Learning optimal opioid prescribing and monitoring: a simulation study of medical residents

Abstract

Objective: Hospitalized patients often receive opioids. There is a lack of consensus regarding evidence-based guidelines or training programs for effective management of pain in the hospital. We investigated the viability of using an Internet-based opioid dosing simulator to teach residents appropriate use of opioids to treat and manage acute pain.

Materials and methods: We used a prospective, longitudinal design to evaluate the effects of simulator training. In face-to-face didactic sessions, we taught 120 (108 internal medicine and 12 family medicine) residents principles of pain management and how to use the simulator. Each trainee completed 10 training and, subsequently, 5 testing trials on the simulator. For each trial, we collected medications, doses, routes and times of administration, pain scores, and a summary score. We used mixed-effects regression models to assess the impact of simulation training on simulation performance scores, variability in pain score trajectories, appropriate use of short- and long-acting opioids, and use of naloxone.

Results: Trainees completed 1582 simulation trials (M = 13.2, SD = 6.8), with sustained improvements in their simulated pain management practices. Over time, trainees improved their overall simulated pain management scores (b = 0.05, P < .01), generated lower pain score trajectories with less variability (b = -0.02, P < .01), switched more rapidly from short-acting to long-acting agents (b = -0.50, P < .01), and used naloxone less often (b = -0.10, P < .01).

Discussion and conclusions: Trainees translated their understanding of didactically presented principles of pain management to their performance on simulated patient cases. Simulation-based training presents an opportunity for improving opioid-based inpatient acute pain management.

Keywords: pain management; patient safety; simulation; training.

Figure 1.

The various interfaces that are part of the opioid dosing simulator. Clockwise (A–F). (A) Presentation of the clinical case. (B) Medication ordering interface. (C) Selecting the “simulation time”. (D) Pain trajectory after administration. (E) 48-h pain score trajectory (ie, at the end of the simulation). (F) Log of medications ordered for a patient.

Figure 2.

(A) Training performance over time based on the overall score for each trial. Over time, the overall performance improved, as shown by the increasing average quantile score. (B) The overall mean pain trajectory for the training and testing trails.

Figure 3.

(A) The variation in the standard deviation of the pain scores, with a decreasing slope. (B) Effect of training on mean time to initiation of long-acting therapy.

Figure 4.

(A) Proportion of trainees using short-acting (morphine IV), long-acting (morphine sustained), and naloxone (antidote) over the course of 48 simulated hours. (B) Proportion of trainees using naloxone as a function of number of simulation trials completed.