Abstract-The insider threat is one of the most pernicious in computer security. Traditional approaches typically instrument systems with decoys or intrusion detection mechanisms to detect individuals who abuse their privileges (the quintessential "insider"). Such an attack requires that these agents have access to resources or data in order to corrupt or disclose them. In this work, we examine the application of process modeling and subsequent analyses to the insider problem. With process modeling, we first describe how a process works in formal terms. We then look at the agents who are carrying out particular tasks, perform different analyses to determine how the process can be compromised, and suggest countermeasures that can be incorporated into the process model to improve its resistance to insider attack.

This paper presents an approach for automatically generating Smart Checklists-context-dependent, dynamically updated views of ongoing medical processes based on current activities and previously validated process models of best practices. This approach addresses not only nominal scenarios but includes guidance when exceptional situations arise. The framework for creating these checklists is described, along with an example and discussion of issues.

This paper summarizes the accomplishments and recent directions of our medical safety project. Our process-based approach uses a detailed, rigorously-defined, and carefully validated process model to provide a dynamically updated, context-aware and thus, “Smart” Checklist to help process performers understand and manage their pending tasks [7]. This paper focuses on support for teams of performers, working independently as well as in close collaboration, in stressful situations that are life critical. Our recent work has three main thrusts: provide effective real-time guidance for closely collaborating teams; develop and evaluate techniques for measuring cognitive load based on biometric observations and human surveys; and, using these measurements plus analysis and discrete event process simulation, predict cognitive load throughout the process model and propose process modifications to help performers better manage high cognitive load situations. This project is a collaboration among software engineers, surgical team members, human factors researchers, and medical equipment instrumentation experts. Experimental prototype capabilities are being built and evaluated based upon process models of two cardiovascular surgery processes, Aortic Valve Replacement (AVR) and Coronary Artery Bypass Grafting (CABG). In this paper we describe our approach for each of the three research thrusts by illustrating our work for heparinization, a common subprocess of both AVR and CABG. Heparinization is a high-risk error-prone procedure that involves complex team interactions and thus highlights the importance of this work for improving patient outcomes.

One goal of medical device certification is to show that a given medical device satisfies its requirements. The requirements that should be met by a device, however, depend on the medical processes in which the device is to be used. Such processes may be complex and, thus, critical requirements may be specified inaccurately or incompletely, or even missed altogether. We are investigating a requirement derivation approach that takes as input a model of the way the device is used in a particular medical process and a requirement that should be satisfied by that process. This approach tries to produce a derived requirement for the medical device that is sufficient to prevent any violations of the process requirement. Our approach combines a method for generating assumptions for assume-guarantee reasoning with one for interface synthesis to automate the derivation of the medical device requirements. The proposed approach performs the requirement derivation iteratively by employing a model checker and a learning algorithm. We implemented this approach and evaluated it by applying it to two small case studies. Our experiences showed that the proposed approach could be successfully applied to abstract models of portions of real-world medical processes and that the derived requirements of the medical devices appeared useful and understandable.

In the surgical setting, team members constantly deal with a high-demand operative environment that requires simultaneously processing a large amount of information. In certain situations, high demands imposed by surgical tasks and other sources may exceed team member’s cognitive capacity, leading to cognitive overload which may place patient safety at risk. In the present study, we describe a novel approach to integrate an objective measure of team member’s cognitive load with procedural, behavioral and contextual data from real-life cardiac surgeries. We used heart rate variability analysis, capturing data simultaneously from multiple team members (surgeon, anesthesiologist and perfusionist) in a real-time and unobtrusive manner. Using audio-video recordings, behavioral coding and a hierarchical surgical process model, we integrated multiple data sources to create an interactive surgical dashboard, enabling the analysis of the cognitive load imposed by specific steps, substeps and/or tasks. The described approach enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies, teaching) and in situations that are prone to errors. This in-depth understanding of the relationship between cognitive load, task demands and error occurrence is essential for the development of cognitive support systems to recognize and mitigate errors during complex surgical care in the operating room.