Data Visualization in “Dashboards”: Brazil Facing Global Leaders in Clinical Trials

Aline Kassuda; Fernando Celso de Campos

DOI: 10.22167/2675-6528-202602625

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The scarcity of strategic views of raw clinical trial data on ClinicalTrials.gov, especially concerning the Brazilian and international scenarios, motivated the present study. The study aimed to apply business intelligence approaches to develop interactive dashboards in Microsoft Power BI® that would allow for the organization, visualization, and monitoring of industry-sponsored clinical trial data, comparing Brazil’s performance with the top three global leaders in the period from 2015 to 2025. To achieve this, the data lifecycle and data storytelling techniques were used, extracting information from the public repository ClinicalTrials.gov via API. The data were manipulated with Python and Power Query®, modeled in a star schema, and visualized in an interactive dashboard in Microsoft Power BI®. The dashboard’s applicability was evaluated by 26 clinical research professionals through an electronic questionnaire. The results demonstrated the solution’s effectiveness, which received validation from professionals for its clarity, ease of navigation, and utility in understanding Brazil’s positioning and reducing analysis time. Overall satisfaction with the dashboard reached an average of 4.46 on a scale of 1 to 5. It was concluded that business intelligence is fundamental to transforming open data into visual intelligence, enabling agile and qualified monitoring of national clinical research in the global scenario and supporting strategic decision-making.

Keywords: Business Intelligence; Monitoring; Narrative; Interactive Dashboard; Clinical Research.

1. Introduction

Clinical research drives medical-scientific innovation, contributing significantly to public health and patient well-being. In this context, clinical trials are fundamental for producing evidence of safety and efficacy of new therapies throughout their different research phases (Kandi and Vadakedath, 2023). To validate this evidence, it is necessary to conduct clinical trials in diverse populations and epidemiological settings, a factor that has driven the globalization of clinical trials beyond the United States of America and Western Europe. Although this phenomenon is a growing trend in industry-sponsored clinical trials, its distribution is still concentrated in high-income countries, revealing important representation gaps (Payedimarri et al., 2023).

Clinical trials generate a high volume of information. Given this, the public registration of research is essential to ensure transparency and scientific quality, in addition to reducing publication bias and selective outcome reporting, representing the fulfillment of an ethical responsibility of researchers. Since 2005, the International Committee of Medical Journal Editors has required registration on public platforms certified by the World Health Organization, such as ClinicalTrials.gov and the Brazilian Registry of Clinical Trials, for publications in major international journals. In Brazil, this same recommendation was adopted in 2007 for publications in the Latin American and Caribbean Health Sciences Literature and the Scientific Electronic Library Online. Brazilian federal resolutions (Board Resolution 39/2008 and Board Resolution 36/2012) made registration mandatory for research with new drugs (Freitas et al., 2016). Among the various public platforms certified by the World Health Organization, ClinicalTrials.gov is considered the largest global repository of clinical trials and provides structured data that favors the application of automations, making the analysis of a large volume of data more efficient (Yu et al., 2024).

The volume and complexity of the collected data make analysis an indispensable step for understanding the clinical trial scenario. In this context, business intelligence approaches assist in processing and organizing this data, transforming it into strategic information that supports decision-making (Bottrighi et al., 2024). For this purpose, tools that optimize the exploratory process are fundamental for obtaining information efficiently. The Python programming language contributes to the automation and unification of large volumes of data, bringing operational efficiency and increased information reliability by reducing the occurrence of errors. Python libraries, such as Pandas, are indispensable for connecting and manipulating data from various sources, making them essential for the robust and integrated preparation of databases that feed panels or dashboards, ensuring the quality of analyses (Ferreira et al., 2024).

The dashboard data needs to be understandable for information users. Data storytelling aids in communicating visualizations by articulating data, visual elements, and narrative, focusing attention on the most relevant aspects and simplifying the interpretation of complex analyses (Echeverria et al., 2018). Knaflic (2019) adds that effective communication with data requires understanding the context and audience, selecting the appropriate visual resource, eliminating elements that do not contribute to the message, and directing the reader’s attention to the essential points. In the routine of clinical research, the use of dashboards functions as an interface that consolidates information from clinical trials. The use of dashboards provides a comprehensive and integrated view of information, facilitating the identification of patterns and the exploration of possible scenarios. For the composition of these dashboards, Microsoft Power BI is an accessible and versatile tool for manipulating, analyzing, and visualizing different datasets. The ease of use and interactivity of visualizations transform data into charts, enabling the exploration of different visual formats, essential characteristics for effective exploratory analysis (Becker and Gould, 2019).

Given the scarcity of visualizations that highlight Brazil’s position against global leaders in conducting clinical trials, the use of business intelligence proves relevant for systematizing Brazil’s data in relation to leading countries in clinical trial registrations in the public repository ClinicalTrials.gov. The use of business intelligence is fundamental for converting open data into visual intelligence, enabling agile and qualified monitoring of national clinical research against the global scenario. In this context, the objective of this work was to apply business intelligence tools to develop interactive dashboards in Microsoft Power BI that allow for the organization, visualization, and monitoring of industry-sponsored clinical trial data, considering Brazil and the three leading countries in registrations in the public repository ClinicalTrials.gov.

2. Material and Methods

This work was classified as applied research, of an exploratory and descriptive nature, according to Gil (2022). The methodological approach was quantitative, using structured data to analyze Brazil’s positioning in global clinical trials and evaluate the developed panel. Technically, the study was bibliographical and documentary, using secondary data from public repositories, such as ClinicalTrials.gov.

The data were obtained from two sources: secondary data extraction from ClinicalTrials.gov and a survey of clinical research professionals’ perceptions through an anonymous electronic form, which did not request personal or identifiable information. The work observed the applicable ethical provisions, waiving registration and evaluation by the CEP/Conep System, in accordance with CNS Resolutions No. 510, of 2016, and No. 466, of 2012.

The work development followed six sequential stages, adapted from the Business Intelligence [BI] processes of Foley and Guillemette (2010): strategic definition, data collection, data manipulation, modeling and structuring, visualization and communication, and evaluation of the dashboard’s applicability.

In the strategic definition stage, the development of the dashboard was planned, accessing ClinicalTrials.gov () due to its global reach and structured data. The scope of the data was delimited to clinical studies initiated between 2015 and 2025, focusing on industry-sponsored interventional trials. The variable selection criteria prioritized relevance, structured data, and international comparability (Bahia, 2021).

Data collection used the ClinicalTrials.gov [API] v2 application programming interface (National Library of Medicine, 2025; Zarin et al., 2011), selected for offering structured and reproducible access. The original data in English were translated into Portuguese, and observations with missing values were excluded to optimize the analyses.

Data manipulation followed the extract, transform, and load [ETL] process (Foley and Guillemette, 2010). Extraction was automated with Python via the ClinicalTrials.gov API v2, including exploratory analysis for inconsistencies. Transformation was hybrid, using Python and Power BI® for standardization, cleaning of duplicates and missing values, and enrichment (Souibgui et al., 2019). The processed data were loaded into the model in Power BI®. Details about codes and country lists are in .

The data modeling and structuring employed the star schema to enable dashboard interactivity and metric calculation. In Microsoft Power BI®, tables were structured with a central fact table and connected dimension tables, managing complex relationships between countries. The dashboard measures were built using the Data Analysis Expressions [DAX] language, grouped into several indicator categories. Detailed formulas are available in the GitHub repository.

The visualization and communication stage consisted of the development of the dashboard in Microsoft Power BI®, designed to organize and communicate the results in a visual, comparable, and structured manner (Foley and Guillemette, 2010). The dashboard was divided into three thematic pages: global view, Brazilian scenario, and comparison of Brazil versus the three leading countries. The visualizations were built with interactive features and diverse graphic elements, following Knaflic (2019) for effective communication. The dashboard is publicly available at .

The evaluation of the panels’ applicability, the final stage, verified the practical utility of the solution. Perceptions of clinical research professionals were collected through an anonymous electronic form, with five-point Likert scale questions. The questions were based on quality attributes for panels suggested by Almasi et al. (2023), such as utility and ease of use. The sample was convenience and voluntary participation, and the questionnaire details are in the GitHub repository.

3. Results and Discussion

This section details the results obtained in each research stage, accompanied by a critical analysis that contextualizes the findings and their implications. The study aimed to apply business intelligence approaches to develop interactive dashboards in Microsoft Power BI® to organize, visualize, and monitor data from industry-sponsored clinical trials, comparing Brazil’s performance with the three global leading countries in the period from 2015 to 2025. The results demonstrate the effectiveness of the proposed solution and its validation by industry professionals, highlighting the potential of business intelligence to transform raw data into strategic and visual information.

The initial stage, named Strategic Definition, was crucial for guiding the construction of the dashboard, establishing alignment with the work’s objective, the analytical scope, and the selection of indicators. The exploration of the ClinicalTrials.gov repository allowed for the identification of a set of suitable variables for calculating the metrics. Among the selected variables, the sponsor class, study start and completion dates, last update date, trial phase, unique study identifier (NCT), location countries, principal sponsor, status, and study type stand out. These variables, together, enabled the characterization of clinical trials regarding their profile, conduct, sponsors, and geographical distribution, according to the National Library of Medicine (2025).

The indicators defined for the work were predominantly strategic, designed to offer a comprehensive view of the international clinical research environment and Brazil’s positioning. This approach is in line with Van de Ven et al. (2023), who emphasize the importance of indicators driven by external perspectives to reflect an organization’s competitive strategy. However, during the exploration of the database, significant limitations were identified regarding two potential variables: the number of patients recruited and the conditions or diseases studied. It was decided not to include these variables in the composition of the indicators to preserve the consistency and comparability of the analyses.

The limitation regarding the number of recruited patients arises from the fact that the value reported in ClinicalTrials.gov corresponds to the global total of the study, without the possibility of breaking down this data by country, which would make comparisons between different nations unfeasible. Regarding the conditions or diseases studied, it was observed that this information did not present standardization by therapeutic areas, revealing high granularity and heterogeneity in its completion. This inconsistency could compromise the reliability of the analyses and lead to duplications or counting errors, justifying the decision to exclude them from the scope of the panel to ensure the integrity of the results.

The Data Collection stage focused on evaluating the methods for obtaining information available on ClinicalTrials.gov. Initially, direct extraction through the platform was examined, but it presented limitations for the defined analytical scope, especially regarding the categorization of the sponsor class. The National Library of Medicine’s documentation (2024) indicates that the system’s sponsor definitions are proprietary, which requires caution in interpretation for comparative purposes. Therefore, the application programming interface (API) was selected as the most suitable option, as it provides more structured, standardized, and reproducible access to records, aligning with the business intelligence approach.

The choice of API represented a methodological advance, allowing for greater standardization, traceability, and reproducibility of the collection process, in addition to favoring integration with subsequent data processing and analysis steps. The variables and observations were translated into Portuguese, increasing the accessibility and understanding of the data within the context of the work. Additionally, observations with missing values were excluded from the database to reduce analytical noise and avoid compromising the analyses. This decision, although it may have implied the loss of some relevant information, increased the analytical viability of the dataset, establishing cuts and limitations that were considered in the interpretations.

The Data Manipulation stage was essential for transforming the raw data from ClinicalTrials.gov into a structured database suitable for the work’s objective, following a hybrid extract, transform, and load (ETL) process that utilized the Python language and Microsoft Power BI®’s Power Query®. Initially, data extraction was performed via the ClinicalTrials.gov API v2, after identifying the field names and data types. Exploratory analysis with Python allowed for the identification of missing values, duplicate data, and other inconsistencies, guiding the processing procedures.

The raw database contained 58,165 observations. The unique study identifier (NCT_Id) was found to have no missing or duplicate values, with 58,165 unique values, ensuring that each observation corresponded to a distinct clinical trial. Fields such as “status”, “lead_sponsor”, “start_date”, and “last_update_post_date” were complete. The “completion_date” field presented 98.78% completeness. The “sponsor_class” and “study_type” fields had only one unique value, which simplified standardization and analysis. These positive aspects of the raw database facilitated the data cleaning and organization process.

However, important points of attention were identified that required treatment. The “phase” (“phases”) variable presented the largest number of missing values, with 14,223 incomplete records, representing 24.45% of the database. The “location countries” (“countries”) variable was second in number of absences, with 3,377 missing records, corresponding to 5.81% of the total. To resolve these inconsistencies, clinical trials with missing phases and countries were removed from the database, resulting in a treated database with 54,788 observations, a reduction of 3,377 records compared to the original raw database.

After treatment, the “phases” variable had its missing values filled with the acronym NA, reaching 100% completeness, while the missing values of the “countries” variable were removed, also resulting in 100% completeness for this variable. The initial processing with Python generated the treated database, which was subsequently refined in Power Query® from Microsoft Power BI®. The main transformations included promoting headers, defining data types, converting date and text columns, and selecting relevant columns for the creation of the fact table (FATO_Ensaios).

To standardize country names, XML files with names in English and Portuguese from the European Union database were imported, creating auxiliary tables (Paises_EN and Paises_PT). An auxiliary query, Paises_EN_Padronizado, was developed to identify and correct country names without automatic matching. The BRIDGE_Paises bridge table was created to resolve the many-to-many relationship between the fact table and the country dimension, as the same clinical trial can be conducted in multiple countries. This data manipulation step was fundamental to ensure the quality and consistency of the analyzed database, demonstrating that handling public databases requires rigorous methodological care to ensure the reliability of the results.

The Modeling and Structuring stage was crucial for organizing the data consistently, using dimensional modeling in a star schema in Microsoft Power BI®. This structural design is preferred due to its performance, ensuring the necessary processing speed for quick access to business information, according to Foley and Guillemette (2010). The model was centered on a fact table containing clinical trial records, connected to dimension tables that categorize and allow comparative filters, such as DIM_Paises, DIM_Status, DIM_Fases, and DIM_Data.

The creation of the BRIDGE_Paises bridge table was an effective solution to represent the multicenter participation of clinical trials, preserving the integrity of the model and the consistency of country counts. After modeling, several measures were built using Data Analysis Expressions (DAX) language to calculate the dashboard indicators. These measures were grouped into categories, including volume and dimension (Total Trials, Total Countries), participation and composition (Phases (%), Country Participation in Global Total (%)), growth and temporal variation (YoY (%), CAGR), historical base and evolution indices (Base100 Index), and classification and competitive positioning (Country Rank, Top1 Sponsor).

The construction of comparative measures between Brazil and the international scenario represented a significant challenge due to the difference in scale between the volumes of clinical trials. To overcome this issue and facilitate the interpretation of visualizations, the use of a fixed-base index was chosen, where the base year was defined as 100 for both Brazil and the global scenario. Furthermore, to compare Brazil with the three leading countries, the annual growth rate was adjusted, using the annual average of the three leading countries in each period, which allowed the comparison to be maintained over time, while smoothing out specific differences between the leading countries themselves. This approach ensured greater reliability of the presented results.

The Visualization and Communication stage resulted in the development of an interactive dashboard in Microsoft Power BI®, structured to organize and communicate results visually and comparably. The dashboard was divided into three thematic pages to guide the user from a macro view of the international scenario to more specific analyses of Brazil and its position relative to global leaders. The first page, “International Scenario”, characterized the global clinical trial environment, presenting a total of 54,788 studies and 164 countries involved, with a global compound annual growth rate (CAGR) of 2.0%.

The United States led the number of studies with 27,529, followed by China with 9,958 and Germany with 7,107. The main global sponsors identified were AstraZeneca, Pfizer, Eli Lilly, Novartis, and GSK. Regarding study phases, 54% were early phase (0 to II), 24% were late phase (III), 18% were post-market (IV), and 4% were not applicable. The global temporal evolution of studies and the annual rate of change were presented, showing the dynamics of the clinical research market over time, with annual variations oscillating between -6.3% and 8.1% in the analyzed period.

The second page, “Brazilian Scenario”, explored Brazil’s participation in the global context, providing a detailed view of the national clinical trial market. It was found that Brazil registered 2,118 studies, with a compound annual growth rate (CAGR) of 3.7% and a 3.9% share of the global total. The main sponsors in Brazil were AstraZeneca, Roche, Novartis, MSD, and Janssen. The distribution of study phases in Brazil showed 68% in early phase (0 to II), 23% in late phase (III), 6% in post-market (IV), and 3% not applicable. The temporal evolution of studies in Brazil and the comparison with the global scenario, using a base index of 100, revealed the country’s growth trajectory, with annual variations ranging between -21% and 28%.

The third page, “Brazil vs. Top 3”, offered a comparative visualization of Brazil’s performance in relation to the three leading countries in clinical trial registrations. Brazil ranked 19th globally, with a 3.9% share. Brazil’s relative distance from the global leader (United States) was -63.8%, representing an absolute distance of -17,571 studies. The leading sponsor in Brazil was AstraZeneca. The distribution of study phases in Brazil, with 68% in early phase and 23% in late phase, was compared with the average of the top three countries. Brazil’s annual variation relative to the Top 3 average and the evolution index based on 100 were also presented, highlighting the country’s competitive positioning.

The dashboard pages facilitated the organization and communication of results, but it is important to emphasize that the visualizations simplify a complex phenomenon. The indicators help describe the clinical research scenario, but they are not sufficient to explain the factors that influence each country’s position. The methodological choice to use an index with a fixed base equal to 100 to compare Brazil with the world, although important for comparing the evolution of the series, reduced the perception of the real difference in magnitude between the volumes. Similarly, the adoption of the annual average of the three leading countries for comparison smoothed out specific differences, which may hide particular behaviors of each of them.

The final stage, Evaluation of the panels’ applicability, involved 26 clinical research professionals who responded to an electronic questionnaire. The sample was predominantly composed of participants with more than ten years of experience, in management positions (managers/directors), and with intermediate or high familiarity with data analysis and panel use. This participant profile lends greater consistency to the interpretation of the positive results obtained, suggesting that the tool was evaluated by an audience with relevant experience in the analyzed context. The questionnaire used a five-point Likert scale, ranging from 1 (Strongly disagree) to 5 (Strongly agree), to measure perception of the tool.

The overall satisfaction level with the dashboard reached an average of 4.46. The items with the best ratings, averaging 4.77, were ease of learning, visual organization, and the tool’s loading and response time. Participants positively highlighted the understanding of Brazil’s positioning relative to leading countries, with an average of 4.73, and the perception of reduced analysis time compared to direct consultation of the ClinicalTrials.gov platform, with an average of 4.65. The dashboard was perceived as clear, easy to use, and useful for supporting analytical activities related to clinical studies, facilitating information retrieval, data comprehension, and insight identification.

The open-ended responses from the questionnaire reinforced the positive perception, but also pointed to opportunities for improvement. The most recurring suggestions included the need to make the analyzed period more explicit, the inclusion of captions or additional explanations in some graphs, and the expansion of the level of detail in the information. It was also suggested to better explain the origin and scope of the data and to incorporate new variables, such as therapeutic classes, number of patients, and distinction between types of sponsors, which indicates a path for the continuous refinement of the tool and the expansion of its explanatory capacity.

In summary, the application of business intelligence approaches enabled the development of interactive dashboards that organize, visualize, and monitor industry-sponsored clinical trial data, providing a clear view of Brazil’s positioning relative to global leaders. The solution proved effective in transforming raw data into visual intelligence, facilitating analysis and strategic decision-making, although the inherent complexity of the data and methodological choices require cautious interpretation of the presented results.

4. Conclusion

This study sought to apply business intelligence approaches to develop interactive dashboards in Microsoft Power BI® that would allow organizing, visualizing, and monitoring data from industry-sponsored clinical trials, comparing Brazil’s performance with the three global leading countries in the period from 2015 to 2025. It was found that the proposed solution, built based on the data lifecycle and data storytelling techniques, extracted information from the public repository ClinicalTrials.gov via API, manipulated it with Python and Power Query®, and modeled it into a star schema. The resulting dashboards organized a total of 54,788 studies in 164 countries, presenting a detailed view of the international landscape, Brazilian participation (2,118 studies, 3.9% of the global total, 19th position), and its comparison with the leaders. The dashboard’s applicability was evaluated by 26 clinical research professionals, who expressed high satisfaction (average of 4.46 out of 5), highlighting its clarity, ease of navigation, and usefulness for understanding Brazil’s positioning and reducing analysis time, demonstrating the tool’s effectiveness in transforming raw data into strategic visual intelligence.

Despite the tool’s practical contribution to the agile and qualified monitoring of national clinical research and support for strategic decision-making, some methodological limitations were identified. The use of a fixed-base index and the annual average of leading countries, although essential for temporal comparison, smoothed out differences in magnitude and specific behaviors. Furthermore, the quantitative focus of the indicators did not encompass important qualitative aspects, such as study complexity or scientific impact. Professionals’ suggestions indicated the need to make the analyzed period more explicit, include legends, and expand the detail of information, incorporating variables such as therapeutic classes, number of patients, and distinction between types of sponsors. For future studies, it is recommended to investigate the panel’s applicability in the daily decision-making process and expand the profile of consulted professionals for validation in different contexts, in addition to implementing the suggested adjustments for the continuous refinement of the tool.

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