A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems.

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts to provide medication decision support. Last, we suggest actionable recommendations for delivering effective clinical decision support using alerts. A review of studies from the medical informatics literature suggests that many basic human factors principles are not followed, possibly contributing to the lack of acceptance of alerts in clinical information systems. We evaluate the limitations of current alerting philosophies and provide recommendations for improving acceptance of alerts by incorporating human factors principles in their design

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Improving Barrier Effectiveness using Human Factors Methods

PresentationThe Process Industry has an established practice of identifying barriers to credit as IPLs (Independent protection layers) through the use of methods such as PHA (Process Hazard Analysis) and LOPA (Layer of Protection Analysis) type studies. However, the validation of IPLs and barriers to ensure their effectiveness especially related to human and organization factors is lagging. The concept of barriers as discrete onion layers comprised of administrative controls, alarms, instruments, mechanical devices, and post-release mitigation is highly idealized. Even worse it is misleading because it blinds us to the reality that all barriers are human. Further, this human base is often made up of small groups of people, comprised of operations, maintenance, and technical staff, with a management layer. The groups of people that maintain and manage all barriers is the most critical factor to ensuring good performance of those barriers in the threat path of a hazard scenario. The methods of PHA and LOPA as currently practiced are not addressing this issue. There is not even awareness of this issue, because the mantra to “ensure independence between protection layers” creates the illusion that barriers can be made independent. The two related issues this paper will address are, (1) the human and organization impact on effectiveness of a single barrier, and (2) the human and organization impact on all barriers in the same threat path. The first issue can be addressed with established human factors and human reliability tools such as Task Analysis, coupled with a public domain human reliability model. The second issue is more complex and requires analyzing the groups of people that cross barrier types and can negatively influence multiple barriers. The methods and concepts will be explained by considering the following barrier types, in a common threat path. The approach described in this paper has been in use for the past two years applied to actual barriers. Critical Alarm with Operator Response Safety Instrumented System Mechanical Pressure Relief Device Demonstrating barrier effectiveness involves both qualitative and quantitative considerations. Demonstrating qualitative effectiveness is done by performing a Task Analysis to identify the degradation factors (human and organization) and degradation factor controls related to the barrier. Demonstrating quantitative effectiveness of the same requires use of a Human Reliability method. Neither of these approaches has been widely adopted in the Process Industry and so there exists a competency gap related to their use. However the need for these tools is evident by the incidents arising in industry due to human and organization factors. Finally, documenting the results on a Bow-tie diagram (the left-hand side) will be demonstrated. Identifying leading process safety indicators embedded in the Bow-tie will be discussed

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

A pre-posterior analysis framework for quantifying the value of seismic monitoring and inspections of buildings

Peer reviewe

Risk-driven behaviour in the African leopard:how is leopard behaviour mediated by lion presence?

Agricultural expansion is restricting many carnivore species to smaller tracts of land, potentially forcing increased levels of overlap between competitors by constraining spatial partitioning. Understanding encounters between competitors is important because competition can influence species densities, distributions, and reproductive success. Despite this, little is known of the mechanisms that mediate coexistence between the African leopard (Panthera pardus) and its competitors. This project used GPS radiocollar data and playback experiments to understand risk-driven changes in the leopard’s behaviour and movement during actual and perceived encounters with lions (Panthera leo). Targeted playbacks of lion roars were used to elucidate immediate and short-lived behavioural responses in leopards when lions were perceived to be within the immediate area. To investigate the post-encounter spatial dynamics of leopard movements, the project used datasets from high-resolution GPS radiocollars deployed on leopards and lions with overlapping territories in the Okavango Delta, Botswana. Leopards were found to adapt behaviours and movements when lions were perceived to be nearby. Specifically, roar playbacks elicited longer periods of vigilance than controls, and movement directions were influenced by speaker locations. Further, leopard movements were quicker and more directional after encountering lions. However, adjustments in behaviour and movement were short-lived. The results provide insights into mechanisms used by the leopard to coexist with its competitors and are a useful case study of the methods that could be used to investigate encounter dynamics within other systems

The Quantified Relationship

The growth of self-tracking and personal surveillance has given rise to the Quantified Self movement. Members of this movement seek to enhance their personal well-being, productivity, and self-actualization through the tracking and gamification of personal data. The technologies that make this possible can also track and gamify aspects of our interpersonal, romantic relationships. Several authors have begun to challenge the ethical and normative implications of this development. In this article, we build upon this work to provide a detailed ethical analysis of the Quantified Relationship. We identify eight core objections to the QR and subject them to critical scrutiny. We argue that although critics raise legitimate concerns, there are ways in which tracking technologies can be used to support and facilitate good relationships. We thus adopt a stance of cautious openness toward this technology and advocate the development of a research agenda for the positive use of QR technologies

Analysis of Heterogeneous Data Sources for Veterinary Syndromic Surveillance to Improve Public Health Response and Aid Decision Making

The standard technique of implementing veterinary syndromic surveillance (VSyS) is the detection of temporal or spatial anomalies in the occurrence of health incidents above a set threshold in an observed population using the Frequentist modelling approach. Most implementation of this technique also requires the removal of historical outbreaks from the datasets to construct baselines. Unfortunately, some challenges exist, such as data scarcity, delayed reporting of health incidents, and variable data availability from sources, which make the VSyS implementation and alarm interpretation difficult, particularly when quantifying surveillance risk with associated uncertainties. This problem indicates that alternate or improved techniques are required to interpret alarms when incorporating uncertainties and previous knowledge of health incidents into the model to inform decision-making. Such methods must be capable of retaining historical outbreaks to assess surveillance risk. In this research work, the Stochastic Quantitative Risk Assessment (SQRA) model was proposed and developed for detecting and quantifying the risk of disease outbreaks with associated uncertainties using the Bayesian probabilistic approach in PyMC3. A systematic and comparative evaluation of the available techniques was used to select the most appropriate method and software packages based on flexibility, efficiency, usability, ability to retain historical outbreaks, and the ease of developing a model in Python. The social media datasets (Twitter) were first applied to infer a possible disease outbreak incident with associated uncertainties. Then, the inferences were subsequently updated using datasets from the clinical and other healthcare sources to reduce uncertainties in the model and validate the outbreak. Therefore, the proposed SQRA model demonstrates an approach that uses the successive refinement of analysis of different data streams to define a changepoint signalling a disease outbreak. The SQRA model was tested and validated to show the method's effectiveness and reliability for differentiating and identifying risk regions with corresponding changepoints to interpret an ongoing disease outbreak incident. This demonstrates that a technique such as the SQRA method obtained through this research may aid in overcoming some of the difficulties identified in VSyS, such as data scarcity, delayed reporting, and variable availability of data from sources, ultimately contributing to science and practice