[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.

Artificial Intelligence Bias in Health Care: Web-Based Survey.

BACKGROUND: Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers. OBJECTIVE: This study's objective was to survey AI specialists in health care to investigate developers' perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures. METHODS: A web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed. RESULTS: A total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively. CONCLUSIONS: This study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.

[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.

Evidence of the medical and economic benefits of implementing hygiene measures by a prevention link physician in trauma surgery: Study protocol for a biphasic multicenter prospective interventional pre-post cohort study using a structured intervention bundle development and tools of behavior change management.

INTRODUCTION: The German Commission for Hospital Hygiene and Infection Prevention recommends nominating one authorized medical specialist in every medical department as an infection prevention link physician (PLP). It has been roughly described that a PLP serves as a link between the infection prevention team and the respective clinical departments. No detailed evidence about the contribution made by PLPs to the decrease of infection rates is available in Germany. The "HygArzt" project aims to demonstrate the medical and economic benefits of the implementation of hygiene measures by PLP in trauma surgery/orthopedics. METHODS: A multicenter interventional pre/post cohort study design was chosen. The study will run for a three-year period, including a pre-, post-, and an intervention phase, in four different hospitals, one of which will serve as pilot. A complex intervention containing evidence-based infection control measures will be developed and implemented by a PLP to proof efficacy. After the successful implementation of the preventive measures in the pilot hospital, the concept will be transposed to the three remaining trauma and orthopedic departments to confirm the transferability and generalizability. To enable the PLPs of the non-pilot departments, a subject-specific training program will be developed based on the study results of the pilot hospital and offered to the PLPs. DISCUSSION: Data are intended to provide evidence that and, if so, to which extent the implementation of specific preventive measures by a medical department-specific PLP is possible and results in a reduction of nosocomial infections in orthopedic surgery and traumatology. CONTRIBUTION TO THE LITERATURE: The present study describes a novel complex study design to prove the effectiveness of intervention measures for infection prevention. The study design and newly developed methodological approach could serve as a model for similar studies on infection prevention in the future. For the first time, the presented research project "HygArzt" focuses on the implementation of hygiene measures by an infection prevention link physician (PLP) and investigates whether nosocomial infections, especially surgical site infections, can be reduced by the measures implemented. TRIAL REGISTRATION: German clinical Trials register DRKS-ID:00013,296. Registered on March 5, 2018, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013296.