4th International Conference on Primary Health Care & 2nd Euro Nursing Congress
September 15-16 2025 | Virtual Event
Teresa Hochstrasser
University of Applied Sciences, Austria
Nursing documentation is essential for patient care, ensuring accurate communication and
enhancing patient safety, but is often perceived as an administrative burden that reduces
time for patient care. Traditional methods – such as handwritten notes or stationary computer
entries – require nurses to leave the bedside, disrupting workflows and increasing the
risk of information loss due to delayed, retrospective entries. The NUDOCU project addresses
these challenges by developing a semi-passive documentation system that uses machine
learning (ML) to predict care activities, enabling bedside documentation via smartphone.
We analyzed 1,330,519 documentation entries from 39,514 patients (2021–2023) at an Austrian
hospital (including timestamps, care activities, patient demographics, diagnoses, and room
assignments). Using this dataset, a Light Gradient-Boosting Machine classification model
was trained to predict the five most probable care activities, considering patient and nurse
context as well as previously documented tasks. The model achieved 80.6% top-5 accuracy
using stratified cross-validation. Predictions were integrated into a smartphone app that
displays suggested care activities ranked by relevance. Nurses can confirm a suggestion or
select another activity via a search function. Integrating ML-based predictions with smartphone-
enabled bedside documentation shows potential to optimize nursing documentation.
By reducing time spent on documentation, the system aims to alleviate the administrative
burden on nurses, allowing more focus on direct patient care. A qualitative evaluation involving
23 nurses revealed good acceptance of the system, with many participants perceiving a
reduction in documentation workload. Further studies should be conducted to evaluate the
system’s scalability and long-term effects in diverse clinical settings.
Teresa Hochstrasser, PhD candidate at Johannes Kepler University, is a research associate at the University of
Applied Sciences Upper Austria. She holds a Bachelor’s degree in Process Management and Business Intelligence
and a Master’s degree in Logistics Engineering Management. Since 2021, she has been involved in the
benchmarking initiative “Leistungsvergleich-Medizin” (LeiVMed), which focuses on benchmarking in Austrian
hospitals. She has presented her research at international conferences, including poster contributions at ISQua’s
International Conferences in 2023 and 2024. This research was funded by the Austrian Research Promotion
Agency (FFG) as part of the project “Nursing Documentation (NUDOCU)” (Grant No. [FO999892173]).