Team Learning Behaviors and Performance: A Meta-Analysis of Direct Effects and Moderators

Under what conditions do team learning behaviors best predict team performance? The current meta-analytic efforts synthesize results from 113 effect sizes and 7758 teams to investigate how different conceptualizations (fundamental, intrateam, and interteam), team characteristics (team size and team familiarity), task characteristics (interdependence, complexity, and type), and methodological characteristics (students vs. nonstudents and measurement choice) affect the relationship between team learning behaviors and team performance. Our results suggest that while different conceptualizations of team learning behaviors independently predict performance, only intrateam learning behaviors uniquely predict performance. A more in-depth investigation into the moderating conditions contradicts the familiar adage of “it depends.” The strength of the relationship between intrateam learning behaviors and team performance did not depend on team familiarity, task complexity, or sample type. However, our results suggested this relationship was stronger in larger teams, teams with moderate task interdependence, teams performing project/action tasks, and studies that use measures that capture a wider breadth of the team learning behavior construct space. These efforts suggest that common boundary conditions do not moderate this relationship. Scholars can leverage these results to develop more comprehensive theories addressing the different conceptualizations of team learning behaviors as well as providing clarity on the scenarios where team learning behaviors are most needed. Further, practitioners can use our results to develop more guided team-based policies that can overcome some of the challenges of forming and developing learning teams.

Coordination and Communication in Healthcare Action Teams

Communication and coordination represent central processes in healthcare action teams. However, we have a limited understanding of how expertise affects these processes and to what extent these effects are shaped by interprofessional differences. The current study addresses these questions by jointly investigating the influence of different aspects of expertise – individual expertise, team familiarity, and expertise asymmetry – on coordination quality and communication openness. We tested our propositions in two hospitals: one in Switzerland (CH, Sample 1) and one in the United Kingdom (UK, Sample 2). Both samples included two-person anesthesia action teams consisting of a physician and a nurse ( NCH = 47 teams, NUK = 48 teams). We used a correlational design with two measurement points (i.e., pre- and postoperation). To consider potential interprofessional differences, we analyzed our data with actor-partner interdependence models. Moreover, we explored differences in the effects of expertise between both hospitals. Our findings suggest that nurses’ expertise is the most important predictor of coordination quality and communication openness. Overall, differences between the two hospitals were more prevalent than interprofessional differences between physicians and nurses. The current study provides a nuanced picture of the effects of expertise, and thereby extends our understanding of interprofessional teamwork.

Determinants of Surgical Case On-Time Start and On-Time Finish in Perioperative Services

Efficient use of the operating room (OR) is crucial for any hospital. One of the major inefficiencies in the OR is surgical cases not starting or finishing on time as scheduled. When a case is delayed, it affects all subsequent cases in that OR. This study uses discrete choice analysis to determine the significant factors, including team familiarity, that influence OR case on-time start and finish. A case is considered on-time if the documented procedure start and finish times are no more than 10 minutes after the scheduled start and finish times. The analysis uses surgical case data from a large tertiary referral hospital and academic center in Greenville, South Carolina. The case data includes all surgical cases (15,091) performed during regular workdays in 2013. Two binary logit models are developed: one for case on-time start and one for case on-time finish. Results indicate that higher team familiarity between surgeon and anesthesiologist, surgeon and circulating nurse, surgeon and scrub nurse, and surgeon and CRNA improve the likelihood of an OR case on-time start and on-time finish. This finding indicates that the OR scheduling staff in the study hospital make a concerted effort to schedule the surgical teams with members who have worked well together in the past.