AI in Nursing Care
Potential Supervisor: Alessia Nowak
Level: Bachelor / Master (adaptable)
Strategic Orientation: Socio-economical Evaluation
Background
The ongoing digital transformation poses significant structural, technical, and organizational challenges for hospitals. With the introduction of the German Hospital Future Act (Krankenhauszukunftsgesetz, KHZG), a national funding framework was established to accelerate digital innovation and strengthen the quality, safety, and efficiency of healthcare delivery. Many hospitals, including Charité, are currently investing in modern IT infrastructures, electronic patient records, and clinical decision support systems. While the technical implementation of these projects is often at the forefront, questions regarding their economic value have become increasingly important. It remains unclear to what extent digital technologies generate measurable cost savings, streamline clinical processes, or create indirect efficiency gains.
A systematic assessment of both the potential savings and the broader economic impact is therefore crucial. Such evaluations support evidence-based decision-making, help prioritize investments, and contribute to the development of sustainable digitalization strategies within the healthcare sector.
Research Directions
This research project examines the economic impact of digitalization initiatives funded under the German Hospital Future Act (KHZG). Potential research questions include:
• What cost-saving potentials arise from selected KHZG measures implemented at Charité?
• How can the economic viability of these digitalization projects be evaluated using cost-benefit analysis?
The objectives are to identify relevant benefit components, develop an appropriate evaluation framework, and derive strategic recommendations if applicable.
Methodology
Methodologically, a systematic literature review, qualitative interviews with project stakeholders, and an economic modelling approach (e.g., cost-benefit analysis) are envisaged.
Potential Data Sources
• Internal project reports and cost calculations related to KHZG-funded initiatives at Charité
• Qualitative interviews (e.g., IT management, project managers, clinical staff)
• Survey data
• Literature on digital health evaluations
Access to internal data depends on approval and confidentiality requirements; interviews need to be arranged by the student; contacts for interviews can be provided.
Project Context
The topic constitutes a standalone research project while remaining closely interconnected with the broader KHZG project initiatives. Although independently designed, it aligns with ongoing digitalization efforts and benefits from insights, structures, and stakeholders involved in the KHZG implementation.
Requirements and Administration (optional)
Desired qualifications include an interest in health economics, technology evaluation, and qualitative research methods. Analytical thinking and a strong interest in digitalization processes within the healthcare sector are advantageous.
References
German Federal Ministry of Health. (2020). Krankenhauszukunftsgesetz (KHZG).
https://www.bundesgesundheitsministerium.de/fileadmin/Dateien/3_Downloads/Gesetze_und_Verordnungen/GuV/K/bgbl1_S.2208_KHZG_28.10.20.pdf
Goodacre, S., & McCabe, C. (2002). An introduction to economic evaluation. Emergency Medicine Journal, 19(3), 198.
Vogel, J., Hollenbach, J., Haering, A., Ehlig, D., & Geissler, A. (2025). Correction to: Digital Maturity Data: Extracting Insights for Health System Management. In Digital Maturity in Hospitals: Strategies, Frameworks, and Global Case Studies to Shape Future Healthcare (pp. C1–C1). Springer Nature Switzerland.
Potential Supervisor: Daniel Fürstenau
Level: Master thesis
Background
The digitalization of nursing care opens up new opportunities for the prevention and reduction of falls through intelligent assistance systems. Current research shows that AI-based approaches can outperform conventional fall risk assessments, while at the same time placing high demands on data quality and data protection. Based on the findings of a three-year collaborative AI nursing care project at Charité, this thesis examines the development of a digital fall monitoring system and discusses whether a click dummy or a functional prototype should be considered an appropriate stage of development.
References
Nanevski I, Jäger S, Schulte-Althoff M, Behnke E, Fürstenau D, Biessmann F
The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment
J Med Internet Res 2025;27:e71034
URL: https://www.jmir.org/2025/1/e71034
DOI: 10.2196/71034
Nanevski I, Mohebi M, Jäger S, Otte K, Schulte-Althoff M, Prasser F, Fürstenau D, Biessmann F
Evaluating the Quality of Synthetic Data in Health Care
Preprint; 2025
DOI: https://doi.org/10.21203/rs.3.rs-6320382/v1
Background
The increasing use of AI-based fall monitoring systems in nursing care raises not only clinical but also economic questions. In particular, it remains unclear to what extent such systems lead to cost savings or additional expenditures compared with existing care practices (“Care as Usual”). The aim of this thesis is to systematically analyse the expected cost effects of AI-based fall monitoring and compare them with standard care in order to provide a sound basis for decision-making regarding the use of digital solutions in nursing care.
Potential Supervisor: Alessia Nowak
Level: Bachelor / Master / both
Strategic Orientation: Socio-economical Evaluation
Background
The ongoing digital transformation poses significant structural, technical, and organizational challenges for hospitals. With the introduction of the German Hospital Future Act (Krankenhauszukunftsgesetz, KHZG), a national funding framework was established to accelerate digital innovation and strengthen the quality, safety, and efficiency of healthcare delivery.
Many hospitals, including Charité, are currently investing in modern IT infrastructures, electronic patient records, and clinical decision support systems. While the technical implementation of these projects is often at the forefront, questions regarding their economic value have become increasingly important. It remains unclear to what extent digital technologies generate measurable cost savings, streamline clinical processes, or create indirect efficiency gains.
A systematic assessment of both the potential savings and the broader economic impact is therefore crucial. Such evaluations support evidence-based decision-making, help prioritize investments, and contribute to the development of sustainable digitalization strategies within the healthcare sector.
Research Directions
This research project examines the economic impact of digitalization initiatives funded under the German Hospital Future Act (KHZG). Potential research questions include:
• What cost-saving potentials arise from selected KHZG measures implemented at Charité?
• How can the economic viability of these digitalization projects be evaluated using cost-benefit analysis?
The objectives are to identify relevant benefit components, develop an appropriate evaluation framework, and derive strategic recommendations if applicable.
Methodology
Methodologically, the project may combine:
• A systematic literature review
• Qualitative interviews with project stakeholders
• An economic modelling approach (e.g., cost-benefit analysis)
Potential Data Sources
• Internal project reports and cost calculations related to KHZG-funded initiatives at Charité
• Qualitative interviews (e.g., IT management, project managers, clinical staff)
• Survey data
• Literature on digital health evaluations
Access to internal data depends on approval and confidentiality requirements. Interviews need to be arranged by the student; contacts for interviews can be provided.
Project Context
The topic constitutes a standalone research project while remaining closely interconnected with the broader KHZG project initiatives. Although independently designed, it aligns with ongoing digitalization efforts and benefits from insights, structures, and stakeholders involved in the KHZG implementation.
Requirements and Administration (optional)
Desired qualifications include an interest in health economics, technology evaluation, and qualitative research methods. Analytical thinking and a strong interest in digitalization processes within the healthcare sector are advantageous.
References
Goodacre, S., & McCabe, C. (2002). An introduction to economic evaluation. Emergency Medicine Journal, 19(3), 198.
Vogel, J., Hollenbach, J., Haering, A., Ehlig, D., & Geissler, A. (2025). Correction to: Digital Maturity Data: Extracting Insights for Health System Management. In Digital Maturity in Hospitals: Strategies, Frameworks, and Global Case Studies to Shape Future Healthcare (pp. C1–C1). Springer Nature Switzerland.
German Federal Ministry of Health. (2020). Krankenhauszukunftsgesetz (KHZG).
Potential Supervisor: Matthias Schulte-Althoff, Alessia Nowak
Level: Master
Strategic Orientation: AI & Analytics or Evaluation
Background
Predicting fall events in clinical settings has remained an unresolved problem for years. Validated fall risk screening and assessment tools show only insufficient predictive performance among older hospital patients (Lee et al., 2023; Matarese et al., 2015). Likewise, there is no evidence that FRATs (Fall Risk Assessment Tools) are superior to clinical judgement (Meyer, Möhler & Köpke, 2018; Cameron et al., 2018).
One key reason is the prevention paradox: preventive measures alter the occurrence of falls and therefore also affect the observed performance of diagnostic tools. If a risk scale is applied effectively, the number of actual falls within the high-risk group decreases. As a result, sensitivity and specificity appear to deteriorate (Defloor, 2004; 2005). The validity of commonly used risk assessment methods is therefore systematically underestimated.
Machine learning models have the potential to better capture complex interactions between risk, preventive measures, and clinical factors (Lee et al., 2025). Initial findings from the KIP study highlight this potential. However, the explanatory power of the pilot study is limited by the low fall incidence (2.5% during the period 01–06/2025; 10 falls among 394 patients). An analysis based on a larger set of routine data (n = 4,700 cases from eight high-risk wards) represents one possible solution.
To date, there has been no analysis of how preventive measures (e.g. bundled fall prevention measures) influence model behaviour.
Research Directions
The aim is to systematically analyse the fall prevention paradox in the context of AI-based prediction models. Methods are to be developed and tested that explicitly account for the influence of nursing prevention measures on diagnostic accuracy and model performance.
Example Research Questions:
- How do nursing prevention measures affect the diagnostic accuracy of ML models for fall prediction?
-
Does model performance improve when preventive measures are explicitly included as features?
-
To what extent do causal models enable the simulation of counterfactual scenarios such as:
What would the fall risk be without the preventive measure?
To what extent does the measure reduce the risk? -
What added value is provided by the expanded routine data (preventive measures, fall assessment) from the evaluation study conducted as part of KIP-SDM?
Data Access & Availability:
The dataset from the KIP-SDM evaluation study is already available and includes 394 patients from several wards at Charité Campus Virchow-Klinikum during the period from January to June 2025.
Data points include diagnoses, medical procedures, nursing data, and preventive measures.
It is planned to further enrich the dataset by extracting routine data for all patients from the participating wards from the hospital information system during the observation period.
Project Context:
The study will build on the findings of the completed three-year KIP-SDM research project funded by the BMFTR.
Requirements and Required Skills
To work on this topic, students should have both an affinity for data and an interest in clinical and/or nursing-related questions. In addition, students should possess a basic understanding of statistical methods and fundamental knowledge of data processing or data science, for example using R or Python.
Students should be willing to engage with a complex, real-world problem at the intersection of information systems, nursing care, and AI. Teamwork and the ability to reflect critically are also essential.
References
Cameron, I. D., Dyer, S. M., Panagoda, C. E., Murray, G. R., Hill, K. D., Cumming, R. G., & Kerse, N. (2018). Interventions for preventing falls in older people in care facilities and hospitals. The Cochrane Database of Systematic Reviews, 9(9), CD005465. https://doi.org/10.1002/14651858.CD005465.pub4
Defloor, T., & Grypdonck, M. F. (2004). Validation of pressure ulcer risk assessment scales: a critique. Journal of Advanced Nursing, 48(6), 613–621. https://doi.org/10.1111/j.1365-2648.2004.03250.x
Defloor, T., & Grypdonck, M. F. (2005). Pressure ulcers: validation of two risk assessment scales. Journal of Clinical Nursing, 14(3), 373–382. https://doi.org/10.1111/j.1365-2702.2004.01058.x
Lee, A., Lee, H., & Lee, S. H. (2025). Digital healthcare approaches for fall detection and prediction in older adults: A systematic review of evidence from hospital and long-term care settings. Medicina (Kaunas), 61(11), 1926. https://doi.org/10.3390/medicina61111926
Lee, V., Appiah-Kubi, L., Vogrin, S., Zanker, J., & Mitropoulos, J. (2023). Current cut points of three falls risk assessment tools are inferior to calculated cut points in geriatric evaluation and management units. Muscles, 2(3), 250–270. https://doi.org/10.3390/muscles2030019
Matarese, M., Ivziku, D., Bartolozzi, F., Piredda, M., & De Marinis, M. G. (2015). Systematic review of fall risk screening tools for older patients in acute hospitals. Journal of Advanced Nursing, 71(6), 1198–1209. https://doi.org/10.1111/jan.12542
Meyer, G., Möhler, R., & Köpke, S. (2018). Reducing waste in evaluation studies on fall risk assessment tools for older people. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, 137–138, 1–7. https://doi.org/10.1016/j.zefq.2018.09.00
Background
The use of clinical routine data offers considerable potential for predicting fall frequency in inpatient and outpatient care. By analysing structured healthcare data, risk-relevant patterns can be identified and preventive measures can be supported at an early stage. Building on existing research activities in Dresden and Halle, the previous line of research is to be taken up and continued. The required data access and cross-site analysis would need to be carried out through the structures of the Medical Informatics Initiative (MII) or the German Health Research Data Portal (FDPG).
