Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration

Schuh, Christian; Bruin, Jeroen S. de; Seeling, Walter

doi:10.1016/j.artmed.2015.11.002

Cited by 27 publications

(27 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior to and surrounding implementation, it is important to communicate with and educate the affected users. 53 Effective training of all the stakeholders and intended users of the CDSS is key to its success 54 and should comprise different aspects such as when (and when not) to use it, how to use it, how to interpret the output of the CDSS, and when to override the CDSS recommendations, among others. It also includes helping users understand how the CDSS will impact their daily activities and how they can provide feedback.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence‐based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Mahadevaiah

Bermejo

et al. 2020

Medical Physics

View full text Add to dashboard Cite

Background Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability. Aim In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance. Results A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well‐trained end‐users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved. Conclusion We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success.

show abstract

Section: Methodsmentioning

confidence: 99%

Artificial intelligence‐based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Mahadevaiah

Bermejo

et al. 2020

Medical Physics

View full text Add to dashboard Cite

show abstract

“…Smart Innovations Personalized Health Referenced Papers [155], [100], [101], [150], [151] [86], [152], [153], [154] [67], [155] to enabling the observation of changes in skin characteristics such as color, texture, moles, and rashes as the monitored person performs his/her daily routines. All of these parameters reflect significant health markers, and this concept is the core principle of smart mirror technologies where at-home surveillance offers new possibilities for more reliable and practical diagnostic measurements.…”

Section: Futuristic Sch Predictive Analyticsmentioning

confidence: 99%

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

et al. 2021

View full text Add to dashboard Cite

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

show abstract

“…CDSS aim to assist physicians and nurses in making better informed clinical decisions and ultimately improve patient safety [4]. Importantly, the implementation of a CDSS in a health care environment requires the integration of the system into the pre-existing dataflow, computational infrastructure, and clinical procedures [3,5].…”

Section: Introductionmentioning

confidence: 99%

The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation

Costa¹,

Wernsdorfer²,

Kehrer³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

Background Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. Objective With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. Methods Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. Results We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. Conclusions AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

show abstract

Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration

Cited by 27 publications

References 25 publications

Artificial intelligence‐based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Artificial intelligence‐based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation

Contact Info

Product

Resources

About