Mismatched concepts in a neonatal intensive care unit (NICU): further issues for computer decision support?

Measuring shared mental models in healthcare

Objective

To review common qualitative and quantitative methods of measuring shared mental models appropriate for use in the healthcare setting.

Background

Shared mental models are the overlap of individuals’ set of knowledge and/or assumptions that act as the basis for understanding and decision making between individuals. Within healthcare, shared mental models facilitate effective teamwork and theorized to influence clinical decision making and performance. With the current rapid growth and expansion of healthcare teams, it is critical that we understand and correctly use shared mental model measurement methods assess optimal team performance. Unfortunately, agreement on the proper measurement of shared mental models within healthcare remains diffuse.

Method

This paper presents methods appropriate to measure shared mental models within healthcare.

Results

Multiple shared mental model measurement methods are discussed with regard to their utility within this setting, ease of use, and difficulties in deploying within the healthcare operational environment. For rigorous analysis of shared mental models, it is recommended that a combination of qualitative and quantitative analyses be employed.

Conclusion

There are multitude of shared mental model measurement methods that can be used in the healthcare domain; although there is no perfect solution for every situation. Researchers can utilize this article to determine the best approach for their needs.

A Comparison of Graphical and Textual Presentations of Time Series Data to Support Medical Decision Making in the Neonatal Intensive Care Unit

OBJECTIVE

To compare expert-generated textual summaries of physiological data with trend graphs, in terms of their ability to support neonatal Intensive Care Unit (ICU) staff in making decisions when presented with medical scenarios.

METHODS

Forty neonatal ICU staff were recruited for the experiment, eight from each of five groups--junior, intermediate and senior nurses, junior and senior doctors. The participants were presented with medical scenarios on a computer screen, and asked to choose from a list of 18 possible actions those they thought were appropriate. Half of the scenarios were presented as trend graphs, while the other half were presented as passages of text. The textual summaries had been generated by two human experts and were intended to describe the physiological state of the patient over a short period of time (around 40 minutes) but not to interpret it.

RESULTS

In terms of the content of responses there was a clear advantage for the Text condition, with participants tending to choose more of the appropriate actions when the information was presented as text rather than as graphs. In terms of the speed of response there was no difference between the Graphs and Text conditions. There was no significant difference between the staff groups in terms of speed or content of responses. In contrast to the objective measures of performance, the majority of participants reported a subjective preference for the Graphs condition.

CONCLUSIONS

In this experimental task, participants performed better when presented with a textual summary of the medical scenario than when it was presented as a set of trend graphs. If the necessary algorithms could be developed that would allow computers automatically to generate descriptive summaries of physiological data, this could potentially be a useful feature of decision support tools in the intensive care unit.