Heather M. Conboy

Process-based derivation of requirements for medical devices

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.

Smart checklists to improve healthcare outcomes

This paper presents an approach for automatically generating Smart Checklists--context-dependent, dynamically updated views of on-going 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.

Cognitive support during high-consequence episodes of care in cardiovascular surgery

Despite significant efforts to reduce preventable adverse events in medical processes, such events continue to occur at unacceptable rates. This paper describes a computer science approach that uses formal process modeling to provide situationally aware monitoring and management support to medical professionals performing complex processes. These process models represent both normative and non-normative situations, and are validated by rigorous automated techniques such as model checking and fault tree analysis, in addition to careful review by experts. Context-aware Smart Checklists are then generated from the models, providing cognitive support during high-consequence surgical episodes. The approach is illustrated with a case study in cardiovascular surgery.

Modal abstraction view of requirements for medical devices used in healthcare processes

Medical device requirements often depend on the healthcare processes in which the device is to be used. Since such processes may be complex, critical requirements may be specified inaccurately, or even missed altogether. We are investigating an automated requirement derivation approach that takes as input a model of the healthcare process along with a model of the device and tries to derive the requirements for that device. Our initial experience with this approach has shown that when the process and device involve complex behaviors, the derived requirements are also often complex and difficult to understand. In this paper, we describe an approach for creating a modal abstraction view of the derived requirements that decomposes each requirement based on its modes, and thus appears to improve understandability.

Intelligent Interruption Management System to Enhance Safety and Performance in Complex Surgical and Robotic Procedures

Procedural flow disruptions secondary to interruptions play a key role in error occurrence during complex medical procedures, mainly because they increase mental workload among team members, negatively impacting team performance and patient safety. Since certain types of interruptions are unavoidable, and consequently the need for multitasking is inherent to complex procedural care, this field can benefit from an intelligent system capable of identifying in which moment flow interference is appropriate without generating disruptions. In the present study we describe a novel approach for the identification of tasks imposing low cognitive load and tasks that demand high cognitive effort during real-life cardiac surgeries. We used heart rate variability analysis as an objective measure of cognitive load, capturing data in a real-time and unobtrusive manner from multiple team members (surgeon, anesthesiologist and perfusionist) simultaneously. 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 identification of specific steps, substeps and tasks that impose low cognitive load. An interruption management system can use these low demand situations to guide the surgical team in terms of the appropriateness of flow interruptions. The described approach also enables us to detect cognitive load fluctuations over time, under specific conditions (e.g. emergencies) or in situations that are prone to errors. An in-depth understanding of the relationship between cognitive overload states, task demands, and error occurrence will drive the development of cognitive supporting systems that recognize and mitigate errors efficiently and proactively during high complex procedures.

Toward improving surgical outcomes by incorporating cognitive load measurement into process-driven guidance

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.

Iterative Analysis to Improve Key Properties of Critical Human-Intensive Processes: An Election Security Example

In this article, we present an approach for systematically improving complex processes, especially those involving human agents, hardware devices, and software systems. We illustrate the utility of this approach by applying it to part of an election process and show how it can improve the security and correctness of that subprocess. We use the Little-JIL process definition language to create a precise and detailed definition of the process. Given this process definition, we use two forms of automated analysis to explore whether specified key properties, such as security and safety policies, can be undermined. First, we use model checking to identify process execution sequences that fail to conform to event-sequence properties. After these are addressed, we apply fault tree analysis to identify when the misperformance of steps might allow undesirable outcomes, such as security breaches. The results of these analyses can provide assurance about the process; suggest areas for improvement; and, when applied to a modified process definition, evaluate proposed changes.