Evaluation of compliance with a computerized protocol: Weaning from mechanical ventilator support using pressure support

Randolph, Adrienne G.; Clemmer, Terry P.; East, Thomas D.; Kinder, A.; Orme, James F.; Wallace, C. J.; Morris, Alan H.

doi:10.1016/s0169-2607(98)00062-5

Cited by 23 publications

(9 citation statements)

References 20 publications

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“…The CDSS can track information about adherence to specific aspects of the protocol. 20,21 High adherence has been demonstrated with adult ventilator CDSS tools, 23,28 and preliminary data suggest that adherence will be reasonably high with pediatric ventilator CDSS tools. 29 Studies in adult ICUs support the benefits of protocol-based ventilator management.…”

Section: Cdss Tools Affect Compliance With Explicit Protocolsmentioning

confidence: 99%

“…20,22 This CDSS was derived from the adult ARDSNet protocols and CDSS tools, which have demonstrated effectiveness over many years. 23,28,30,34 Little is known about the use in pediatric medicine of CDSS tools that were derived from adult tools. Success with an insulinmanagement CDSS tool simultaneously deployed in adult and pediatric ICUs suggest that such translation is possible.…”

Section: Cdss Knowledge Developmentmentioning

confidence: 99%

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Computerized Decision Support Systems for Mechanical Ventilation in Children

Sward

Newth

2015

J Pediatr Intensive Care

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Mechanical ventilation is an effective treatment in the ICU but can have significant adverse effects. Approaches from adult research have been adopted in pediatric critical care despite known differences in respiratory physiology and ICU processes. There continues to be considerable variation in how ventilators are managed. Computerized decision support systems implement explicit protocols, and are designed to make mechanical ventilation management safer, more consistent, and more lung protective. Variable results and low or unknown compliance with protocols and CDSS tools have been reported. To date, there has been limited research regarding CDSS for mechanical ventilation in children.

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Section: Cdss Tools Affect Compliance With Explicit Protocolsmentioning

confidence: 99%

Section: Cdss Knowledge Developmentmentioning

confidence: 99%

Computerized Decision Support Systems for Mechanical Ventilation in Children

Sward

Newth

2015

J Pediatr Intensive Care

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“…Instead of computerizing a specific recipe for ventilation management 37,47 , in designing NéoGanesh we tried to respect the golden rules of knowledge engineering: make an explicit model of medical tasks and reasoning involved, and distinguish between the conceptual model physiological models do not always represent useful information to the clinician in decision making. For instance, to follow up the recovery of patient after anaesthesia, pharmacological equations are imprecise and not used in practice; iii) the decision making process of clinicians may be less variable than the complex physiology of patients.…”

Section: A Knowledge-based System Working In Closed-loopmentioning

confidence: 99%

“…The work performed since several years at the LDS Hospital for the elaboration of computerized protocols is relevant here 17,32,37 . The algorithmic-oriented approach chosen at the LDS Hospital leads to a complex logic where temporal aspects are intricate.…”

Section: Low-level Controllersmentioning

confidence: 99%

“…Instead of computerizing a specific recipe for ventilation management 37,47 , in designing NéoGanesh we tried to respect the golden rules of knowledge engineering: make an explicit model of medical tasks and reasoning involved, and distinguish between the conceptual model (knowledge level, see for instance Figure 3) and the representation paradigms (symbolic level) used to implement it 13 . NéoGanesh is based on current AI techniques: a knowledge representation that mixes objects, rules and temporal abstractions 16 in a distributed architecture 14 .…”

Section: A Knowledge-based System Working In Closed-loopmentioning

confidence: 99%

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Knowledge-based systems for automatic ventilatory management

Dojat¹,

Brochard²

2001

Respiratory Care Clinics of North America

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To cite this version:Michel Dojat, Laurent Brochard. Knowledge-based systems for automatic ventilatory management.. Respiratory Care Clinics, 2001, 7 (3), pp.379-96, viii. IntroductionDespite their enormous potential to facilitate bedside management, the practical role of computers in critical care environments is generally restricted to the storage and the retrieval of data coming from electronic medical devices and hospital information networks. Benefits of the use of computers in health care may be extended by the design of computerized medical assistants that can efficiently discharge the clinical staff of repetitive tasks (which, in practice, often are not performed) and, importantly, help practitioners to make efficient decisions in time. In intensive care and anesthesia, the demand for computerized medical assistants is potentially considerable, in order to filter and synthesize the growing mass of clinical parameters and information available. The progressive introduction of computerized protocols has been proposed to standardize the bedside decision making process for mechanical ventilation and to reduce unnecessary variation among practitioners 32 , reinforcing the potential impact of computerized medical assistants. Designing such assistants for intelligent monitoring, diagnosis and therapy planning tasks in Intensive Care and Anesthesia is a challenging goal that requires the modeling of several levels of knowledge ranging from low level data interpretation to high level cognitive tasks, such as planning (for a review of recent research work in this field see 12,25 ). Our goal in this special issue is to show how knowledge-based computerized assistants can be used to practically improve the closed-loop control of mechanical ventilation. Control and Planning: Two Key Points for Automatic Ventilation Management 3Modern methods of mechanical ventilation partially assist the patient's ventilation by adding a variable amount of mechanical support to his/her spontaneous activity. In this context, since the needs of the patient are evolutive, it is essential to continuously control the ventilatory support, in order to avoid excessive work of breathing and effort, discomfort and dyspnea on the one hand, or excessive support, hyperinflation and dyssynchrony on the other hand. In parallel to this ideal automatic adaptation, it may be necessary to plan the long term adaptation of the therapy according to specific medical goals. For instance, it may be indicated to gradually decrease the level of assistance in order to facilitate the weaning from the ventilator or to take into account large variations of physiological needs during the patient wake-up from anesthesia or drug intoxication.Planning and control are two different tasks that have a common goal: choosing actions over time to influence a process, based on some model of that process 9 . Control is a local task to determine what to do the next instant. Planning is a strategic task to regulate the process evolution. For control and plann...

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