Pharma Next

Pharma Next unifies Trial data into real-time insights with Tableau Next, leveraging powerful visualizations, AI Concierge, Slack Collaboration, and intelligent actions for smarter, faster decisions

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About the Project

Inspiration

The pharmaceutical industry faces enormous challenges in managing clinical trial data. From recruitment to patient retention, every step generates massive amounts of data spread across silos. Leadership teams often lack real-time insights to answer crucial questions:

  • Which campaigns deliver the best enrollment outcomes?
  • How does campaign performance vary across geographies or patient demographics?
  • How much does it truly cost to recruit and retain a patient?
  • What is the patient adherence rate for scheduled site visits?
  • Which trial phases have the highest dropout rates, and why?
  • What types of adverse events are being reported most frequently?
  • Are adverse events more common in specific therapeutic areas or demographics?
  • What is the impact of patient dropouts or adverse events on trial success and finances?
  • How much cost is wasted on patients who drop out before completion?
  • How should budgets be reallocated across campaigns and trial phases to improve ROI?

Why does this matter? Without real-time, integrated insights, Pharma Next risks delayed trial outcomes, jeopardized patient safety, and increased costs directly impacting its ability to deliver life-saving medicines quickly and maintain a competitive edge in the pharmaceutical industry. These insights are not just numbers on a dashboard. They directly impact trial success, patient safety, and ultimately, the speed at which life-saving medicines reach the market.

Solution

To address these challenges, we built Pharma Next an integrated analytics platform powered by Salesforce and Tableau Next. Our solution transforms fragmented clinical trial data into real-time, actionable insights that empower leadership teams to move from reactive reporting to proactive decision-making.

Key capabilities include:

  • Unified Dashboards & Visualizations
    Interactive Tableau dashboards that bring together marketing, operational, safety, and financial KPIs in one place.

  • Advanced Metrics & KPIs
    Real-time tracking of enrollment success rates, patient adherence, dropout distribution, and cost per participant.

  • AI Concierge (Agentic Capabilities)
    Natural language Q&A that allows users to simply ask questions like “Which campaign has the highest Enrollments?” without navigating complex reports.

  • Slack Collaborations
    Alerts and insights are delivered directly into Slack for faster collaboration and decision-making.

  • Intelligent Data Actions
    Go beyond visualization trigger proactive actions such as Analyzing Dropout trend and Recommending strategies, Triggering Email to Leadership from Dashboard

By combining these features, Pharma Next empowers pharma leaders to reduce costs, accelerate trial timelines, improve patient safety, and increase overall trial success rates.

What We Learned

  • How to design a semantic data model that powers natural language queries in Tableau Next.
  • Building AI Concierge for agentic analytics, so business users can simply ask questions instead of searching dashboards.
  • Leveraging Slack Collaboration for real-time collaboration on metrics.
  • Enabling Intelligent Data Actions, where leadership can trigger Salesforce Flows, AI Actions, or external APIs directly from the dashboard.

How We Built It

  1. Data Preparation:

    • Prepared datasets for campaigns, participants, Patients, Clinical Trials, enrollments, visits, Medical conditions, medications, and costs.
    • Created derived cost models (e.g., VisitCost, EnrollmentCost) to simulate financial impact.
    • Created Calculated Fields and Metrics from the data model to use in Visualization and AI Concierge

  2. Visualization Layer:

    • Built interactive dashboards in Tableau Next showing KPIs: enrollment rates, dropouts, adherence, adverse events, and campaign ROI.

  3. AI Concierge:

    • Added field descriptions to the semantic model and Defined Business Preferences.
    • Enabled natural language queries like:
      "Show campaign type with the highest enrollments and compare costs."

  4. Slack & Collaboration:

    • Embedded Tableau metrics into Slack channels for instant team alignment. Allow sharing Matrices on Slack channels

  5. Intelligent Actions:

    • Buttons on dashboards invoke Salesforce Flows/Apex for tasks like:
      • Requesting retention strategy recommendations.
      • Drafting and Sending Email to Leadership

Challenges We Faced

  • Data modeling: Creating realistic datasets that simulate pharma operations.
  • Semantic layer tuning: Ensuring Tableau AI generated accurate responses required precise field descriptions.
  • Balancing complexity: AI Concierge struggled with very complex calculations, so we powered it with Calculated Fields and Matrices.

Why It Matters

Clinical trials aren’t just about data; they impact patient safety, trial success, and time-to-market for life-saving medicines.
By turning complex trial data into real-time insights + intelligent actions, Pharma Next helps pharma companies move from reactive reporting to proactive decision-making.

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