A Care Quality Dashboard for General Practitioners Managing Patients with Diabetes Mellitus Type 2: User- Centered Design and Evaluation of a Prototype

A Care Quality Dashboard for General Practitioners Managing Patients with Diabetes Mellitus Type 2: User- Centered Design and Evaluation of a Prototype | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Care Quality Dashboard for General Practitioners Managing Patients with Diabetes Mellitus Type 2: User- Centered Design and Evaluation of a Prototype Lola Jo Ackermann, Odile-Florence Giger, Marinja Prinicpe, Michael Brändle, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8347867/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Diabetes mellitus type 2 (T2D) is a growing burden in Switzerland, where general practitioners face increasing workload. To evaluate the quality of T2D care, the Swiss Society of Endocrinology and Diabetology (SGED) developed the SGED score. However, its practical use is hampered by paper-based workflows and fragmented documentation. Currently, there is no dashboard specifically for the visualization of the SGED score, which overviews aggregated population parameters such as HbA1c or blood pressure. To address this gap, this study derived functional requirements for a dashboard and developed a high-fidelity prototype through an iterative, user-centered design process, in collaboration with healthcare professionals. This approach explores how such a dashboard needs to align with clinical needs to enhance usability and promote adoption in routine T2D management. Methods An iterative, user-centered three-step approach was employed, involving 14 semi-structured interviews with Swiss T2D healthcare professionals. Step 1 involved defining the project scope and identifying functional requirements. Step 2 collected more requirements and prioritized all of them using the “Must Have”, “Should Have”, “Could Have”, “Won’t Have” (MoSCoW) method. In step 3, a high-fidelity Figma dashboard prototype was developed and iteratively refined based on the requirements and user feedback. Results Key functional requirements included reminder and alert functions, color-coded critical values, demographic overviews, trend analyses, benchmarking within networks, and exportable reports. Additional needs emerged for patient-level views, integrated checklists, inclusion of comorbidities, and personal or practice-specific goal-setting features. Iterative refinements based on user feedback improved clarity, usability, and visual appeal. Some participants highlighted the dashboard’s intuitive design, clear and diverse visualizations, and benchmarking functionalities, describing it as both engaging and efficient. Others raised concerns about limited suitability for daily clinical workflows, potential integration challenges with existing systems, and the need for interactive, patient-centered features to support routine care. Conclusion The proposed dashboard could enhance T2D care through features like population overviews, long-term visualizations, and anonymized benchmarking. Successful clinical adoption will heavily depend on interoperability and seamless integration into existing workflows. The identified requirements provide a foundation for future digital T2D management systems. Diabetes mellitus type 2 user-centered prototype dashboard interviews Switzerland SGED score Figures Figure 1 Figure 2 Figure 3 1 Background Diabetes mellitus type 2 (T2D) is an escalating public health challenge, placing a growing burden on healthcare systems worldwide. Projections suggest that by 2050, the number of individuals living with T2D will reach over 640 million, with more than one in three adults aged 20–79 in Western countries affected ( 1 ). Switzerland reflects these global trends, making it crucial to address the associated challenges. In Switzerland, general practitioners (GPs) play a key role in T2D management ( 2 ) by serving as the first line of defense in monitoring patients with T2D. However, they are facing increasing pressure: workloads are steadily increasing, while the number of practicing GPs is declining ( 3 , 4 ). These conditions have been linked to difficulties in consistently providing high-quality care for T2D patients, undermining care quality ( 5 ). To this end, the Swiss Society of Endocrinology and Diabetology (SGED) developed the SGED score, a descriptive overview of how well the T2D population of a practice is managed by its healthcare professionals. It assesses eight key clinical indicators: general diabetes control, body mass index, nicotine abuse, HbA1c, blood pressure, LDL cholesterol, nephropathy screening, retinopathy screening, and foot examination ( 6 ). Despite its clinical relevance, the score exists either only in paper-based form or requires additional documentation in secondary tools ( 7 ), such as BlueCCM provided by BlueCare, a chronic care indicator tool for managing T2D patients ( 8 ). This not only increases administrative burden but also introduces risks of manual error and prevents continuous tracking of patient progress or data-driven evaluation of care quality or treatment effectiveness ( 9 , 10 ). At present, there is no digital dashboard that comprehensively and user-friendly visualizes the SGED score ( 7 ). To address this gap, we systematically derived functional requirements for a SGED score dashboard in collaboration with healthcare professionals, building on previous stakeholder analysis in T2D care in Switzerland ( 7 ). Such requirement engineering represents an early stage of the software development cycle ( 11 ), ensuring that the resulting system aligns with user needs and clinical workflows, in this case, primarily those of healthcare professionals such as GPs. Based on these requirements, an iterative design process was conducted to develop and evaluate a high-fidelity dashboard prototype that visualizes the SGED score. Prototyping represents a crucial phase in product development, facilitating early validation of design concepts ( 12 ) and supporting subsequent adoption and integration into routine clinical practice. Guided by principles of user-centered design ( 13 , 14 ), this study examines the following research questions: How can the functional requirements for a SGED score dashboard be identified and prioritized to align with the needs of healthcare professionals in T2D management? How should a user-centered SGED score dashboard be designed and iteratively refined to ensure accessibility, usability, and practical relevance in clinical workflows? 2 Methods To answer the research questions, 14 semi-structured interviews, including both individuals and groups, were conducted with various healthcare professionals, including GPs, Medical Practice Assistants (MPAs), Medical Practice Coordinators (MPCs), and other specialists. We focused on this user group because they play a vital role in T2D management and serve as the primary caregivers for patients, providing essential insights and feedback for the development of a potential SGED score visualization dashboard. Following established best practices for user-centered design, which emphasize ongoing user involvement to improve usability and fit in real-world settings ( 14 , 15 ), we adopted an iterative, user-centered three-step approach (see Fig. 1 ). First, requirements were defined to establish the project scope and guide development of the semi-structured interviews (Step 1). Second, more functional requirements were collected through user interviews to ensure a clear understanding of user needs. This was achieved by brainstorming and the prioritization of requirements using the “Must Have”, “Should Have”, “Could Have”, “Won’t Have” (MoSCoW) method, which places an element into one of the four categories (Step 2) ( 16 ). Third, a high-fidelity prototype of the SGED score dashboard was created using the Figma Starter plan ( 17 ), which was then iteratively refined based on user feedback (Step 3). Step 1: Define project scope and requirements In the first step, we defined functional requirements to establish the project scope and provide a foundation for the semi-structured interviews. Previous work ( 7 ) and literature ( 18 – 20 ) informed the identification of key functional requirements for a meaningful dashboard prototype in a real-world T2D management context. These requirements from the predefined set (see Table 1 , Additional File 8) were used as a starting point for step 2. Table 1 Overview over additional files and their content File name File format Title of data Description of data Additional File 1 .docx Consolidated criteria for reporting qualitative studies using the COREQ (Consolidated Criteria for Reporting Qualitative Research) 32-item checklist Completed 32-item COREQ checklist used to ensure comprehensive and transparent reporting of qualitative research methods and findings. Additional File 2 .docx Study information and consent form Information sheet and consent form provided to all study participants, outlining the study purpose, procedures, data handling, and participant rights. Additional File 3 .docx Semi-structured interview guide List of guiding questions and topics used during interviews with stakeholders, including general practitioners and other healthcare professionals. Additional File 4 .docx Interviewee’s backgrounds Summary table detailing the professional background, role, and relevant experience of each interviewee (anonymized). Additional File 5 .docx Coding Framework Structured framework describing the themes and thematic codes applied during qualitative data analysis. Additional File 6 .docx Narrative elements for the prototype Collection of narrative components informing the design of the digital diabetes management dashboard prototype. Additional File 7 .docx SGED Score Dashboard Prototype in German Mockups of the digital SGED score dashboard prototype, presented in German. Additional File 8 .docx Predefined functional requirements for the dashboard List of the system’s functional requirements defined prior to the design and implementation phases. Additional File 9 .docx Example for the “Must Have”, “Should Have”, “Could Have”, “Won’t Have” (MoSCoW) method prioritization framework Example illustrating how dashboard features were prioritized according to the MoSCoW framework. Step 2: Understand and refine user needs The second step centered on requirement engineering for a dashboard to visualize the SGED score. Semi-structured interviews were carried out to explore user needs and expectations. Brainstorming sessions helped expand and refine the predefined functional requirements. All predefined and user-generated requirements were then prioritized using the MoSCoW method, which categorizes them into “Must Have”, “Should Have”, “Could Have”, and “Won’t Have” elements ( 16 ). Requirements were assigned to these groups based on their relative importance for implementation. Since the MoSCoW method (see Table 1 , Additional File 9) produces a nominal scale, all requirements within the same category are considered to have equal priority, without ranking one higher or lower than another within the group ( 16 ). This equal priority approach makes it an easily implementable and favorable prioritization technique ( 21 , 22 ). This structured prioritization enabled us to focus on the most essential functionalities while still capturing secondary needs for potential future development. Step 3: Develop a high-fidelity prototype In the final step, the functional requirements were first translated into a persona, followed by the development of ten user stories and one scenario (see Table 1 , Additional File 6). These narrative elements ensured that the design process remained user-centered while also providing a structured basis for prototype development ( 23 ). The requirements were then transformed into a high-fidelity prototype of the dashboard using Figma ( 17 ), a collaborative web-based design tool used to visualize and prototype user interfaces. The prototype was then evaluated iteratively with users to gather feedback on usability and effectiveness. Insights from these evaluations informed successive refinements of the prototype, ensuring that the dashboard evolved in alignment with user expectations and practical needs. Through this cyclical process of design and feedback, the dashboard was continuously improved to achieve both usability and relevance. 2.1 Semi-structured interviews We developed a semi-structured interview guide (see Table 1 , Additional File 3), allowing interviewers to ask unplanned, spontaneous questions when appropriate ( 24 ). This flexibility ensured that each interview could capture individual perspectives and provide deep insights into the current situation, as well as gather meaningful feedback on functional requirements and the prototype. Before the main part of the interview, participants were asked a few background questions about their work and their experience with the SGED score, providing context for the discussion. The interviews then proceeded through four sequential steps. First, participants were invited to brainstorm potential functional requirements relevant for a SGED score dashboard. Next, they provided feedback on the predefined requirements to assess their relevance and clarity. In the third step, participants helped prioritize the identified requirements using the MoSCoW method, distinguishing the features into the four distinct categories ( 16 ). Finally, the session concluded with a demonstration of the dashboard prototype, during which participants shared their impressions and suggested improvements. 2.2 Selection and recruitment of interviewees This study received approval from the institution's ethics committee and was deemed exempt from formal review (date: October 27, 2024). Interviews were carried out between June and August 2025. Written informed consent was obtained from all participants prior to their involvement in the study (see Table 1 , Additional File 2). Participants were initially selected through purposive sampling to include individuals with extensive expertise in the field of T2D management. This was additionally supplemented with snowball sampling by asking the initial interviewees to suggest further participants who met the selection criteria, creating a referral chain ( 25 ). Recruitment concluded once theoretical saturation was reached, defined as the point at which three consecutive interviews failed to produce new insights ( 26 , 27 ). 2.3 Data collection and data analysis All interviews were recorded via Microsoft Teams. Initial transcripts were automatically generated by Microsoft Teams and subsequently reviewed in detail, with any missing segments manually completed by revisiting the recordings. Afterward, transcripts were anonymized by assigning a unique identifier to each participant. Data analysis was performed concurrently using ATLAS.ti (version 25.0.1) ( 28 ). To maintain methodological rigor and ensure transparency, the study adhered to the COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines (see Table 1 , Additional File 1). The transcripts were analyzed inductively using a thematic analysis ( 30 ), allowing themes to emerge directly from participants’ experiences and feedback regarding the SGED score dashboard. The thematic analysis was conducted by one researcher. 3 Results A total of 14 individuals participated in 10 interviews, comprising four group interviews with two participants and six individual interviews. The sample included six GPs, two MPAs, three MPCs, and three other physicians, all based in Switzerland. Background information on the interviewees is provided in Table 1 , Additional File 4. The resulting coding framework is shown in Table 1 , Additional File 5. For the analysis of the predefined functional requirements (subsection 3.1.1), responses were aggregated at the interview level (n = 10) to capture shared dynamics within the group settings. In contrast, the prioritization of user-generated functional requirements (subsection 3.1.2) and the analysis of prototype feedback (section 3.2) were conducted at the individual level (n = 14) to better reflect personal perceptions and experiences. 3.1 Functional Requirements In this section, we present the functional requirements identified for the visualization dashboard of the SGED score, using the MoSCoW framework. These requirements represent the outcome of steps 1 and 2 of the method. They serve to highlight potential elements for the prototype and are derived from the challenges and needs of healthcare professionals involved in the management of T2D. This section is divided into two subparts. Part one focuses on the prioritization of the predefined functional requirements (see Fig. 2 ), while part two highlights the prioritization of user-generated requirements that emerged from the brainstorming sessions with participants. 3.1.1 Predefined Functional Requirements (see Fig. 2 ) Eight out of 10 interviews (8/10) considered a reminder or alert mechanism to be essential in a SGED score dashboard, for instance, to notify healthcare professionals of missing entries or upcoming assessments. Similarly, eight interviews (8/10) emphasized the need for labeling and flagging critical values at the aggregate level, for example through color coding: “If something is missing in the collective, it must be clearly identifiable.” (P7). While seven (7/10) regarded a demographic overview of the T2D patient collective , such as age or gender distribution, as an important element, one (1/10) completely disagreed with this view. Six (6/10) described long-term trend analyses as a “Must Have” feature for evaluating care over time, whereas the remaining four (4/10) considered such analyses desirable but most definitely not necessary. While half of the interviews (5/10) viewed benchmarking within the managed care network as a valuable feature: “ It would be exciting if I could compare myself with other family doctors. To see where I stand. That way, I could improve.” (P7), others voiced concerns: “ I don't actually think it's a bad thing because it creates transparency, but on the other hand, I know that it might be a feature that generates the most resistance.” (P4). Half (5/10) emphasized the importance of visualizations of the overall SGED score directly within the dashboard. An export and reporting function was perceived by six (6/10) as a “Should Have” feature. Filtering options for subpopulations , such as specific age groups, were mentioned by three (3/10) as necessary: “ You need to be able to filter and drill down into details if something interests you.” (P10), by four (4/10) mentioned as important but not essential, whilst one (1/10) criticized that this feature is in no way desirable. Also, one interview (1/10) was completely against the customizable user flow of the dashboard, and only two (2/10) categorized it as a “Must Have” feature, making it not considered as critical: “If it's piloted well, I don't think it needs to be adjusted individually. Then it should be sufficient as it is.” (P11). Lastly, opinions differed more strongly regarding role-based access . While two interviews (2/10) argued in favor of such a feature, the majority (8/10) believed it would be counterproductive: “ For me, this goes completely against the principle of care within a multi-professional team.“ (P4). 3.1.2 User-Generated Functional Requirements Four out of fourteen participants (4/14) expressed a desire to view individual patients within the visualization dashboard, which currently focuses only on displaying the overall SGED score. They emphasized the importance of accessing patient-specific metrics, typically stored in individual patient records, to better understand and improve population-level outcomes: “Having an overview of the entire population is good, but it's also important that I can break it down to the individual patient. Because I can only improve the population if I improve the individual.” (P7). Four interviewees (4/14) highlighted the need for implemented checklists to support the systematic documentation of SGED criteria during routine consultations. These checklists should cover all relevant SGED criteria and serve as a practical tool to verify that no essential data or quality indicators are overlooked. They should be accessible directly within the dashboard or downloadable as templates if needed. Several participants emphasized the importance of going beyond the SGED score. For instance, four (4/14) mentioned the need to display patient comorbidities , since many patients have additional diseases: “Things get interesting when different diseases occur together, for example, hypertension in the context of diabetes or cardiac problems.” (P7). Three participants (3/14) expressed interest in incorporating data and related to lifestyle counselling and daily patient-self management : “Much of it lies in patient self-management. There are no tools that have action plans that can be given to patients or that have movement logs. There are many protocols out there, but ideally they would be unified.” (P14). Two interviewees (2/14) wished for the inclusion of additional laboratory data beyond the SGED score to provide a more detailed clinical overview. Lastly, two (2/14) argued that the recording of individual goals should be possible: “What might also be exciting is being able to set your own goals. For example: “I want to improve by 50% on this item.” Then you can set it yourself and see: that's what I set out to do back then. That could serve as a self-motivation.” (P10). Overall, most of the user-generated functional requirements were identified by GPs rather than by MPAs or MPCs. 3.2 Prototype This section summarizes the key findings from the user feedback on the prototype dashboard for visualizing the SGED score. The feedback was collected in open-ended interviews, where participants were not restricted by predefined questions but instead had the freedom to share comments, suggestions, or criticisms based on their own perspectives. The feedback was integrated into the design through an iterative process. After each interview, the prototype was refined to better align with the expressed preferences and needs of the users. As a result, participants in later interviews were already engaging with a more advanced and tailored version of the dashboard, shaped by the input of earlier users. It should be noted, however, that the current prototype functions purely as a design prototype rather than a fully implemented system. To illustrate the identified functional requirements, mockup data was generated and embedded into the prototype for demonstration purposes. All visualizations have been translated to English, and for the interviews, the German version was used (see Table 1 , Additional File 7). The visualizations below showcase the most important views of the prototype, implemented based on the defined functional requirements. 3.2.1 Ease of Use and Design Clarity Four out of fourteen interviewees (4/14) praised the clear and straightforward user interface : “The design is very appealing, clear, and simple with just a few clicks. Very effective.” (P13). This makes the prototype well suited for T2D healthcare professionals, who do not have much time to navigate applications in a clinical setting since they are already under lots of time pressure. Furthermore, four (4/14) expressed their gratitude for the presence and implementation of the visualizations regarding the SGED score, since they are informative yet easily understandable. Two (2/14) specifically mentioned that they appreciate the variety of visualizations for similar scenarios provided, making it customizable to preferences and exciting to engage: “I like to play around a bit with different graphics. I find that exciting and helpful.” (P1). However, difficulties with interpretation of data were also common in earlier interviews. Six (6/14) misinterpreted various visualizations due to the absence of appropriate and declarative legends or information pop-ups. Also, the choice of colors in the visualizations occasionally caused confusion, suggesting that a more consistent color scheme would improve clarity. 3.2.2 Functional Features Five (5/14) perceived the benchmarking within the managed care network (see Fig. 3 , Panel 4) as particularly helpful. They appreciated the ability to compare results within the network: “ Even if you don't do well yourself, seeing that everyone else isn't doing well either can still be a valuable assessment.” (P3). Participants explained that benchmarking promotes transparency and helps identify areas of improvement or recognize when structural factors are hindering effective T2D management. One participant added that not only individual professionals will profit from this, but also entire practices or networks when they analyze and compare the effects of managed care. At the same time, four (4/14) stressed anonymized benchmarking : “Otherwise, a bias will arise: only those who are already doing well will participate, and a barrier will arise for those who are perhaps not so well-prepared at the moment.” (P13). Three (3/14) appreciated the filtering options (see Fig. 3 , Panel 2), as they enable more precise and context-aware evaluations: “That makes perfect sense. If you want to look at the degree of target achievement for HbA1c, for example, it varies greatly, also depending on age. A patient over 90 is satisfactorily treated with an HbA1c of 8. On the other hand, for a 50-year-old type 2 diabetic, 7.5 is not yet satisfactory; it is only satisfactory at 6.5 or below.” (P5). However, two (2/14) wished to have filtering options directly integrated into other views. One (1/14) suggested adding filters to the trends view to directly pinpoint which subpopulation was affected. Another (1/14) proposed including filters in the export function: “Perhaps it would be useful if you could check boxes for subpopulations again when exporting a file, to see directly what belongs to them in a more detailed manner.” (P10). Four participants (4/14) voiced their interest in viewing long-term trends (see Fig. 3 , Panel 3), noting that visualizing changes over time helps them track improvements or declines in care quality and detect systemic issues. One (1/14) emphasized that it is important to have adjustable time intervals, allowing users to monitor progress within specific periods. Two (2/14) criticized the reminder mechanism (see Fig. 3 , Panel 6), describing it as pressuring and unhelpful, since such reminders are rather appropriate in an individual patient context. One (1/14) prefers to identify critical values themselves and therefore does not want to receive constant notifications. Lastly, one (1/14) particularly valued the export and reporting function (see Fig. 3 , Panel 5), viewing it as a useful tool for sharing visualizations and data with health insurers, showcasing the effectiveness of chronic care within managed care contracts. 3.2.3 Clinical Relevance and Integration Opinions diverged regarding the prototype’s relevance in everyday clinical practice. Four (4/14) voiced overall positive feedback and would be interested in using the dashboard in a clinical setting due to potential positive effects on T2D management: “You can check in from time to time: How are things looking? How do we see ourselves? That's certainly motivating, I think. You see, Ah, I've gotten better here, not there, we still need to work a bit harder there." (P12). Seven (7/14) saw a limited perceived benefit and adoption potential in everyday clinical work. They argued that such a visualization dashboard is of limited relevance for routine care, as analyses of the T2D population at the practice-level are only occasionally needed. Six of these (6/14) emphasized the absence of individual patient overviews , noting that daily practice focuses primarily on patient-specific treatment decisions: “When it comes to the population perspective, you might look at the overall data once a year. But in a regular doctor's office, I don't just go by the score. I need a patient cockpit for that.” (P8). These six (6/14) suggested that, given the limited time available, the healthcare professionals should rather dedicate their time to direct patient interactions. Similarly, three (3/14) were concerned that the dashboard could increase administrative workload , which could hinder its adoption. In addition, five (5/14) criticized the current prototype for its limited flexibility and lack of interactivity , describing it as too rigid for practical use. They highlighted that it only displays existing data without the possibility to record or adjust data directly within the system. Four (4/14) are also conflicted about the feasibility of real-world implementation , expressing uncertainty about how the dashboard could be integrated into existing clinical information systems (CIS): “There are 60 to 70 different CIS (clinical information systems), and you need access to all of them…” (P10). Finally, three participants (3/14) suggested that to achieve broader adoption, the dashboard must be extended beyond the SGED score to include data on comorbidities, prediabetic patients, or other chronic conditions. Without such an expansion, they feared its usefulness would remain limited to a narrow segment of T2D management. 4 Discussion We next discuss the key challenges, potential strategies for adoption and future work regarding the visualization dashboard prototype for the SGED score. 4.1 Key Challenges One of the most critical and currently unresolved challenges in T2D management in Switzerland is the fragmented landscape of CIS ( 31 ). This fragmentation poses a fundamental barrier to the development and implementation of interoperable systems ( 32 ). Moreover, the lack of interoperability leads to additional costs, as data often has to be entered or reconciled manually ( 33 ). Across interviews, it became evident that the absence of standardized interfaces or a commonly used national decision-support CIS is a core impediment. Each CIS operates in isolation today, meaning data cannot be easily exchanged or integrated across systems, making it difficult to compile and visualize the SGED score in a consistent and structured manner across multiple systems. This prevents automation, limits decision functionalities, and undermines the utility of performance feedback, and this is not only happening in the context of T2D ( 32 ). Therefore, the challenge of bringing together disparate data must be considered both a technical prerequisite and the most urgent priority for implementing any kind of clinical dashboard. Without addressing this foundational issue, digital support systems such as the SGED visualization dashboard risk remaining conceptual rather than operational. Another key challenge concerns the limited relevance of the SGED score dashboard in everyday clinical practice with respect to individual patients. While the SGED score is calculated based on aggregated patient data, the SGED score is a performance measure for the entire T2D population at a given practice ( 34 ). Hence, visualizations of the SGED score serve as aggregated population-level indicators and not for individual patient details. Nonetheless, healthcare professionals rely on patient-specific data and tools tailored to clinical decision-making during consultations with T2D patients ( 20 ). As a result, a visualization dashboard is more likely to only be used periodically for performance monitoring and quality assurance ( 18 ) rather than in day-to-day clinical routines. This population-oriented nature limits its perceived practicality for a broad range of healthcare professionals. 4.2 Potential Strategies for Adoption To increase the likelihood of successful adoption, several implementation strategies can be considered. For the SGED score dashboard to be used at scale, it must be embedded into the daily workflows of primary care in a way that minimizes administrative burden while maximizing clinical usefulness. Interoperability is the central requirement for this ( 20 ). Data entered by patients or healthcare professionals must be synchronized seamlessly within the GP’s CIS. Such interoperability ensures that information is not siloed and can be accessed easily across different platforms. Two potential implementation pathways can be distinguished. First, the SGED score dashboard could function as a stand-alone secondary system, which would require standardized national interfaces to enable data exchange across different CIS. This approach would allow practices to maintain their existing software but would depend heavily on robust data integration standards. Second, the dashboard could be integrated into existing decision-support systems or CIS. This would likely facilitate adoption, as healthcare professionals would not need to adapt to an entirely new system. Leveraging existing infrastructures could reduce technical complexity and would ensure a smoother workflow integration ( 35 ). Nonetheless, an existing system would need to be open to integrating the identified functional requirements into their functionality. Whichever path is chosen, the emphasis must remain on reducing redundancy and additional workload to minimize further tasks for already pressured healthcare professionals ( 3 , 4 ). The dashboard should ideally provide automated data synchronization and actionable insights ( 20 ) rather than becoming another layer of administrative effort. Ensuring that the tool supports rather than disrupts existing practices will be decisive for its long-term acceptance. 4.3 Future Work Several open questions remain regarding the feasibility and future of the SGED score dashboard. A central question is whether the system can be seamlessly integrated into current clinical workflows, using one of the potential implementation strategies from subsection 4.2. Another open issue is the rigidity of the SGED score itself ( 7 ). Many interviewees noted that the score, in its current form, is too inflexible, lacking the customization needed to reflect diverse realities of T2D management, such as accounting for individual patient comorbidities or lifestyle factors. This rigidity reduces its direct applicability and limits healthcare professionals’ motivation to engage with it beyond the financial incentives received as compensation. Therefore, future work should explore whether the SGED score could be made more adaptable to the needs of individual practices, potentially by turning it into a more personalized and individual score. For example, by allowing customizable indicators or integrating additional chronic disease metrics ( 7 , 20 ). Finally, there remains a broader strategic and policy-level question: how can interoperability and data sharing be promoted at a national level to enable digital quality monitoring tools like the SGED score dashboard? Addressing these systemic issues would not only enhance the feasibility of this prototype but also contribute to the broader digital transformation of chronic care in Switzerland. 5 Limitations This study has several limitations that should be acknowledged. First, the sample size of 14 participants (derived from four group interviews and six individual interviews) was relatively small, which limits the generalizability of the findings. Furthermore, participants were not sampled representatively across Switzerland, since the interviewees were from the German-speaking region. Second, there may have been a selection bias, as those with a particular interest in digital health or structured diabetes care may have been more likely to participate, potentially skewing the feedback. Moreover, despite being conducted online, the interviews may have been subject to social desirability bias, with participants expressing more overly positive responses toward the prototype than in a more neutral setting. Third, the study examined a hypothetical SGED score dashboard rather than an operational system. Consequently, many of the insights reflect anticipated behavior rather than observed usage. While participants provided thoughtful reflections on both positive and negative aspects of the prototype, their responses may not fully predict actual adoption or engagement under real-world conditions, especially when considering variations in workload, clinical routines, or policy constraints. Fourth, the transcripts were only analyzed by one researcher, which may have introduced subjective bias and limited the reliability of the interpretations. Finally, while this study focused on identifying functional requirements and generating an initial prototype for a visualization dashboard for the SGED score, it did not investigate practical aspects of implementation, such as potential commercial partners or explicit integration pathways within existing systems. Future work will need to address these questions to determine the feasibility, effectiveness, and sustainability of the dashboard in clinical practice. 6 Conclusion This study explored the design of a visualization dashboard for the SGED score, a tool intended to provide an aggregated overview of T2D care quality at the practice level in Switzerland. Through qualitative interviews with key stakeholders, including GPs, MPAs, and MPCs, we identified key functional requirements, user preferences, and barriers to implementation. These challenges included system fragmentation, workflow integration challenges, and the population-level focus of the SGED score. While participants generally recognized the dashboard’s potential to support performance monitoring and quality improvement, successful adoption will ultimately depend on interoperability, usability, and alignment with existing clinical routines. Overall, the findings provide a foundation for the further development and evaluation of digital tools that enhance chronic disease management and inform strategies for scalable implementation in primary care. Abbreviations T2D Diabetes mellitus type 2 SGED Swiss Society of Endocrinology and Diabetology GP General Practitioner MPA Medical Practice Assistant MPC Medical Practice Coordinator MoSCoW “Must Have”, “Should Have”, “Could Have”, “Won’t Have” COREQ Consolidated Criteria for Reporting Qualitative Research CIS Clinical Information System Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. This study did not involve the collection of clinical or otherwise sensitive health data. In view of its minimal-risk profile and its exclusive focus on expert knowledge and lived experience, the study was exempt from a formal review and approval by the Ethics Committee of the University of St.Gallen. Participants were fully briefed on the study’s purpose and procedures before the interviews. Written consent was obtained, including permission to record audio. Participants were informed of their right to withdraw at any time, and all data were anonymized and securely stored to ensure confidentiality. Consent for publication Not applicable Competing Interests OFG, TK, and MJ are affiliated with the Centre for Digital Health Interventions, a joint initiative of the Institute for Implementation Science in Health Care, University of Zurich, the Department of Management, Technology, and Economics at ETH Zurich, and the Institute of Technology Management and School of Medicine at the University of St Gallen. CDHI is funded in part by Novo Nordisk Pharma, Switzerland, the Swiss health insurer CSS, the Austrian healthcare provider MavieNext (UNIQA), and the Swiss investor company MTIP. TK is also a co-founder of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, neither Novo Nordisk, CSS, Pathmate Technologies, MavieNext, nor MTIP was involved in the design, analysis, or writing of this research. Funding This research was co-funded by Novo Nordisk Pharma AG, Switzerland, through the grant Digitalization of Diabetes Management Score (D-SGED-S). It was also co-funded by the Swiss health insurer CSS. The funders had no role in the design of the study, data collection, analysis, interpretation, or writing of the manuscript. Author Contribution LA, MJ, and TK developed the concept of this study. TK supervised the study. LA and OG conducted the semi-structured interviews with the healthcare professionals. LA performed the data analysis. LA developed the prototype. All authors read and approved the final manuscript. Acknowledgement We are grateful for all interview participants who generously shared their time, expertise, and personal insights. The contributions were invaluable and played a crucial role in shaping this research on the SGED score dashboard prototype. Furthermore, we acknowledge financial support from Novo Nordisk Pharma AG. Data Availability All data generated or analyzed during this study are included in this article and its supplementary information files (Additional Files 1-9). References Global [Internet]. Diabetes Atlas. [cited 2025 Sept 29]. Available from: https://diabetesatlas.org/data-by-location/global/ AerzteZeitung.de [Internet]. 2020 [cited 2025 Sept 24]. Hausarztpraxis ist bei Diabetes Anlaufstation Nummer eins. Available from: https://www.aerztezeitung.de/Politik/Hausarztpraxis-ist-bei-Diabetes-Anlaufstation-Nummer-eins-408419.html Schweizer Radio und Fernsehen (SRF) [Internet]. 2023 [cited 2025 Sept 24]. Hausarztpraxen in Not - «Es ist zu viel»: Hilferufe der Hausärztinnen. 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Interoperability of heterogeneous health information systems: a systematic literature review. BMC Med Inf Decis Mak. 2023;23(1):18. Ndlovu K, Scott RE, Mars M. Interoperability opportunities and challenges in linking mhealth applications and eRecord systems: Botswana as an exemplar. BMC Med Inf Decis Mak. 2021;21(1):246. Christ E, Czock A, Renström F, Ammeter T, Ebrahimi F, Zechmann S et al. Evaluation of type 2 diabetes care management in nine primary care practices before and after implementation of the Criteria of Good Disease Management of Diabetes established by the Swiss Society of Endocrinology and Diabetology. Swiss Med Wkly. 2022 July 26;152(2930):w30197. Solomon J, Dauber-Decker K, Richardson S, Levy S, Khan S, Coleman B, et al. Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study. JMIR Form Res. 2023;7:e44065. Additional Declarations Competing interest reported. OFG, TK, and MJ are affiliated with the Centre for Digital Health Interventions, a joint initiative of the Institute for Implementation Science in Health Care, University of Zurich, the Department of Management, Technology, and Economics at ETH Zurich, and the Institute of Technology Management and School of Medicine at the University of St Gallen. CDHI is funded in part by Novo Nordisk Pharma, Switzerland, the Swiss health insurer CSS, the Austrian healthcare provider MavieNext (UNIQA), and the Swiss investor company MTIP. TK is also a co-founder of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, neither Novo Nordisk, CSS, Pathmate Technologies, MavieNext, nor MTIP was involved in the design, analysis, or writing of this research. Supplementary Files AdditionalFile5.docx AdditionalFile3.docx AdditionalFile2.docx AdditionalFile1.docx AdditionalFile8.docx AdditionalFile4.docx AdditionalFile6.docx AdditionalFile9.docx AdditionalFile7.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 18 Dec, 2025 Editor invited by journal 17 Dec, 2025 Editor assigned by journal 15 Dec, 2025 Submission checks completed at journal 15 Dec, 2025 First submitted to journal 12 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2","display":"","copyAsset":false,"role":"figure","size":108127,"visible":true,"origin":"","legend":"\u003cp\u003ePrioritization of predefined functional requirements by participants using the MoSCoW framework\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/b2d0de1bf8b5e92c41e29166.png"},{"id":100360899,"identity":"a6b31af1-7395-4dd7-aee6-e5e80c9d7fae","added_by":"auto","created_at":"2026-01-16 07:44:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":271711,"visible":true,"origin":"","legend":"\u003cp\u003eScreenshots of the SGED score dashboard prototype; Panel 1. visualization of the overall SGED score, Panel 2. filtering options for subpopulations, Panel 3. long term trends, Panel 4. anonymized benchmarking within the network, Panel 5. export and reporting function, Panel 6. Reminder and alert mechanism.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/a9885272446acf0e1c2e6a13.png"},{"id":100380775,"identity":"2764a55d-eb3c-48d9-85bd-997ce516f473","added_by":"auto","created_at":"2026-01-16 10:33:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1674817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/3c49fa3d-c7a9-458b-b8c3-b0755f0883b3.pdf"},{"id":100361174,"identity":"fdc6bed7-e04f-4c58-b036-819c8a4995d7","added_by":"auto","created_at":"2026-01-16 07:44:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27738,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/7ffa5a058c9defcb3e09add6.docx"},{"id":100360557,"identity":"3bd873ff-963a-4602-8557-484bb76a5ffa","added_by":"auto","created_at":"2026-01-16 07:39:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29486,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/52b9efcc2a3152e67eea2de8.docx"},{"id":100360525,"identity":"d90749c5-8f0b-4607-b37a-6f77d66a0a19","added_by":"auto","created_at":"2026-01-16 07:39:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":40848,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/27297a528f127eff3a98c156.docx"},{"id":100004098,"identity":"4c7e6942-711a-49c9-80e5-d70bd80091be","added_by":"auto","created_at":"2026-01-12 05:24:44","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29925,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/550ba77117d9b7ba72ce6d75.docx"},{"id":100004113,"identity":"d1b2c003-5bae-4195-ad60-c5fbf0bebf77","added_by":"auto","created_at":"2026-01-12 05:24:44","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":31360,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile8.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/91f3e3ba7b7b907f1f0d3bbe.docx"},{"id":100004106,"identity":"ce2d4eea-197d-4cf2-af20-20808ac87755","added_by":"auto","created_at":"2026-01-12 05:24:44","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":26586,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/1277ad2acbc0336a723a0c1a.docx"},{"id":100004108,"identity":"1cfd9808-7d26-4f55-aef3-6e347d3bf736","added_by":"auto","created_at":"2026-01-12 05:24:44","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":323124,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/9ad3a06c94803d750c7d5f46.docx"},{"id":100360918,"identity":"c88d6db4-c654-4b9e-b4a8-0a0d886cfb1f","added_by":"auto","created_at":"2026-01-16 07:44:11","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":345431,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile9.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/28185d023c17da716d7593e8.docx"},{"id":100004111,"identity":"be818d71-fa4b-464e-b9b2-18d1ff4657a1","added_by":"auto","created_at":"2026-01-12 05:24:44","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":3917854,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile7.docx","url":"https://assets-eu.researchsquare.com/files/rs-8347867/v1/736d53499264bb109e6664a9.docx"}],"financialInterests":"Competing interest reported. OFG, TK, and MJ are affiliated with the Centre for Digital Health Interventions, a joint initiative of the Institute for Implementation Science in Health Care, University of Zurich, the Department of Management, Technology, and Economics at ETH Zurich, and the Institute of Technology Management and School of Medicine at the University of St Gallen. CDHI is funded in part by Novo Nordisk Pharma, Switzerland, the Swiss health insurer CSS, the Austrian healthcare provider MavieNext (UNIQA), and the Swiss investor company MTIP. TK is also a co-founder of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, neither Novo Nordisk, CSS, Pathmate Technologies, MavieNext, nor MTIP was involved in the design, analysis, or writing of this research.","formattedTitle":"A Care Quality Dashboard for General Practitioners Managing Patients with Diabetes Mellitus Type 2: User- Centered Design and Evaluation of a Prototype","fulltext":[{"header":"1 Background","content":"\u003cp\u003eDiabetes mellitus type 2 (T2D) is an escalating public health challenge, placing a growing burden on healthcare systems worldwide. Projections suggest that by 2050, the number of individuals living with T2D will reach over 640\u0026nbsp;million, with more than one in three adults aged 20\u0026ndash;79 in Western countries affected (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Switzerland reflects these global trends, making it crucial to address the associated challenges. In Switzerland, general practitioners (GPs) play a key role in T2D management (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) by serving as the first line of defense in monitoring patients with T2D. However, they are facing increasing pressure: workloads are steadily increasing, while the number of practicing GPs is declining (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These conditions have been linked to difficulties in consistently providing high-quality care for T2D patients, undermining care quality (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo this end, the Swiss Society of Endocrinology and Diabetology (SGED) developed the SGED score, a descriptive overview of how well the T2D population of a practice is managed by its healthcare professionals. It assesses eight key clinical indicators: general diabetes control, body mass index, nicotine abuse, HbA1c, blood pressure, LDL cholesterol, nephropathy screening, retinopathy screening, and foot examination (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Despite its clinical relevance, the score exists either only in paper-based form or requires additional documentation in secondary tools (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), such as BlueCCM provided by BlueCare, a chronic care indicator tool for managing T2D patients (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This not only increases administrative burden but also introduces risks of manual error and prevents continuous tracking of patient progress or data-driven evaluation of care quality or treatment effectiveness (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). At present, there is no digital dashboard that comprehensively and user-friendly visualizes the SGED score (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address this gap, we systematically derived functional requirements for a SGED score dashboard in collaboration with healthcare professionals, building on previous stakeholder analysis in T2D care in Switzerland (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Such requirement engineering represents an early stage of the software development cycle (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), ensuring that the resulting system aligns with user needs and clinical workflows, in this case, primarily those of healthcare professionals such as GPs. Based on these requirements, an iterative design process was conducted to develop and evaluate a high-fidelity dashboard prototype that visualizes the SGED score. Prototyping represents a crucial phase in product development, facilitating early validation of design concepts (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and supporting subsequent adoption and integration into routine clinical practice.\u003c/p\u003e \u003cp\u003eGuided by principles of user-centered design (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), this study examines the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow can the functional requirements for a SGED score dashboard be identified and prioritized to align with the needs of healthcare professionals in T2D management?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow should a user-centered SGED score dashboard be designed and iteratively refined to ensure accessibility, usability, and practical relevance in clinical workflows?\u003c/p\u003e "},{"header":"2 Methods","content":"\u003cp\u003eTo answer the research questions, 14 semi-structured interviews, including both individuals and groups, were conducted with various healthcare professionals, including GPs, Medical Practice Assistants (MPAs), Medical Practice Coordinators (MPCs), and other specialists. We focused on this user group because they play a vital role in T2D management and serve as the primary caregivers for patients, providing essential insights and feedback for the development of a potential SGED score visualization dashboard.\u003c/p\u003e \u003cp\u003eFollowing established best practices for user-centered design, which emphasize ongoing user involvement to improve usability and fit in real-world settings (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), we adopted an iterative, user-centered three-step approach (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). First, requirements were defined to establish the project scope and guide development of the semi-structured interviews (Step 1). Second, more functional requirements were collected through user interviews to ensure a clear understanding of user needs. This was achieved by brainstorming and the prioritization of requirements using the \u0026ldquo;Must Have\u0026rdquo;, \u0026ldquo;Should Have\u0026rdquo;, \u0026ldquo;Could Have\u0026rdquo;, \u0026ldquo;Won\u0026rsquo;t Have\u0026rdquo; (MoSCoW) method, which places an element into one of the four categories (Step 2) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Third, a high-fidelity prototype of the SGED score dashboard was created using the Figma Starter plan (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), which was then iteratively refined based on user feedback (Step 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStep 1: Define project scope and requirements\u003c/p\u003e \u003cp\u003eIn the first step, we defined functional requirements to establish the project scope and provide a foundation for the semi-structured interviews. Previous work (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and literature (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) informed the identification of key functional requirements for a meaningful dashboard prototype in a real-world T2D management context. These requirements from the predefined set (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 8) were used as a starting point for step 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview over additional files and their content\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFile name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFile format\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitle of data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription of data\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsolidated criteria for reporting qualitative studies using the COREQ (Consolidated Criteria for Reporting Qualitative Research) 32-item checklist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompleted 32-item COREQ checklist used to ensure comprehensive and transparent reporting of qualitative research methods and findings.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy information and consent form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInformation sheet and consent form provided to all study participants, outlining the study purpose, procedures, data handling, and participant rights.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-structured interview guide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eList of guiding questions and topics used during interviews with stakeholders, including general practitioners and other healthcare professionals.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterviewee\u0026rsquo;s backgrounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSummary table detailing the professional background, role, and relevant experience of each interviewee (anonymized).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoding Framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructured framework describing the themes and thematic codes applied during qualitative data analysis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNarrative elements for the prototype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCollection of narrative components informing the design of the digital diabetes management dashboard prototype.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSGED Score Dashboard Prototype in German\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMockups of the digital SGED score dashboard prototype, presented in German.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredefined functional requirements for the dashboard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eList of the system\u0026rsquo;s functional requirements defined prior to the design and implementation phases.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional File 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.docx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample for the \u0026ldquo;Must Have\u0026rdquo;, \u0026ldquo;Should Have\u0026rdquo;, \u0026ldquo;Could Have\u0026rdquo;, \u0026ldquo;Won\u0026rsquo;t Have\u0026rdquo; (MoSCoW) method prioritization framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExample illustrating how dashboard features were prioritized according to the MoSCoW framework.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStep 2: Understand and refine user needs\u003c/p\u003e \u003cp\u003eThe second step centered on requirement engineering for a dashboard to visualize the SGED score. Semi-structured interviews were carried out to explore user needs and expectations. Brainstorming sessions helped expand and refine the predefined functional requirements. All predefined and user-generated requirements were then prioritized using the MoSCoW method, which categorizes them into \u0026ldquo;Must Have\u0026rdquo;, \u0026ldquo;Should Have\u0026rdquo;, \u0026ldquo;Could Have\u0026rdquo;, and \u0026ldquo;Won\u0026rsquo;t Have\u0026rdquo; elements (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Requirements were assigned to these groups based on their relative importance for implementation. Since the MoSCoW method (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 9) produces a nominal scale, all requirements within the same category are considered to have equal priority, without ranking one higher or lower than another within the group (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This equal priority approach makes it an easily implementable and favorable prioritization technique (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This structured prioritization enabled us to focus on the most essential functionalities while still capturing secondary needs for potential future development.\u003c/p\u003e \u003cp\u003eStep 3: Develop a high-fidelity prototype\u003c/p\u003e \u003cp\u003eIn the final step, the functional requirements were first translated into a persona, followed by the development of ten user stories and one scenario (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 6). These narrative elements ensured that the design process remained user-centered while also providing a structured basis for prototype development (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The requirements were then transformed into a high-fidelity prototype of the dashboard using Figma (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), a collaborative web-based design tool used to visualize and prototype user interfaces. The prototype was then evaluated iteratively with users to gather feedback on usability and effectiveness. Insights from these evaluations informed successive refinements of the prototype, ensuring that the dashboard evolved in alignment with user expectations and practical needs. Through this cyclical process of design and feedback, the dashboard was continuously improved to achieve both usability and relevance.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Semi-structured interviews\u003c/h2\u003e \u003cp\u003eWe developed a semi-structured interview guide (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 3), allowing interviewers to ask unplanned, spontaneous questions when appropriate (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This flexibility ensured that each interview could capture individual perspectives and provide deep insights into the current situation, as well as gather meaningful feedback on functional requirements and the prototype. Before the main part of the interview, participants were asked a few background questions about their work and their experience with the SGED score, providing context for the discussion. The interviews then proceeded through four sequential steps. First, participants were invited to brainstorm potential functional requirements relevant for a SGED score dashboard. Next, they provided feedback on the predefined requirements to assess their relevance and clarity. In the third step, participants helped prioritize the identified requirements using the MoSCoW method, distinguishing the features into the four distinct categories (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Finally, the session concluded with a demonstration of the dashboard prototype, during which participants shared their impressions and suggested improvements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection and recruitment of interviewees\u003c/h2\u003e \u003cp\u003e This study received approval from the institution's ethics committee and was deemed exempt from formal review (date: October 27, 2024). Interviews were carried out between June and August 2025. Written informed consent was obtained from all participants prior to their involvement in the study (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 2). Participants were initially selected through purposive sampling to include individuals with extensive expertise in the field of T2D management. This was additionally supplemented with snowball sampling by asking the initial interviewees to suggest further participants who met the selection criteria, creating a referral chain (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Recruitment concluded once theoretical saturation was reached, defined as the point at which three consecutive interviews failed to produce new insights (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection and data analysis\u003c/h2\u003e \u003cp\u003eAll interviews were recorded via Microsoft Teams. Initial transcripts were automatically generated by Microsoft Teams and subsequently reviewed in detail, with any missing segments manually completed by revisiting the recordings. Afterward, transcripts were anonymized by assigning a unique identifier to each participant. Data analysis was performed concurrently using ATLAS.ti (version 25.0.1) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). To maintain methodological rigor and ensure transparency, the study adhered to the COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 1). The transcripts were analyzed inductively using a thematic analysis (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), allowing themes to emerge directly from participants\u0026rsquo; experiences and feedback regarding the SGED score dashboard. The thematic analysis was conducted by one researcher.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eA total of 14 individuals participated in 10 interviews, comprising four group interviews with two participants and six individual interviews. The sample included six GPs, two MPAs, three MPCs, and three other physicians, all based in Switzerland. Background information on the interviewees is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 4. The resulting coding framework is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 5.\u003c/p\u003e \u003cp\u003eFor the analysis of the predefined functional requirements (subsection 3.1.1), responses were aggregated at the interview level (n\u0026thinsp;=\u0026thinsp;10) to capture shared dynamics within the group settings. In contrast, the prioritization of user-generated functional requirements (subsection 3.1.2) and the analysis of prototype feedback (section 3.2) were conducted at the individual level (n\u0026thinsp;=\u0026thinsp;14) to better reflect personal perceptions and experiences.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Functional Requirements\u003c/h2\u003e \u003cp\u003eIn this section, we present the functional requirements identified for the visualization dashboard of the SGED score, using the MoSCoW framework. These requirements represent the outcome of steps 1 and 2 of the method. They serve to highlight potential elements for the prototype and are derived from the challenges and needs of healthcare professionals involved in the management of T2D. This section is divided into two subparts. Part one focuses on the prioritization of the predefined functional requirements (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while part two highlights the prioritization of user-generated requirements that emerged from the brainstorming sessions with participants.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Predefined Functional Requirements (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eEight out of 10 interviews (8/10) considered a \u003cb\u003ereminder or alert mechanism\u003c/b\u003e to be essential in a SGED score dashboard, for instance, to notify healthcare professionals of missing entries or upcoming assessments. Similarly, eight interviews (8/10) emphasized the need for \u003cb\u003elabeling and flagging critical values\u003c/b\u003e at the aggregate level, for example through color coding: \u003cem\u003e\u0026ldquo;If something is missing in the collective, it must be clearly identifiable.\u0026rdquo;\u003c/em\u003e (P7). While seven (7/10) regarded a \u003cb\u003edemographic overview of the T2D patient collective\u003c/b\u003e, such as age or gender distribution, as an important element, one (1/10) completely disagreed with this view. Six (6/10) described \u003cb\u003elong-term trend analyses\u003c/b\u003e as a \u0026ldquo;Must Have\u0026rdquo; feature for evaluating care over time, whereas the remaining four (4/10) considered such analyses desirable but most definitely not necessary. While half of the interviews (5/10) viewed \u003cb\u003ebenchmarking within the managed care network\u003c/b\u003e as a valuable feature: \u0026ldquo;\u003cem\u003eIt would be exciting if I could compare myself with other family doctors. To see where I stand. That way, I could improve.\u0026rdquo;\u003c/em\u003e (P7), others voiced concerns: \u0026ldquo;\u003cem\u003eI don't actually think it's a bad thing because it creates transparency, but on the other hand, I know that it might be a feature that generates the most resistance.\u0026rdquo;\u003c/em\u003e (P4). Half (5/10) emphasized the importance of \u003cb\u003evisualizations of the overall SGED score\u003c/b\u003e directly within the dashboard. An \u003cb\u003eexport and reporting function\u003c/b\u003e was perceived by six (6/10) as a \u0026ldquo;Should Have\u0026rdquo; feature. \u003cb\u003eFiltering options for subpopulations\u003c/b\u003e, such as specific age groups, were mentioned by three (3/10) as necessary: \u0026ldquo;\u003cem\u003eYou need to be able to filter and drill down into details if something interests you.\u0026rdquo;\u003c/em\u003e (P10), by four (4/10) mentioned as important but not essential, whilst one (1/10) criticized that this feature is in no way desirable. Also, one interview (1/10) was completely against the \u003cb\u003ecustomizable user flow\u003c/b\u003e of the dashboard, and only two (2/10) categorized it as a \u0026ldquo;Must Have\u0026rdquo; feature, making it not considered as critical: \u003cem\u003e\u0026ldquo;If it's piloted well, I don't think it needs to be adjusted individually. Then it should be sufficient as it is.\u0026rdquo;\u003c/em\u003e (P11). Lastly, opinions differed more strongly regarding \u003cb\u003erole-based access\u003c/b\u003e. While two interviews (2/10) argued in favor of such a feature, the majority (8/10) believed it would be counterproductive: \u0026ldquo;\u003cem\u003eFor me, this goes completely against the principle of care within a multi-professional team.\u0026ldquo;\u003c/em\u003e (P4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 User-Generated Functional Requirements\u003c/h2\u003e \u003cp\u003eFour out of fourteen participants (4/14) expressed a desire to \u003cb\u003eview individual patients\u003c/b\u003e within the visualization dashboard, which currently focuses only on displaying the overall SGED score. They emphasized the importance of accessing patient-specific metrics, typically stored in individual patient records, to better understand and improve population-level outcomes: \u003cem\u003e\u0026ldquo;Having an overview of the entire population is good, but it's also important that I can break it down to the individual patient. Because I can only improve the population if I improve the individual.\u0026rdquo;\u003c/em\u003e (P7). Four interviewees (4/14) highlighted the need for implemented \u003cb\u003echecklists\u003c/b\u003e to support the systematic documentation of SGED criteria during routine consultations. These checklists should cover all relevant SGED criteria and serve as a practical tool to verify that no essential data or quality indicators are overlooked. They should be accessible directly within the dashboard or downloadable as templates if needed. Several participants emphasized the importance of going beyond the SGED score. For instance, four (4/14) mentioned the need to display \u003cb\u003epatient comorbidities\u003c/b\u003e, since many patients have additional diseases: \u003cem\u003e\u0026ldquo;Things get interesting when different diseases occur together, for example, hypertension in the context of diabetes or cardiac problems.\u0026rdquo;\u003c/em\u003e (P7). Three participants (3/14) expressed interest in incorporating data and related to \u003cb\u003elifestyle counselling and daily patient-self management\u003c/b\u003e: \u003cem\u003e\u0026ldquo;Much of it lies in patient self-management. There are no tools that have action plans that can be given to patients or that have movement logs. There are many protocols out there, but ideally they would be unified.\u0026rdquo;\u003c/em\u003e (P14). Two interviewees (2/14) wished for the inclusion of additional \u003cb\u003elaboratory data\u003c/b\u003e beyond the SGED score to provide a more detailed clinical overview. Lastly, two (2/14) argued that the recording of \u003cb\u003eindividual goals\u003c/b\u003e should be possible: \u003cem\u003e\u0026ldquo;What might also be exciting is being able to set your own goals. For example: \u0026ldquo;I want to improve by 50% on this item.\u0026rdquo; Then you can set it yourself and see: that's what I set out to do back then. That could serve as a self-motivation.\u0026rdquo;\u003c/em\u003e (P10). Overall, most of the user-generated functional requirements were identified by GPs rather than by MPAs or MPCs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prototype\u003c/h2\u003e \u003cp\u003eThis section summarizes the key findings from the user feedback on the prototype dashboard for visualizing the SGED score. The feedback was collected in open-ended interviews, where participants were not restricted by predefined questions but instead had the freedom to share comments, suggestions, or criticisms based on their own perspectives.\u003c/p\u003e \u003cp\u003eThe feedback was integrated into the design through an iterative process. After each interview, the prototype was refined to better align with the expressed preferences and needs of the users. As a result, participants in later interviews were already engaging with a more advanced and tailored version of the dashboard, shaped by the input of earlier users. It should be noted, however, that the current prototype functions purely as a design prototype rather than a fully implemented system. To illustrate the identified functional requirements, mockup data was generated and embedded into the prototype for demonstration purposes. All visualizations have been translated to English, and for the interviews, the German version was used (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional File 7). The visualizations below showcase the most important views of the prototype, implemented based on the defined functional requirements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Ease of Use and Design Clarity\u003c/h2\u003e \u003cp\u003eFour out of fourteen interviewees (4/14) praised the \u003cb\u003eclear and straightforward user interface\u003c/b\u003e: \u003cem\u003e\u0026ldquo;The design is very appealing, clear, and simple with just a few clicks. Very effective.\u0026rdquo;\u003c/em\u003e (P13). This makes the prototype well suited for T2D healthcare professionals, who do not have much time to navigate applications in a clinical setting since they are already under lots of time pressure. Furthermore, four (4/14) expressed their gratitude for the presence and implementation of the \u003cb\u003evisualizations\u003c/b\u003e regarding the SGED score, since they are informative yet easily understandable. Two (2/14) specifically mentioned that they appreciate the \u003cb\u003evariety of visualizations\u003c/b\u003e for similar scenarios provided, making it customizable to preferences and exciting to engage: \u003cem\u003e\u0026ldquo;I like to play around a bit with different graphics. I find that exciting and helpful.\u0026rdquo;\u003c/em\u003e (P1). However, \u003cb\u003edifficulties with interpretation of data\u003c/b\u003e were also common in earlier interviews. Six (6/14) misinterpreted various visualizations due to the absence of appropriate and declarative legends or information pop-ups. Also, the choice of colors in the visualizations occasionally caused confusion, suggesting that a more consistent color scheme would improve clarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Functional Features\u003c/h2\u003e \u003cp\u003eFive (5/14) perceived the \u003cb\u003ebenchmarking\u003c/b\u003e within the managed care network (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Panel 4) as particularly helpful. They appreciated the ability to compare results within the network: \u0026ldquo;\u003cem\u003eEven if you don't do well yourself, seeing that everyone else isn't doing well either can still be a valuable assessment.\u0026rdquo;\u003c/em\u003e (P3). Participants explained that \u003cb\u003ebenchmarking promotes transparency\u003c/b\u003e and helps identify areas of improvement or recognize when structural factors are hindering effective T2D management. One participant added that not only individual professionals will profit from this, but also entire practices or networks when they analyze and compare the effects of managed care. At the same time, four (4/14) stressed \u003cb\u003eanonymized benchmarking\u003c/b\u003e: \u003cem\u003e\u0026ldquo;Otherwise, a bias will arise: only those who are already doing well will participate, and a barrier will arise for those who are perhaps not so well-prepared at the moment.\u0026rdquo;\u003c/em\u003e (P13). Three (3/14) appreciated the \u003cb\u003efiltering options\u003c/b\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Panel 2), as they enable more precise and context-aware evaluations: \u003cem\u003e\u0026ldquo;That makes perfect sense. If you want to look at the degree of target achievement for HbA1c, for example, it varies greatly, also depending on age. A patient over 90 is satisfactorily treated with an HbA1c of 8. On the other hand, for a 50-year-old type 2 diabetic, 7.5 is not yet satisfactory; it is only satisfactory at 6.5 or below.\u0026rdquo;\u003c/em\u003e (P5). However, two (2/14) wished to have filtering options directly integrated into other views. One (1/14) suggested adding filters to the trends view to directly pinpoint which subpopulation was affected. Another (1/14) proposed including filters in the export function: \u003cem\u003e\u0026ldquo;Perhaps it would be useful if you could check boxes for subpopulations again when exporting a file, to see directly what belongs to them in a more detailed manner.\u0026rdquo;\u003c/em\u003e (P10). Four participants (4/14) voiced their interest in viewing \u003cb\u003elong-term trends\u003c/b\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Panel 3), noting that visualizing changes over time helps them track improvements or declines in care quality and detect systemic issues. One (1/14) emphasized that it is important to have adjustable time intervals, allowing users to monitor progress within specific periods. Two (2/14) criticized the \u003cb\u003ereminder mechanism\u003c/b\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Panel 6), describing it as pressuring and unhelpful, since such reminders are rather appropriate in an individual patient context. One (1/14) prefers to identify critical values themselves and therefore does not want to receive constant notifications. Lastly, one (1/14) particularly valued the \u003cb\u003eexport and reporting function\u003c/b\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Panel 5), viewing it as a useful tool for sharing visualizations and data with health insurers, showcasing the effectiveness of chronic care within managed care contracts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Clinical Relevance and Integration\u003c/h2\u003e \u003cp\u003eOpinions diverged regarding the prototype\u0026rsquo;s relevance in everyday clinical practice. Four (4/14) voiced overall \u003cb\u003epositive feedback\u003c/b\u003e and would be interested in using the dashboard in a clinical setting due to potential positive effects on T2D management: \u003cem\u003e\u0026ldquo;You can check in from time to time: How are things looking? How do we see ourselves? That's certainly motivating, I think. You see, Ah, I've gotten better here, not there, we still need to work a bit harder there.\"\u003c/em\u003e (P12). Seven (7/14) saw a \u003cb\u003elimited perceived benefit\u003c/b\u003e and adoption potential in everyday clinical work. They argued that such a visualization dashboard is of limited relevance for routine care, as analyses of the T2D population at the practice-level are only occasionally needed. Six of these (6/14) emphasized the absence of \u003cb\u003eindividual patient overviews\u003c/b\u003e, noting that daily practice focuses primarily on patient-specific treatment decisions: \u003cem\u003e\u0026ldquo;When it comes to the population perspective, you might look at the overall data once a year. But in a regular doctor's office, I don't just go by the score. I need a patient cockpit for that.\u0026rdquo;\u003c/em\u003e (P8). These six (6/14) suggested that, given the limited time available, the healthcare professionals should rather dedicate their time to direct patient interactions. Similarly, three (3/14) were concerned that the dashboard could increase \u003cb\u003eadministrative workload\u003c/b\u003e, which could hinder its adoption. In addition, five (5/14) criticized the current prototype for its \u003cb\u003elimited flexibility and lack of interactivity\u003c/b\u003e, describing it as too rigid for practical use. They highlighted that it only displays existing data without the possibility to record or adjust data directly within the system. Four (4/14) are also conflicted about the \u003cb\u003efeasibility of real-world implementation\u003c/b\u003e, expressing uncertainty about how the dashboard could be integrated into existing clinical information systems (CIS): \u003cem\u003e\u0026ldquo;There are 60 to 70 different CIS (clinical information systems), and you need access to all of them\u0026hellip;\u0026rdquo;\u003c/em\u003e (P10). Finally, three participants (3/14) suggested that to achieve broader adoption, the dashboard must be \u003cb\u003eextended beyond the SGED score\u003c/b\u003e to include data on comorbidities, prediabetic patients, or other chronic conditions. Without such an expansion, they feared its usefulness would remain limited to a narrow segment of T2D management.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWe next discuss the key challenges, potential strategies for adoption and future work regarding the visualization dashboard prototype for the SGED score.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key Challenges\u003c/h2\u003e \u003cp\u003eOne of the most critical and currently unresolved challenges in T2D management in Switzerland is the fragmented landscape of CIS (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This fragmentation poses a fundamental barrier to the development and implementation of interoperable systems (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Moreover, the lack of interoperability leads to additional costs, as data often has to be entered or reconciled manually (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Across interviews, it became evident that the absence of standardized interfaces or a commonly used national decision-support CIS is a core impediment. Each CIS operates in isolation today, meaning data cannot be easily exchanged or integrated across systems, making it difficult to compile and visualize the SGED score in a consistent and structured manner across multiple systems. This prevents automation, limits decision functionalities, and undermines the utility of performance feedback, and this is not only happening in the context of T2D (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Therefore, the challenge of bringing together disparate data must be considered both a technical prerequisite and the most urgent priority for implementing any kind of clinical dashboard. Without addressing this foundational issue, digital support systems such as the SGED visualization dashboard risk remaining conceptual rather than operational. Another key challenge concerns the limited relevance of the SGED score dashboard in everyday clinical practice with respect to individual patients. While the SGED score is calculated based on aggregated patient data, the SGED score is a performance measure for the entire T2D population at a given practice (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Hence, visualizations of the SGED score serve as aggregated population-level indicators and not for individual patient details. Nonetheless, healthcare professionals rely on patient-specific data and tools tailored to clinical decision-making during consultations with T2D patients (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). As a result, a visualization dashboard is more likely to only be used periodically for performance monitoring and quality assurance (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) rather than in day-to-day clinical routines. This population-oriented nature limits its perceived practicality for a broad range of healthcare professionals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Potential Strategies for Adoption\u003c/h2\u003e \u003cp\u003eTo increase the likelihood of successful adoption, several implementation strategies can be considered. For the SGED score dashboard to be used at scale, it must be embedded into the daily workflows of primary care in a way that minimizes administrative burden while maximizing clinical usefulness. Interoperability is the central requirement for this (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Data entered by patients or healthcare professionals must be synchronized seamlessly within the GP\u0026rsquo;s CIS. Such interoperability ensures that information is not siloed and can be accessed easily across different platforms.\u003c/p\u003e \u003cp\u003eTwo potential implementation pathways can be distinguished. First, the SGED score dashboard could function as a stand-alone secondary system, which would require standardized national interfaces to enable data exchange across different CIS. This approach would allow practices to maintain their existing software but would depend heavily on robust data integration standards. Second, the dashboard could be integrated into existing decision-support systems or CIS. This would likely facilitate adoption, as healthcare professionals would not need to adapt to an entirely new system. Leveraging existing infrastructures could reduce technical complexity and would ensure a smoother workflow integration (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Nonetheless, an existing system would need to be open to integrating the identified functional requirements into their functionality. Whichever path is chosen, the emphasis must remain on reducing redundancy and additional workload to minimize further tasks for already pressured healthcare professionals (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The dashboard should ideally provide automated data synchronization and actionable insights (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) rather than becoming another layer of administrative effort. Ensuring that the tool supports rather than disrupts existing practices will be decisive for its long-term acceptance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Future Work\u003c/h2\u003e \u003cp\u003eSeveral open questions remain regarding the feasibility and future of the SGED score dashboard. A central question is whether the system can be seamlessly integrated into current clinical workflows, using one of the potential implementation strategies from subsection 4.2. Another open issue is the rigidity of the SGED score itself (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Many interviewees noted that the score, in its current form, is too inflexible, lacking the customization needed to reflect diverse realities of T2D management, such as accounting for individual patient comorbidities or lifestyle factors. This rigidity reduces its direct applicability and limits healthcare professionals\u0026rsquo; motivation to engage with it beyond the financial incentives received as compensation. Therefore, future work should explore whether the SGED score could be made more adaptable to the needs of individual practices, potentially by turning it into a more personalized and individual score. For example, by allowing customizable indicators or integrating additional chronic disease metrics (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Finally, there remains a broader strategic and policy-level question: how can interoperability and data sharing be promoted at a national level to enable digital quality monitoring tools like the SGED score dashboard? Addressing these systemic issues would not only enhance the feasibility of this prototype but also contribute to the broader digital transformation of chronic care in Switzerland.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Limitations","content":"\u003cp\u003eThis study has several limitations that should be acknowledged. First, the sample size of 14 participants (derived from four group interviews and six individual interviews) was relatively small, which limits the generalizability of the findings. Furthermore, participants were not sampled representatively across Switzerland, since the interviewees were from the German-speaking region. Second, there may have been a selection bias, as those with a particular interest in digital health or structured diabetes care may have been more likely to participate, potentially skewing the feedback. Moreover, despite being conducted online, the interviews may have been subject to social desirability bias, with participants expressing more overly positive responses toward the prototype than in a more neutral setting. Third, the study examined a hypothetical SGED score dashboard rather than an operational system. Consequently, many of the insights reflect anticipated behavior rather than observed usage. While participants provided thoughtful reflections on both positive and negative aspects of the prototype, their responses may not fully predict actual adoption or engagement under real-world conditions, especially when considering variations in workload, clinical routines, or policy constraints. Fourth, the transcripts were only analyzed by one researcher, which may have introduced subjective bias and limited the reliability of the interpretations. Finally, while this study focused on identifying functional requirements and generating an initial prototype for a visualization dashboard for the SGED score, it did not investigate practical aspects of implementation, such as potential commercial partners or explicit integration pathways within existing systems. Future work will need to address these questions to determine the feasibility, effectiveness, and sustainability of the dashboard in clinical practice.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study explored the design of a visualization dashboard for the SGED score, a tool intended to provide an aggregated overview of T2D care quality at the practice level in Switzerland. Through qualitative interviews with key stakeholders, including GPs, MPAs, and MPCs, we identified key functional requirements, user preferences, and barriers to implementation. These challenges included system fragmentation, workflow integration challenges, and the population-level focus of the SGED score. While participants generally recognized the dashboard\u0026rsquo;s potential to support performance monitoring and quality improvement, successful adoption will ultimately depend on interoperability, usability, and alignment with existing clinical routines. Overall, the findings provide a foundation for the further development and evaluation of digital tools that enhance chronic disease management and inform strategies for scalable implementation in primary care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes mellitus type 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSwiss Society of Endocrinology and Diabetology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Practitioner\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Practice Assistant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Practice Coordinator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoSCoW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e\u0026ldquo;Must Have\u0026rdquo;, \u0026ldquo;Should Have\u0026rdquo;, \u0026ldquo;Could Have\u0026rdquo;, \u0026ldquo;Won\u0026rsquo;t Have\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOREQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsolidated Criteria for Reporting Qualitative Research\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki. This study did not involve the collection of clinical or otherwise sensitive health data. In view of its minimal-risk profile and its exclusive focus on expert knowledge and lived experience, the study was exempt from a formal review and approval by the Ethics Committee of the University of St.Gallen. Participants were fully briefed on the study\u0026rsquo;s purpose and procedures before the interviews. Written consent was obtained, including permission to record audio. Participants were informed of their right to withdraw at any time, and all data were anonymized and securely stored to ensure confidentiality.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003e OFG, TK, and MJ are affiliated with the Centre for Digital Health Interventions, a joint initiative of the Institute for Implementation Science in Health Care, University of Zurich, the Department of Management, Technology, and Economics at ETH Zurich, and the Institute of Technology Management and School of Medicine at the University of St Gallen. CDHI is funded in part by Novo Nordisk Pharma, Switzerland, the Swiss health insurer CSS, the Austrian healthcare provider MavieNext (UNIQA), and the Swiss investor company MTIP. TK is also a co-founder of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, neither Novo Nordisk, CSS, Pathmate Technologies, MavieNext, nor MTIP was involved in the design, analysis, or writing of this research.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was co-funded by Novo Nordisk Pharma AG, Switzerland, through the grant Digitalization of Diabetes Management Score (D-SGED-S). It was also co-funded by the Swiss health insurer CSS. The funders had no role in the design of the study, data collection, analysis, interpretation, or writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLA, MJ, and TK developed the concept of this study. TK supervised the study. LA and OG conducted the semi-structured interviews with the healthcare professionals. LA performed the data analysis. LA developed the prototype. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e We are grateful for all interview participants who generously shared their time, expertise, and personal insights. The contributions were invaluable and played a crucial role in shaping this research on the SGED score dashboard prototype. Furthermore, we acknowledge financial support from Novo Nordisk Pharma AG.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this article and its supplementary information files (Additional Files 1-9).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal [Internet]. Diabetes Atlas. [cited 2025 Sept 29]. 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JMIR Form Res. 2023;7:e44065.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes mellitus type 2, user-centered prototype, dashboard, interviews, Switzerland, SGED score","lastPublishedDoi":"10.21203/rs.3.rs-8347867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8347867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiabetes mellitus type 2 (T2D) is a growing burden in Switzerland, where general practitioners face increasing workload. To evaluate the quality of T2D care, the Swiss Society of Endocrinology and Diabetology (SGED) developed the SGED score. However, its practical use is hampered by paper-based workflows and fragmented documentation. Currently, there is no dashboard specifically for the visualization of the SGED score, which overviews aggregated population parameters such as HbA1c or blood pressure. To address this gap, this study derived functional requirements for a dashboard and developed a high-fidelity prototype through an iterative, user-centered design process, in collaboration with healthcare professionals. This approach explores how such a dashboard needs to align with clinical needs to enhance usability and promote adoption in routine T2D management.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn iterative, user-centered three-step approach was employed, involving 14 semi-structured interviews with Swiss T2D healthcare professionals. Step 1 involved defining the project scope and identifying functional requirements. Step 2 collected more requirements and prioritized all of them using the \u0026ldquo;Must Have\u0026rdquo;, \u0026ldquo;Should Have\u0026rdquo;, \u0026ldquo;Could Have\u0026rdquo;, \u0026ldquo;Won\u0026rsquo;t Have\u0026rdquo; (MoSCoW) method. In step 3, a high-fidelity Figma dashboard prototype was developed and iteratively refined based on the requirements and user feedback.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKey functional requirements included reminder and alert functions, color-coded critical values, demographic overviews, trend analyses, benchmarking within networks, and exportable reports. Additional needs emerged for patient-level views, integrated checklists, inclusion of comorbidities, and personal or practice-specific goal-setting features. Iterative refinements based on user feedback improved clarity, usability, and visual appeal. Some participants highlighted the dashboard\u0026rsquo;s intuitive design, clear and diverse visualizations, and benchmarking functionalities, describing it as both engaging and efficient. Others raised concerns about limited suitability for daily clinical workflows, potential integration challenges with existing systems, and the need for interactive, patient-centered features to support routine care.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe proposed dashboard could enhance T2D care through features like population overviews, long-term visualizations, and anonymized benchmarking. Successful clinical adoption will heavily depend on interoperability and seamless integration into existing workflows. The identified requirements provide a foundation for future digital T2D management systems.\u003c/p\u003e","manuscriptTitle":"A Care Quality Dashboard for General Practitioners Managing Patients with Diabetes Mellitus Type 2: User- Centered Design and Evaluation of a Prototype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:24:38","doi":"10.21203/rs.3.rs-8347867/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-11T08:51:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T08:18:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263096210856747998093738186052565466400","date":"2026-01-26T18:38:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-02T10:00:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240409426167615347311306633979702222613","date":"2025-12-24T01:43:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T17:45:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-17T10:29:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-16T00:45:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-16T00:44:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2025-12-12T17:01:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fee0cc77-99bb-4f11-be41-4f09543172df","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T06:27:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 05:24:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8347867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8347867","identity":"rs-8347867","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}