against 4 criteria. The 3 highest scoring use cases were point of care troponin testing in: possible Acute Myocardial Infarction, lactate testing in suspected sepsis and in trauma. We developed the process for scoring the point of care tests to be completed close to a proposed trial to allow for a changes in technology. Conclusions: We successfully designed a Multiple Criteria Decision Analysis to identify use cases and candidate tests for inclusion in a future platform trial of in vitro point of care testing in UK Emergency Medical Services. We identified 3 use cases for evaluation in a platform trial of in vitro point of care testing: troponin testing in possible acute myocardial infarction, lactate testing in suspected sepsis and lactate testing to identify occult haemorrhage in trauma.