Search

Two experiments used a spatial navigation task to study the relationship between individual differences in working memory capacity and interrupted task performance. The results of experiment one show that participants with low working memory capacity (WMC) are more susceptible to the negative effects of interruptions than participants with high WMC. The results of additional analyses indicate that both groups differ in their strategies used to memorize material from the primary task. A second experiment manipulated memory strategy use for high and low memory span participants and found that low span participants performed at the level of high spans when using a strategy that is more typically used by high span participants. However, this performance improvement did not show during interrupted tasks. Overall, these results suggest that individual memory capacity differences affect performance during interrupted tasks by determining selection of memory strategies and by limiting performance of participants. [ABSTRACT FROM AUTHOR]

Objective: The objective was to evaluate a configural vital signs (CVS) display designed to support rapid detection and identification of physiological deterioration by graphically presenting patient vital signs data.Background: Current display technology in the intensive care unit (ICU) is not optimized for fast recognition and identification of physiological changes in patients. To support nurses more effectively, graphical or configural vital signs displays need to be developed and evaluated.Method: A CVS display was developed based on findings from studies of the cognitive work of ICU nurses during patient monitoring. A total of 42 ICU nurses interpreted data presented either in a traditional, numerical format (n = 21) or on the CVS display (n = 21). Response time and accuracy in clinical data interpretation (i.e., identification of patient status) were assessed across four scenarios.Results: Data interpretation speed and accuracy improved significantly in the CVS display condition; for example, in one scenario nurses required only half of the time for data interpretation and showed up to 1.9 times higher accuracy in identifying the patient state compared to the numerical display condition.Conclusion: Providing patient information in a configural display with readily visible trends and data variability can improve the speed and accuracy of data interpretation by ICU nurses.Application: Although many studies, including this one, support the use of configural displays, the vast majority of ICU monitoring displays still present clinical data in numerical format. The introduction of configural displays in clinical monitoring has potential to improve patient safety. [ABSTRACT FROM AUTHOR]

FEATURE AT A GLANCE: Checklists, with their goal to increase adherence to protocols, are gaining popularity in health care despite some serious limitations. Here I present adherence engineering (AE), a conceptual framework that aims to increase adherence to protocols. AE provides guidance for the development of equipment that supports the successful completion of structured tasks. An example of how AE principles might be implemented is shown in the context of a clinical task in health care. Nonadherence to protocol when performing this task can have severe consequences for patient safety. The application of AE has the potential to improve human performance in a wide range of contexts. [ABSTRACT FROM AUTHOR]

Use of current health care equipment for medical procedures (e.g., central line insertions and central line care) is primarily dependent on the cognition of the health care worker. That is, the present design of equipment (typically numerous, separately packaged individual items) provides minimal information about the optimal order of procedure steps and no defenses against human error, such as omitting steps in procedure. In this article, we propose patient safety may be improved by redesigning equipment to integrate a 'checklist' using sequencing, color coding, and visual icons. We hypothesize this reduces cognitive demand by off-loading knowledge into the world, creating affordances that provide guidance reducing the likelihood of errors and promoting adherence to best practices. © 2011Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Checklists and protocols have been successfully introduced into aviation with improved safety as a result of this effort. In recent years there have been attempts to introduce protocols and checklists to health care with the goal of improving patient safety. The present study investigates adherence to protocols in the intensive care unit (ICU) in a number of nursing tasks. The results of this work indicate that adherence to protocols varies dramatically between tasks. This finding suggests that adherence is often a function of task and equipment design. In addition, the context in which protocols are being used also potentially determines their success. Overall, the results indicate that protocols can be introduced in health care, but additional considerations have to be taken into account to ensure their successful application. © 2011Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This paper presents a parallel algorithm for fast word search to determine the set of biological words of an input DNA sequence. The algorithm is designed to scale well on state-of-the-art multiprocessor/multicore systems for large inputs and large maximum word sizes. The pattern exhibited by many sequential solutions to this problem is a repetitive execution over a large input DNA sequence, and the generation of large amounts of output data to store and retrieve the words determined by the algorithm. As we show, this pattern does not lend itself to straightforward standard parallelization techniques. The proposed algorithm aims to achieve three major goals to overcome the drawbacks of embarrassingly parallel solution techniques: (i) to impose a high degree of cache locality on a problem that, by nature, tends to exhibit nonlocal access patterns, (ii) to be lock free or largely reduce the need for data access locking, and (iii) to enable an even distribution of the overall processing load among multiple threads. We present an implementation and performance evaluation of the proposed algorithm on DNA sequences of various sizes for different organisms on a dual processor quad-core system with a total of 8 cores. We compare the performance of the parallel word search implementation with a sequential implementation and with an embarrassingly parallel implementation. The results show that the proposed algorithm far outperforms the embarrassingly parallel strategy and achieves a speed-up’s of up to 6.9 on our 8-core test system. [ABSTRACT FROM AUTHOR]

The adoption of health information technology (HIT) has facilitated efforts to increase the quality and efficiency of health care services and decrease health care overhead while simultaneously generating massive amounts of digital information stored in electronic health records (EHRs). However, due to patient safety issues resulting from the use of HIT systems, there is an emerging need to develop and implement hazard detection tools to identify and mitigate risks to patients. This paper presents a new methodological framework to develop hazard detection models and to demonstrate its capability by using the US Department of Veterans Affairs' (VA) Corporate Data Warehouse, the data repository for the VA's EHR. The overall purpose of the framework is to provide structure for research and communication about research results. One objective is to decrease the communication barriers between interdisciplinary research stakeholders and to provide structure for detecting hazards and risks to patient safety introduced by HIT systems through errors in the collection, transmission, use, and processing of data in the EHR, as well as potential programming or configuration errors in these HIT systems. A nine-stage framework was created, which comprises programs about feature extraction, detector development, and detector optimization, as well as a support environment for evaluating detector models. The framework forms the foundation for developing hazard detection tools and the foundation for adapting methods to particular HIT systems. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the data quality of ventilator settings recorded by respiratory therapists using a computer charting application and assess the impact of incorrect data on computerized ventilator management protocols. DESIGN An analysis of 29,054 charting events gathered over 12 months from 678 ventilated patients (1,736 ventilator days) in four intensive care units at a tertiary care hospital.Measurements: Ten ventilator settings were examined, including fraction of inspired oxygen (Fio (2)), positive end-expiratory pressure (PEEP), tidal volume, respiratory rate, peak inspiratory flow, and pressure support. Respiratory therapists entered values for each setting approximately every two hours using a computer charting application. Manually entered values were compared with data acquired automatically from ventilators using an implementation of the ISO/IEEE 11073 Medical Information Bus (MIB). Data quality was assessed by measuring the percentage of time that the two sources matched. Charting delay, defined as the interval between data observation and data entry, also was measured.Results: The percentage of time that settings matched ranged from 99.0% (PEEP) to 75.9% (low tidal volume alarm setting). The average charting delay for each charting event was 6.1 minutes, including an average of 1.8 minutes spent entering data in the charting application. In 559 (3.9%) of 14,263 suggestions generated by computerized ventilator management protocols, one or more manually charted setting values did not match the MIB data.Conclusion: Even at institutions where manual charting of ventilator settings is performed well, automatic data collection can eliminate delays, improve charting efficiency, and reduce errors caused by incorrect data. [ABSTRACT FROM AUTHOR]

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue's impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners' understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide "lagging indicators" of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming. [ABSTRACT FROM AUTHOR]

Purpose: Communicating about COVID-19 vaccine side effects and efficacy is crucial for promoting transparency and informed decision-making, but there is limited evidence on how to do so effectively. Design: A within-subjects experiment. Setting: Online survey from January 21 to February 6, 2021. Subjects: 596 US Veterans and 447 non-Veterans. Intervention: 5 messages about COVID-19 vaccine side effects and 4 messages about COVID-19 vaccine efficacy. Measures: COVID-19 vaccine interest (1 = "I definitely do NOT want the vaccine" to 7 = "I definitely WANT the vaccine" with the midpoint 4 = "Unsure"). Confidence about COVID-19 vaccine efficacy (1= "Not at all confident," 2 = "Slightly confident," 3 = "Somewhat confident," 4 = "Moderately confident," 5 = "Extremely confident"). Results: Compared to providing information about side effects alone (M = 5.62 [1.87]), messages with additional information on the benefits of vaccination (M = 5.77 [1.82], P <.001, d z =.25), reframing the likelihood of side effects (M = 5.74 [1.84], P <.001, d z =.23), and emphasizing that post-vaccine symptoms indicate the vaccine is working (M = 5.72 [1.84], P <.001, d z =.17) increased vaccine interest. Compared to a vaccine efficacy message containing verbal uncertainty and an efficacy range (M = 3.97 [1.25]), messages conveying verbal certainty with an efficacy range (M = 4.00 [1.24], P =.042, d z=.08), verbal uncertainty focused on the upper efficacy limit (M = 4.03 [1.26], P <.001, d z =.13), and communicating the point estimate with certainty (M = 4.02 [1.25], P <.001, d z =.11) increased confidence. Overall, Veteran respondents were more interested (M Veterans = 5.87 [1.72] vs M NonVeterans = 5.45 [2.00], P <.001, d =.22) and confident (M Veterans = 4.13 [1.19] vs M NonVeterans = 3.84 [1.32], P <.001, d =.23) about COVID-19 vaccines than non-Veterans. Conclusions: These strategies can be implemented in large-scale communications (e.g., webpages, social media, and leaflets/posters) and can help guide healthcare professionals when discussing vaccinations in clinics to promote interest and confidence in COVID-19 vaccines. [ABSTRACT FROM AUTHOR]