AI in occupational health management: How artificial intelligence is revolutionising occupational health management

Artificial intelligence (AI) is no longer just a topic for the IT department - it is in the process of fundamentally changing many areas of a company. Even in the occupational health management (OHM) opens the Use of AI new possibilities: from the early risk detection about Tailor-made health programmes through to the automation of complex processes. Now is the right time for HR, health officers and CEOs to familiarise themselves with the potential of AI in BGM because preventive thinking not only saves costs, but also strengthens the health and motivation of employees in the long term.

How digitalisation and AI are changing BGM

Due to the progressive Digitisation and the availability of large quantities of Health data the use of AI-supported systems increasingly attractive. AI can quickly process and analyse information from various sources and convert it into usable recommendations. Where manual questionnaires and long evaluation times used to characterise day-to-day occupational health management, today Intelligent, dynamic systemsthat recognise individual risks and provide targeted support.

This development is leading to a paradigm shift: From standardised measures to individualised health promotion - and from reactive care to preventive measuresthat recognise risks before they become problems.

Advantages of AI in OHM at a glance

1. Increased efficiency
   AI-based systems analyse large amounts of data efficiently and within a few seconds. This saves time and resources and relieves BGM teams of routine tasks.
2. Automate processes
   Reminders for health checks, individual training or nutrition plans and analyses can be automated and large amounts of data can be mapped quickly and precisely. 
3. Personalisation
   AI recognises individual stresses and preferences and recommends tailored measures - e.g. relaxation exercises for stressed managers, individual health plans, preventative measures or targeted back programmes for warehouse staff.
4. Early detection of risks
   Analysing health data (e.g. sick days, surveys, wearable data, technology) makes it possible to identify potential problems at an early stage before they lead to failures.
5. GDPR-compliant data processing
   Instead of analysing data manually, data can be extracted directly from digital tools - GDPR-compliant and at the touch of a button.

Practical application examples of AI in BGM

1st Isa from DeepCare - AI-supported technology

Isa is a modern healthcare solution that supports companies in flexibly providing customised healthcare services. Thanks to AI-supported algorithms Isa analyses anonymised user data and makes recommendations for suitable health offers. Employees receive a personalised training plan - whether for Stress management, Movement or Nutrition.

Example: An office worker with frequent neck pain automatically receives suggestions for short stretching exercises during working hours - combined with mindfulness exercises for stressful phases.

2. preventive health measures

AI can take preventative action by analysing historical data (e.g. days of absence, surveys, digital activity). This gives companies early indications of increasing stress in certain departments or among certain groups of people - e.g. longer screen time for office workers or high musculoskeletal stress in the warehouse.

3. diagnosis and support with treatments

In collaboration with medical professionals, the AI-based tools recognise the first indicators of certain diseases. For example, by analysing movement via an app or wearables. In the event of abnormalities, specific recommendations are made for further medical clarification or operational support.

4. personalised health promotion

Based on individual employee preferences as well as previous illnesses, activity profiles or even sleep and activity data, the AI can suggest customised measures - e.g. sleep training for overtired trainees, targeted cardiovascular training for shift workers or nutritional coaching for field staff.

Recommendations for the successful implementation of AI in OHM

1. Clear use case first
   Don't start with the technology, but with a specific problem: e.g. high workload in production or low utilisation of existing offerings.
2. Thinking about data protection
   Health data is sensitive. Ensure GDPR compliance and data protection regulations, anonymisation and transparent communication with employees.
3. Promoting participation
   Actively involve employees - through simple onboarding sessions, BGM newsletters, feedback opportunities and clear language without technical jargon.
4. Making successes visible
   Document progress, e.g. through reduced absenteeism, increased utilisation or positive feedback - this not only improves employee health, but also increases acceptance.
5. Cooperation with experienced providers
   Rely on providers who have experience in the AI in BGM bring along - like DeepCare with Isawhich impresses with its intuitive use and personalised content.

Conclusion: AI in OHM - not a future topic, but one that can be implemented now

The use of AI in occupational health management offers enormous potential - not only to relieve HR and BGM teams, but above all for a Modern, effective health promotionthat takes employees seriously and supports them individually. Companies that are already technology-based, personalised healthcare programmes investing in our employees creates real added value: for the health of the workforce and competitiveness in the future.