[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.

[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.

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.

Measuring Patient Experience and Patient Satisfaction-How Are We Doing It and Why Does It Matter? A Comparison of European and U.S. American Approaches.

(1) Background: Patients' experiences and satisfaction with their treatment are becoming increasingly important in the context of quality assurance, but the measurement of these parameters is accompanied by several disadvantages such as poor cross-country comparability and methodological problems. The aim of this review is to describe and summarize the process of measuring, publishing, and utilizing patient experience and satisfaction data in countries with highly developed healthcare systems in Europe (Germany, Sweden, Finland, Norway, the United Kingdom) and the USA to identify possible approaches for improvement. (2) Methods: Articles published between 2000 and 2021 that address the topics described were identified. Furthermore, patient feedback in social media and the influence of sociodemographic and hospital characteristics on patient satisfaction and experience were evaluated. (3) Results: The literature reveals that all countries perform well in collecting patient satisfaction and experience data and making them publicly available. However, due to the use of various different questionnaires, comparability of the results is difficult, and consequences drawn from these data remain largely unclear. (4) Conclusions: Surveying patient experience and satisfaction with more unified as well as regularly updated questionnaires would be helpful to eliminate some of the described problems. Additionally, social media platforms must be considered as an increasingly important source to expand the range of patient feedback.

Implications of an HbA1c-based Diabetes Screening on Prevalence and Effect of Dysglycemia in Patients With COVID-19.

CONTEXT: In patients with severe acute respiratory syndrome coronavirus type 2 infection, diabetes is associated with poor COVID-19 prognosis. However, case detection strategy is divergent and reported prevalence varies from 5% to 35%. OBJECTIVE: We examined how far the choice of screening tools affects the detection rate of dysglycemia and in consequence the estimation of diagnosis-associated risk for moderate (mo) or severe (s) COVID-19. METHODS: Non-intensive care unit inpatients with COVID-19 were screened systematically at admission for diabetes (D) and prediabetes (PreD) by glycated hemoglobin A1c (HbA1c) (A), random blood glucose (B), and known history (C) from November 1, 2020 to March 8, 2021. Dysglycemia rate and effect on COVID-19 outcome were analyzed in 2 screening strategies (ABC vs BC). RESULTS: A total of 578 of 601 (96.2%) of admitted patients were screened and analyzed. In ABC, prevalence of D and PreD was 38.2% and 37.5%, respectively. D was significantly associated with an increased risk for more severe COVID-19 (adjusted odds ratio [aOR] [moCOVID-19]: 2.27, 95% CI, 1.16-4.46 and aOR [sCOVID-19]: 3.26, 95% CI, 1.56-6.38). Patients with PreD also presented more often with more severe COVID-19 than those with normoglycemia (aOR [moCOVID-19]: 1.76, 95% CI, 1.04-2.97 and aOR [sCOVID-19]: 2.41, 95% CI, 1.37-4.23). Screening with BC failed to identify only 96% of PreD (206/217) and 26.2% of D diagnosis (58/221) and missed associations of dysglycemia and COVID-19 severity. CONCLUSION: Pandemic conditions may hamper dysglycemia detection rate and in consequence the awareness of individual patient risk for COVID-19 severity. A systematic diabetes screening including HbA1c reduces underdiagnosis of previously unknown or new-onset dysglycemia, and enhances the quality of risk estimation and access of patients at risk to a diabetes-specific intervention.

Are medical students equipped for digital studies?Have their hopes and fears been confirmed during Covid-19? What should we consider in the future?

Digitisation in the education of future doctors was still in its infancy before the Covid pandemic. For the successful implementation of digital teaching, students need the technical equipment and the necessary skills to use it in a meaningful way. Furthermore, it requires a willingness to adapt the learning environment and to take responsibility for self-directed learning. At the beginning of 2020, faculties were forced to convert all teaching to digital formats. Initial research shows that students prefer face-to-face teaching. To determine whether medical students were prepared for digital studies and what should be considered for the future, we analysed surveys at the beginning of online studies and after two Corona semesters at a medical faculty. We were able to show that although our students had good technology equipment, they had a rather negative attitude towards online teaching for various reasons and developed negative emotions. Deficits in design of educational material, and personal learning habits raised concern. A lack of guidance and a lack of interaction with fellow students contributed to this. Adjustments in these areas will be necessary in the future to provide students with positive access to digital studies and thus increase learning success.

Telmisartan on top of antihypertensive treatment does not prevent progression of cerebral white matter lesions in the prevention regimen for effectively avoiding second strokes (PRoFESS) MRI substudy.

BACKGROUND AND PURPOSE: High blood pressure is one of the main risk factors for cerebral white matter lesions (WMLs). There is limited evidence from one randomized trial that blood pressure-lowering is able to slow WML progression. We investigated whether telmisartan prevents WML progression in the imaging substudy of the Prevention Regimen for Effectively Avoiding Second Strokes (PRoFESS) trial. METHODS: This predefined substudy comprised 771 patients (mean age, 65 years) with recent ischemic stroke of noncardioembolic origin who received telmisartan or placebo during a mean follow-up of 27.9 (SD, 7.6) months and had 2 evaluable MRI examinations after index stroke and at study closeout. All MRI scans were centrally adjudicated for progression of periventricular and subcortical WML by 2 neuroradiologists blinded to treatment allocation. RESULTS: Mean blood pressure was 3.0/1.3 mm Hg lower with telmisartan compared with placebo at follow-up MRI. There was no statistically significant difference in progression of the mean periventricular WML score (least squares mean difference, 0.14; 95% CI, -0.12 to 0.39; P=0.29) and mean subcortical WML diameter (least squares mean difference, -0.35 mm; 95% CI, -1.00 to 0.31 mm; P=0.30) during follow-up between patients on telmisartan and placebo. CONCLUSIONS: Treatment with telmisartan on top of existing antihypertensive medication did not result in significant blood pressure-lowering and did not prevent the progression of WML in patients with a recent ischemic stroke in this patient cohort. Our analysis is limited by the relatively short follow-up period. Clinical Trial Registration- URL: http://clinicaltrials.gov. Unique Identifier: NCT00153062.

Risk of recurrent stroke in patients with silent brain infarction in the Prevention Regimen for Effectively Avoiding Second Strokes (PRoFESS) imaging substudy.

BACKGROUND AND PURPOSE: Silent brain infarctions are associated with an increased risk of stroke in healthy individuals. Risk of recurrent stroke in patients with both symptomatic and silent brain infarction (SBI) has only been investigated in patients with cardioembolic stroke in the European Atrial Fibrillation Trial. We assessed whether patients with recent noncardioembolic stroke and SBI detected on MRI are at increased risk for recurrent stroke, other cardiovascular events, and mortality. METHODS: The prevalence of SBI detected on MRI was assessed in 1014 patients enrolled in the imaging substudy of the Prevention Regimen for Effectively Avoiding Second Strokes (PRoFESS) trial. The primary outcome was first recurrence of stroke in patients with both symptomatic stroke and SBI in comparison with age- and sex-matched patients with stroke without SBI. Secondary outcomes were a combined vascular end point, other vascular events, and mortality. The 2 groups were compared using conditional logistic regression. RESULTS: Silent brain infarction was detected in 207 (20.4%) of the 1014 patients. Twenty-seven (13.0%) patients with SBI and 19 (9.2%) without SBI had a recurrent stroke (OR, 1.42; 95% CI, 0.79-2.56; P=0.24) during a mean follow-up of 2.5 years. Similarly, there was no statistically significant difference for all secondary outcome parameters between patients with SBI and matched patients without SBI. CONCLUSIONS: The presence of SBI in patients with recent mild noncardioembolic ischemic stroke could not be shown to be an independent risk factor for recurrent stroke, other vascular events, or a higher mortality rate. CLINICAL TRIAL REGISTRATION: URL: http://clinicaltrials.gov. Unique identifier: NCT00153062.