[Use of AI-based applications by hospital staff: task profiles and qualification requirements]. / Einsatz von KI-basierten Anwendungen durch Krankenhauspersonal: Aufgabenprofile und Qualifizierungsbedarfe.

BACKGROUND: Artificial intelligence (AI) is becoming increasingly important for the future development of hospitals. To unlock the large potential of AI, job profiles of hospital staff members need to be further developed in the direction of AI and digitization skills through targeted qualification measures. This affects both medical and non-medical processes along the entire value chain in hospitals. The aim of this paper is to provide an overview of the skills required to deal with smart technologies in a clinical context and to present measures for training employees. METHODS: As part of the "SmartHospital.NRW" project in 2022, we conducted a literature review as well as interviews and workshops with experts. AI technologies and fields of application were identified. RESULTS: Key findings include adapted and new task profiles, synergies and dependencies between individual task profiles, and the need for a comprehensive interdisciplinary and interprofessional exchange when using AI-based applications in hospitals. DISCUSSION: Our article shows that hospitals need to promote digital health literacy skills for hospital staff members at an early stage and at the same time recruit technology- and AI-savvy staff. Interprofessional exchange formats and accompanying change management are essential for the use of AI in hospitals.

[Development of an advanced practice nurse (APN) role for nutrition management: A needs assessment using a mixed methods approach]. / Die Entwicklung der Rolle einer Advanced Practice Nurse (APN) für das Ernährungsmanagement.

Development of an advanced practice nurse (APN) role for nutrition management: A needs assessment using a mixed methods approach Abstract. Background: Nurses are attributed to play a key role in nutrition management. This field has emerged to be a subject of advanced nursing practice. Aim: Conducting a needs assessment on the role profile of an advanced practice nurse (APN) in nutrition management according to the PEPPA framework. Methods: Mixed methods design. In a cross-sectional study on the current practice, the diagnostic accuracy of nurses' nutrition screening using Nutritional Risk Screening (NRS 2002) compared with independent assessment by a nutrition expert using NRS 2022 was examined. In case of a positive screening result, reasons were determined using an in-depth assessment. In addition, semi-structured, guideline-based interviews were conducted and content-analysed. Results: The identification of patients at risk by nurses' nutrition screening showed a need for improvement (sensitivity: 56%, specificity: 96%; n = 195). Commonly identified reasons for (risk of) malnutrition (n = 51) were lack of desire to eat/lack of appetite or increased caloric needs due to illness. Development opportunities and expectations for an APN were specified based on the interviews (n = 20). They refer to skill enhancement, support within the interprofessional team in complex treatment cases and a stronger nursing role in nutrition management. Conclusions: Based on the needs assessment, the APN's areas of responsibility were identified and assigned to the Hamric model, and implementation strategies could be derived.

[The transformative effect of artificial intelligence in hospitals : The focus is on the individual]. / Die transformative Wirkung von künstlicher Intelligenz im Krankenhaus : Der Mensch steht im Mittelpunkt.

Rapid advances in digital technology and the promising potential of artificial intelligence (AI) are changing our everyday lives and have already impacted on hospital procedures. The use of AI applications, in particular, enables a wide range of possible uses and has considerable potential for improving medical and nursing care. In radiological diagnostics, for example, there are already many well-researched applications for AI-based image evaluation. In this article further AI developments are presented, which can help to relieve medical staff in order to create more time for direct patient care. In addition, essential aspects regarding the development and transfer of AI-based applications are highlighted. It is crucial that the integration of AI into medical practice is carried out with the utmost care and prudence. Data protection and ethical aspects need to be considered and respected at all times. Ensuring the reliability and integrity of AI systems is essential to earn the trust of both patients and healthcare professionals. A comprehensive inspection for possible bias within the underlying data and algorithms is indispensable. In this field of tension between promising possibilities and ethical challenges, the digital transformation in medicine and care can be designed to increase patient safety and to relieve staff.

Electronic Patient-Reported Outcome Measures (ePROMs) Improve the Assessment of Underrated Physical and Psychological Symptom Burden among Oncological Inpatients.

For advanced cancer inpatients, the established standard for gathering information about symptom burden involves a daily assessment by nursing staff using validated assessments. In contrast, a systematic assessment of patient-reported outcome measures (PROMs) is required, but it is not yet systematically implemented. We hypothesized that current practice results in underrating the severity of patients' symptom burden. To explore this hypothesis, we have established systematic electronic PROMs (ePROMs) using validated instruments at a major German Comprehensive Cancer Center. In this retrospective, non-interventional study, lasting from September 2021 to February 2022, we analyzed collected data from 230 inpatients. Symptom burden obtained by nursing staff was compared to the data acquired by ePROMs. Differences were detected by performing descriptive analyses, Chi-Square tests, Fisher's exact, Phi-correlation, Wilcoxon tests, and Cohen's r. Our analyses pointed out that pain and anxiety especially were significantly underrated by nursing staff. Nursing staff ranked these symptoms as non-existent, whereas patients stated at least mild symptom burden (pain: meanNRS/epaAC = 0 (no); meanePROM = 1 (mild); p < 0.05; r = 0.46; anxiety: meanepaAC = 0 (no); meanePROM = 1 (mild); p < 0.05; r = 0.48). In conclusion, supplementing routine symptom assessment used daily by nursing staff with the systematic, e-health-enabled acquisition of PROMs may improve the quality of supportive and palliative care.