With the start of the 21st century, patient safety as a topic of special interest has attracted increasing attention in both academia and clinical practice. As technology has continued to develop since then, questions and focal points surrounding the topic have also shifted. In particular, questions regarding the impact of digitalization on patient safety and its measurement are now of high interest. This work aims to develop a maturity assessment instrument in the form of a criteria set for measuring structural requirements for digital patient safety in hospitals. Based on the results of a literature review and a derivation of maturity objects (MO) from known maturity models, 64 criteria across 11 categories were developed. Written comments of two digital patient safety experts as well as subsequent interviews were used to evaluate and refine the criteria catalog. The resulting catalog offers hospitals guidance for detecting possible areas of structural improvements in their information systems with regard to patient safety and represents a unique instrument for assessing digital maturity in this particular area.

Artificial intelligence (AI) tends to emerge as a relevant component of medical care, previously reserved for medical experts. A key factor for the utilization of AI is the user’s trust in the AI itself, respectively the AIt’s decision process, but AI-models are lacking information about this process, the so-called Black Box, potentially affecting usert’s trust in AI. This analysis’ objective is the description of trust-related research regarding AI-models and the relevance of trust in comparison to other AI-related research topics in healthcare. For this purpose, a bibliometric analysis relying on 12985 article abstracts was conducted to derive a co-occurrence network which can be used to show former and current scientific endeavors in the field of healthcare based AI research and to provide insight into underrepresented research fields. Our results indicate that perceptual factors such as “trust” are still underrepresented in the scientific literature compared to other research fields.