Healthcare AdministratorsvsRegistered Nurses

until most of the change has landed

High adoption

until most of the change has landed

Medium adoption

AI is automating scheduling, revenue cycle and reporting in health systems, moving administrators towards AI governance and change leadership.

AI augmenting diagnostics, monitoring, and administrative tasks.

1. Operational AI for capacity and staffing
2. Revenue cycle automation
3. Office copilots for reporting and communication
4. Procurement of clinical AI
5. High sector adoption
6. Low observed usage by managers themselves

1. Need to Improve Patient Outcomes & Safety
2. Shortage of Healthcare Professionals & Staff Burnout
3. Advancements in AI for Medical Image Analysis & Diagnostics
4. Availability of Large Healthcare Datasets (for AI training)
5. Demand for More Efficient Healthcare Delivery
6. Rise of Telehealth & Remote Patient Monitoring
7. Focus on Personalized Medicine & Care
8. Pressure to Reduce Healthcare Costs
9. Integration of AI into Electronic Health Record (EHR) Systems
10. Patient Expectations for More Accessible & Proactive Care

1. AI governance and risk management
2. Change leadership
3. Data and operational analytics
4. Procurement and vendor management
5. Regulatory and compliance knowledge
6. Financial stewardship

1. Clinical Judgment & Critical Thinking
2. Empathy & Patient Communication
3. Technical Proficiency with AI Clinical Tools
4. Data Interpretation & Validation
5. Interdisciplinary Collaboration (with AI insights)
6. Adaptability & Continuous Learning (of new tech)
7. Ethical AI Application & Patient Privacy
8. Patient Advocacy in an AI-Augmented Setting

1. Epic
2. Qventus
3. Waystar
4. Workday
5. Microsoft 365 Copilot

1. Epic / Cerner (EHRs with increasing AI capabilities)
2. Viz.ai (AI for stroke detection and care coordination)
3. Current Health / Biofourmis (Remote Patient Monitoring & AI Analytics)
4. Nuance Dragon Medical One (Voice recognition for clinical documentation)
5. Various internal predictive models developed by hospital systems (e.g., for sepsis prediction)