New technologies are increasingly entering healthcare, such as digital procedures and precision medicine pharmaceuticals. These are technology-assisted medical approaches in which digital tools – including algorithms, data platforms, or artificial intelligence – are used for individualized diagnostic and therapeutic procedures as well as targeted pharmaceuticals. But how sustainable is precision medicine? And what impact does AI have? This analysis examines the three dimensions of ecological, economic, and social sustainability.

Digital twins, which can predict individual responses to cancer therapies or determine a person’s risk for obesity or other diseases based on their biomarkers: this is made possible by procedures and pharmaceuticals of precision medicine. These aim to optimize treatments according to the genetic, biological, and clinical characteristics of patients. Digital tools and artificial intelligence are increasingly important drivers for optimizing and expanding the application of precision medicine. What are the positive and negative impacts of this development? On society, the climate, and healthcare staff? In short: how social, economic, and ecological is precision medicine? The following examines these procedures and pharmaceuticals along these three dimensions of sustainability:

Social

According to a survey conducted by the German Hospital Institute on behalf of the German Hospital Federation, physicians and nursing staff spend an average of three hours per day on documentation work – time that is therefore not available for patient care.

Digital tools and artificial intelligence (AI) can help optimize processes, improve diagnoses, and use resources more efficiently – for example, through automated documentation, duty roster creation, or operating room management. This relieves medical staff and creates more time for patient care. Additionally, AI can advance research on rare diseases through data-based, cross-institutional networking. Telemedicine and digital health applications (DiGA) contribute to better care in underserved regions by supporting diagnosis and treatment, particularly benefiting immobile individuals. Considering demographic change, telemedicine and video consultations will also become essential to maintaining a consistent level of care.

At the same time, the use of AI-based tools raises ethical questions: Who is responsible for AI decisions, and how are potential errors addressed? There is a risk that AI could reproduce discriminatory patterns or reduce people to their genetic characteristics. Therefore, clear regulations must exist to protect patient data and safeguard human dignity without stifling innovation. An important step in this direction is the EU AI Act, which establishes legal frameworks for AI use. Simultaneously, physicians must develop sufficient competencies to critically assess and interpret AI decisions.

Many healthcare professionals resist AI, for example because of limited transparency and insufficient training. Concerns about being replaced by AI are also common. In truth, the nature of the profession is more likely to be transformed, with a greater focus on interpersonal, creative, and solution-oriented skills.

Ecological

The German healthcare system accounts for approximately 5.2% of national greenhouse gas emissions – a significant share that highlights the sector’s potential for reduction. Medical products and their supply chains contribute the most, accounting for 71% of emissions within healthcare. This underscores the importance of ecological strategies in areas such as procedures and pharmaceuticals of precision medicine.

A key lever for emission reduction lies in digitalization. Digital solutions such as telemedicine, e-health applications, or electronic health records (ePA) can significantly reduce resource consumption. Studies show that e-health services could avoid 6–8% of projected transport and energy emissions in healthcare by 2030. Implementing the ePA also carries potential savings: approximately 6,000 tons of CO₂-equivalents could be avoided annually, mainly through reduced paper consumption, fewer transport routes (such as for medical letters), and the avoidance of unnecessary duplicate examinations. At the same time, patients and doctors benefit from immediate access to relevant health data.

However, digitalization is not purely beneficial for the environment. An often underestimated ecological drawback is the so-called rebound effect: increasing use of digital technologies generates efficiency gains but also increases demand for IT infrastructure. New digital products and applications emerge, raising energy requirements – for example, through the operation of numerous data centers consuming large amounts of electricity and resources. Paradoxically, this means that digitalization itself can create new emission drivers.

Economic

A more efficient healthcare system and lower costs – digital procedures and precision medicine are often promoted with this promise. Whether they truly fulfill this potential remains unclear, as robust evidence and long-term data are lacking. Research-driven pharmaceutical companies at least anticipate greater efficiency and more targeted use of resources. The German Medical Association emphasizes that savings from precision medicine are difficult to estimate, as they often become visible only in the long term while costs arise immediately.

In the EU, small, molecularly defined patient groups are increasingly the focus of clinical research, facilitated by accelerated approval procedures for precision medicines. However, critics warn that this could allow therapies with limited data to reach the market earlier.

Patients must therefore be adequately protected while also benefiting from precision medicine. Targeted treatment, effective prevention, or avoidance of disease consequences can enable faster reintegration into daily and professional life. This not only reduces productivity losses but also stabilizes the workforce and tax revenues – a gain for both the healthcare system and the individuals themselves.

AI can play a decisive role in this development: increasingly precise early detection and risk prediction – for example, childhood obesity – allow targeted prevention and significant cost savings. It becomes possible to predict how cancer patients will respond to a specific chemotherapy. Estimates suggest that this could save 74 billion euros over the next ten years. Electronic health record keeping also improves information exchange among physicians, avoids duplicate examinations, and relieves patients.

Despite these opportunities, AI expert and CTO of AI specialists Arne Janning warns in the 10xD – Digital Health Magazine: “The most common mistake is the lack of integration into existing systems. A lot of money is invested in AI solutions without first analyzing processes, adapting IT infrastructure, or adequately training staff.”

Summary of Recommendations

Ecological:

• Transition to renewable energy within your own operations and consider it when selecting service providers, such as cloud vendors, to keep greenhouse gas emissions as low as possible.
• Prepare CO₂ inventories, product assessments, and/or sustainability reports (ideally aligned with the European Sustainability Reporting Standard) to clarify your environmental impact, identify improvement opportunities, and evaluate your current sustainability performance.
• Aim to implement and operationalize environmental and energy management systems – such as the Eco-Management and Audit Scheme (EMAS), ISO 14001, or ISO 50001 – to systematically improve environmental performance, increase energy efficiency, and support the organization’s long-term sustainability goals.
• When writing code and developing software, ensure that it is designed for maximum energy efficiency and resource conservation during use. This includes efficient algorithms, avoiding unnecessary calculations or load peaks, and minimizing excessive use of memory, CPU, or network resources.
• Establish clear guidelines that define which tasks should use AI tools and which should rely on less energy-intensive tools. For instance, a classic Google search requires significantly less energy than a ChatGPT request. This approach helps reduce greenhouse gas emissions.

Economic:

• Recognize and leverage sustainability as a competitive advantage. Effective communication, recognized labels, and sustainability certifications can strengthen this position, increase brand value, and boost customer trust.
• Achieve cost savings through the efficient use of renewable energy and energy-saving processes – for example, by managing energy peaks and reducing consumption through modern machinery, regular maintenance, and cleaning.
• Develop products with interface standards such as FHIR and HL7 to reduce development costs and enable scalability.
• Establish a strong digitalization strategy to save not only greenhouse gas emissions but also resources like paper – and reduce costs over the long term.
• Analyze internal processes, review and adapt the IT infrastructure, train employees, and only then invest in AI solutions that truly fit your needs.

Social:

• Apply digital (AI) tools and incorporate them when addressing challenges to support employees in their work. For example, use them for documentation to improve working conditions and allow more time for patients, product development, research, or other tasks.
• Systematically build trust in new technologies through training and professional development to ensure sustainable efficiency gains and employee satisfaction.
• Ensure accessibility and barrier-free design of your (digital) products for all user groups so that all people – regardless of their abilities – have equal access to information and services. Pay particular attention to older people, those with disabilities, and individuals with low digital literacy.
• Develop guidelines to uphold data protection standards when using digital (AI) tools and communicate transparently with employees and patients. This ensures that (AI) tools are used responsibly.

Conclusion

In the context of sustainability in digital procedures and precision medicine pharmaceuticals, it becomes clear that AI can play a central role. Its use has enormous potential to transform healthcare but must be applied thoughtfully and strategically. Attempts to optimize too many areas simultaneously can quickly lead to overload and inefficiency. At the same time, targeted AI integration can automate processes, significantly relieve medical staff, and contribute decisively during periods of skilled labor shortages. Properly implemented, AI conserves social and economic resources and can also have ecological benefits – for instance, through more efficient use of energy-intensive medical infrastructure. The prerequisite is trust – from both users and patients.

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