AI RISK MANAGEMENT FRAMEWORK
Course 3318
2 Hours
$129
AI in healthcare offers incredible opportunities but also unprecedented risks. This course provides healthcare leaders with the tools and strategies to build a robust risk management framework, ensuring AI adoption safety, security, and compliance.
Target Audience
- Compliance officers
- Risk management professionals
- IT leaders and healthcare administrators
- AI project managers
Course Format:
Live Virtual Lectures
Interactive Activities:
- Hands-on exercises, case studies, and group discussions to foster practical learning.
Resources and Materials:
- Risk assessment templates.
- Sample risk management framework.
- Compliance and monitoring checklists.
Certificate of Completion
Payment Information: We accept credit card payments.
Course Objectives
- Understand the unique risks posed by AI in healthcare, including data privacy, ethical concerns, and operational vulnerabilities.
- Learn to identify, assess, and prioritize risks in AI systems.
- Gain tools and strategies to implement a comprehensive risk management framework tailored to healthcare organizations.
- Develop skills to monitor and update the framework as regulations, technologies, and risks evolve.
Course Modules Overview:
Understanding AI Risks in Healthcare
- Key categories of AI risks: compliance, ethical, operational, and cybersecurity.
- Unique challenges of using AI with protected health information (PHI).
- Overview of case studies highlighting AI-related failures in healthcare.
Risk Identification and Assessment
- How to identify risks at different stages of AI implementation (design, deployment, monitoring).
- Assessing risk severity and likelihood: prioritization frameworks.
- Tools for conducting comprehensive risk assessments.
Designing a Risk Management Framework
- Key components of an AI risk management framework:
- Integrating risk management into existing organizational workflows.
Mitigating and Monitoring Risks
- Strategies for mitigating AI risks, including access controls, data encryption, and algorithm transparency.
- Setting up monitoring systems for early detection of risks.
- Auditing and reporting on AI system performance and risk status.
Adapting to Evolving Risks
- Emerging risks in AI: regulatory changes, technological advances, and new ethical dilemmas.
- How to future-proof your risk management framework.
- Building a culture of continuous improvement in AI risk management.
Group Rates Available!
Bring your team and save – perfect for healthcare organizations looking to train multiple leaders.
Discounts available for organizations enrolling 5+ participants.
Class Schedule
Rates below are for each participant.
January 29, 2025
11:00 AM – 1:00 PM EST
AI Risk Management Framework Virtual, EST
$129
Class ID: 3318
February 13, 2025
4:00 PM – 6:00 PM EST
AI Risk Management Framework Virtual, EST
$129
Class ID: 3318
March 6, 2025
4:00 PM – 6:00 PM EST
AI Risk Management Framework Virtual, EST
$129
Class ID: 3318
Frequently Asked Questions
What is unique about AI risk management compared to traditional healthcare risk management?
AI introduces new complexities, such as algorithmic bias, data drift, and transparency challenges. Unlike traditional systems, AI evolves over time, meaning risks can emerge or change as the system learns or as new data is introduced.
How does this course help in addressing algorithmic bias?
The course provides tools to identify and mitigate biases in AI systems, ensuring equitable outcomes. You’ll learn about testing algorithms for fairness and implementing safeguards to prevent unintended consequences.
What are “data drift” risks, and how are they managed?
Data drift occurs when the data used by an AI system changes over time, potentially leading to inaccurate predictions or decisions. This course teaches monitoring techniques and strategies to recalibrate AI systems to maintain performance and compliance.
Can this framework be applied to third-party AI tools?
Yes. The course covers how to evaluate and manage risks associated with third-party AI vendors, including contractual agreements, compliance assurance, and ongoing performance monitoring.
How do I prioritize risks when resources are limited?
The course introduces prioritization frameworks that help organizations allocate resources to the most critical risks based on their potential impact and likelihood, ensuring efficient and effective risk management.
Does the course address the financial risks of AI in healthcare?
Yes, we discuss cost-related risks, such as over-investment in underperforming AI systems or unexpected costs due to non-compliance, and strategies to mitigate them through proper planning and assessment.
How does the course address the cultural shift required for AI risk management?
The course emphasizes the importance of fostering a culture of accountability and proactive risk awareness. You’ll learn strategies for engaging leadership and staff in adopting risk management practices.
How do I ensure my risk management framework stays relevant as technology evolves?
The course provides guidance on designing a flexible framework that can adapt to new regulations, technologies, and organizational needs, ensuring long-term effectiveness.