Internal medicine paging curriculum to improve physician-nurse interprofessional communication: a single center pilot study.
Lauren A HeidemannSamantha KempnerEric WalfordRyan ChippendaleJames T FitzgeraldHelen K MorganPublished in: Journal of interprofessional care (2020)
Effective physician-nurse communication is critical to patient safety, yet internal medicine trainees are rarely given feedback on this skill. In order to address this gap, we developed a 4-week simulated paging curriculum for senior medical students. Standardized Registered Nurses administered five acute inpatient paging cases to students via telephone and scored communication on a 10-point global scale (1 = highly ineffective to 10 = highly effective) and seven communication domains using a 5-point Likert-type scale. The domains included precision/clarity, instructive, directing, assertive, ability to solicit information, engaged, and structured communication. Students received verbal and written feedback from the nurses on communication skills and clinical decision-making. Our primary goal was to determine if student-nurse communication improved throughout the curriculum. Data were analyzed using multivariate ANOVAs with repeated measures. Twenty-seven students participated. Global communication scores increased significantly from case 1 to case 5 (7.1 to 8.7, p < .01). The following communication domains increased significantly: precision (3.8 to 4.4, p < .01), instructive (3.6 to 4.7, p < .01), directing (4.0 to 4.6, p = .02), assertiveness (4.0 to 4.7, p = .04), engaged (4.1 to 4.7, p < .01). In conclusion, this curriculum can be an innovative approach to improve physician-nurse communication using standardized registered nurses to deliver structured feedback to medical trainees.
Keyphrases
- medical students
- primary care
- patient safety
- quality improvement
- healthcare
- emergency department
- mental health
- clinical trial
- randomized controlled trial
- machine learning
- artificial intelligence
- acute respiratory distress syndrome
- big data
- emergency medicine
- study protocol
- deep learning
- drug induced
- mechanical ventilation