From early diagnostics to optimized resource allocation and streamlined administrative workflows, AI in healthcare promises to transform how care is delivered. But as healthcare organizations embrace AI, they must also grapple with an emerging challenge: how to responsibly manage the new risks AI introduces.
To fully harness the potential of artificial intelligence (AI), healthcare organizations must go beyond traditional compliance and cybersecurity checklists. They need a comprehensive Enterprise Risk Management (ERM) framework that includes the unique, fast-evolving AI risks in healthcare. Backed by robust incident reporting software, this framework can help track, manage, and reduce AI-related risks in real time.
AI is already embedded in key areas of healthcare, including:
These applications are transforming care delivery.
For instance, AI systems analyze patient data to support diagnoses and treatment plans, while machine learning algorithms forecast patient deterioration or ER surges. Imaging tools powered by AI can detect anomalies with high accuracy, and AI-driven chatbots improve patient engagement by handling routine queries and appointment scheduling.
According to McKinsey, AI could save the U.S. healthcare system up to $360 billion annually by improving efficiency and reducing avoidable adverse outcomes. In imaging diagnostics alone, some AI tools have demonstrated accuracy rates above 94%.
However, these advances come with inherent risks.
AI introduces a range of strategic, operational, and clinical risks that traditional frameworks may not fully capture.
The COSO 2017 ERM Framework includes five components: Governance & Culture, Strategy & Objective-Setting, Performance, Review & Revision, and Information, Communication & Reporting. Each can be tailored to address AI-related risks.
AI risk management begins with leadership. Boards and executives must embed AI oversight into the organization’s culture, ensuring that ethical use, transparency, and accountability are prioritized.
This includes establishing clear responsibilities for AI oversight, fostering collaboration between technical and clinical teams, and promoting a culture of continuous learning around AI safety and implications.
To align AI in healthcare initiatives with broader organizational goals, leaders should assess how AI impacts their competitive positioning and mission.
This involves conducting megatrend and SWOT analyses, defining AI risk tolerance levels, and mapping out dependencies across data systems, personnel, and vendors. A clear understanding of how AI supports or challenges strategic objectives is crucial for effective planning.
Real-time risk identification and prioritization are essential. Healthcare organizations need to assess where AI is integrated—from diagnostic tools to billing systems—and determine the potential risks at each touchpoint.
Engaging clinicians, IT leaders, and data scientists in this evaluation helps ensure risks are captured comprehensively. Both qualitative insights and quantitative assessments, such as likelihood and impact scoring, are useful in prioritizing risks.
Incident reporting software is an indispensable tool in managing AI risks in healthcare. These platforms allow staff to document and categorize issues such as algorithm malfunctions, unexpected outcomes, or patient complaints linked to AI tools. Over time, this data reveals patterns and highlights systemic weaknesses.
With a centralized and searchable repository, organizations can more easily detect recurring problems like model drift or data misclassification. Teams can track response timelines, resolution steps, and identify areas needing retraining or system updates. Ultimately, this proactive approach helps healthcare organizations shift from reactive to preventive risk management.
Because AI healthcare technology evolves rapidly, ERM processes must be regularly reviewed and refined. Healthcare leaders should stay updated on regulatory developments, assess whether past responses have been effective, and modify protocols as needed. Continuous improvement is critical to maintaining an effective risk posture.
Communication is the linchpin of effective risk management.
AI-related risks and incidents should be integrated into existing dashboards and reporting channels.
Frontline clinicians and staff need simple, accessible ways to report anomalies, and leadership must be equipped with insights that support decision-making and regulatory compliance. Transparency in these processes builds internal alignment and fosters trust with patients and stakeholders alike.
To implement an effective AI risk strategy, healthcare organizations should consider the following actionable steps:
Ensure all AI algorithms are trained on diverse datasets to avoid bias and enhance fairness. Models should be designed to allow traceability and explainability, so that clinical decisions can be interpreted by both clinicians and patients. Establish internal review processes to validate AI outputs regularly.
When working with third-party AI vendors, demand transparency. Contracts should require audit trails, performance testing results, and documentation of how algorithms are maintained and updated. Evaluate vendors not just on functionality, but also on compliance with ethical and regulatory standards.
Provide ongoing training for all stakeholders—from clinicians to administrative staff—on how to safely interact with AI tools. This includes understanding the benefits of AI in healthcare, along with its limitations; how to question AI outputs when necessary; and how to report anomalies through the organization’s incident reporting system.
Assign a compliance lead or team to monitor changing laws and guidance on AI in healthcare, including updates to HIPAA, GDPR, and emerging national or state-level AI legislation. Embed compliance checkpoints into your AI system lifecycle.
AI systems can fail. Whether due to vendor shutdowns, cyberattacks, or model drift, organizations must ensure continuity.
Develop data access protocols, alternative workflows, and communication plans that activate when AI systems are offline or compromised.
Use incident reporting software not only to log AI-related issues but also to analyze them. Look for trends that suggest deeper problems, such as repeated algorithm failures or consistent data mismatches. Feed these insights back into both technical updates and process improvements.
Each of these strategies contributes to a resilient, responsible approach to AI in healthcare. By treating AI risks with the same rigor as clinical and operational safety, organizations can ensure AI tools support rather than jeopardize care.
AI is reshaping healthcare technology, offering immense promise for better patient outcomes and smarter systems. But without a clear risk management framework, those benefits could be overshadowed by unintended harm.
By adapting the COSO ERM Framework to account for AI and investing in digital tools like incident reporting software, healthcare organizations can stay ahead of AI-related risks. In doing so, they not only protect patients and staff but also build a stronger foundation for innovation.
Take the next step toward safer, smarter healthcare. See how Performance Health Partners’ award-winning incident reporting system can help your organization proactively identify, manage, and prevent AI-related risks, all while improving patient outcomes. Book a demo with our team today.