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Patient-centered Care Models Taking Center Stage in Life Sciences
Read how patient-centric care models and pharma innovation are ushering new trends in life sciences to improve patient outcomes and enrich lives.
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Join the TCS ADD™ forum at Pace Port™ London to explore AI/generative AI in clinical R&D and pharmacovigilance and how it is transforming life sciences.
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Read how using intelligent technology to transform systematic literature reviews from multiple dimensions can help create a cost-effective process.
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02 Dec 2022
Tata Consultancy Services’ comprehensive PV services, device complaint management, outcome-based pricing model for AI-led safety operations, and its flagship ADDTM Safety platform, cited as strengths.
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01 Jul 2025
With an ever-growing abundance of data in today’s ecosystem, there is a significant role played by data analytics in enhancing day-to-day tasks of an individual or a business. Data dashboards provide centralized access to information in visual or structured formats. These dashboards comprise a collection of visual reports that display key performance indicators (KPI), important metrics, and key data points—usually in real-time—to monitor the health of a business. In the clinical trial process, as well, analytics play a vital role in risk-based monitoring.
The role of KPIs
In the pharmaceutical industry, KPIs and key risk indicators (KRI) are critical where subject-safety, data quality, and early risk assessments play a crucial role. To digitize the process of clinical trials without compromising important quality checks, the industry needs a solution that is powered by KPIs and KRIs, and follows a proactive approach to quickly identify issues and accelerate the process. Risk-based monitoring targets and maps resources around risks related to subjects and efficacy. Monitoring critical processes and data through efficient visualization tools, such as charts, bar graphs, etc., offers quality insights and improves productivity in pharma research. Visualization in clinical trials should be designed such that it quickly and precisely identifies the outlines and issues alerts to end users in real time.
Effective analytical interfaces
Knowing how to structure the data is the first step in developing a robust analytics interface that can help in making smarter decisions. Selecting the right kind of data visualization from a list is the next and most important step, since it enhances usability of the dashboard. Listed below are a few aspects that should be considered while developing a data-intensive analytics application.
Unified, user-first approaches
The information provided by data dashboards should be concise, crisp, and in real time. From an end-user perspective, all data visualizations on a screen should form a unified view. The main question that should be asked throughout the dashboard design process is whether the intended user interface is seamless and effortless for users to learn and work on. Pilot studies and focus group discussions demonstrating existing knowledge and prior success prove extremely valuable here.
Relevant data dashboard screens
Dashboards should allow users to have a seamless experience. The information furnished by dashboards should be precise, with a provision to have optimal visuals for each KPI. Visual tools such as graphs and charts present data in an easily understandable format. Understanding the purpose of each chart or graph helps users choose the most appropriate tool for requirements.
For example, bar charts and column charts are primarily used to summarize data in categories or provide value-based metrics, whereas line graphs display continuous data over time. Pie charts display related category breakdowns, and a gauge chart measures KPIs against set targets. The most appropriate chart for a dashboard’s end objectives should be chosen carefully. From a visual standpoint, RAG (red, amber, green) thresholds are critical to the presentation of KRIs.
The role of user research in the development process
The process of developing dashboards is centered on users and begins with creating user personas, collecting information about user roles, and mapping out user stories. User research plays a pivotal role during all stages of development and involves creating wireframes, testing, and validation. This can be used as a guide for developers through wireframes that contain action-oriented information to understand functional needs and eliminate gaps during development.
User experience (UX)
Users will judge any application on its ease of navigation and ability to reduce effort. Here are a few important aspects of any dashboard screen:
Structure: The page should be structured in a way that draws attention to significant data points and follows UX standards.
Colors: The use of monochromatic colors on a dashboard helps to provide a clean appearance.
Contrast: Contrast, when used sparingly, can help communicate a point or quickly distinguish outliers.
Repetition and consistency: Repetition and consistency across the board can make a design intuitive and engaging for the user.
Responsiveness: A device-agnostic design format will add more value to the application.
Conclusion
Analytics play a vital role in the digitization process of clinical trials, especially with the plethora of data around us. Thus, it is imperative that dashboards are created with user personas and experiences in mind. A sound knowledge of visualization and UX principles will help develop and architect applications that add value to the pharmaceutical industry.
About the author
Image of Parul Dua
Parul Dua
Parul Dua is working as a Technical Lead for TCS ADD Platform with the Life Sciences unit at TCS. In her career spanning over 4 years, she has experience in application development with hands-on experience in analytical dashboards. She holds a master’s degree in Computer Application from SASTRA University. Her areas of interest include web designing, data analytics, and machine learning.
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