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SHR Neuro Krebs Kardio Lipid Stoffw Microb

Berger, MF; Petritsch, J; Hecker, A; Pustak, S; Michelitsch, B; Banfi, C; Kamolz, LP; Lumenta, DB.
Paper-and-Pencil vs. Electronic Patient Records: Analyzing Time Efficiency, Personnel Requirements, and Usability Impacts on Healthcare Administration.
J Clin Med. 2024; 13(20): Doi: 10.3390/jcm13206214 [OPEN ACCESS]
Web of Science PubMed FullText FullText_MUG

Führende Autor*innen der Med Uni Graz: Lumenta David Benjamin
Co-Autor*innen der Med Uni Graz: Banfi Chiara; Hecker Andrzej Stanislaw; Kamolz Lars-Peter; Michelitsch Birgit
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Abstract:: Background: This study investigates the impact of transitioning from paper and pencil (P&P) methods to electronic patient records (EPR) on workflow and usability in surgical ward rounds. Methods: Surgical ward rounds were audited by two independent observers to evaluate the effects of transitioning from P&P to EPR. Key observations included the number of medical personnel and five critical workflow aspects before and after EPR implementation. Additionally, usability was assessed using the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ). Results: A total of 192 P&P and 160 EPR observations were analyzed. Physicians experienced increased administrative workload with EPR, while nurses adapted more easily. Ward teams typically consisted of two physicians and three or four nurses. Usability scores rated the system as "Not Acceptable" across all professional groups. Conclusions: The EPR system introduced usability challenges, particularly for physicians, despite potential benefits like improved data access. Usability flaws hindered system acceptance, highlighting the need for better workflow integration. Addressing these issues could improve efficiency and reduce administrative strain. As artificial intelligence becomes more integrated into clinical practice, healthcare professionals must critically assess AI-driven tools to ensure safe and effective patient care.
Find related publications in this database (Keywords): digital medicine; electronic patient record; electronic health record; clinical decision support; data science