Created June 17, 2015, last updated 10 months ago.
Collection: 29, Score: 911, Trend score: 0, Read count: 912, Articles count: 10, Created: 2015-06-17 03:17:17 UTC. Updated: 2021-02-09 21:38:35 UTC.
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Cognitive aids are prompts designed to help users complete a task or series of tasks. They may take the form of posters, flowcharts, checklists, or even mnemonics. It has been suggested that the use of cognitive aids improves performance and patient outcomes during anesthetic emergencies; however, a systematic assessment of the evidence is lacking. ⋯ Cognitive aids should continue to be developed from established clinical guidelines where guidelines exist. They would also benefit from more extensive simulation-based usability testing before use. Further evidence is required to explore the effects of cognitive aids in anesthetic emergencies, how they affect team function, and their design considerations.
Randomized Controlled Trial
A hardcopy or paper cognitive aid has been shown to improve performance during the management of simulated local anesthetic systemic toxicity (LAST) when given to the team leader. However, there remains room for improvement to ensure a system that can achieve perfect adherence to the published guidelines for LAST management. Recent research has shown that implementing a checklist via a designated reader may be of benefit. Accordingly, we sought to investigate the effect of an electronic decision support tool (DST) and designated "Reader" role on team performance during an in situ simulation of LAST. ⋯ In a prospective, randomized single-blinded study, a designated Reader with an electronic DST improved adherence to guidelines in the management of an in situ simulation of LAST. Such tools are promising in the future of medicine, but further research is needed to ensure the best methods for implementing them in the clinical arena.
We present a prototype of a decision support system for anesthesia that applies set covering theory. The system is designed to generate dynamically configured check-lists for intra-operative problems. These lists have the potential to help anesthesiologists detect and manage problems in a timely manner. ⋯ A set covering algorithm that accommodates multiple problem sets was used to implement the prototype. A simulated case and the system behavior are presented. The ultimate goals of a system such as the one presented are to function as an intelligent alarm module for electronic monitors and to facilitate the task of correcting intra-operative problems.
Randomized Controlled Trial
Decision support systems (DSSs) have been successfully implemented into clinical practice offering clinical suggestions and treatment options with excellent results in various clinical settings. Although their results appeared promising, showing that DSSs can increase anesthesiologists' vigilance and patient safety during surgery, DSSs have never been used before to help anesthesiologists in identifying critical events in patients under spinal analgesia with sedation. We have developed and clinically evaluated a DSS for this specific task. ⋯ The number of critical events/h occurring and the duration of surgery were similar in both groups. The number of hypoxemia episodes was significantly less (P = 0.036) in the DSS group (0.7 ± 1.0 vs. 1.4 ± 2.2 for the Control Group). The DSS tested in this trial could help the clinician to detect and treat critical events more efficiently and in a shorter length of time.
Intraoperative hypotension and hypertension are associated with adverse clinical outcomes and morbidity. Clinical decision support mediated through an anesthesia information management system (AIMS) has been shown to improve quality of care. We hypothesized that an AIMS-based clinical decision support system could be used to improve management of intraoperative hypotension and hypertension. ⋯ With automatic acquisition of arterial blood pressure and inhaled drug concentration variables in an AIMS, near real-time notification was effective in reducing the duration and frequency of hypotension with concurrent >1.25 MAC inhaled drug episodes. However, since phenylephrine infusion is manually documented in an AIMS, the impact of notification messages was less pronounced in reducing episodes of hypertension with concurrent phenylephrine infusion. Automated data capture and a higher frequency of data acquisition in an AIMS can improve the effectiveness of an intraoperative clinical decision support system.
The way hemodynamic therapies are delivered today in anesthesia and critical care is suboptimal. Hemodynamic variables are not always understood correctly and used properly. The adoption of hemodynamic goal-directed strategies, known to be clinically useful, is poor. ⋯ However, most therapeutic decisions cannot be based on a limited number of output variables. Therefore, one should focus on the development of systems designed to relieve clinicians from very simple and repetitive tasks. Whether intraoperative goal-directed fluid therapy may be one of these tasks remains to be evaluated.
Guidelines to minimize the incidence of postoperative nausea and vomiting (PONV) have been implemented in many hospitals. In previous studies, we have demonstrated that guideline adherence is suboptimal and can be improved using decision support (DS). In this study, we investigate whether DS improves patient outcome through improving physician behaviour. ⋯ Automated reminders can improve patient outcome by improving guideline adherence.
Adherence to advanced cardiac life support (ACLS) guidelines during in-hospital cardiac arrest (IHCA) is associated with improved outcomes, but current evidence shows that sub-optimal care is common. Successful execution of such protocols during IHCA requires rapid patient assessment and the performance of a number of ordered, time-sensitive interventions. Accordingly, we sought to determine whether the use of an electronic decision support tool (DST) improves performance during high-fidelity simulations of IHCA. ⋯ Use of an electronic DST provided a significant improvement in the management of simulated IHCA by senior medical students as measured by adherence to published guidelines.
Several formulas have been developed to guide resuscitation in severely burned patients during the initial 48 hrs after injury. These approaches require manual titration of fluid that may result in human error during this process and lead to suboptimal outcomes. The goal of this study was to analyze the efficacy of a computerized open-loop decision support system for burn resuscitation compared to historical controls. ⋯ Implementation of a computer decision support system for burn resuscitation in the intensive care unit resulted in improved fluid management of severely burned patients. All measures of crystalloid fluid volume were reduced while patients were maintained within urinary output targets a higher percentage of the time. The addition of computer decision support system technology improved patient care.
It has been suggested that predicting difficult tracheal intubation is useless because of the poor predictive capacity of individual signs and scores. The authors tested the hypothesis that an accurate prediction of difficult tracheal intubation using simple clinical signs is possible using a computer-assist model. ⋯ Computer-assisted models using complex interaction between variables enable an accurate prediction of difficult tracheal intubation with a low proportion of patients in the inconclusive zone. An external validation of the model is now required.
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