Leitlinienbasierter Clinical Decision Support – Anforderungen an evidenzbasierte Entscheidungsunterstützungssysteme

Börm, Philipp

doi:10.1055/a-1284-3193

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…CDS("clinical decision support")-Systeme stellen einen attraktiven Lösungsansatz für diese Herausforderungen dar. Derartige Computersoftware kann auf Grundlage der Patientendaten evidenzbasierte Handlungsempfehlungen ausgeben und so den ärztlichen Entscheidungsprozess, insbesondere in Hinblick auf multidimensionale Datensätze, unterstützen [4].…”

Section: High-content-immunprofiling-technologien Als Prädiktive Anal...unclassified

Immunologie der Schwangerschaft: von lokalen und systemischen Protagonisten zum High-Content-Immunprofiling

2022

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ZusammenfassungEine zielgerichtete Adaptation des mütterlichen Immunsystems an die Schwangerschaft ist essenziell, um einen komplikationslosen Schwangerschaftsverlauf zu ermöglichen. Hierfür ist ein komplexes Zusammenspiel von fetalen Trophoblastzellen, mütterlichen Immunzellen und dezidualen Stromazellen an der fetomaternalen Grenzzone notwendig. Auch systemisch erfolgen grundlegende immunologische Veränderungen. Darüber hinaus unterliegt die mütterliche Immunantwort einer zeitlichen Dynamik und passt sich den wechselnden Anforderungen der fortschreitenden Schwangerschaft an. Weiterhin unterliegt die mütterliche Immunantwort der Modulation von multiplen Einflussfaktoren, z. B. Hormonen. Im Beitrag werden Kernaspekte der immunologischen Adaptation an die Schwangerschaft beleuchtet, neuartige technologische Ansätze des Monitorings vorgestellt und klinische Anwendungspotenziale diskutiert.

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Section: High-content-immunprofiling-technologien Als Prädiktive Anal...unclassified

Immunologie der Schwangerschaft: von lokalen und systemischen Protagonisten zum High-Content-Immunprofiling

2022

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“…Increasingly, machine-learning techniques are used, where large data sets are used to learn from further events and recognize specific patterns. Both methods base on artificial intelligence (AI) that combine various applications [ 10 ]. In the field of infectious diseases, AI-based DSSs have been increasingly used to assist clinicians´ decision-making in antibiotic management in hospital settings [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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

Tokgöz,

Krayter,

Hafner

et al. 2024

BMC Med Inform Decis Mak

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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.

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“…The system retrieves data and transforms it into an output following distinct rules. A further segmentation can be made into Bayesian networks, causal-probabilistic networks and rule-based systems: The latter are usually based on medical guidelines [2]. Non-knowledge based CDSS require a clinical data source and generate recommendations using artificial intelligence (AI) including machine learning or statistical pattern recognition [3][4].…”

Section: Introductionmentioning

confidence: 99%

User-Oriented Requirements for AI-Based Clinical Decision Support Systems in Sepsis: Study Protocol for a Mixed Methods Study (Preprint)

Raszke,

Giebel,

Abels

et al. 2024

Preprint

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BACKGROUND Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a non-user-oriented development approach, resulting in reduced user acceptance. OBJECTIVE This study has two research objectives. Firstly, problems and corresponding solutions that hinder or support the development and implementation of AI-based CDSS are identified. Secondly, this study aims to increase user acceptance by creating a user-oriented requirement profile, using the example of sepsis. METHODS The study is based on a mixed methods approach combining (i) a scoping review, (ii) focus groups with physicians and professional caregivers and (iii) semi-structured interviews with relevant stakeholders. The research modules mentioned provide the basis for the development of a (iv) survey, including a discrete choice experiment (DCE) with physicians. The survey is followed by the development of a requirement profile for AI-based CDSS and the derivation of policy recommendations for action, which are evaluated in a (v) expert roundtable discussion. RESULTS This study provides an overview of the barriers and corresponding solutions related to the development and implementation of AI-based CDSS. Using sepsis as an example, a user-oriented requirement profile for AI-based CDSS is developed. CONCLUSIONS The results of the study represent the first attempt to create a comprehensive user-oriented requirement profile for the development of sepsis-specific AI-based CDSS. In addition, general recommendations are derived, in order to reduce barriers in the development and implementation of AI-based CDSS.

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Leitlinienbasierter Clinical Decision Support – Anforderungen an evidenzbasierte Entscheidungsunterstützungssysteme

Cited by 8 publications

References 19 publications

Immunologie der Schwangerschaft: von lokalen und systemischen Protagonisten zum High-Content-Immunprofiling

Immunologie der Schwangerschaft: von lokalen und systemischen Protagonisten zum High-Content-Immunprofiling

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

User-Oriented Requirements for AI-Based Clinical Decision Support Systems in Sepsis: Study Protocol for a Mixed Methods Study (Preprint)

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