Infrastructure Rating Tool: Using Decision Support Tools to Enhance ASCE Infrastructure Report Card Process

Clinical Decision Support Tools

BackgroundDuring the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs’ underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree’s nodes step-by-step in an interactive manner.ObjectiveThis study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts.MethodsWe developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention’s guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants’ accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants’ ratings of the DSTs’ usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants’ perception of the tools. We used ANOVAs and t tests to assess statistical significance.ResultsOur survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P<.001 for all 4 t tests). ANOVA detected no significant differences regarding mental effort between the 3 study groups. Differences between the 2 intervention groups were of small effect sizes and nonsignificant for all 3 measures of the quality of decision support and most measures of participants’ perception of the DSTs.ConclusionsWhen the decision space is limited, as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more with 1 type of user interface than the other.Trial RegistrationDeutsches Register Klinischer Studien DRKS00028136; https://tinyurl.com/4bcfausx (retrospectively registered)

This toolkit brings together those with first-hand experience of designing, delivering, evaluating and participating in a Longitudinal Integrated Clerkship (LIC) within a UK Higher Education Institution and those working closely on programmes focussing on Health Education England (HEE)'s and NHS England's national priorities. In August 2022, a collaborative workshop was held for students and tutors participating in a London-based LIC in 2021–2022, faculty with prior experience in running LICs, and HEE representatives. The aim of the workshop was to co-produce a toolkit to guide undergraduate institutions, who may wish to introduce an LIC within their medical school curriculum that aligns to these national priorities. Although this toolkit primarily focuses on a UK audience, we anticipate that other health systems facing a need for similar educational reform may also find use for this toolkit. The NHS Long Term Plan,1 the HEE Future Doctor Report2 and The Enhance Programme3 have outlined key national priorities for the future of health and social care (Figure 1), including how we can train our workforce to deliver these aims. These priorities include embedding generalist skills in early career doctors, so they can better provide person-centred care in the context of complex multi-morbidity, while considering the impact of deep-rooted health inequity and social determinants of health. They can better provide person-centred care in the context of complex multi-morbidity, while considering the impact of deep-rooted health inequity and social determinants of health. This toolkit has been written for medical schools but may also be beneficial for other undergraduate and postgraduate health educators, who are considering setting up longitudinal educational programmes to meet their local and national health and workforce priorities. This document may also enable health and social care providers and third sector organisations, who are partnering up to support educational programmes, to better understand how longitudinal courses may benefit their health priorities. Currently, undergraduate and postgraduate training is fragmented in its provision of educational supervision and patient care. The lack of continuity of relationships with patients, supervisors and peers, can make it harder to effectively address the increasing complexity of multi-morbidity at an individual and population level. Because this fragmentation continues in the educational experience of postgraduates, it has a domino effect on undergraduates placed within those fragmented clinical settings—it becomes easy to see how this cyclical lack of continuity could perpetuate workforce burnout and poor retention.4, 5 This cyclical lack of continuity could perpetuate workforce burnout and poor retention. There is also a need for the future workforce to better understand the effects of health inequity, both at an individual and community level. It is well-recognised that certain groups of patients have poorer health outcomes than others.6 However, a deeper understanding of local population health priorities is difficult to achieve within our current, fragmented teaching and training programmes and instead requires being embedded into a community over a period of time. A deeper understanding of local population health priorities is difficult to achieve within our current, fragmented teaching and training programmes and instead requires being embedded into a community over a period of time. LICs (Figure 2) are an ideal educational model to address the issues of fragmentation of the student experience, and the need for students to have a better grasp of local population health. LICs place a greater emphasis on continuity for students and patients,7 greater responsibility for patient care, with more rewarding outcomes for students/trainees, and their patients and communities.8 Furthermore, many of the LICs in the United Kingdom are based in primary care,9 which provides a fertile ground for students to develop meaningful longitudinal patient relationships, and allows students to be embedded within a local community. The discussions within our workshop were informed by the existing literature on LICs which provides an international lens on how to develop LICs, what benefit they can provide, and their pitfalls.10-12 This toolkit builds on this literature with our lived experience within a UK health care and higher education environment, and aims to re-frame the importance of LICs as an educational model that better aligns to new integrated care priorities in the United Kingdom. For consistency in this toolkit, we use the term 'Longitudinal Integrated Clerkship' as it is most commonly known within the literature. However, there is debate about whether this term is relevant to the UK context and whether a more general term such as 'longitudinal placement' would better capture what currently exists and is achievable within the UK landscape.13 Design & Development of an LIC Evaluation and Research of an LIC To ensure an LIC that is sustainable, it is important to identify and consult with key stakeholders from the outset. Consideration of your institution's culture will also be important to ensure the success of an LIC, particularly with regard to the assessment process and how the LIC will be perceived within the hidden curriculum.14 A co-created mission statement for your LIC can help to ensure that all stakeholders are on the same trajectory. The overarching aim of your LIC is likely to depend on your local context. For example, in your local area, a main driver may be the need to address workforce recruitment and retention. Alternatively, addressing health inequity within underserved communities in the local area may be your main driver. When considering learning outcomes for your LIC, these can be considered under the headings of educational and health outcomes to ensure mutual benefit for those served by health systems and educational institutions. Similarly to a programme's mission statement, LIC outcomes are best defined with input from educational, health and community stakeholders. Investment at this stage from all stakeholders will be important as there may be conflicting priorities that will need to be worked through. Educational outcomes should align with the broader priorities and values of your institution, as well as national priorities, such as the Medical Licencing Assessment and those from HEE. Creation of a curriculum blueprint will be useful at this stage. Health outcomes would consider NHS policy documents (such as the Five Year Forward view), as well as local policies relevant to health context. The success of an LIC will depend on how the principles of the longitudinal model are adapted to fit with existing educational and service delivery models in your institution and local area. While there are existing frameworks of what an LIC might look like, these are based on the international literature, and local context should be considered for the successful delivery of an LIC. For example, from current literature, an LIC should be long enough for students to establish meaningful relationships, and Worley et al. suggest that an appropriate length of time should be from 6 to 12 months.15 We would suggest the length of time should be conducive to students having repeated encounters with the same patients, educators and peers to maximise relationships. There is no formal consensus on how 'repeated encounters' should be defined but the aim is for students to experience patient care over time in different settings with different health care practitioners. Facilitating sustained patient–student partnerships across the course of an LIC lies at the heart of this educational model. It is through these partnerships that mutual benefit can be garnered. The aim is for students to experience patient care over time in different settings with different health care practitioners. Table 1 outlines some important factors to consider during the design of your LIC. Consider the overarching outcomes of your LIC. Use this to decide what stage of medical school it is best delivered in. Early years E.g., Key LIC outcome = deeper understanding of the health inequity and preventative care for your population. You may wish to introduce these foundational concepts via an LIC at an early stage of clinical training, when there may be more curriculum space to dedicate to these concepts, and students are establishing their values around health equity. Later years E.g., Key LIC outcome = enhancing preparation for practice in line with national priorities In this case, the LIC may be better suited for those who are about to graduate New medical schools or those undergoing curriculum review may wish to implement an LIC for an entire cohort Medical schools with an established curriculum could pilot an LIC with a small group of students, with a phased roll-out in future years, allowing for improvements in your model if you fully roll out. Or there are many LICs that are small cohort student-selected programmes. If do you choose to run a pilot LIC, consider how you will advertise and recruit prospective students. The format of an LIC can vary internationally. Within the United Kingdom, LIC structure can range from a full time LIC to 1 day a week. Also consider the balance between Consider if your original LIC outcomes can be achieved in your chosen format and whether you can evaluate and assess these outcomes (see below for evaluation and assessment) within the constraints of your given structure. LICs can take place in a variety of settings with one setting usually acting as the main base for student learning Adequate supervision ensures clinical and educational safety for students and the patients they care for. This is an important consideration across all clinical settings. Also consider how undergraduates and postgraduates, and students from different health professions within the same learning environment can be brought together to enhance learning for both groups, e.g., through group supervision, tutorials or patient care. Assessment should be considered from the outset of LIC planning to ensure there is alignment with learning outcomes, as well as medical school and national assessments. Consider How and when these assessments will be reviewed should also be factored into the timetable for students and supervisors. LICs are likely to require a shift in mindset and logistics from the existing culture of learning at your institution, not only from the perspective of students, but also placement supervisors and central faculty. Adequate support for all relevant groups will help to identify early teething problems and ensure smoother transitions during implementation (see Table 2). With any educational intervention, particularly one that is new to an institution, there will be multiple reasons to collect data. For future iterations of the course, it is important to establish what worked well and what could be improved. Broadly, this type of data would be regarded as evaluation. Additionally, different stakeholder groups will have particular outcomes they are interested in, and these data might fall under the category of research. When deciding what data to collect, it is worth looking back at the primary LIC objectives, and planning from the outset how evaluation will be conducted and data collected, alongside the design and development of the overall LIC. This will help make early decisions regarding why the data are being collected, when and from whom, and ensures timely ethics and funding applications. Reviewing the existing literature at this stage will help shape both the LIC and your evaluation and research questions. Kirkpatrick's hierarchy can be a useful heuristic when considering what type of data to collect—for example, collecting qualitative data from different stakeholders on their perceptions and experience of the LIC. You may also choose to collect specific quantitative student or patient outcome data. When deciding who to collect data from, consider the whole range of stakeholders. For example, if a key aim of your LIC is to improve patient access to health care in your local area, it would be important to hear from patients themselves on their experiences. When deciding who to collect data from, consider the whole range of stakeholders. While data are often collected to look at what additional value an intervention provided, it is equally important to ensure no harm has inadvertently been inflicted on stakeholder groups in the process. This is also something to consider when deciding what parameters should be evaluated. The timeframe of data collection is also an important consideration. In keeping with a longitudinal process, some research questions may be better answered by looking at different points across the course, to capture how data change over time. When writing up and presenting data, consider involving all stakeholders in this process. It would be worth looking at presenting and publishing avenues that reach a broad audience—for example, health care arenas, patient and community facing publications, and medical educators. The future health workforce will require new complex skills to manage increasingly complex population needs. Current undergraduate training needs to consider how it is preparing future graduates to develop the skills needed to adapt to the rapidly changing health care landscape. An LIC places continuity and integration at its core and is an ideal educational model to embed these key skills within the curriculum. This, in turn, could help prepare our future workforce in providing person-centred integrated care that meets population need. This toolkit can be used to guide those considering educational reform in line with population need. Finally, it is the collaborative partnerships with students, policy makers, educators, and health and social care providers that will help ensure alignment between population priorities, workforce needs and medical education. Current undergraduate training needs to consider how it is preparing future graduates to develop the skills needed to adapt to the rapidly changing health care landscape. We would like to thank Mohammed-Hareef Asunramu, one of our LIC students, and Monika Gupta from Health Education England's London Enhancing Generalist Skills Team for their contribution to this work. The authors have no conflict of interest to disclose. No data have been used in this submission that would require formal ethical approval. All participants of our workshop have been listed as authors and have contributed and consented to the work.

Gentle remediation options (GRO) are risk management strategies/technologies that result in a net gain (or at least no gross reduction) in soil function as well as risk management. They encompass a number of technologies, including the use of plant (phyto‐), fungi (myco‐), and/or bacteria‐based methods, with or without chemical soil additives or amendments, for reducing contaminant transfer to local receptors by in situ stabilization, or extraction, transformation, or degradation of contaminants. Despite offering strong benefits in terms of risk management, deployment costs, and sustainability for a range of site problems, the application of GRO as practical on‐site remedial solutions is still in its relative infancy, particularly for metal(loid)‐contaminated sites. A key barrier to wider adoption of GRO relates to general uncertainties and lack of stakeholder confidence in (and indeed knowledge of) the feasibility or reliability of GRO as practical risk management solutions. The GREENLAND project has therefore developed a simple and transparent decision support framework for promoting the appropriate use of gentle remediation options and encouraging participation of stakeholders, supplemented by a set of specific design aids for use when GRO appear to be a viable option. The framework is presented as a three phased model or Decision Support Tool (DST), in the form of a Microsoft Excel‐based workbook, designed to inform decision‐making and options appraisal during the selection of remedial approaches for contaminated sites. The DST acts as a simple decision support and stakeholder engagement tool for the application of GRO, providing a context for GRO application (particularly where soft end‐use of remediated land is envisaged), quick reference tables (including an economic cost calculator), and supporting information and technical guidance drawing on practical examples of effective GRO application at trace metal(loid) contaminated sites across Europe. This article introduces the decision support framework. ©2015 Wiley Periodicals, Inc.

A Return to Training Days Gone By: A Case for a Uniform Report Card System for Gynaecologic Surgery.

The health care environment is rife with issues that are urgently in need of solutions that can readily be addressed using appropriate tools for decision support. Recent developments in RFID technology facilitate this process through continuous provision of instantaneous item-level information. We consider an existing decision support framework and instantiate this framework using an example from the health care domain using RFID-generated item-level information. We illustrate the process by developing a health care knowledge-based system and evaluate its performance.

The influence of cognitive science and psychology on decision theory is bringing about changes to assumptions about decision making, and, as a consequence, the way that decisions should be modeled and supported. The three articles published in this Special Issue on intelligent decision support and modeling reflect an emerging literature that incorporates psychology into decision modeling and decision support. This literature represents only a starting point, and much remains to be done in terms of acknowledging the influence of psychology on engineering decisions. Decision support tools will probably never completely make up for engineers’ lack of the cognitive capacity needed to make the multitude of decisions associated with engineering design in a fully informed, unbiased way. Incorporating rational choice theory and psychology into decision support tools seems to be a fruitful path toward promoting optimal decisions in engineering. The last two to three decades have brought about important changes to the field of decision theory, particularly in response to the generally accepted principle that the act of cognitive representation and framing of decisions should follow the axioms of expected utility theory. The standard theory for decision making is based on subjective expected utility theory (e.g., Savage, 1954; Schmeidler, 1989), which involves the enumeration of possibilities, an analysis of the possible outcomes, and the selection of the utility-maximizing decision (Gilboa & Schmeidler, 2001). In engineering design, decision theory is generally applied as a systematic procedure for selecting design variables when there is uncertainty over the preferences associated with the objectives (Thurston, 1991, 2001). In schools of engineering, operations research, computer science, and business around the world, students continue to be taught a set of methodologies consonant with rational choice theory (Wood, 2004), which is founded upon the analysis of information as the basis of decision. In short, decision support and modeling in engineering design generally follows a framework of selecting design variables that optimize the expected utility of the design, and, in so doing, casts engineering design within a rational process (Hazelrigg, 1998). The perceived increased accuracy and rigor of the decisions and the decision support systems built upon the premise of utility maximization can mask the realities of eliciting preferences from engineers, which may be subject to psychological biases. Kahneman and Tversky (Tversky & Kahneman, 1974; Kahneman & Tversky, 1979) drew attention to the realities of human decision making in describing the heuristics that human beings employ in decision making under uncertainty, which are subject to psychological biases that can lead to systematic and predictable errors. In recent years, Kahneman and colleagues have published a series of articles dealing with strategies to correct for these psychological biases (e.g., Kahneman & Lovallo, 1993; Kahneman et al., 2011) and entire fields of behavioral finance, behavioral economics, and behavioral strategy have grown up around the application of cognitive science and psychology to the theory and practice of decision making under uncertainty in specific contexts. This influence is now being felt in the engineering design domain in the modeling and support of engineering decisions, which is built upon a rich heritage of studies on the cognitive and behavioral strategies of engineers. The three articles published in this Special Issue reflect a shift away from normative subjective utility maximization as the only model for decision making and decision support toward greater consideration to the exigencies of engineering decision making in practice. All three articles contribute to decision support and modeling and treat these as integrated issues rather than as compartmentalized problems. The article “Bayesian Project Diagnosis for the Construction Design Process” by Matthews and Philip is perhaps the most “traditional” of the three articles. The article deals with the problem of forecasting potential problems in construction processes. We use traditional in the sense that Bayesian modeling is an accepted method for estimating the probability of an event or outcome of interest (Marshall & Oliver, 1995). They model the construction process as a Markov chain, with transition probabilities associated with progressing through various Reprint requests to: Andy Dong, Faculty of Engineering and Information Technologies, University of Sydney, Engineering Building (J05), Sydney 2006, Australia. E-mail: andy.dong@sydney.edu.au Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2012), 26, 371–373. # Cambridge University Press 2012 0890-0604/12 $25.00 doi:10.1017/S0890060412000248

Decision support tools can aid decision making by systematically incorporating information, accounting for uncertainties, and facilitating evaluation between alternatives. Without user buy‐in, however, decision support tools can fail to influence decision‐making processes. We surveyed fishery researchers, managers, and fishers affiliated with the Lake Whitefish Coregonus clupeaformis fishery in the 1836 Treaty Waters of Lakes Huron, Michigan, and Superior to assess opinions of current and future management needs to identify barriers to, and opportunities for, developing a decision support tool based on Lake Whitefish recruitment projections with climate change. Approximately 64% of 39 respondents were satisfied with current management, and nearly 85% agreed that science was well integrated into management programs. Though decision support tools can facilitate science integration into management, respondents suggest that they face significant implementation barriers, including lack of political will to change management and perceived uncertainty in decision support outputs. Recommendations from this survey can inform development of decision support tools for fishery management in the Great Lakes and other regions.

Patient perceptions of a decision support tool to assist with young women’s contraceptive choice

Aerial application of dispersants are an effective means of responding to oil spills in coastal waters and the deeper waters of the Outer Continental Shelf or Gulf of Mexico. To ensure the safety of responders and nearby wildlife, a buffer area is put in place around the spilled oil to be treated, within which spraying operations are conducted. In 2015, a research project was initiated to develop a prototype Decision Support Tool (DST) designed specifically for estimating the spray drift during the aerial application of dispersants on an oil spill. In 2019, an initiative was undertaken to further develop the DST and address known data gaps in the modeling used in the prototype, expand on the aircraft included in the tool, and include a contour plot output of dispersant deposition. The DST has been designed specifically for estimating the spray drift during the aerial application of dispersants on an oil spill through the use of complex Computational Fluid Dynamics (CFD) modeling. The DST program operational space was developed based on direct input from Oil Spill Response Operators (OSROs) for ten airframes currently used in the United States for aerial response operations, including both turbo propeller and turbo fan engine types. The DST employs a database of results generated using the latest in CFD modeling technology to examine flow structures and drift effects created by various operating conditions, coupled with specific configurations of different oil spill response aircraft and their spray systems (boom and nozzle configurations). The DST uses a Response Surface Curve (RSC) for each airframe to predict the drift extent of dispersant particles and mass deposition concentration, the RSC for each airframe was derived from a database of results generated using the latest CFD modeling technology. The studies conducted to generate data for the DST RSCs provided considerable insight into the relationships between the particle dispersant behavior for different airframe types. Trends were identified in particle dispersion behavior when airframes were flown with a heavy payload (full weight) compared to lighter payload (empty weight). These trends change depending on the airframe used and, more specifically, the location and arrangement of the boom used to release the droplets relative to the location of the main wing. Change in Particle Size Distribution (PSD) was also investigated for flight operations of one airframe and the impact on the drift extent reported. The DST will provide oil spill responders with information related to the extent of any areas potentially impacted by dispersant drift. This will assist the operational control personnel in establishing setback distances, information which becomes increasingly important as a spill escalates beyond a Tier 1 response where the size of the spill, and the resources committed, become significant. In addition, the DST generates a contour plot of mass deposition at ground level based on the operational and environmental parameters input to the program, providing the user with a graphical display of where the majority of the aerial dispersant is predicted to land. While the analysis and tool development are complete, a formal peer review has not been completed at the time of the paper publication.

Air traffic management decision support tools have shown the capability to incr ease arrival traffic throughput of congested Terminal Radar Approach Control (TRACON) facilities without significantly impacting air traffic controller workload. NASA Ames Research Center, in cooperation with the Federal Aviation Administration (FAA), is playing a leading role in identifying air traffic management problems, developing and prototyping concepts, and performing field trials for such decision support tools. The Center -TRACON Automation System (CTAS) is a suite of decision support tools devel oped by NASA Ames Research Center, and is included in the FAA's Free Flight Program. This paper describes the concept and development plan of the Expedite Departure Path (EDP) component of CTAS. EDP is a decision support tool aimed at providing TRACON Tr affic Management Coordinators (TMCs) with pertinent departure traffic loading and scheduling information, and radar controllers with advisories for tactical control of TRACON departure traffic. EDP employs the CTAS trajectory synthesis routine to provide conflict -free altitude, speed and heading advisories. These advisories will assist the TRACON departure controller in efficiently sequencing, spacing and merging departure aircraft into the en route traffic flow. The anticipated benefits of EDP include a reduction in airborne delay for departure aircraft, reduced fuel burn and reduced noise impact due to expedited climb trajectories. EDP will eventually share information with both surface and arrival decision support tools to form an integrated decision support system capable of planning, coordinating and executing highly efficient terminal airspace operations.

The renovation of existing buildings usually involves decision-making processes aiming at reducing energy consumption and building maintenance costs. The goal of this paper is to prioritize what components/systems need to be replaced. It focuses on renovation strategies from the maintenance density and energy consumption point of view. A Decision Support Framework (DSF) was developed for future development of Decision Support System. Data warehousing methodologies were used to extract, store and analyze maintenance data. A Web application was developed to display the frequency of faults and density of maintenance of six distinct buildings at University College Cork (UCC). A generic Decision Support Model (DSM) with six sections was developed, which was implemented to a computerized semi-automatic tool – Decision Support Tool (DST). Integrating the analysis results and the DST, five renovation suggestions were proposed.

Insights for developing effective decision support tools for environmental sustainability

This paper seeks to identify the most environmental friendly way of conducting a refurbishment of Broendby Strand, with focus on PCB remediation. The actual identification is conducted by comparing four remediation techniques using urban metabolism fused with life cycle assessment (UM-LCA) in combination with information relating to cost and efficiency of the compared techniques. The methodological goal of our paper is to test UM-LCA as a decision support tool and discuss application of the method in relation to large refurbishment projects. To assess the environmental performance of PCB-remediation techniques, the UM-LCA method was applied. By combining UM and LCA methodologies, the total environmental impact potentials of the remediation techniques were calculated. To build an inventory for each technique, we contacted and interviewed experts and studied existing literature, cases, and projects in order to compile information on practical details of the techniques. To process the collected inventory data, we used the simplified product system modeling software Quantis Suite 2.0 (QS2.0). In order to validate the results from the simplified software, we carried out the exact same analysis using a more complex tool—OpenLCA 1.5. Based on the assessment results, we compared the remediation techniques and identified the techniques with the smallest and largest environmental impact potentials. The results obtained are presented, and the technique with the smallest impact identified. A comparison between the two software tools applied is made, and differences between the two are discussed in detail. Further discussed is how possible inventory errors affect the results and if any assumptions should be considered as critical for the final results. Furthermore, are the remediation efficiencies of each technique and the cost of each method considered and compared. Finally, UM-LCA’s ability to work as a tool for decision support is discussed and possible ways of implementing the method in sustainable decision-making is considered. In this study, it is found that the most environmental friendly PCB-remediation technique is thermal desorption, whereas the technique with the largest environmental impact potential is sand blasting, due to the environmental impacts induced in relation to disposal of the building waste. It is concluded that the UM-LCA method can be applied as a tool for decision support, and if economic aspects are incorporated, the UM-LCA approach could be an essential approach for designing sustainable buildings.

PurposePressure is growing globally for larger businesses to improve the energy performance of the buildings in which they operate. Property or facility managers are usually responsible for these improvements through energy‐led refurbishment. The number and complexity of possible interventions pose challenges that these professionals attempt to meet by using decision support tools (DSTs). The work aims to identify key features of DSTs for energy‐led building refurbishment and define an optimum approach.Design/methodology/approachA desk study examined ten DSTs reported in the available literature and evaluated them against a set of desirable attributes that a property or facility manager would value for this task.FindingsThe results of the desk study concluded that no DST offers every feature and that there is an opportunity for a new DST for energy‐led refurbishment. An optimum DST template is proposed, consisting of a seven‐step process from assessment of the existing state of the building through to continuous evaluation and improvement of the refurbished building.Originality/valueThe work combines the best features of available DSTs into a novel optimised strategy for energy‐led refurbishment of non‐domestic buildings, which is geographically non‐specific and could be applied anywhere in the world.