What it takes to scale AI in health care
Health systems need the ability to test multiple approaches and conduct experiments more rapidly.
By
Matthew Solan
September 18, 2026
Clinical Scorecard: What it takes to scale AI in health care
At a Glance
Category Detail
Condition Artificial Intelligence in Health Care Key Mechanisms Integration of AI into clinical and operational workflows, infrastructure readiness, and change management. Target Population Health systems and clinical caregivers Care Setting Health care organizations implementing AI technologies
Key Highlights
Scaling AI requires understanding existing workflows and improving them. Health systems must evaluate AI applications based on strategic objectives. Infrastructure readiness includes data, computing, and application layers. Cybersecurity is increasingly important as reliance on technology grows. Realistic expectations about AI's capabilities are essential for successful implementation. Guideline-Based Recommendations
Diagnosis
Management
Identify and prioritize AI projects based on their potential impact. Monitoring & Follow-up
Assess the integration of AI into existing health care processes. Risks
Prepare for cybersecurity threats associated with AI implementation. Patient & Prescribing Data
Patients within health systems utilizing AI technologies.
AI-ready data is crucial for effective health care transformation.
Clinical Best Practices
Adopt an 'AI-first' approach to reimagine health care delivery. Ensure data is in an AI-ready format for effective utilization. Encourage rapid experimentation and learning in AI applications. Related Resources & Content
