Klaus Christoffersen

1. How to make automated systems team players

When designing a joint system for a complex, dynamic, open environment, where the consequences of poor performance by the joint system are potentially grave, the need to shape the machine agents into team players is critical. Traditionally, the assumption has been that if a joint system fails to perform adequately, the cause can be traced to so-called “human error.” However, if one digs a little deeper, they find that the only reason many of these joint systems perform adequately at all is because of the resourcefulness and adaptability that the human agents display in the face of uncommunicative and uncooperative machine agents. The ability of a joint system to perform effectively in the face of difficult problems depends intimately on the ability of the human and machine agents to coordinate and capitalize upon the unique abilities and information to which each agent has access.For automated agents to become team players, there are two fundamental characteristics which need to be designed in from the beginning: observability and directability. In other words, users need to be able to see what the automated agents are doing and what they will do next relative to the state of the process, and users need to be able to re-direct machine activities fluently in instances where they recognize a need to intervene. These two basic capabilities are the keys to fostering a cooperative relationship between the human and machine agents in any joint system.

A method for measuring system safety and latent errors associated with pediatric procedural sedation.

The practice of sedating patients in the hospital for diagnostic and therapeutic procedures may be associated with life-threatening respiratory depression. We describe a method that uses a simulated event to identify latent system failures. A simulated scenario was developed that was reproducible with realistic physiology that degraded over time if no interventions occurred and improved when treated appropriately. Management of the scenario was observed in an ideal setting, a radiology department, and an emergency department. Event management was videotaped. The simulator’s physiological data were saved automatically at 5-s intervals. Deviations from “best practice” were measured by using a set of video markers for event detection, diagnosis, and treatment. The simulator data files were used to calculate time out of range for critical variables. Hypoxia and hypotension lasted 4.5 and 5.5 min in the radiology and emergency departments, respectively, compared with 0 min in the gold standard setting. Many latent failures were identified by reviewing the video. This study supports the feasibility of using available human simulation as a crash-test dummy to more objectively quantify rescue system performance in actual sedation care settings. This method revealed vulnerabilities in personnel and in care systems even though sedation care regulatory requirements were met.

A Model of Communication in Support of Distributed Anomaly Response and Replanning

This paper proposes a descriptive model of the contents of communication among distributed practitioners engaged in the cognitive work of anomaly response and replanning. The model was used for a re-analysis of data on coordinative activities in response to an anomaly in a past space shuttle mission, and for a new analysis of log-based communication among flight controllers during normal space station operations. The model was found to be useful for supporting the discovery of patterns in communication among practitioners in complex work domains by providing a “coding scheme” that transforms domain-specific data into domain-independent protocols. The results of these model-based analyses have important implications for the design of computer-supported cooperative work.

Discovering the events expert practitioners extract from dynamic data streams: the modified unit marking technique

One of the cornerstones of expert performance in complex domains is the ability to perceive problem situations in terms of their task-relevant semantic properties. One such class of properties consists of phenomena that are defined in terms of patterns of change over time, i.e., events. A basic pre-requisite for working towards tools to support event recognition is a method for understanding the events that expert practitioners find meaningful in a given field of practice. In this article we present the modified unit marking procedure (mUMP), a technique adapted from work on social perception to facilitate identification of the meaningful phenomena which observers attend to in a dynamic data array. The mUMP and associated data analysis techniques are presented with examples from a first of a kind study where they were used to elicit and understand the events practitioners found meaningful in a scenario from an actual complex work domain.