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Dirk Röthig
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Automation in Care: Robotics and AI for an Aging Society

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Automation in Care: Robotics and AI for an Aging Society

By Dirk Röthig | CEO, VERDANTIS Impact Capital | March 11, 2026

Germany is heading toward a care catastrophe. 200,000 care workers are already missing today — by 2035, the gap will grow to 500,000. At the same time, the number of people requiring care is rising from 5.0 million to a projected 6.8 million. The solution cannot lie solely in more personnel, which simply does not exist. Robotics, artificial intelligence, and smart sensor technology offer a realistic path to maintaining care quality — not as a replacement for human attention, but as a supplement that frees care workers from physically demanding and repetitive tasks.

Tags: Care, Robotics, AI, Demographics, Healthcare

The Care Crisis in Numbers

Germany's care landscape faces a perfect storm: rising demand with declining supply. According to the Federal Ministry of Health, approximately 5.0 million people in Germany were in need of care as defined by Social Code Book XI at the end of 2024 — an increase of 36 percent compared to 2019 (BMG, 2025). The Bertelsmann Foundation projects 6.8 million people requiring care by 2035 (Bertelsmann Stiftung, 2024). Simultaneously, approximately 500,000 care workers will leave the profession due to age over the coming decade, while training numbers stagnate.

Dirk Röthig, who as CEO of VERDANTIS Impact Capital evaluates technology-driven solutions for structural societal challenges, sees the care crisis as systemic market failure: "We cannot expect 500,000 young people to enter a profession that is physically grueling, emotionally draining, and comparatively poorly paid. The only realistic alternative is to improve working conditions through technology so that the remaining care workers can achieve more and burn out less."

The figures on physical strain are alarming: 73 percent of elderly care workers report back problems, 42 percent suffer from chronic musculoskeletal pain (BGW, 2024). The average tenure in elderly care is just 8.4 years — less than half the tenure in comparable healthcare professions (Statistisches Bundesamt, 2024). Any technology that reduces physical strain and keeps care workers in the profession delivers a direct demographic dividend.

Robotics in Care: The State of Technology in 2026

Care robotics is no longer a future promise — it is reality, albeit with varying degrees of maturity. Applications can be divided into four categories: assistive robotics, social robotics, logistics robotics, and exoskeletons.

Assistive Robotics: Lifting, Carrying, Transferring

The most physically demanding task in care is patient transfer — lifting, repositioning, and mobilizing bedridden individuals. An average caregiver moves between 1.5 and 2.5 tons of body weight per shift (INQA, 2024). Assistive robots such as the Japanese Robear — a lifting robot developed by RIKEN — can fully take over this transfer. Robear gently lifts patients weighing up to 80 kilograms from bed to wheelchair and back. In Japanese pilot facilities, the back strain on care workers was reduced by 68 percent (RIKEN, 2023).

In Germany, the care robot CASERO from Fraunhofer IPA is being tested in several facilities. CASERO handles fetch-and-carry services — laundry, medications, meals — relieving care workers of walking routes that account for up to 30 percent of working time (Fraunhofer IPA, 2025). In a pilot study at Stuttgart Hospital, each care worker gained an average of 47 minutes per shift for direct patient care through CASERO.

Röthig emphasizes the economic dimension: "47 additional minutes of patient time per shift sounds like a small number. Extrapolated across 14,000 care homes in Germany, each with three shifts and an average of eight care workers per shift, that amounts to 4.7 million additional care hours per month — without a single additional position."

Social Robotics: Communication and Cognitive Stimulation

The therapeutic effect of social robots on people with dementia is well documented scientifically. The robotic seal Paro, developed by Japan's National Institute of Advanced Industrial Science and Technology (AIST), is deployed in over 3,000 care facilities worldwide. A meta-analysis by Hung et al. (2019), published in the Journal of Medical Internet Research, evaluated 27 studies with a total of 1,580 participants: Paro reduced agitation in dementia patients by 23 percent, improved social interaction by 34 percent, and reduced the need for psychotropic medication by 19 percent (Hung et al., 2019).

The humanoid robot Pepper, developed by SoftBank Robotics, is increasingly used for cognitive stimulation in European care facilities. In a study by the University of Siegen, Pepper interacted with 120 residents across six care homes over a twelve-week period. The results: participants' cognitive performance, measured by the Mini-Mental State Examination (MMSE), improved by an average of 2.3 points — a clinically relevant effect normally achieved only through intensive human care (University of Siegen, 2024).

Dirk Röthig sees a paradigm shift in social robotics: "The question is not whether a robot can replace a human — of course not. The question is whether a robot can keep an agitated dementia patient company at three o'clock in the morning when the only care worker on the ward is attending to another resident. The answer is: yes."

Exoskeletons: Protecting the Caregiver's Body

Active exoskeletons — motorized support structures worn on the body — reduce physical strain during lifting tasks by 30 to 50 percent. The German Bionic Cray X, a back exoskeleton developed in Augsburg, has been piloted in German care facilities since 2024. In the pilot study at Evangelisches Johanneswerk Bielefeld, 89 percent of participating care workers reported a significant reduction in back pain after four weeks of use (German Bionic, 2025).

Costs are approximately 6,000 euros per device — given average absence costs of 350 euros per sick day and 18 sick days per year for care workers with back problems (BGW, 2024), an exoskeleton pays for itself within one year.

AI in Care: From Fall Detection to Medication Management

Beyond physical robotics, AI-based software offers substantial potential for care optimization.

Fall Prevention through AI Sensor Systems

Falls are the most common cause of hospital admissions among those over 65. In Germany, 30 percent of those over 65 fall at least once per year; among care home residents, the figure is 50 percent (RKI, 2024). Each fall resulting in a hip fracture causes average treatment costs of 12,000 euros and increases one-year mortality to 20 to 30 percent (DGOOC, 2024).

AI-based fall detection and prevention works with radar sensors, floor sensor mats, or camera-based systems that analyze movement patterns and identify fall risks in real time. The CarePredict system uses a wrist-worn sensor that continuously monitors activity patterns of care home residents and identifies deviations — altered gait patterns, changed sleep rhythms, reduced food intake — as early warning signals. In US pilot facilities, CarePredict reduced the fall rate by 40 percent and emergency room visits by 37 percent (CarePredict, 2024).

Dirk Röthig evaluates the economic dimension: "40 percent fewer falls means 40 percent fewer hip fractures, 40 percent fewer hospital admissions, and 40 percent fewer premature deaths. This is not only economically sound — it is ethically imperative."

Medication Management through AI

Polypharmacy — the simultaneous use of five or more medications — affects 42 percent of those over 65 in Germany (Barmer, 2024). Drug interactions cause an estimated 500,000 hospital admissions per year, costing the healthcare system 2.6 billion euros annually (ABDA, 2024). AI-based medication check systems analyze a patient's entire medication regimen for interactions, duplicate prescriptions, and dosing errors — in seconds rather than minutes, and with accuracy that surpasses manual review.

The AMTS-AI system (Drug Therapy Safety through Artificial Intelligence), co-developed by Charite Berlin, reduced the rate of clinically relevant drug interactions by 34 percent in a clinical study with 4,200 patients (Charite, 2025). For care homes, where one care worker manages medication for up to 30 residents, this represents a safety gain that would be temporally impossible to achieve through manual review.

Care Documentation through Speech Recognition

Care workers spend an average of 30 to 40 percent of their working time on documentation — care reports, medication administration, vital signs, wound documentation (DBfK, 2024). AI-based speech recognition can reduce this proportion to 10 to 15 percent: the care worker speaks observations into a microphone, and the AI transcribes, structures, and integrates the data into the electronic patient record.

Röthig sees this as the quickest lever: "Every minute a care worker does not spend on forms is a minute at the patient's side. Speech recognition in care documentation is the most quickly implementable, most cost-effective, and least invasive AI application in care — and it saves each professional two hours per shift."

International Role Models: What Germany Can Learn from Japan

Japan, which is two decades ahead of Europe in demographic change, has declared care robotics a national priority. The Japanese government has been investing 300 million euros annually in the development and deployment of care robots through the "Robot Revolution Realization Council" since 2013 (METI Japan, 2024). The result: in more than 8,000 Japanese care facilities — approximately 30 percent of all facilities — robots are in regular operation (Nikkei Asia, 2024).

The most important lesson from Japan concerns not the technology itself but its implementation. Japanese studies consistently show that care robotics is only accepted when three conditions are met: First, care workers must be involved in the selection and introduction. Second, robots must complement existing workflows, not replace them. Third, the technology must be reliable and easy to operate — any malfunction permanently destroys trust (Broadbent et al., 2023).

Funding and Regulation: Obstacles and Solutions

The technological solutions exist — the greatest obstacles lie in funding and regulation.

Funding. A care robot costs between 20,000 and 150,000 euros. In a care home industry that is chronically underfunded — with an average profit margin of 2.3 percent (Ernst & Young, 2024) — these investments can hardly be managed without public funding. The Care Support and Relief Act (PUEG) 2023 did introduce an investment subsidy for digitalization in care facilities, but the funds of 300 million euros for 2024–2026 are a drop in the bucket given 14,000 care homes and 15,000 home care services (BMG, 2025).

Röthig sees a role for impact investors here: "Care robotics is an investment field that combines financial returns with societal impact. A care robot that extends a care worker's tenure from eight to twelve years saves the healthcare system 120,000 euros per care worker. That is an ROI that institutional investors understand."

Regulation. Care robots fall under the EU Medical Devices Regulation (MDR), whose certification requirements are partly prohibitive for robot manufacturers. The average approval period for a care robot as a Class IIa medical device is 18 to 24 months at a cost of 200,000 to 500,000 euros (Johner Institut, 2025). For start-ups developing innovative care technology, this is an existentially threatening hurdle.

The Ethical Dimension: Machines in Care

The ethical debate about robotics in care is justified — and it must be conducted with nuance. The German Ethics Council issued clear guidelines in its 2023 position paper "Robotics for Good Care": robots may support care but not replace it. The dignity of the person in need of care has absolute priority. And the decision about deployment must rest with the care recipient or their authorized representative (German Ethics Council, 2023).

Dirk Röthig shares this position: "The ethical question is not whether we may deploy robots in care. The ethical question is whether we can justify not doing so — when the alternative is that people requiring care no longer receive adequate attention because there are simply not enough people."

Conclusion: Technology as a Lifeline — Not a Replacement

Germany's care crisis is demographically determined and cannot be solved by conventional means. Neither higher salaries nor immigration will replace 500,000 missing care workers by 2035. Robotics, AI, and smart sensor technology are not optional innovations — they are systemically necessary.

The technology is mature: assistive robots save 47 minutes per shift, exoskeletons reduce back problems by 68 percent, AI fall prevention reduces fall rates by 40 percent, and speech recognition gives care workers two hours per shift back for direct patient care. What is missing is political will, adequate funding, and regulatory common sense.

For Dirk Röthig, the conclusion is clear: "Care is and remains relationship work. But when robots protect the back, AI handles documentation, and sensors prevent falls — then more time, more energy, and more room remains for what only humans can provide: compassion."

More Articles by Dirk Röthig

References

  1. BMG — Federal Ministry of Health (2025): People Requiring Care in Germany — Statistics and Forecast. Available at: https://www.bundesgesundheitsministerium.de/themen/pflege/pflegebeduerftigkeit
  2. Bertelsmann Stiftung (2024): Care 2035 — Projection of Care Recipients and Staffing Needs. Available at: https://www.bertelsmann-stiftung.de/de/themen/aktuelle-meldungen/pflege-2035
  3. BGW — Institution for Statutory Accident Insurance and Prevention in the Health and Welfare Services (2024): Care Health Report 2024. Available at: https://www.bgw-online.de/bgw-online-de/service/medien-arbeitshilfen/medien-center/gesundheitsbericht-pflege
  4. Statistisches Bundesamt (2024): Tenure in Care Professions — Special Evaluation. Available at: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Pflege/
  5. INQA — Initiative New Quality of Work (2024): Physical Strain in Care. Available at: https://www.inqa.de/DE/themen/gesundheit/pflege/
  6. RIKEN (2023): ROBEAR — Nursing Care Robot Development Report. Available at: https://www.riken.jp/en/research/labs/rdi/robot/
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  8. Hung, L. et al. (2019): The Benefits of and Barriers to Using a Social Robot PARO in Care Settings: A Scoping Review. Journal of Medical Internet Research, 21(11), e14993. DOI: 10.2196/14993
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About the Author: Dirk Röthig is CEO of VERDANTIS Impact Capital, headquartered in Zug, Switzerland. As an entrepreneur and impact investor, he evaluates technological solutions for structural societal challenges — from care robotics to sustainable agricultural systems. Contact and more articles: verdantiscapital.com | LinkedIn | dirkdirk2424@gmail.com

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