KRIs, KPIs and Dashboards to Monitor AI/ML Use-Cases</div><div style="text-align: center;"><button class="read-more-button">Continue Reading</button></div><div class="read-more-hidden">& Governance Performance

Published on 22/11/2025

KRIs, KPIs and Dashboards to Monitor AI/ML Use-Cases & Governance Performance

The rapidly evolving landscape of clinical trials necessitates the integration of advanced technological solutions, particularly in artificial intelligence (AI) and machine learning (ML). This article aims to provide an in-depth tutorial on the development and monitoring of Key Risk Indicators (KRIs), Key Performance Indicators (KPIs), and dashboards to assess AI/ML utilization and governance within clinical trials, particularly in Amgen clinical trials.

Step 1: Understanding the Fundamentals of KRIs and KPIs

Before implementing KRIs and KPIs for monitoring AI/ML in clinical trials, it is crucial to understand the definitions and differences between these two concepts:

Key Risk Indicators (KRIs): These are metrics used to provide early signals of potential risks that can jeopardize the success of a clinical trial. In the context of AI/ML, KRIs may focus on data quality, model accuracy, or prediction fidelity.
Key Performance Indicators (KPIs): KPIs reflect how effectively a clinical trial is achieving its key objectives. They can pertain to patient recruitment rates, data completeness, or time to database lock.

Both KRIs and KPIs serve as essential tools for clinical operations, allowing stakeholders to make informed decisions and optimize resource allocation. This foundational understanding will set the stage for effective application in the context of AI and ML.

Step 2: Identifying Relevant Use Cases for AI/ML in Clinical Trials

The application of AI/ML in clinical trials can significantly enhance efficiency and effectiveness. The following are common use cases relevant to Amgen clinical trials and beyond:

Patient Recruitment: AI can analyze electronic health records and social media data to identify suitable candidates for clinical trials, thus improving clinical trial site feasibility.
Data Monitoring: Machine learning algorithms can automate the monitoring of trial data in real-time for discrepancies, improving data quality.
Predictive Analytics: Using historical data, AI can predict patient outcomes and adverse events, allowing for timely interventions.

These use cases highlight the versatility of AI/ML and underscore the need for systematic monitoring through KRIs and KPIs.

Step 3: Establishing Governance Frameworks for AI/ML Applications

Governance outlines the structure and procedures necessary for oversight and compliance in clinical trials. When implementing AI/ML technologies, a robust governance framework is paramount:

Data Governance: Establish protocols for data integrity, accuracy, and security. This may include guidelines stipulated by regulatory authorities such as the FDA and the EMA.
Algorithmic Governance: Define processes for validating AI/ML algorithms, ensuring they perform reliably within the clinical trial context.
Regulatory Compliance: Formulate policies that ensure adherence to regulations concerning patient data privacy and ethical standards within the trial.

Effective governance minimizes risks associated with AI/ML applications in clinical trials, allowing organizations to harness these technologies responsibly.

Step 4: Developing KRIs and KPIs for AI/ML Utilize Cases

With a clear understanding of governance, it is time to define specific KRIs and KPIs tailored to your clinical trial’s AI/ML applications. Here’s how to approach the development process:

1. Determine Objectives

Align KRIs and KPIs with the overall objectives of the clinical trial. For example, if the aim is to enhance patient recruitment, a KPI could be the number of qualified patients screened per month.

2. Engage Stakeholders

Engaging stakeholders from clinical operations, data science, and regulatory affairs throughout the process ensures that the measures chosen are relevant and comprehensive.

3. Choose Relevant Metrics

Each KRI and KPI should be measurable and actionable. Consider metrics that gauge the success of AI applications, such as:

Accuracy of predictive models (KRI)
Time taken for patient recruitment (KPI)
Frequency of data discrepancies identified by AI (KRI)

Incorporating both qualitative and quantitative measures ensures a holistic view of AI/ML performance.

Step 5: Implementing Dashboards for Real-Time Monitoring

Once KRIs and KPIs have been established, the next step is to create dashboards that facilitate real-time monitoring. A well-designed dashboard can enhance decision-making:

1. Choose the Right Tools

Select data visualization and dashboarding tools compatible with existing data systems. Popular options include Tableau, Power BI, and proprietary clinical trial management systems.

2. Design for Clarity

The dashboard should prioritize clarity and user-friendliness. Key elements include:

Visual representations of KRIs and KPIs (charts, graphs)
Alerts for out-of-range metrics
Filtering options for deeper insights by various factors (trial site, patient demographics)

3. Ensure Accessibility

Dashboards should be accessible to all relevant stakeholders, enabling informed decision-making across clinical operations, regulatory affairs, and medical affairs teams.

Step 6: Routine Review and Continuous Improvement

The field of AI/ML in clinical trials is not static; thus, continuous improvement is essential. Establish a routine for reviewing KRIs and KPIs:

Regular Meetings: Schedule periodic discussions to evaluate the effectiveness of monitoring efforts and the overall success of AI implementations.
Adapt Metrics: As the technology evolves and more is learned from its application, adjust KRIs and KPIs to align with emerging challenges and opportunities.
Stakeholder Feedback: Actively seek feedback from users of the dashboards to identify any usability issues and additional needs.

Step 7: Reporting and Accountability

Effective governance extends to reporting mechanisms. Regular reports should outline the performance of AI/ML interventions based on the identified KRIs and KPIs:

Performance Reports: Summarize results against benchmarks, providing insights into what is working and what areas require attention.
Compliance Reports: Highlight adherence to governance frameworks and regulatory standards, such as those outlined by the ICH.

Accountability is enhanced when stakeholders can clearly see the results of AI/ML initiatives, fostering a culture of transparency and quality improvement.

Conclusion

In summary, the implementation of KRIs, KPIs, and dashboards for AI/ML use cases is integral to ensuring quality and compliance in clinical trials, especially in the context of evolving technologies like those seen in Amgen clinical trials and other drug development processes. By following the steps outlined in this guide, clinical operations, regulatory affairs, and medical affairs professionals can establish a framework that not only supports effective governance but also drives clinical trial success.