Applying ordered network analysis to video-recorded physician-nurse interactions to examine communication patterns associated with shared understanding in inpatient oncology care settings

Abstract

Objectives: The main aim of this study was to demonstrate how ordered network analysis of video-recorded interactions combined with verbal response mode (VRM) coding (eg, edification, disclosure, reflection and interpretation) can uncover specific communication patterns that contribute to the development of shared understanding between physicians and nurses. The major hypothesis was that dyads that reached shared understanding would exhibit different sequential relationships between VRM codes compared with dyads that did not reach shared understanding.

Design: Observational study design with the secondary analysis of video-recorded interactions.

Setting: The study was conducted on two oncology units at a large Midwestern academic health care system in the USA.

Participants: A total of 33 unique physician-nurse dyadic interactions were included in the analysis. Participants were the physicians and nurses involved in these interactions during patient care rounds.

Primary and secondary outcome measures: The primary outcome measure was the development of shared understanding between physicians and nurses, as determined by prior qualitative analysis. Secondary measures included the frequencies, orders and co-occurrences of VRM codes in the interactions.

Results: A Mann-Whitney U test showed that dyads that reached shared understanding (N=6) were statistically significantly different (U=148, p=0.00, r=0.93) from dyads that did not reach shared understanding (N=25) in terms of the sequential relationships between edification and disclosure, edification and advisement, as well as edification and questioning. Dyads that reached shared understanding engaged in more edification followed by disclosure, suggesting the importance of this communication pattern for reaching shared understanding.

Conclusions: This novel methodology demonstrates a robust approach to inform interventions that enhance physician-nurse communication. Further research could explore applying this approach in other healthcare settings and contexts.

Keywords: Cognition; Nurses; ONCOLOGY; Physicians; STATISTICS & RESEARCH METHODS.

Figure 1

ONA mean network summarising all 33 physician–nurse interactions. Purple circles are plotted points for the physician-nurse models, and black nodes represent the codes, the coloured circle within a node represents self-connections. The larger the node size is the higher frequency and the code is being used as a response to other nodes. Directed connections are represented as triangles, with thicker and more saturated triangles represent stronger connections. The chevrons on the triangles indicate the direction of connections. ONA, ordered network analysis.

Figure 2

Mean ordered networks for the physician–nurse dyads who reached a shared understanding (in blue; left) and those who did not (in red; right).