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Project

KI-Para-Mi

KI-getriebener Paradigmenwechsel durch Mitarbeiter-zentrische Schicht- und Dienstplanung zur Verringerung des Pflegenotstands

KI-getriebener Paradigmenwechsel durch Mitarbeiter-zentrische Schicht- und Dienstplanung zur Verringerung des Pflegenotstands

  • Duration:

The goal of the KI-Para-Mi project is to develop an intelligent personnel planning system for flexible shift scheduling in nursing, which above all takes into account the interests of the employees. The shortage of qualified nursing personnel is a major topics that shapes public debate and political agenda around the globe. Better medical care and rising life expectancy are leading to an increased demand for skilled nursing staff. The gap between demand and actual supply of personnel is growing increasingly. In addition, the average length of stay in the nursing profession is much shorter than in other occupational fields, which places a heavy burden on the body and mind of employees in the nursing profession. Furthermore, classic rigid shift models are still in use, which do not allow flexible shift and duty scheduling. A re-scheduling of already defined shifts is usually only possible with great effort. The inflexible shift plans also make it more difficult to work part time and return to work, e.g. after parental leave, and lead to many trained specialists leaving the profession.

The digital personnel planning system of the project partner Planerio GmbH is to be extended by an AI concept. With the help of AI methods and machine learning algorithms for huge search spaces and ML-based optimization, the wishes and short-term needs of the employees are to be calculated optimally and more flexibly, based directly on the availability and preferences of users.

Partners

Planerio GmbH

Sponsors

BMBF - Federal Ministry of Education and Research

01IS19038B

BMBF - Federal Ministry of Education and Research

Publications about the project

Ellák Somfai; Benjámin Baffy; Kristian Fenech; Changlu Guo; Rita Hosszú; Dorina Korózs; Fabrizio Nunnari; Marcell Pólik; Daniel Sonntag; Attila Ulbert; András Lorincz

In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2102.09199, Pages 1-14, arXiv, 2021.

To the publication

Fabrizio Nunnari; Md Abdul Kadir; Daniel Sonntag

In: Andreas Holzinger; Peter Kieseberg; A. Min Tjoa; Edgar Weippl (Hrsg.). Machine Learning and Knowledge Extraction. International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) (CD-MAKE-2021), August 17-20, Virtual, Pages 241-253, LNCS, Vol. 12844, ISBN 978-3-030-84060-0, Springer International Publishing, 2021.

To the publication

Fabrizio Nunnari; Abraham Ezema; Daniel Sonntag

In: Stefan Edelkamp; Ralf Möller; Elmar Rueckert (Hrsg.). KI 2021: Advances in Artificial Intelligence. German Conference on Artificial Intelligence (KI-2021), September 27 - October 1, Germany, Pages 179-193, ISBN 978-3-030-87626-5, Springer International Publishing, 2021.

To the publication