scholarly journals Simulations for Mechanical Ventilation in Children: Review and Future Prospects

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Olivier Flechelles ◽  
Annie Ho ◽  
Patrice Hernert ◽  
Guillaume Emeriaud ◽  
Nesrine Zaglam ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Closed Loop ◽  
Ventilatory Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Safety Issues ◽  
Mechanical Ventilation Duration ◽  
Different Types ◽  
Ventilator Performance ◽  
Ventilatory Management

Mechanical ventilation is a very effective therapy, but with many complications. Simulators are used in many fields, including medicine, to enhance safety issues. In the intensive care unit, they are used for teaching cardiorespiratory physiology and ventilation, for testing ventilator performance, for forecasting the effect of ventilatory support, and to determine optimal ventilatory management. They are also used in research and development of clinical decision support systems (CDSSs) and explicit computerized protocols in closed loop. For all those reasons, cardiorespiratory simulators are one of the tools that help to decrease mechanical ventilation duration and complications. This paper describes the different types of simulators described in the literature for physiologic simulation and modeling of the respiratory system, including a new simulator (SimulResp), and proposes a validation process for these simulators.

Alternative Modes of Mechanical Ventilation

2018 ◽  
Vol 29 (4) ◽  
pp. 396-404
Author(s):  
John J. Gallagher
Keyword(s):  
Mechanical Ventilation ◽  
Closed Loop ◽  
Treatment Options ◽  
Ventilatory Support ◽  
Dual Control ◽  
Mechanical Ventilators ◽  
Lung Protection ◽  
Ventilation Modes ◽  
Volume Controlled ◽  
Pressure Controlled

Modern mechanical ventilators are more complex than those first developed in the 1950s. Newer ventilation modes can be difficult to understand and implement clinically, although they provide more treatment options than traditional modes. These newer modes, which can be considered alternative or nontraditional, generally are classified as either volume controlled or pressure controlled. Dual-control modes incorporate qualities of pressure-controlled and volume-controlled modes. Some ventilation modes provide variable ventilatory support depending on patient effort and may be classified as closed-loop ventilation modes. Alternative modes of ventilation are tools for lung protection, alveolar recruitment, and ventilator liberation. Understanding the function and application of these alternative modes prior to implementation is essential and is most beneficial for the patient.

scholarly journals Recent advances in the technology of anesthesia

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 375
Author(s):  
Christian Seger ◽  
Maxime Cannesson
Keyword(s):  
Closed Loop ◽  
Perioperative Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Consumer Health ◽  
Technology Innovations ◽  
Health Markets ◽  
Operating Suite ◽  
Delivery Technology ◽  
Generation Technology

The practice of anesthesiology is inextricably dependent upon technology. Anesthetics were first made possible, then increasingly safe, and now more scalable and efficient in part due to advances in monitoring and delivery technology. Herein, we discuss salient advances of the last three years in the technology of anesthesiology. Consumer technology and telemedicine have exploded onto the scene of outpatient medicine, and perioperative management is no exception. Preoperative evaluations have been done via teleconference, and copious consumer-generated health data is available. Regulators have acknowledged the vast potential found in the transfer of consumer technology to medical practice, but issues of privacy, data ownership/security, and validity remain. Inside the operating suite, monitoring has become less invasive, and clinical decision support systems are common. These technologies are susceptible to the “garbage in, garbage out” conundrum plaguing artificial intelligence, but they will improve as network latency decreases. Automation looms large in the future of anesthesiology as closed-loop anesthesia delivery systems are being tested in combination (moving toward a comprehensive system). Moving forward, consumer health companies will search for applications of their technology, and loosely regulated health markets will see earlier adoption of next-generation technology. Innovations coming to anesthesia will need to account for human factors as the anesthesia provider is increasingly considered a component of the patient care apparatus.

Knowledge Bases, Clinical Decision Support Systems, and Rapid Learning in Oncology

2015 ◽  
Vol 11 (2) ◽  
pp. e206-e211 ◽  
Author(s):  
Peter Paul Yu
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Rapid Learning ◽  
Types Of Knowledge ◽  
Different Types

This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into clinical decision support systems.

scholarly journals Current Knowledge about Providing Drug–Drug Interaction Services for Patients—A Scoping Review

Pharmacy ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 69
Author(s):  
Tora Hammar ◽  
Sara Hamqvist ◽  
My Zetterholm ◽  
Päivi Jokela ◽  
Mexhid Ferati
Keyword(s):  
Patient Safety ◽  
Scoping Review ◽  
Health Care Professionals ◽  
Current Knowledge ◽  
Medication Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Unintended Consequences ◽  
Safety Issues ◽  
Scoping Reviews

Drug–drug interactions (DDIs) pose a major problem to patient safety. eHealth solutions have the potential to address this problem and generally improve medication management by providing digital services for health care professionals and patients. Clinical decision support systems (CDSS) to alert physicians or pharmacists about DDIs are common, and there is an extensive body of research about CDSS for professionals. Information about DDIs is commonly requested by patients, but little is known about providing similar support to patients. The aim of this scoping review was to explore and describe current knowledge about providing digital DDI services for patients. Using a broad search strategy and an established framework for scoping reviews, 19 papers were included. The results show that although some patients want to check for DDIs themselves, there are differences between patients, in terms of demands and ability. There are numerous DDI services available, but the existence of large variations regarding service quality implies potential safety issues. The review includes suggestions about design features but also indicates a substantial knowledge gap highlighting the need for further research about how to best design and provide digital DDI to patients without risking patient safety or having other unintended consequences.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

scholarly journals Clinician acceptance of complex clinical decision support systems for treatment allocation of patients with chronic low back pain

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Stephanie Jansen-Kosterink ◽  
Lex van Velsen ◽  
Miriam Cabrita
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Low Back ◽  
Research Model ◽  
Online Questionnaire ◽  
Treatment Allocation

Abstract Background The uptake of complex clinical decision support systems (CDSS) in daily practice remains low, despite the proven potential to reduce medical errors and to improve the quality of care. To improve successful implementation of a complex CDSS this study aims to identify the factors that hinder, or alleviate the acceptance of, clinicians toward the use of a complex CDSS for treatment allocation of patients with chronic low back pain. Methods We tested a research model in which the intention to use a CDSS by clinicians is influenced by the perceived usefulness; this usefulness, in turn is influenced by the perceived service benefits and perceived service risks. An online survey was created to test our research model and the data was analysed using Partial Least Squares Structural Equation Modelling. The study population consisted of clinicians. The online questionnaire started with demographic questions and continued with a video animation of the complex CDSS followed by the set of measurement items. The online questionnaire ended with two open questions enquiring the reasons to use and not use, a complex CDSS. Results Ninety-eight participants (46% general practitioners, 25% primary care physical therapists, and 29% clinicians at a rehabilitation centre) fully completed the questionnaire. Fifty-two percent of the respondents were male. The average age was 48 years (SD ± 12.2). The causal model suggests that perceived usefulness is the main factor contributing to the intention to use a complex CDSS. Perceived service benefits and risks are both significant antecedents of perceived usefulness and perceived service risks are affected by the perceived threat to autonomy and trusting beliefs, particularly benevolence and competence. Conclusions To improve the acceptance of complex CDSSs it is important to address the risks, but the main focus during the implementation phase should be on the expected improvements in patient outcomes and the overall gain for clinicians. Our results will help the development of complex CDSSs that fit more into the daily clinical practice of clinicians.

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