Postoperative Nausea and Vomiting Clinical Trial
Official title:
Real-time Decision Support for Postoperative Nausea and Vomiting (PONV) Prophylaxis
The purpose of this study is to determine how automated recommendations are best presented to optimize the adherence to guidelines on prophylaxis for nausea and vomiting after surgery.
Nausea and vomiting after surgery (PONV) is a common side effect of the surgical procedure,
general anesthesia and opioid use occurring in about one third of patients. In addition to
being very unpleasant for patients, it is associated with longer recovery room stays and
increased costs. Much research has been done on prophylactic interventions that may be
applied during the surgical procedure to prevent PONV. Current national guidelines recommend
that a risk score is used to decide on the number of prophylactic interventions to administer
to a patient. Based on specific characteristics of individual patients and the procedures
that they are about to undergo, such a risk score predicts the risk of PONV for each
individual. According to the national guidelines, patients with higher risks of PONV should
receive more prophylactic interventions. However, in a busy operating room where the
anesthesia provider performs multiple patient care tasks, closely following the
recommendations to minimize the risk of PONV is often difficult.
Computers may help anesthesia providers to adhere to best practices for PONV prevention by
providing so-called decision support. A decision support system for PONV automatically
calculates the risk of PONV for an individual patient and presents this predicted risk to the
anesthesia provider on the computer screen that is being used by the anesthesia team for
record keeping. In recent studies, such decision support systems have been demonstrated to
improve adherence to PONV guidelines, especially when a recommendation on the number of
interventions is added to the predicted risk. However, in these studies there was still quite
some room for improvement of the adherence to PONV guidelines. In general, implementation
science is only beginning to understand how such decision support systems are best used to
improve medical decision making and minimize practice variations among providers. Further
study of how the design of decision support systems impacts the decision making of healthcare
providers is therefore warranted.
In this proposed study, the investigators will implement several decision support elements
for PONV that aim to help anesthesia providers to adhere to the departmental PONV guidelines
during the anesthetic case. The study consists of three phases. The first phase is the
preintervention phase - i.e. before the decision support has been implemented. The second
phase is the first intervention phase with one CDSS feature added. The third phase is the
second intervention phase with another CDSS feature added.
The decision support elements will provide information about the patient's predicted risk of
PONV and the number of prophylactic interventions that the departmental guidelines recommend
based on that risk. We will start with preoperative email notifications, followed by an
element within the anesthesia information management system (AIMS) that are displayed around
the start and end of the procedure. All forms of decision support only provide
recommendations. The anesthesia provider is free to act on the message or ignore the
notifications.
The investigators will compare the adherence to PONV guidelines and the actual occurrence of
PONV (both nausea and emetic events: vomiting and retching) in the post-anesthesia care unit
(PACU) between all study phases and between the different interventions. The goal of the
comparison is to evaluate which decision support elements have an added value to optimize
guideline adherence for PONV prophylaxis.
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