In a time when nursing shortages are becoming increasingly urgent, Artificial Intelligence (AI) offers promising possibilities to relieve nursing staff and improve quality of care. But how can AI be safely and meaningfully implemented in nursing homes? Which use cases are already feasible today? And what ethical and legal aspects must be considered?

Potential of AI in Nursing Homes

The use of AI in care can offer numerous advantages:

• Time savings: Routine tasks like documentation can be partially automated

• Quality assurance: Systematic monitoring of care processes

• Decision support: Risk assessments and recommendations for action Translation into over 20 languages

• Staff relief: More time for direct resident care especially in times of skilled labor shortages, AI can help improve working conditions and ensure quality of care.

Practical Use Cases for AI in Care

1. Digital Care Documentation

Care documentation consumes much of staff time daily. AI can support by:

• Automatic creation of care reports based on templates Intelligent text modules adapted to individual resident needs

• Detection of inconsistencies or missing information Integration of the structural model to reduce bureaucracy in care documentation

Practical example: A caregiver enters brief notes about the measure performed, and the AI creates a complete, professionally correct documentation entry that only needs to be reviewed and confirmed.

2. Medication Management

The safe administration of medications is a critical area:

• Creation and updating of digital medication plans

• Checking for interactions and contraindications

• Reminder functions for medication administration

• Documentation of medication intake

Practical example: An AI-supported app creates a clear medication plan based on medical prescriptions, points out possible interactions, and documents administration.

3. Quality Assurance and Testing

AI can support quality assurance in nursing homes:

• Creation of checklists for internal quality checks

• Automatic evaluation of quality indicators Preparation for external audits (MDK) Identification of improvement potential

Practical example: Before an upcoming MDK audit, the AI analyzes care documentation, identifies possible weaknesses, and creates a checklist for preparation.

4. Transfer Management When transferring residents between different facilities:

• Automated creation of transfer forms

• Summarization of relevant information from care documentation

• Ensuring completeness of all required information

• Seamless information transfer between care facilities

Practical example: When transferring a resident to the hospital, the AI creates a complete transfer form with all relevant information on care needs, medication, and special requirements.

Safe Implementation of AI in Nursing Homes

Data Protection and Security

Handling sensitive health data requires special care: GDPR compliance: All AI solutions must meet strict data protection requirements Evaluating hosting options: Cloud solutions vs. on-premise systems Access rights: Granular permission concepts for different user groups Encryption: Secure transmission and storage of all data

Ethical Aspects

The use of AI in care raises ethical questions:

• Human control: AI should support caregivers, not replace them

• Transparency: Traceability of AI recommendations

• Resident autonomy: Self-determination and dignity must be preserved

• Justice: Equal access to AI-supported care

Introduction step-by-step

Dr. Manfred Criegee-Rieck, CIO of Nuremberg Hospital, recommends a pragmatic approach that can also be applied to nursing homes:

• Determine hosting option: Cloud or own server?

• Create technical foundation: Infrastructure and interfaces

• Develop use cases with teams: Involve nursing staff

• Gradually connect more data sources: From simple to complex

Prioritization by AI Maturity

Not every use case is suitable for getting started. A sensible sequence is:

1. Knowledge management: Finding and structuring information

2. Administration: Documentation, applications, transfer forms

3. Automation: Appointment scheduling, resource management

4. Decision support: Risk analyses, care planning

Challenges and Solutions

Staff Acceptance

The introduction of AI systems may face skepticism:

• Early involvement: Include caregivers in planning from the beginning.

• Start with a small team.

• Training: Understandable introduction to AI functionality for all employees

• Benefit orientation: Demonstrate concrete advantages for daily work

• Feedback culture: Continuous improvement based on feedback

Technical Integration

• Connecting to existing systems can be challenging:

• Interfaces: Standardized APIs for data exchange

• Modular structure: Gradual integration of individual functions

• Pilot projects: Test phase with limited scope

• Support: Technical assistance during implementation

Conclusion: AI as Support, Not Replacement

Artificial intelligence offers promising possibilities to improve care in homes and relieve staff. The key to success lies in a pragmatic, step-by-step approach focusing on concrete use cases that bring immediate benefits. The principle should always be: AI is a tool to support caregivers, not to replace them. Human attention and care remain the heart of good nursing – AI can merely help ensure that more time is available for this. With a well-thought-out implementation concept that considers data protection, ethics, and the needs of all involved, AI can already make a valuable contribution to meeting the challenges in care today.