Fatal Error: How Patriot Overlooked a Scud

Timing errors and temporal uncertainty in clinical databases-A narrative review

A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

Dynamic Analysis of Software Systems with Aperiodic Impulse Rejuvenation

This paper aims to obtain the dynamical solution and instantaneous availability of software systems with aperiodic impulse rejuvenation. Firstly, we formulate the generic system with a group of coupled impulsive differential equations and transform it into an abstract Cauchy problem. Then we adopt a difference scheme and establish the convergence of this scheme by applying the Trotter–Kato theorem to obtain the system’s dynamical solution. Moreover, the instantaneous availability as an important evaluation index for software systems is derived, and its range is also estimated. At last, numerical examples are shown to illustrate the validity of theoretical results.

Writing software for the clinic

Medical physicists often write computer programs to support scientific, educational, and clinical endeavors. Errors in scientific and educational software can waste time and effort by producing meaningless results, but errors in clinical software can contribute to patient injuries. Although the ultimate goal of error-free software is impossible to achieve except in very small programs, there are many good design, implementation, and testing practices that can be used by small development groups to significantly reduce errors, improve quality, and reduce maintenance. The software development process should include four basic steps: specifications, design, implementation, and testing. A specifications document defining what the software is intended to do is valuable for clearly delimiting the scope of the project and providing a benchmark for evaluating the final product. Keep the software design simple and straightforward. Document assumptions, and check them. Emphasize maintainability, portability, and reliability rather than speed. Use layers to isolate the application from hardware and the operating system. Plan for upgrades. Expect the software to be used in unplanned ways. Whenever possible, be generous with RAM and disk storage; hardware is cheaper than development and maintenance. During implementation, use well-known algorithms whenever possible. Use prototypes to try out ideas. Use generic modules, version numbering, unique file names, defensive programming, and operating system and language/compiler defaults. Avoid binary data files and clever tricks. Remember that real numbers are not exact in a computer. Get it right before making it faster. Document the software extensively. Test continuously during development; the later a problem is found, the more it costs to fix. Use a written procedure to test the final product exactly as a typical user would run it. Allow no changes after clinical release. Expect to spend at least an additional 50% of the initial development effort on testing, fixing errors, and getting the software into routine operation.