Decision Support Tool For Design Research Articles

Abstract PO2-10-12: The devil is in the details: Workflow analysis of breast cancer treatment decision making and implications for decision support design

Abstract While numerous decision aids have been developed to support treatment decision-making in breast cancer1, widespread implementation remains limited due to challenges integrating these tools in clinical workflows2. Given these persistent issues, we need to better understand the complex workflows and needs of decision aid users - patients, their family caregivers, and breast cancer clinicians. Previous studies have explored various aspects of breast cancer team workflows3,4. Despite this work, it is still unclear how these complex workflows support or hinder decision aid use. In this study, we conducted an in-depth analysis of breast cancer team workflow to elicit design requirements for informatics tools to support patient-centered decision-making. This study was part of a larger project aimed at designing a COMputeried PAtient-centered Collaborative Technology (COMPACT) to support personalized decision-making for breast cancer patients. We conducted observations of clinicians and patients at one breast center, using a tablet computer with smart pen to take notes on all aspects of clinic workflow. We uploaded all observation notes into a qualitative data analysis software, Dedoose. Two researchers coded the observations in an inductive, consensus-based process. We met to review our codes and identify key themes emerging in the data. We observed 95 hours and 127 patient encounters across 20 clinician-centered and 8 patient-centered observations. We identified 10 themes related to the design of decision support, which we grouped into the 4 components of workflow6 (see Table 1). We identified numerous patient-specific factors communicated from the patient to the clinical team that influenced treatment decision-making. These “decision factors” covered the broader scope of the patient’s life that was important in the decision-making process (e.g., upcoming family wedding, work). Patients were frequently “knowledge brokers” and were asked to remember and relay information across members of the care team. We identified 28 different tools and technologies used to support treatment decision-making. Patient family caregivers (e.g., aunt, husband) played an important role in treatment decision-making. Based on these findings, we developed design requirements for informatics tools to better support patients (and clinicians). Our study highlights the complexity and interdependence of breast cancer treatment decision-making. Patients frequently made treatment decisions that influenced subsequent decisions. Decision aids typically focus on one decision or one care specialty and do not support consideration of several treatment options at one time1. Future research is needed to determine if and how decision support tools could incorporate the broader scope and interdependence between decisions to support patient-centered care. We highlight the need for informatics tools to consider the diverse and unique decision factors (e.g., hobbies) that are crucial to patient-centered decision-making. Decision support could help to elicit and incorporate these factors during treatment decision-making. However, the variable nature of these patient-specific factors presents a challenge to decision support design. Further research is needed to determine how to optimally incorporate such factors into decision support tools. We found inadequate system support for clinicians’ tasks including limited feedback loops to notify clinicians of completed tasks (e.g., mammogram, genetic testing). As a result, patients were often responsible for relaying information to and between different members of the clinical team. Given that patients only remember 50% of what clinicians tell them during visits7, a better system is needed to improve the reliability of information transfer. Our design guidelines can be used to improve the design of decision support tools and ultimately improve patient-centered decision-making. Table 1. Workflow components, themes, and associated challenges to decision support design Citation Format: Megan Salwei, Carrie Reale. The devil is in the details: Workflow analysis of breast cancer treatment decision making and implications for decision support design [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-10-12.

Evaluation of a multi-user requirements axiomatic design decision support tool for manufacturing process selection

AbstractManufacturing process selection presents numerous challenges to designers, including product complexity, consideration of production volumes and part tolerances. This paper introduces a design support tool based on the axiomatic design model to systematically transform requirements into functions and technological capabilities. The results from an evaluation of the implemented prototype tool in the field of medical device design demonstrates its usefulness in selecting the most suitable candidate manufacturing process for a given artifact, while taking into account multiple user requirements.

Strategic team design for sustainable effectiveness: A data-driven analytical perspective and its implications

Teams are building blocks of organizations and essential inputs of organizational success. This article studies a data-driven analytical approach that exploits the rich data accumulated in organizations in the digital era to design teams, including prescribing team composition and formation decisions. We propose to evaluate a team regarding its performance and temporal stability, referred to as sustainable effectiveness (SE). Our approach estimates the team's performance and stability using machine learning models. It then optimizes an integrated objective of the team's performance and stability through mixed-integer programming models formulated according to predictive models. Consequently, this approach mines meaningful team compositions from historical data and guides strategic team formation accordingly. We conduct empirical studies using authentic data from our partner company in the real estate brokerage industry. The findings reveal that teams that adhere to our model's recommendations achieve an average percentage improvement of 153.1% to 156.5% higher than the benchmark teams, particularly when recruiting one or two members in their actual SE during the post-formation period. We further disclose the mechanism underlying this improvement from the perspective of changes in team compositions. Our study provides a decision support tool for team design and ensuing team dynamic management.

Modeling the impact of future rainfall changes on the effectiveness of urban stormwater control measures

The convergence of urban expansion, deteriorating infrastructure, and a changing climate will escalate the risks of stormwater pollution and urban flooding in the coming decades. Using outputs from an ensemble of global climate models to drive a high spatial resolution stormwater model, we analyzed climate change impacts on urban stormwater runoff and control measures for 23 cities across the United States. Runoff model outputs for two future emissions scenarios ending in 2055 were compared against a historical scenario to assess changes. All cities showed increases in average annual stormwater runoff, with changes up to 30% over the next 30 years due to a greater frequency of high intensity storm events. Runoff model outputs showed substantial variation across cities with untreated stormwater runoff increasing by as much as 48%. Patterns of future runoff impacts within cities will affect the performance of distributed treatment strategies such as Green Stormwater Infrastructure (GSI) to meet municipal water quality improvement and runoff reduction goals. Results indicate that adoption of adaptable design standards and decision support tools that readily accommodate projected precipitation changes are critical for supporting more resilient designs of stormwater control measures.

Control intervention design for preclinical and clinical trials: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable.

Control comparator selection is a critical trial design issue. Preclinical and clinical investigators who are doing trials of stroke recovery and rehabilitation interventions must carefully consider the appropriateness and relevance of their chosen control comparator as the benefit of an experimental intervention is established relative to a comparator. Establishing a strong rationale for a selected comparator improves the integrity of the trial and validity of its findings. This Stroke Recovery and Rehabilitation Roundtable (SRRR) taskforce used a graph theory voting system to rank the importance and ease of addressing challenges during control comparator design. "Identifying appropriate type of control" was ranked easy to address and very important, "variability in usual care" was ranked hard to address and of low importance, and "understanding the content of the control and how it differs from the experimental intervention" was ranked very important but not easy to address. The CONtrol DeSIGN (CONSIGN) decision support tool was developed to address the identified challenges and enhance comparator selection, description, and reporting. CONSIGN is a web-based tool inclusive of seven steps that guide the user through control comparator design. The tool was refined through multiple rounds of pilot testing that included more than 130 people working in neurorehabilitation research. Four hypothetical exemplar trials, which span preclinical, mood, aphasia, and motor recovery, demonstrate how the tool can be applied in practice. Six consensus recommendations are defined that span research domains, professional disciplines, and international borders.

Control intervention design for preclinical and clinical trials: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable.

Control comparator selection is a critical trial design issue. Preclinical and clinical investigators who are doing trials of stroke recovery and rehabilitation interventions must carefully consider the appropriateness and relevance of their chosen control comparator as the benefit of an experimental intervention is established relative to a comparator. Establishing a strong rationale for a selected comparator improves the integrity of the trial and validity of its findings. This Stroke Recovery and Rehabilitation Roundtable (SRRR) taskforce used a graph theory voting system to rank the importance and ease of addressing challenges during control comparator design. "Identifying appropriate type of control" was ranked easy to address and very important, "variability in usual care" was ranked hard to address and of low importance, and "understanding the content of the control and how it differs from the experimental intervention" was ranked very important but not easy to address. The CONtrol DeSIGN (CONSIGN) decision support tool was developed to address the identified challenges and enhance comparator selection, description, and reporting. CONSIGN is a web-based tool inclusive of seven steps that guide the user through control comparator design. The tool was refined through multiple rounds of pilot testing that included more than 130 people working in neurorehabilitation research. Four hypothetical exemplar trials, which span preclinical, mood, aphasia, and motor recovery, demonstrate how the tool can be applied in practice. Six consensus recommendations are defined that span research domains, professional disciplines, and international borders.

A multi-objective simulation-based decision support tool for wine supply chain design and risk management under sustainability goals

Sustainable Supply Chain Network Design (SCND) poses a complex challenge, as it often requires trade-offs in decision-making. The problem becomes even more challenging when there is a need to integrate risk management into the design process. It is steered by this challenge that the purpose of the paper is twofold. First, it proposes a Simulation-Based Decision Support System (SBDSS) for a real case study of a Portuguese wine SCND problem. Second, it analyzes different wine Supply Chain (SC) configurations under risk situations and sustainability objectives. The main contributions of this research are as follows. First, it concerns the method that was employed, which, contrarily to traditional approaches in literature (e.g., stochastic or genetic programming), consists of a simulation approach, in which agents and their interactions in an environment with uncertainty are modeled. Second, for the generation of non-dominated solutions, a simheuristic was employed, which combines multi-objective optimization with simulation. Finally, the type of industry that was considered, due to the lack of cases focusing on it in literature. The results suggest that the proposed approach determines a set of Pareto-front solutions that consider the sustainability dimensions of SCND, as well as risk situations. Furthermore, the implications that several parameters have on the sustainability of the designed SCs were assessed, as well as the impacts that the considered risks have on the performance of SCs. In particular, it was found that transportation risks impacted considerably more the performance of the SC than the risks on the prices of energy and raw materials.

A framework for strategic online en-route operations: Integrating traffic flow and strategic conflict managements

This paper proposes an innovative operational framework for strategic online en-route operations, in the trajectory-based operations context. This framework captures the interactions of traffic flow and strategic conflict managements and integrates them in a more harmonized way. The two cores of this framework are a traffic situation awareness method based on digital airspace and a trajectory selection model. The traffic situation awareness method can efficiently calculate the traffic complexity to meet the real-time requirement. The trajectory selection model captures the controller workload accurately and the interactions between traffic flow and strategic conflict managements. Randomized numerical experiments in high-density scenarios are conducted to explore the potential impact of integrating traffic flow and strategic conflict managements. Results show that compared with the existing hierarchical operations method, integrating traffic flow and strategic conflict managements not only significantly improves airspace capacity utilization, but also further reduces the number of trajectory conflicts. The method proposed provides a new reference for developing the operational framework for high-density en-route airspace, in the future TBO context. In addition, this method has fine computational efficiency and it can also assist in the design of decision support tools for en-route operations.

An Integrated User Interface of Assessment and Optimization for Architectural Façade Shading Designs in Taiwan

In response to sustainable development goals, the architectural industry aims to decrease the high proportion of emissions and energy use in the construction sector. Therefore, the design method of building performance optimization (BPO) has been advocated in recent studies as a method for accomplishing high-performance building design. However, BPO remains difficult to implement in practice due to the lack of a definite process and supporting tools for architects/designers in the early design process. The purpose of this paper is to propose a BPO framework and integrated design decision support (DDS) interface to provide a visual and science-based analysis and assist designers working with high-performance building façade designs. The framework and DDS tool are then tested by designers through a practice design of the headquarters façade. All the designers started and implemented the facade optimization design in a short training session, although they reported that the developed support tools still needed to be improved in terms of also integrating optimization tools. The characteristics of the user interface help considerably with comparing and making decisions in optimal solutions. The results emphasize the importance of developing design support tools for practical adoption from practical designers’ perspectives.

Decision-making and related outcomes of patients with complex care needs in primary care settings: a systematic literature review with a case-based qualitative synthesis

BackgroundIn primary care, patients increasingly face difficult decisions related to complex care needs (multimorbidity, polypharmacy, mental health issues, social vulnerability and structural barriers). There is a need for a pragmatic conceptual model to understand decisional needs among patients with complex care needs and outcomes related to decision. We aimed to identify types of decisional needs among patients with complex care needs, and decision-making configurations of conditions associated with decision outcomes.MethodsWe conducted a systematic mixed studies review. Two specialized librarians searched five bibliographic databases (Medline, Embase, PsycINFO, CINAHL and SSCI). The search strategy was conducted from inception to December 2017. A team of twenty crowd-reviewers selected empirical studies on: (1) patients with complex care needs; (2) decisional needs; (3) primary care. Two reviewers appraised the quality of included studies using the Mixed Methods Appraisal Tool. We conducted a 2-phase case-based qualitative synthesis framed by the Ottawa Decision Support Framework and Gregor’s explicative-predictive theory type. A decisional need case involved: (a) a decision (what), (b) concerning a patient with complex care needs with bio-psycho-social characteristics (who), (c) made independently or in partnership (how), (d) in a specific place and time (where/when), (e) with communication and coordination barriers or facilitators (why), and that (f) influenced actions taken, health or well-being, or decision quality (outcomes).ResultsWe included 47 studies. Data sufficiency qualitative criterion was reached. We identified 69 cases (2997 participants across 13 countries) grouped into five types of decisional needs: ‘prioritization’ (n = 26), ‘use of services’ (n = 22), ‘prescription’ (n = 12), ‘behavior change’ (n = 4) and ‘institutionalization’ (n = 5). Many decisions were made between clinical encounters in situations of social vulnerability. Patterns of conditions associated with decision outcomes revealed four decision-making configurations: ‘well-managed’ (n = 13), ‘asymmetric encounters’ (n = 21), ‘self-management by default’ (n = 8), and ‘chaotic’ (n = 27). Shared decision-making was associated with positive outcomes. Negative outcomes were associated with independent decision-making.ConclusionOur results could extend decision-making models in primary care settings and inform subsequent user-centered design of decision support tools for heterogenous patients with complex care needs.

Decision support tool for sustainable design of emerging technologies

Decision support tool for sustainable design of emerging technologies

Enhancing the Urban Resilience to Flood Risk Through a Decision Support Tool for the LID-BMPs Optimal Design

Enhancing the Urban Resilience to Flood Risk Through a Decision Support Tool for the LID-BMPs Optimal Design

Using a computer based selection model for sizing of solar panels and battery back-up systems for use in a floating in-pond raceway

The In-pond Raceway (IPR) is a relatively new option for production aquaculture, which depends on water moving devices to provide constant flow. Failure of these devices will often result in a catastrophic loss of fish in the system. Thus, robust power backup systems are necessary to mitigate risk in the event of prolonged power outage. Because these systems must be dependable and many suitable locations for these raceways are in ponds in remote locations, farmers have considered utilizing off-grid solar photovoltaic (PV) systems with battery storage. PV with battery storage eliminates the expense of running utility power to remote areas and eliminates the associated risk of power failure when these lines go down. However, the PV and battery storage must be properly sized to ensure continuously available power. This study modeled energy requirements using a daily energy balance for these unique systems. The model was then utilized to determine whether sized systems could withstand varying electric loads based on monitored hourly climatological conditions at seven different locations within the state of Kentucky in the United States. Determining the appropriate size was an iterative process, where the battery storage and PV array were incrementally increased until the model predicted that the system would provide continuous power year-round. The model was also used to examine the effects of undersized systems. Energy depletion events (when not enough energy remained to drive the water moving device) were clustered in groups of multiple days in winter months over the twenty-two-year dataset. Therefore, undersized systems may be appropriate if there was no intention of stocking fish in winter. Establishing this model led to the development of a design decision support tool aimed at potential new Aquaculture producers and a series of recommendation tables, with the intention of streamlining the initial design process for this novel technology.

Assessing the role of short-term weather forecasts in fire manager tactical decision-making: a choice experiment

BackgroundWeather plays an integral role in fire management due to the direct and indirect effects it has on fire behavior. However, fire managers may not use all information available to them during the decision-making process, instead utilizing mental shortcuts that can bias decision-making. Thus, it is important to evaluate if (and how) fire managers use information like weather forecasts when making tactical decisions. We explore USDA Forest Service fire manager confidence in relative humidity, precipitation, and wind models. We then use a choice experiment where key weather attributes were varied to explore how sensitive fire managers were to changes in specific weather variables when choosing to directly or indirectly attack a fire that is transitioning to extended attack.ResultsRespondents were less confident in the accuracy of wind and precipitation forecasts than relative humidity or weather forecasts more generally. The influence of weather information on the decision depended on the framing used in the choice experiment; specifically, whether respondents were told the initial strategy had been to directly or indirectly attack the fire. Across conditions, fire managers generally preferred to indirectly attack the fire. Decisions about the tactics to apply going forward were more sensitive to time in season when the fire was occurring and wind and precipitation forecasts than to other attributes.ConclusionsThe results have implications for the design of decision support tools developed to support fire management. Results suggest how fire managers’ use of fire weather information to evaluate forecast conditions and adjust future management decisions may vary depending on the management decision already in place. If fire weather-based decision support tools are to support the use of the best available information to make fire management decisions, careful attention may be needed to debias any effect of prior decisions. For example, decision support tools may encourage users to “consider the opposite,” i.e., consider if they would react differently if different initial decision with similar conditions were in place. The results also highlight the potential importance of either improving wind and precipitation forecast models or improving confidence in existing models.

Aircraft Deconfliction via Mathematical Programming: Review and Insights

Computer-aided air traffic management has increasingly attracted the interest of the operations research community. This includes, among other tasks, the design of decision support tools for the detection and resolution of conflict situations during flight. Even if numerous optimization approaches have been proposed, there has been little debate toward homogenization. We synthesize the efforts made by the operations research community in the past few decades to provide mathematical models to aid conflict detection and resolution at a tactical level. Different mathematical representations of aircraft separation conditions are presented in a unifying analysis. The models, which hinge on these conditions, are then revisited, providing insight into their computational performance.

The impact of negotiators’ motivation on the use of decision support tools in preparation for negotiations

AbstractThorough preparation for a negotiation is considered critical for the achievement of successful relational and substantive results. Careful specification of preferences and determining the negotiation offer scoring systems is one of the most important preparation activities. To facilitate this process, preference elicitation aids have been designed and implemented in decision and negotiation support systems (NSSs). This paper shows that negotiators’ motivation affects the use of simple elicitation aid and elicited preferences. We identify three types of motivations: epistemic, social, identity, and assign the factors that describe them. Then, using the dataset from electronic negotiation experiments, we apply logistic regression to identify those motivations that allow distinguishing negotiators who make errors in the determination of the scoring systems from those who do not make them. The key result allows us to identify relational‐ and learning‐oriented goals of the identity motivation as having a significant and direct impact on the negotiators’ classification. Accommodating and competing approaches of social motivation impact agents' accuracy with the differences observed for gender. Surprisingly, epistemic motivation represented by rationality and experientiality factors does not affect users’ accuracy in the prenegotiation phase. The results obtained can be used to design decision support tools adjusted to the motivational profiles of the NSS users.

Better rules for judging joules: Exploring how experts make decisions about household energy use

Public understanding of home energy use is rife with biases and misunderstandings that can stymie the adoption of efficient technologies and conservation practices. Studying how energy experts make energy-related judgments can help design decision support tools to correct misperceptions held by novices. Here we conduct interviews with electrical engineers (n = 10), physicists (n = 10), and energy analysts (n = 10) to document expert judgments about energy use and to identify their cognitive shortcuts (heuristics) for household energy decision making. Performance on an energy estimation task confirmed that energy experts have more accurate estimates of home energy use than novices. We document 24 unique expert heuristics related to device functions, components, and observable cues used by experts while making energy-use judgments. A follow-up survey with the experts indicated that these expert heuristics are generally more accurate than novice heuristics. The library of heuristics created in this study can be useful additions to education programs designed to improve public energy literacy and decision making.

Research and Development Directions for Design Support Tools for Circular Building

To support the construction sector in its transition to a circular economy, many design instruments and decision support tools have been and are still being developed. This development is uncoordinated and raises confusion among building designers and advising engineers, slowing down the tools’ adoption in practice. Moreover, it is unclear if the available design tools are able to fulfil the needs of design professionals at all. Therefore, this research identifies the knowledge challenges for the “supply and demand” of design tools for a circular construction practice. It focuses on Flanders, given the importance the topic receives in the region’s policy programme and among practitioners. This study builds on a thorough literature review, and on inventorying and categorising instruments and ongoing developments. By comparing that review with the needs that were identified during interviews with a focus group, it was possible to pinpoint designers’ needs for support tools and outline three urgent research tracks. More generally, it was found that the needs of our focus group are only partially reflected by the available design tools and the ongoing developments. This identified mismatch advocates for a more participatory and practice-oriented research approach when developing design support tools for circular building.

Opportunities and Recommendations for Improving Medication Safety: Understanding the Medication Management System in Primary Care Through an Abstraction Hierarchy.

BackgroundDespite making great strides in improving the treatment of diseases, the minimization of unintended harm by medication therapy continues to be a major hurdle facing the health care system. Medication error and prescription of potentially inappropriate medications (PIMs) represent a prevalent source of harm to patients and are associated with increased rates of adverse events, hospitalizations, and increased health care costs. Attempts to improve medication management systems in primary care have had mixed results. Implementation of new interventions is difficult because of complex contextual factors within the health care system. ion hierarchy (AH), the first step in cognitive work analysis (CWA), is used by human factors practitioners to describe complex sociotechnical systems. Although initially intended for the nuclear power domain and interface design, AH has been used successfully to aid the redesign of numerous health care systems such as the design of decision support tools, mobile patient monitoring apps, and a telephone triage system.ObjectiveThis paper aims to refine our understanding of the primary care office in relation to a patient’s medication through the development of an AH. Emphasis was placed on the elements related to medication safety to provide guidance for the design of a safer medication management system in primary care.MethodsThe AH development was guided by the methodology used by seminal CWA literature. It was initially developed by 2 authors and later fine-tuned by an expert panel of clinicians, social scientists, and a human factors engineer. It was subsequently refined until an agreement was reached. A means-ends analysis was performed and described for the nodes of interest. The model represents the primary care office space through functional purposes, values and priorities, function-related purposes, object-related processes, and physical objects.ResultsThis model depicts the medication management system at various levels of abstraction. The resulting components must be balanced and coordinated to provide medical treatment with limited health care resources. Understanding the physical and informational constraints on activities that occur in a primary care office depicted in the AH defines areas in which medication safety can be improved.ConclusionsNumerous means-ends relationships were identified and analyzed. These can be further evaluated depending on the specific needs of the user. Recommendations for optimizing a medication management system in a primary care facility were made. Individual practices can use AH for clinical redesign to improve prescribing and deprescribing practices.

A decision support tool for climate-informed and socioeconomic urban design

We develop a novel statistical decision-theoretic framework for urban design from an outdoor thermal comfort (OTC), social and economic perspectives. We combine those aspects into spatio-temporal risk measures which provide a compact representation of the overall quality of an urban design scenarios (UDS) set. We then formulate the selection of the optimal design as an optimization problem which is easy to solve and has a clear interpretation. To illustrate how our framework can be used in practice, we present a real-world study, which is based on a set of UDS that aim to improve the OTC of a specific site in Singapore. We show how our framework provides decision makers the flexibility to make choices relevant to their design objectives, leading to informed and interpretable design option selection.