Real-time Decision Support for Postoperative Nausea and Vomiting (PONV) Prophylaxis

Postoperative Nausea and Vomiting

Decision support, Prediction models, PONV, prophylaxis, Decision Support Systems, Clinical, Decision Support Techniques

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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