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The Industry 5.0 concept has placed human needs at the heart of industrial processes. This raises the question of how new technologies can enhance employee decision-making processes and influence the evolution of team autonomy. Recent studies have shown that the best way to measure these impacts is to conduct experiments in complex and realistic environmental settings. However, the main methods cannot satisfy this requirement while controlling the events and associated variables, whereas a set of use cases can. Therefore, a model should be defined to generate and structure these use cases while validating their relevance. Following the decomposition of the global research objective and case-definition recommendations, this study proposes a framework for designing complementary use cases to evaluate the impact of new technologies on emerging autonomy models in a structured, realistic, and global manner. Based on widely recognised related work, the 6-step framework helps define a coherent context specifying the business process model, agent, autonomy, technologies to be implemented, their fields of action, detailed variable collection protocol, and experimental setup. A cross-analysis of existing cases from the literature and empirical use of the framework validated the relevance of the model in designing experimental environments that are close to real-world settings. [ABSTRACT FROM AUTHOR]

Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a "working database", the system now includes program management modules and literature databases, which are all accessible via a web interface. Originally designed to manage data in the ecological Research Unit 816 (SE Ecuador), the open software is now being used in several other environmental research programs, demonstrating its broad applicability. While the system was mainly developed for abiotic and biotic tabular data in the beginning, the new research program demands full capabilities to work with area-wide and high-resolution big models and remote sensing raster data. Thus, a raster engine was recently implemented based on the Geo Engine technology. The great variety of pre-implemented desktop GIS-like analysis options for raster point and vector data is an important incentive for researchers to use the system. A second incentive is to implement use cases prioritized by the researchers. As an example, we present machine learning models to generate high-resolution (30 m) microclimate raster layers for the study area in different temporal aggregation levels for the most important variables of air temperature, humidity, precipitation, and solar radiation. The models implemented as use cases outperform similar models developed in other research programs. Dataset: The link to the datasets is as follows: https://respect.app.geoengine.io (accessed on 29 November 2024). [ABSTRACT FROM AUTHOR]

Digital product passport (DPP) is a key regulatory element of the latest European Union's Circular Economy Action Plan. By enhancing product traceability and supply chain transparency, its upcoming mandatory implementation aspires to improve resource circularity, cut emissions, and strengthen supply chain governance. However, the extant literature lacks a comprehensive understanding of DPP use cases in the supply chain and how they enable sustainable and circular supply chain management (SCM). To address this knowledge gap, this study first provides an overview of the current state of DPP in policy and practice. Based on a structured review of 82 DPP supply chain use cases in academic literature, we synthesise the functional areas that DPP can add value to improving supply chain sustainability and circularity. Informed by the review findings, we first discuss theoretical and practical implications of DPP-enabled sustainable and circular SCM, and then prioritise research needs in the emerging research domain. [ABSTRACT FROM AUTHOR]

For the reuse of traction batteries, many different scenarios exist, for example, stationary storage farms or fast charging stations. Another second-life usage scenario is the reuse of batteries as home energy storage in combination with a photovoltaic installation in a private household. This application is the focus of the present study. Home energy storage is a reasonable possibility for storing renewable energy and conserving resources, but it also includes multiple challenges regarding reliability and safety requirements. Within this study, these challenges are investigated. A battery inspection concept was developed, and a logistic model for considering the legal requirements was created. Data from different use cases were selected, and their structure was homogenized. To assess their safety, fire tests were performed. In addition, a concept for a reliability assessment that provides the possibility to evaluate the suitability of a battery for a second-life application based on usage data in its first life was developed. Based on the results, a prototype of a second-life storage system was built from traction battery cells removed from electric vehicles. This prototype is currently used to store energy from a photovoltaic system, and its usage data were collected. [ABSTRACT FROM AUTHOR]

Among the novel 3D visualization technologies of our era, light field displays provide the complete 3D visual experience without the need for any personal viewing device. Due to the lack of such constraint, these displays may be viewed by any number of observers simultaneously, and the corresponding use case contexts may also involve a virtually unlimited numbers of users; any number that the valid viewing area of the display may accommodate. While many instances of the utilization of this technology operate with static contents, camera animation may also be relevant. While the topic of light field camera animation has already been addressed on an initial level, there are still numerous research efforts to be carried out. In this paper, we elaborate on the lessons learned from implementing light field camera animation. The paper discusses the associated implications, limitations, potentials, and future research efforts. Each of these areas are approached from the perspectives of use cases, visual content, and quality assessment, as well as capture and display hardware. Our work highlights the existing research gaps in the investigated topic, the severe issues related to visualization sharpness, and the lack of appropriate datasets, as well as the constraints due to which novel contents may be captured by virtual cameras instead of real capture systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal for Technology in Theory & Practice / Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis (TATuP) is the property of Oekom Verlag GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract This study provides a detailed exploration of how innovation management and digital entrepreneurship models can help transform technical use cases in smart grid contexts into viable business cases, thereby bridging the gap between technical potential and market application in the field of energy informatics. It focuses on the I-GReta project Use Cases (UCs). The study employs methodologies like Use Case Analysis, Portfolio Mapping of Innovation Level, Innovation Readiness Level, and the Tech Solution Business Model Canvas (TSBMC) to analyse and transition from technical use cases to viable business cases. This approach aligns technological solutions with market demands and regulatory frameworks, leveraging digital entrepreneurship models to navigate market challenges and foster energy management, sustainability, and digitalization.

Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system now includes program management modules and literature databases, which are all accessible via a web interface. Originally designed to manage data in the ecological Research Unit 816 (SE Ecuador), the open software is now being used in several other environmental research programs, demonstrating its broad applicability. While the system was mainly developed for abiotic and biotic tabular data in the beginning, the new research program demands full capabilities to work with area-wide and high-resolution big models and remote sensing raster data. Thus, a raster engine was recently implemented based on the Geo Engine technology. The great variety of pre-implemented desktop GIS-like analysis options for raster point and vector data is an important incentive for researchers to use the system. A second incentive is to implement use cases prioritized by the researchers. As an example, we present machine learning models to generate high-resolution (30 m) microclimate raster layers for the study area in different temporal aggregation levels for the most important variables of air temperature, humidity, precipitation, and solar radiation. The models implemented as use cases outperform similar models developed in other research programs.

Teachers are key users of classroom assessment data; however, their needs and preferences are often overlooked in the design and development of the assessments themselves. We used principles of Human-Centered Design (HCD) to systematically solicit, study, and integrate teachers' needs in the design and development of a classroom-based early mathematics assessment. We recruited 18 teachers to participate in a series of research activities that align with HCD methodology. Data informed the articulation of the use case and test specifications, thereby elevating teachers' voices to improve test-development decisions that consider the end users from the start. [ABSTRACT FROM AUTHOR]

In a blockchain network, a rule set called consensus mechanism is used to create and finalize a block. In a proof-of-stake (PoS), consensus-based blockchain network, nodes become validators, minters, or stakeholders' nodes to complete the consensus mechanism. In these networks, when a node becomes a validator node, its details need to be saved because the details of the validators are used in the network for many important decisions, such as selecting block proposers for the consensus process. In this paper, we present Sea Shield, which uses a validator chain to save a node's information when it becomes a validator or leaves its responsibility as a validator in the PoS-based blockchain network. The validator chain is a blockchain that can run with the main chain of a PoS-based blockchain. The internal features of the validator chain are similar to those of the blockchain. We designed and simulated a consensus mechanism to create and finalize the block for the validator chain with no forks. We present a process by which a node may join or unjoin as a validator in a PoS-based blockchain network to improve the overall security of the main chain-consensus process. [ABSTRACT FROM AUTHOR]

Cognitive automation (CA) moves beyond rule-based business process automation to target cognitive knowledge and service work. This allows the automation of tasks and processes, for which automation seemed unimaginable a decade ago. To organizations, these CA use cases offer vast opportunities to gain a significant competitive advantage. However, CA imposes novel challenges on organizations' decisions regarding the automation potential of use cases, resulting in low adoption and high project failure rates. To counteract this, we draw on an action research study with a leading European manufacturing company to develop and test a model for assessing use cases' amenability to CA. The proposed model comprises four dimensions: cognition, data, relationship, and transparency requirements. The model proposes that a use case is less (more) amenable to CA if these requirements are high (low). To account for the model's industry-agnostic generalizability, we draw on an internal evaluation within the action research company and three additional external evaluations undertaken by independent project teams in three distinct industries. From a practice perspective, the model will help organizations make more informed decisions in selecting use cases for CA and planning their respective initiatives. From a research perspective, the identified determinants affecting use cases' amenability to CA will enhance our understanding of CA in particular and artificial intelligence as the driving force behind CA in general. [ABSTRACT FROM AUTHOR]

Requirements Engineering (RE) is the fundamental concept in software engineering scope. Automatic analysis for identifying the requirements can decrease the time consumption, delay, and cost of software development as well. To achieve the best analysis of texts, Natural Language Processing helps to disambiguate the meanings to a great extent. Automatic RE can help much more because automatic RE has more accuracy as well as very low latency in the feature extraction, contrary to manual extraction. The current paper focuses on these issues and suggests a framework for the spontaneous detecting of requirements from customers' documents. The new framework applies to the first stage of requirements analysis. For this reason and according to the evaluation results, the study's new framework could detect topics, use cases, and topic groups by at least 97%, 92%, and 95%, respectively, on the customers' real requirement documents. [ABSTRACT FROM AUTHOR]

This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers. [ABSTRACT FROM AUTHOR]

This study provides a detailed exploration of how innovation management and digital entrepreneurship models can help transform technical use cases in smart grid contexts into viable business cases, thereby bridging the gap between technical potential and market application in the field of energy informatics. It focuses on the I-GReta project Use Cases (UCs). The study employs methodologies like Use Case Analysis, Portfolio Mapping of Innovation Level, Innovation Readiness Level, and the Tech Solution Business Model Canvas (TSBMC) to analyse and transition from technical use cases to viable business cases. This approach aligns technological solutions with market demands and regulatory frameworks, leveraging digital entrepreneurship models to navigate market challenges and foster energy management, sustainability, and digitalization. [ABSTRACT FROM AUTHOR]

Use case scenarios play an essential role in innovation projects and influence the design of technologies. In this research article, I examine how use cases are generated, varied, and anticipated. To do so, I analyze interviews with international innovation experts (N = 14), e.g., Chief Technology Officer, Innovation Director, or User Experience Researcher. A heterogeneity of practices for the design of use cases, the creation of use case variants, and the anticipation of a possible extension of use cases can be identified. Anti-personas and worst-case scenarios are tools specifically aimed at identifying malicious use cases. A surprising result is the absence of special AI‑based procedures for use case generation, variation, and anticipation, indicating untapped potential for innovation projects.

As cities evolve, the need for systems that aid in their management becomes more necessary. Regarding traffic, and specifically the issues generated by vehicle parking, various smart parking system solutions have emerged in recent years. Although there is considerable available literature, most of it focuses on specific parts of the solution, such as providing a system architecture, analyzing types of sensors, or studying different communication methods. However, there is no proposed guide to follow for tackling the design and deployment of a comprehensive smart parking system. To address this gap, a possible workflow is proposed to guide the design and implementation of such systems. The article describes the step-by-step guide, as well as how it has been applied in the specific case of designing a limited-time paid parking system for the Girona City Council. The system aims to monitor the occupancy of various parking spaces in a zone and visibly indicate (using an LED) whether the vehicle parked in a space is within the allowed time limit or has exceeded it. It is worth noting that the city of Girona already has a LoRaWAN network, so the system development was carried out leveraging its existence. The application of the guide to the specific case is accompanied by examples of real sensors used, field measurements conducted to study user behavior, as well as radio coverage simulations and sensor network simulations that allow analysis of the effect of using message acknowledgments (ACK) or ADR. Although the use of the proposed guide is shown in a specific case, it can be easily extrapolated to other application cases.

Among the novel 3D visualization technologies of our era, light field displays provide the complete 3D visual experience without the need for any personal viewing device. Due to the lack of such constraint, these displays may be viewed by any number of observers simultaneously, and the corresponding use case contexts may also involve a virtually unlimited numbers of users; any number that the valid viewing area of the display may accommodate. While many instances of the utilization of this technology operate with static contents, camera animation may also be relevant. While the topic of light field camera animation has already been addressed on an initial level, there are still numerous research efforts to be carried out. In this paper, we elaborate on the lessons learned from implementing light field camera animation. The paper discusses the associated implications, limitations, potentials, and future research efforts. Each of these areas are approached from the perspectives of use cases, visual content, and quality assessment, as well as capture and display hardware. Our work highlights the existing research gaps in the investigated topic, the severe issues related to visualization sharpness, and the lack of appropriate datasets, as well as the constraints due to which novel contents may be captured by virtual cameras instead of real capture systems.

This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers.

The complexity of industrial challenges is emerging together with the exponentially developing information and communication technologies (ICT) that provide several implementation approaches for systems engineering. It is difficult for a single company to follow technological development and remain a pioneer in every topic. Still, the industrial challenges require experts in each technology. Therefore, collaboration among technology providers is an opportunity to gather all resources and competences needed for full delivery. A joint offering (JO) is a solution, that is, co‐created in collaboration between two or more actors that usually have complementary technological skills or value‐creation logics. A JO has several doubts relating to the use case (UC) in hand, the feasibility of the joint solution to be co‐created, the resources and skills needed to deliver the joint solution, the partners, the business value creation, the elements of the contract and the ownership of the outcome, just to mention a few. The Joint Offering Evaluation Framework (JOEF) uncovers these issues and supports decision‐making before the development of a JO starts. The JOEF comprises the Joint Offering Playbook and the Business Value Toolset (BVT). The Playbook offers seven viewpoints with checklists and tools for IT solution providers considering collaboration and co‐creation for a solution that they cannot deliver or sell alone. The BVT evaluates and illustrates the business value of a JO from the viewpoints of both creation and delivery, together with value capture and assessment. The JOEF was piloted with a digital twin (DT) UC from the pulp and paper industry. [ABSTRACT FROM AUTHOR]

In today's demanding healthcare landscape, the use of theoretical frameworks is paramount for navigating the complexities of digital health challenges. The Context Optimisation Model for Person-centred Analysis and Systematic Solutions (COMPASS) theoretical framework and implementation model serves as an invaluable direction tool in planning, implementing, and evaluating digital healthcare initiatives. This paper showcases the tangible value of the COMPASS implementation model through a use case scenario involving an accredited exercise physiologist and a healthcare user with Type 2 Diabetes Mellitus who seeks credible information via a mobile digital device. Within this example, the COMPASS model demonstrates the ability to enhance systematic processes, streamline the workflow of health professionals and develop their capabilities to actively contribute to the transformative realm of digital health. Through exploration of the use case and the significance of the systematic processes as a research direction, the empowerment of health professionals to play pivotal roles in ongoing digital health transformation is emphasised. The COMPASS model emerges as a powerful tool, guiding health professionals and organisations towards innovative and sustainable solutions in the dynamic landscape of digital healthcare. [ABSTRACT FROM AUTHOR]

Use case‐based utility analysis of acquisition technologies in bridge construction BIM‐based project management is currently still under development in infrastructure and engineering, especially in bridge construction. Particularly in the execution phase, only research has been done on how information may be acquired digitally and automatically at the construction site. Automated data acquisition may greatly facilitate the acquisition of relevant data and information during the construction phase, it may increase safety and make more efficient use of personnel at a construction site, reduce workloads and ensure a short‐cycle information provision, thus optimizing costs. Basically, each data acquisition should serve a specific use case and be triggered according to the lean philosophy and the pull principle. So far, there is no standard or recommendation for the decision‐making process, which acquisition technology should be used for which use case in order to generate the greatest benefit. In this paper, a use case based utility analysis for data acquisition technologies in bridge construction is presented. Blind spots of previous research are addressed to ensure a structured and transparent decision‐making process. [ABSTRACT FROM AUTHOR]

The digitalization of the energy sector enables a broad range of new digital use cases and business models. For instance, blockchain-technology can be used for the verification of tamper-resistant storage of asset data (asset logging) or manipulation-resistant guarantees of origin for electricity (labeling). Yet, it is associated with high implementation and operating effort. But many of these use cases require similar players, interfaces, data sets and data processing, so that synergies can result from a joint implementation. We thus evaluate these synergies in implementation and operating effort for use cases in the field of asset logging and labeling using a bottom-up evaluation of the components based on a methodology of Dossow (Energies 16:2424, 2022). Additionally, we extend this methodology to analyze the scalability of the use cases by assessing the relative effort reduction for an increasing number of players involved. The analysis already shows substantial synergies for combinations of two use cases. Yet, especially for combinations of three or more use cases a high effort reduction potential is derived. The highest synergies are obtained among the asset logging use cases, while a combination of asset logging and labeling use cases shows lower synergies in comparison. The analysis of the scaling of the use cases demonstrates that for labeling use cases the main effort driver is the number of consumers, while for asset logging use cases the number of asset operators shows to be more relevant. Thus, scaling effects outweigh the effort reduction potential of use case combinations especially for combinations of asset logging and labeling cases. [ABSTRACT FROM AUTHOR]

Recent advances in thermochemical storage technology have introduced a novel closed-loop thermal energy storage (TES) system, known as the heat battery (HB), which is believed to have great potential for aiding the energy transition in the built environment because of its higher energy density and neglectable storage loss compared to conventional TES systems. In order to investigate the potential use cases of the HB and provide practical feedback for its further development, this research employs a simulation-based approach to analyze its influence on building performance in various use cases within Dutch residential buildings. Stakeholders including the homeowner, distribution system operator, and district heating system operator are identified, and a preliminary list of use cases is defined based on relevant literature and input from the HB developer. The simulation approach is conducted to predict key performance indicators for each stakeholder. The Kruskal-Wallis test was employed to sort and scrutinize the simulation outcomes and discern the significance of each use case element. The findings demonstrated that the HB holds the potential to diminish both the operational energy cost by up to 30% for the homeowners and the peak heating load transmitted from the building to the district heating system. [ABSTRACT FROM AUTHOR]

The proliferation of fifth-generation (5G) networks has opened up new opportunities for the deployment of cellular vehicle-to-everything (C-V2X) systems. However, the large-scale implementation of 5G-based C-V2X poses critical challenges requiring thorough investigation and resolution for successful deployment. This paper aims to identify and analyze the key challenges associated with the large-scale deployment of 5G-based C-V2X systems. In addition, we address obstacles and possible contradictions in the C-V2X standards caused by the special requirements. Moreover, we have introduced some quite influential C-V2X projects, which have influenced the widespread adoption of C-V2X technology in recent years. As the primary goal, this survey aims to provide valuable insights and summarize the current state of the field for researchers, industry professionals, and policymakers involved in the advancement of C-V2X. Furthermore, this paper presents relevant standardization aspects and visions for advanced 5G and 6G approaches to address some of the upcoming issues in mid-term timelines. [ABSTRACT FROM AUTHOR]

Context. Use cases are widely used as a means of formulating requirements in the development of information systems. All subsequent design stages depend on the quality of their presentation. Structuring use cases can significantly increase their understanding and maintenance in the face of changing requirements.. Objective. Flexible technologies involve working in small teams. The existing communication between teams is not sufficient to highlight sub use cases at the project level. There is a need for automated analysis of the corpus of all use cases. Method. A mathematical model of a use case which makes it possible to define the criteria for comparing scenarios and eliminate the redundancy of descriptions is proposed. A four-step method for restructuring use cases has been developed. At the first stage, use cases are presented in a formalized form. At the second, they are stored in the repository, which ensures their quick search and placement. At the third stage, procedures of scenario comparison are performed. Scenario similarity criteria are proposed. At the fourth stage, the formation of subordinate use cases is carried out, their texts are coordinated with all interested teams, and the use cases that cause subordinate use cases are corrected. Results. Experiments providing the formalized compilation of use cases by several development teams followed by automated restructuring were carried out to test the proposed solutions. As a result, new subordinate use cases were correctly identified and the scope of use of previously formed ones was expanded. There was a significant reduction in the time for restructuring. Conclusions. The proposed method of restructuring use cases improves the clarity and consistency of requirements, the possibility of their adjustment and maintenance, and reduces the compilation time. The method can be used in the design of any information system, where the requirements are presented in the form of use cases. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article considers theoretical and practical aspects in planning information technology projects, and determines the urgency of this issue. Planning is the beginning of any targeted activity, in particular to achieve the goals of successful project implementation and consists of processes and requirements to determine the set of tasks and product timelines. When planning processes, the basis is a certain technology. Different models are used to model the technology of IT project development: the basic model is a waterfall based on the initial planning of the project, and on its basis other models are considered, which are considered hybrid. As for large, complex projects, when working with them, more complex planning methods are often used. An example is the Agile methodology, which was designed to help developers create a project that can be quickly adapted and transformed according to requests. Also suitable for projects with a high degree of uncertainty. That is, the purpose of the study is to improve the technology of planning information technology projects taking into account the method of use case. This method involves the development of a use case diagram and the construction of a use case, the calculation of the unadjusted weight of the actor and the unadjusted weight of the UUCW use case, an assessment of the technical and organizational complexity of the project. All this makes it possible to determine the total cost of the software. To achieve this goal, Viseven data was used and a number of problems in the project management system, which are covered in this article, were identified. Solving one of the problems, in particular, the large loss of time for processing (more than 50%) due to unclear criteria for evaluating the implementation of projects at the stage of initialization and planning. It is proposed to plan the duration and cost of the project based on the method of use case and adjustment factors, taking into account the complexity of its technical and organizational implementation. The paper substantiates these steps with appropriate calculations and conclusions.

Background: Multiple sclerosis (MS), epilepsy, and depression are chronic central nervous system conditions in which remote measurement technology (RMT) may offer benefits compared with usual assessment. We previously worked with clinicians, patients, and researchers to develop 13 use cases for RMT: 5 in epilepsy (seizure alert, seizure counting, risk scoring, triage support, and trend analysis), 3 in MS (detecting silent progression, detecting depression in MS, and donating data to a biobank), and 5 in depression (detecting trends, reviewing treatment, self-management, comorbid monitoring, and carer alert). Objective: In this study, we aimed to evaluate the use cases and related implementation issues with an expert panel of clinicians external to our project consortium. Methods: We used a Delphi exercise to validate the use cases and suggest a prioritization among them and to ascertain the importance of a variety of implementation issues related to RMT. The expert panel included clinicians from across Europe who were external to the project consortium. The study had 2 survey rounds (n=23 and n=17) and a follow-up interview round (n=9). Data were analyzed for consensus between participants and for stability between survey rounds. The interviews explored the reasons for answers given in the survey. Results: The findings showed high stability between rounds on questions related to specific use cases but lower stability on questions relating to wider issues around the implementation of RMT. Overall, questions on wider issues also had less consensus. All 5 use cases for epilepsy (seizure alert, seizure counting, risk scoring, triage support, and trend analysis) were considered beneficial, with consensus among participants above the a priori threshold for most questions, although use case 3 (risk scoring) was considered less likely to facilitate or catalyze care. There was very little consensus on the benefits of the use cases in MS, although this may have resulted from a higher dropout rate of MS clinicians (50%). Participants agreed that there would be benefits for all 5 of the depression use cases, although fewer questions on use case 4 (triage support) reached consensus agreement than for depression use cases 1 (detecting trends), 2 (reviewing treatment), 3 (self-management), and 5 (carer alert). The qualitative analysis revealed further insights into each use case and generated 8 themes on practical issues related to implementation. Conclusions: Overall, these findings inform the prioritization of use cases for RMT that could be developed in future work, which may include clinical trials, cost-effectiveness studies, and the commercial development of RMT products and services. Priorities for further development include the use of RMT to provide more accurate records of symptoms and treatment response than is currently possible and to provide data that could help inform patient triage and generate timely alerts for patients and carers. [ABSTRACT FROM AUTHOR]