Overview of the Evaluation Process for AI Technologies in Healthcare

This document outlines the key principles and lessons learned from evaluating artificial intelligence (AI) technologies in healthcare, particularly drawing from experiences with the AI in Health and Care Award. The evaluation process emphasizes the importance of careful planning, collaboration, and iterative engagement throughout the deployment phases.

Key Principles and Best Practices:
Thorough Planning and Iterative Engagement:

Ensure that evaluations include patient superusers and clinician-facing organizations from the beginning. Engage with the deployment sites to refine the evaluation process and identify key areas for improvement early on.
Baseline Analysis and Technology Integration:

Allow several months for baseline analysis and to integrate AI technologies into clinical workflows before assessing their impact. This period helps to understand the system's functionality and user experience in real-world settings.
Avoiding Bias:

Take into account patient characteristics and clinicians' experiences to ensure a balanced evaluation and avoid potential biases in the results.
Regular Checks on Technology Usage and Data Quality:

Continuously monitor the usage and data quality of AI technologies to ensure they remain effective and produce reliable results over time.
Early Dialogue and Written Agreements:

Establish early dialogue between stakeholders regarding data collection and sharing arrangements. Clear, written agreements can help prevent delays and misunderstandings during the evaluation process.
Case Studies and Mixed Methods:

The document presents case studies that demonstrate challenges faced during evaluations and the approaches taken to address them. Mixed methods of research are used to understand the impact of AI technologies and identify barriers to successful implementation.
Theoretical Frameworks and National Guidance:

Reference to national guidance and theoretical frameworks provides a structured approach for evaluating AI technologies, covering phases from design and feasibility testing to real-world deployment.
By following these guidelines, AI technologies in healthcare can be more effectively evaluated, ensuring that they meet clinical needs and have a positive impact on patient outcomes while addressing potential challenges in their deployment and use.

Source: https://www.england.nhs.uk/publication/planning-and-implementing-real-world-artificial-intelligence-ai-evaluations-lessons-from-the-ai-in-health-and-care-award/

Region:  United Kingdom