DOI: 10.1097/SIH.0b013e3181663592

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PMID: 19088635

Issn Print: 1559-2332

Publication Date: 2008/04/01


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Team Performance Assessment in Healthcare: Facing the Challenge

Tanja Manser

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Excerpt

It has been widely recognized in the patient safety literature that team performance is crucial to providing safe patient care, and “team failure” has become a key factor addressed by many system-based interventions to improve patient safety.1–3 Teamwork has become a focus of medical education standards,4,5 and the simulation community has responded to this by designing and implementing simulation-based team training programs for many specialties and increasingly for multispecialty teams.6–9 However, if team performance cannot be assessed accurately, efforts to define specific training needs and to improve team performance may be futile. Thus, medical educators and human factors researchers are now facing the challenges of team performance assessment—some of which are known from other safety critical industries and some of which may be unique to healthcare.
This edition of Simulation in Healthcare features an article by Rosen and colleagues outlining best practices for team performance assessment.10 The authors are to be congratulated for providing a concise overview of the challenges of team performance assessment in the context of simulation-based training. Building on an extensive body of research on team performance assessment in various safety critical domains, they provide a list of 11 “best practices,” recommending that these best practices should also guide simulation-based team performance assessment in healthcare.
The future success of simulation-based research and training in healthcare most likely will be determined by how successfully the simulation community responds to the challenges associated with performance assessment of both clinical and nontechnical skills (ie, the cognitive and social skills required in any operational task involving decision-making and team work11) on an individual and team level. I would like to explore the issue of how the current (published) practice of team performance measurement in healthcare does compare with the best practices described by Rosen and colleagues (Table 1).
The authors begin their list of best practices by stating that team performance measurement needs to be grounded in theory because the theoretical model referred to defines the elements of a measure (eg, backup behavior and leadership can be elements of a team performance measure) (see best practice #1). However, healthcare researchers and educators may find it difficult to choose because theoretical models focusing on different aspects of team performance are still under development.12 Most conceptual models of team performance are presented within the framework of inputs (eg, characteristics of the individual, the task, and the organizational structure), processes (eg, communication, decision making), and outputs (eg, quality and quantity of products or services, improved knowledge of team members) (IPO models).12,13 The main advantage of an IPO model is that it allows for temporal considerations, describing a team process by which inputs are transformed into outputs that then serve as inputs into another process. Thus, the focus of performance assessment is shifted from the output to the process by which an output is achieved. Of course, to provide a complete picture of team performance, both aspects need to be included (see best practice #8). Empirical data from healthcare looking at the relationships among inputs, processes, and outcomes14–17 will contribute to an increasingly complete picture of team performance in healthcare and beyond.
To be relevant in practice, measures defined on the basis of a theoretical model need to capture critical competencies and thus have to be adapted to the types of teams and team tasks studied (see best practice #3). The research on nontechnical skills in anesthesia,18 surgery,19 and neonatal resuscitation,20 where many aspects discussed by Rosen and colleagues were considered carefully, is an excellent example of scientific developments that can be used for research and training purposes during simulation as well as in clinical settings.
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