Stefan C. Christov

Exception Handling Patterns for Process Modeling

Process modeling allows for analysis and improvement of processes that coordinate multiple people and tools working together to carry out a task. Process modeling typically focuses on the normative process, that is, how the collaboration transpires when everything goes as desired. Unfortunately, real-world processes rarely proceed that smoothly. A more complete analysis of a process requires that the process model also include details about what to do when exceptional situations arise. We have found that, in many cases, there are abstract patterns that capture the relationship between exception handling tasks and the normative process. Just as object-oriented design patterns facilitate the development, documentation, and maintenance of object-oriented programs, we believe that process patterns can facilitate the development, documentation, and maintenance of process models. In this paper, we focus on the exception handling patterns that we have observed over many years of process modeling. We describe these patterns using three process modeling notations: UML 2.0 Activity Diagrams, BPMN, and Little-JIL. We present both the abstract structure of the pattern as well as examples of the pattern in use. We also provide some preliminary statistical survey data to support the claim that these patterns are found commonly in actual use and discuss the relative merits of the three notations with respect to their ability to represent these patterns.

Rigorously Defining and Analyzing Medical Processes: An Experience Report

This paper describes our experiences in defining the processes associated with preparing and administrating chemotherapy and then using those process definitions as the basis for analyses aimed at finding and correcting defects. The work is a collaboration between medical professionals from a major regional cancer center and computer science researchers. The work uses the Little-JIL language to create precise process definitions, the Propel system to specify precise process requirements, and the FLAVERS system to verify that the process definitions adhere to the requirement specifications. The paper describes how these technologies were applied to successfully identify defects in the chemotherapy process. Although this work is still ongoing, early experiences suggest that this approach can help reduce medical errors and improve patient safety. The work has also helped us to learn about the desiderata for process definition and analysis technologies, both of which are expected to be broadly applicable to other domains.

Experience modeling and analyzing medical processes: UMass/baystate medical safety project overview

This paper provides an overview of the UMass/Baystate Medical Safety project, which has been developing and evaluating tools and technology for modeling and analyzing medical processes. We describe the tools that currently comprise the Process Improvement Environment, PIE. For each tool, we illustrate the kinds of information that it provides and discuss how that information can be used to improve the modeled process as well as provide useful information that other tools in the environment can leverage. Because the process modeling notation that we use has rigorously defined semantics and supports creating relatively detailed process models (for example, our models can specify alternative ways of dealing with exceptional behavior and concurrency), a number of powerful analysis techniques can be applied. The cost of eliciting and maintaining such a detailed model is amortized over the range of analyses that can be applied to detect errors, vulnerabilities, and inefficiencies in an existing process or in proposed process modifications before they are deployed.

A benchmark for evaluating software engineering techniques for improving medical processes

The software engineering and medical informatics communities have been developing a range of approaches for reasoning about medical processes. To facilitate the comparison of such approaches, it would be desirable to have a set of medical examples, or benchmarks, that are easily available, described in considerable detail, and characterized in terms of the real-world complexities they capture. This paper presents one such benchmark and discusses a list of desiderata that medical benchmarks can be evaluated against.

Exception handling patterns for processes

Using exception handling patterns in process models can raise the abstraction level of the models, facilitating both their writing and understanding. In this paper, we identify several useful, general-purpose exception handling patterns and demonstrate their applicability in business process and software development models.

Formally Defining Medical Processes

OBJECTIVES To demonstrate a technology-based approach to continuously improving the safety of medical processes. METHODS The paper describes the Little-JIL process definition language, originally developed to support software engineering, and shows how it can be used to model medical processes. The paper describes a Little-JIL model of a chemotherapy process and demonstrates how this model, and some process analysis technologies that are also briefly described, can be used to identify process defects that pose safety risks. RESULTS Rigorously modeling medical processes with Little-JIL and applying automated analysis techniques to those models helped identify process defects and vulnerabilities and led to improved processes that were reanalyzed to show that the original defects were no longer present. CONCLUSIONS Creating detailed and precisely defined models of medical processes that are then used as the basis for rigorous analyses can lead to improvements in the safety of these processes.

Considerations for online deviation detection in medical processes

Medical errors are a major cause of unnecessary suffering and even death. To address this problem, we are investigating an approach for automatically detecting when an executing process deviates from a set of recommended ways to perform that process. Such deviations could represent errors and, thus, detecting and reporting deviations as they occur could help catch errors before something bad happens. This paper presents the proposed deviation detection approach, identifies some of the major research issues that arise, and discusses strategies to address these issues. A preliminary evaluation is performed by applying the approach to a part of a detailed process model. This model has been developed in an in-depth case study on modeling and analyzing a blood transfusion process.