Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation.

BACKGROUND: Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. OBJECTIVE: The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. METHODS: An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. RESULTS: Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. CONCLUSION: The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

Competencies required by patients and health professionals regarding telerehabilitation: A scoping review.

Background: Telerehabilitation offers patients alternative access to therapy and has become more prominent during the COVID-19 pandemic. Despite the increasing attractiveness of such programs, there are research gaps regarding the required competencies in the demand-oriented technology use in rehabilitative care. Objective: The study aims at collecting evidence on competencies required by patients and health professionals for using telerehabilitation. We analyse tasks and requirements associated with telerehabilitation and derive and systematise relevant competencies. Methods: We conducted a scoping review and analysed MEDLINE, Psyndex, EMBASE, Cochrane Library, and Web of Science for empirical studies and grey literature from 2017 to May 2022. Articles had to be in English/German and refer to medical rehabilitation accompanied by health professionals taking place in the patient's home. Results: One hundred ten articles were included, covering video conferencing systems, applications with video, audio, or visual therapy content, or wearables. Depending on the program, tasks before, during, and after therapy sessions differ, as do whether these are performed by health professionals, patients, or the technology. Users need digital, health-related, social, personal, and health professionals also professional competencies. This comprises telerehabilitation, technical, health-related, and clinical knowledge, a range of physical, cognitive, social-interactive, technical, and clinical skills, a positive attitude towards telerehabilitation and experience. Whether sociodemographic factors promote successful use is unclear. Conclusions: Telerehabilitation requires a variety of different competencies from patients and health professionals - going beyond the sphere of technical skills. This highlights the need for an evaluation of existing programs for promoting competencies in the use of telerehabilitation and refinement of the programs in line with demands.