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.

Over the past decade, health information technology (IT) has enabled the amount of digital information stored in electronic health records (EHRs) to expand greatly. However, according to some studies, hazards in health IT can lead to changes in clinical decisions, care processes, and care outcomes, as well as other issues. Thus, the effects of health IT hazards on patient safety have been at the forefront of recent patient safety research. Nonetheless, hazard detection in health IT remains a challenge. In this paper, the authors assume that safety-related issues in health IT would exhibit anomalous characteristics in EHR data. Although all hazards will exhibit some anomalous characteristics, not all anomalies can be regarded as hazards. The authors hypothesize that errors in health IT could lead to interruptions in the sequence of clinical actions. To this end, the problem of detecting anomalous sequences in big EHR data is considered. This paper focuses on dynamic event sequences, which are a series of clinical actions in motion. The authors propose an adaptive anomaly detection approach that uses higher-order network representation to detect anomalous sequences. Furthermore, the authors propose a contiguous subsequence anomaly detection approach that identifies abnormal subsequences in the detected anomalous sequences. The proposed approaches are tested by using synthetic and real-world EHR data. The proposed methods outperform existing state of the art anomaly detection techniques. To reduce the computational complexity associated with the operational implementation of the proposed approaches, the Apache Spark environment was leveraged, and a much shorter run time together with improved performance were achieved, especially for data with more than 60,000 sequences.

MOTIVATION: De novo motif discovery algorithms find statistically over-represented sequence motifs that may function as transcription factor binding sites. Current methods often report large numbers of motifs, making it difficult to perform further analyses and experimental validation. The motif selection problem seeks to identify a minimal set of putative regulatory motifs that characterize sequences of interest (e.g. ChIP-Seq binding regions). RESULTS: In this study, the motif selection problem is mapped to variants of the set cover problem that are solved via tabu search and by relaxed integer linear programing (RILP). The algorithms are employed to analyze 349 ChIP-Seq experiments from the ENCODE project, yielding a small number of high-quality motifs that represent putative binding sites of primary factors and cofactors. Specifically, when compared with the motifs reported by Kheradpour and Kellis, the set cover-based algorithms produced motif sets covering 35% more peaks for 11 TFs and identified 4 more putative cofactors for 6 TFs. Moreover, a systematic evaluation using nested cross-validation revealed that the RILP algorithm selected fewer motifs and was able to cover 6% more peaks and 3% fewer background regions, which reduced the error rate by 7%. AVAILABILITY AND IMPLEMENTATION: The source code of the algorithms and all the datasets are available at https://github.com/YichaoOU/Set_cover_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent.

BACKGROUND: An important focus of genomic science is the discovery and characterization of all functional elements within genomes. In silico methods are used in genome studies to discover putative regulatory genomic elements (called words or motifs). Although a number of methods have been developed for motif discovery, most of them lack the scalability needed to analyze large genomic data sets. METHODS: This manuscript presents WordSeeker, an enumerative motif discovery toolkit that utilizes multi-core and distributed computational platforms to enable scalable analysis of genomic data. A controller task coordinates activities of worker nodes, each of which (1) enumerates a subset of the DNA word space and (2) scores words with a distributed Markov chain model. RESULTS: A comprehensive suite of performance tests was conducted to demonstrate the performance, speedup and efficiency of WordSeeker. The scalability of the toolkit enabled the analysis of the entire genome of Arabidopsis thaliana; the results of the analysis were integrated into The Arabidopsis Gene Regulatory Information Server (AGRIS). A public version of WordSeeker was deployed on the Glenn cluster at the Ohio Supercomputer Center. CONCLUSION: WordSeeker effectively utilizes concurrent computing platforms to enable the identification of putative functional elements in genomic data sets. This capability facilitates the analysis of the large quantity of sequenced genomic data.

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.

OBJECTIVE: Computerized Provider Order Entry (CPOE) with electronic documentation, and computerized decision support dramatically changes the information environment of the practicing clinician. Prior work patterns based on paper, verbal exchange, and manual methods are replaced with automated, computerized, and potentially less flexible systems. The objective of this study is to explore the information management strategies that clinicians use in the process of adapting to a CPOE system using cognitive task analysis techniques. DESIGN: Observation and semi-structured interviews were conducted with 88 primary-care clinicians at 10 Veterans Administration Medical Centers. MEASUREMENTS: Interviews were taped, transcribed, and extensively analyzed to identify key information management goals, strategies, and tasks. Tasks were aggregated into groups, common components across tasks were clarified, and underlying goals and strategies identified. RESULTS: Nearly half of the identified tasks were not fully supported by the available technology. Six core components of tasks were identified. Four meta-cognitive information management goals emerged: 1) Relevance Screening; 2) Ensuring Accuracy; 3) Minimizing memory load; and 4) Negotiating Responsibility. Strategies used to support these goals are presented. CONCLUSION: Users develop a wide array of information management strategies that allow them to successfully adapt to new technology. Supporting the ability of users to develop adaptive strategies to support meta-cognitive goals is a key component of a successful system.

Dynamic real-time systems require adaptive resource management to accommodate varying processing needs. We address the problem of resource management with multiple shared resources for soft real-time systems consisting of tasks that have discrete QoS settings that correspond to varying resource usage and varying utility. Given an amount of available resource, the problem is to provide on-line control of the tasks' QoS settings so as to optimize the overall system utility. We propose several heuristic algorithms that will be shown to be compatible with the requirements imposed by our control theoretical resource management framework: (1) By only making incremental adjustments to QoS settings as available resources change, they provide low run-time complexity, making them suitable for use in on-line resource managers. (2) Differences between actual utility and optimal utility do not accumulate over time, so there is no long-term degradation in performance. (3) The lower and upper bound on actual utility can be calculated dynamically based on current system conditions, and absolute bounds can be calculated statically in advance. (4) It is possible to respond to the actual resource possible, allowing all resources to be used and tolerating misspecification of task resource requirements

OBJECTIVE: Authors developed a picture-graphics display for pulmonary function to present typical respiratory data used in perioperative and intensive care environments. The display utilizes color, shape and emergent alerting to highlight abnormal pulmonary physiology. The display serves as an adjunct to traditional operating room displays and monitors. DESIGN: To evaluate the prototype, nineteen clinician volunteers each managed four adverse respiratory events and one normal event using a high-resolution patient simulator which included the new displays (intervention subjects) and traditional displays (control subjects). Between-group comparisons included (i) time to diagnosis and treatment for each adverse respiratory event; (ii) the number of unnecessary treatments during the normal scenario; and (iii) self-reported workload estimates while managing study events. MEASUREMENTS: Two expert anesthesiologists reviewed video-taped transcriptions of the volunteers to determine time to treat and time to diagnosis. Time values were then compared between groups using a Mann-Whitney-U Test. Estimated workload for both groups was assessed using the NASA-TLX and compared between groups using an ANOVA. P-values < 0.05 were considered significant. RESULTS: Clinician volunteers detected and treated obstructed endotracheal tubes and intrinsic PEEP problems faster with graphical rather than conventional displays (p < 0.05). During the normal scenario simulation, 3 clinicians using the graphical display, and 5 clinicians using the conventional display gave unnecessary treatments. Clinician-volunteers reported significantly lower subjective workloads using the graphical display for the obstructed endotracheal tube scenario (p < 0.001) and the intrinsic PEEP scenario (p < 0.03). CONCLUSION: Authors conclude that the graphical pulmonary display may serve as a useful adjunct to traditional displays in identifying adverse respiratory events.

Some distributed real-time systems interact with external environments that change dynamically, and it is necessary to take the external variables into account when performing task allocation. We developed an approximation algorithm for task allocation, and it finds allocations that are maximally robust against dynamic changes in multiple external variables. Such an algorithm will help to reduce expensive reallocations triggered by changes in unpredictable environments. The algorithm has a polynomial running time, and its robustness optimality is given by an approximation ratio, which equals 2.41 asymptotically, when workloads are large and workload independent utilization of tasks is insignificant.

Summary form only given. We propose architecture and a general optimization framework for dynamic, distributed real-time systems. Interesting features of this model include the consideration of adaptive applications and utility functions. We extend by formalizing the corresponding multicriterial optimization problem. As the most difficult part of this problem, we identified the evaluation and comparison of the quality of single allocations and sets of allocations, respectively. To this end, we propose and examine metrics for measuring the goodness of solutions within our general resource management framework. These metrics lay the basis for further work on developing both online and offline algorithms to tackle the general optimization problem and provide an efficient adaptive resource manager for dynamic, distributed real-time systems.

Summary form only given. We examine several heuristic algorithms for the maximum allowable workload (MAW) problem for real-time systems with tasks having variable workloads. Briefly, the problem concerns the allocation of tasks to m processors, where each task t is characterized by a function t.r(w) that gives the running time of the task in terms of its workload (or input size) w. The objective of the maximum allowable workload problem is to find an allocation of tasks to processors so that the allocation is feasible (no task misses its deadline) when each task is given a workload of w or smaller and w is maximized. This optimization problem uses a robustness measure that is closely related to the MAIL (maximum allowable increase in load) metric recently proposed by Gertphol et al. The main contribution of this paper is the comparison of several heuristic algorithms for the MAW-RMS problem. Hillclimbing, random search, simulated annealing, and first-fit heuristics are presented and evaluated via simulation. As we show here, the first-fit greedy heuristic produces solutions of a reasonable quality compared to the other algorithms. In addition, we demonstrate the applicability of our model in air defense systems.

A new method was developed to increase the saliency of changing variables in a cardiovascular visualization for use by anesthesiologists in the operating room (OR). Clinically meaningful changes in patient physiology were identified and then mapped to the inherent psychophysical properties of the visualization. A long history of psychophysical research has provided an understanding of the parameters within which the human information processing system is able to detect changes in the size, shape and color of visual objects (Gescheider, 1976, Spence, 1990, and Baird, 1970). These detection thresholds are known as just noticeable differences (JNDs) which characterize the amount of change in an object's attribute that is recognizable 50% of the time. A prototype version of the display has been demonstrated to facilitate anesthesiologist's performance while reducing cognitive workload during simulated cardiac events (Agutter et al., 2002). In order to further improve the utility of the new cardiovascular visualization, the clinically relevant changes in cardiovascular variables are mapped to noticeable perceptual changes in the representational elements of the display. The results of the method described in this paper are used to merge information from the psychophysical properties of the cardiovascular visualization, with clinically relevant changes in the patient's cardiovascular physiology as measured by the clinical meaningfulness questionnaire. The result of this combination will create a visualization that is sensitive to changes in the cardiovascular health of the patient and communicates this information to the user in a meaningful, salient and intuitive manner.

OBJECTIVE: Data representations on today's medical monitors need to be improved to advance clinical awareness and prevent data vigilance errors. Simply building graphical displays does not ensure an improvement in clinical performance because displays have to be consistent with the user's clinical processes and mental models. In this report, the development of an original pulmonary graphical display for anesthesia is used as an example to show an iterative design process with built-in usability testing. DESIGN: The process reported here is rapid, inexpensive, and requires a minimal number of subjects per development cycle. Three paper-based tests evaluated the anatomic, variable mapping, and graphical diagnostic meaning of the pulmonary display. MEASUREMENTS: A confusion matrix compared the designer's intended answer with the subject's chosen answer. Considering deviations off the diagonal of the confusion matrix as design weaknesses, the pulmonary display was modified and retested. The iterative cycle continued until the anatomic and variable mapping cumulative test scores for a chosen design scored above 90% and the graphical diagnostic meaning test scored above 75%. RESULTS: The iterative development test resulted in five design iterations. The final graphical pulmonary display improved the overall intuitiveness by 18%. The display was tested in three categories: anatomic features, variable mapping, and diagnostic accuracy. The anatomic intuitiveness increased by 25%, variable mapping intuitiveness increased by 34%, and diagnostic accuracy decreased slightly by 4%. CONCLUSION: With this rapid iterative development process, an intuitive graphical display can be developed inexpensively prior to formal testing in an experimental setting.

The anesthesiologist is faced with a fire-hose of information in the operating room. Data from the patient monitors include numerical data, waveforms, control settings, and alarm conditions. During an unexpected event, the anesthesiologist must quickly assess available information in order to diagnose and treat the patient before the patient is injured. However, human error is associated with more than 80% of critical anesthesia incidents and more than 50% of anesthetic deaths.1 In a recent study at the University of Washington, Department of Anesthesiology, 32% of the reported human error incidents were related …

Systems for the archival and retrieval of images are used in many areas, for example medical applications, news agencies, etc. The state-of-the-art approach for image description considers a priori extracted features. The disadvantageous reduction of the image content onto a few low-level features limits the applicability of image databases. A search for objects and other important image components requires dynamic feature extraction. The related computational and storage requirements exceed the possibilities of computer architectures with a single processing element. Therefore we developed a cluster platform, which supports the implementation of this novel retrieval approach in existing systems. We introduce the basic principles of image retrieval with dynamic feature extraction and a cluster platform. The main focus regards thereby the workload balancing across the cluster. For this purpose we developed a scheduling heuristic and executed performance measurements with the implemented prototype. The obtained results are discussed.