Addressing misconceptions of flow disruption studies in “Is non-stop always better? Examining assumptions behind the concept of flow disruptions in studies of robot-assisted surgery”

Abstract

This letter to the editor provides a response to “Is non-stop always better? Examining assumptions behind the concept of flow disruptions in studies of robot-assisted surgery.” The authors provide much needed clarification on misconceptions of flow disruption studies. The evolving methodology is not aimed at creating a “non-stop” flow, or optimizing efficiency, but understanding the clinical process from a systems perspective.

Dear Sir/Madam,

Constructive criticism and careful thought are an essential component of scientific endeavor, so we welcomed your recent article by Bruun et al. (2021) [1]. While the central criticism, that the methodology surrounding surgical flow disruptions (FDs) is focused on working “non-stop”, does not reflect the historical, theoretical, or practical underpinning of FD methods, nor the richness of the knowledge and improvement that has arisen from it, we appreciate the opportunity to address some of the controversies and challenges around conceptualizations of FDs that it presents.

FDs were defined in a study of cardiac surgery by Wiegmann et al. (2007) [2] building on findings in congenital heart surgery that seemingly innocuous intraoperative deviations affected mortality and morbidity [3]. Both studies exemplify the importance of looking beyond individuals to the functioning of clinical systems for ways to improve patient outcomes. FDs fundamentally demonstrate mismatches between the demands of the work and the ability of the system (people; tasks; technologies; workspace; organization) to support it [4]. Identifying where they are misaligned helps to understand what must be overcome, and what is necessary for success, in real-world surgical practice (“work as imagined” vs “work as done”) [5]. The FD method has now been used across a broad range of acute care contexts, demonstrating how clinical processes vary with experience, non-technical skills, checklists, teamwork training, and much more. Studies in robot-assisted surgery have revealed challenges associated with sophisticated OR technology that surgical teams face every day, across organizational and national boundaries, that until recently had not been systematically studied, nor well acknowledged. In that light, the characterization of FDs being focused on “non-stop” surgery is misrepresentative and reductionist.

As Bruun et al. point out FDs have been classified differently across studies. The methodology has evolved across different specialties; for different study purposes and designs; to refine observational reliability; and because there is no “one way” to view a clinical system [6]. Bruun et al. also make an important point about some of the deeper challenges of understanding clinical systems. While some FDs—such as observed damage to a surgical robot from an overhead boom—are almost certainly undesirable, some may reflect safety behaviors, may be necessary for progress, or demonstrate necessary systemic trade-offs. For example, a phone call into the OR can be disruptive, but incredibly important; or a surgical pause can be valuable for planning the next section of the operation but may also indicate higher cognitive or team demands. The combination of quantitative and qualitative data within FD methodology allows detailed exploration of each event, and if necessary, further classification. Not all FDs are “bad” but they all show us how the system is working, and where further insight or enquiry might be warranted.

In failing to acknowledge the long history, wide application, practical purpose, and deeper theoretical foundations of this work, Bruun and colleagues missed the fundamental value of FDs, which is not that “pauses are bad” and must be avoided, but what those ‘pauses’ tell us about how clinical systems function in the first place. FD methodology is not focused on efficiency or “non-stop” operating, but on understanding what it takes for surgery to go well, by studying where it deviates. We discuss the challenges, nuances, and state of the science in a recent review that addresses these issues and offers deeper insights [7]. We also welcome thoughtful, informed, and constructive debate about how these methods can be refined, the misconceptions addressed, and the science of understanding clinical systems improved from a clinician-centered perspective.