Applying hierarchical task analysis to medication administration errors

Medication use in hospitals is a complex process and is dependent on the successful interaction of health professionals functioning within different disciplines. Errors can occur at any one of the five main stages of prescribing, documenting, dispensing or preparation, administering and monitoring. The responsibility for the error is often placed on the nurse, as she or he is the last person in the drug administration chain whilst more pressing underlying causal factors remain unresolved. This paper demonstrates how hierarchical task analysis can be used to model drug administration and then uses the systematic human error reduction and prediction approach to predict which errors are likely to occur. The paper also puts forward design solutions to mitigate these errors

Predicting pilot error on the flight deck: Validation of a new methodology and a multiple methods and analysts approach to enhancing error prediction sensitivity

The Human Error Template (HET) is a recently developed methodology for predicting designed induced pilot error. This article describes a validation study undertaken to compare the performance of HET against three contemporary Human Error Identification (HEI) approaches when used to predict pilot errors for an approach and landing task and also to compare individual analyst error predictions to an approach to enhancing error prediction sensitivity: the multiple analysts and methods approach, whereby multiple analyst predictions using a range of HEI technique are pooled. The findings indicate that, of the four methodologies used in isolation, analysts using the HET methodology offered the most accurate error predictions, and also that the multiple analysts and methods approach was more successful overall in terms of error prediction sensitivity than the three other methods but not the HET approach. The results suggest that when predicting design induced error, it is appropriate to use domain specific approaches and also a toolkit of different HEI approaches and multiple analysts in order to heighten error prediction sensitivity

Deliberate ignorance in project risk management

The management of project risk is considered a key discipline by most organisations involved in projects. Best practice project risk management processes are claimed to be self-evidently correct. However, project risk management involves a choice between which information is utilized and which is deemed to be irrelevant and hence excluded. Little research has been carried out to ascertain the manifestation of barriers to optimal project risk management such as 'irrelevance'; the deliberate inattention of risk actors to risk. This paper presents the results of a qualitative study of IT project managers, investigating their reasons for deeming certain known risks to be irrelevant. The results both confirm and expand on Smithson's [Smithson, M., 1989. Ignorance and Uncertainty. Springer-Verlag, New York] taxonomy of ignorance and uncertainty and in particular offer further context related insights into the phenomenon of 'irrelevance' in project risk management. We suggest that coping with 'irrelevance' requires defence mechanisms, the effective management of relevance as well as the setting of, and sticking to priorities. (C) 2009 Elsevier Ltd and IPMA. All rights reserved

A novel qualitative prospective methodology to assess human error during accident sequences

Numerous theoretical models and techniques to assess human error were developed since the 60's. Most of these models were developed for the nuclear, military, and aviation sectors. These methods have the following weaknesses that limit their use in industry: the lack of analysis of underlying causal cognitive mechanisms, need of retrospective data for implementation, strong dependence on expert judgment, focus on a particular type of error, and/or analysis of operator behaviour and decision-making without considering the role of the system in such decisions. The purpose of the present research is to develop a qualitative prospective methodology that does not depend exclusively on retrospective information, that does not require expert judgment for implementation and that allows predicting potential sequences of accidents before they occur. It has been proposed for new (or existent) small and medium- scale facilities, whose processes are simple. To the best of our knowledge, a methodology that meets these requirements has not been reported in literature thus far. The methodology proposed in this study was applied to the methanol storage area of a biodiesel facility. It could predict potential sequences of accidents, through the analysis of information provided by different system devices and the study of the possible deviations of operators in decision-making. It also enabled the identification of the shortcomings in the human-machine interface and proposed an optimization of the current configuration.Fil: Calvo Olivares, Romina Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Rivera, Selva Soledad. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Núñez Mc Leod, Jorge Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentin

System hazards in managing laboratory test requests and results in primary care: medical protection database analysis and conceptual model

Objectives To analyse a medical protection organisation's database to identify hazards related to general practice systems for ordering laboratory tests, managing test results and communicating test result outcomes to patients. To integrate these data with other published evidence sources to inform design of a systems-based conceptual model of related hazards. Design A retrospective database analysis. Setting General practices in the UK and Ireland. Participants 778 UK and Ireland general practices participating in a medical protection organisation's clinical risk self-assessment (CRSA) programme from January 2008 to December 2014. Main outcome measures Proportion of practices with system risks; categorisation of identified hazards; most frequently occurring hazards; development of a conceptual model of hazards; and potential impacts on health, well-being and organisational performance. Results CRSA visits were undertaken to 778 UK and Ireland general practices of which a range of systems hazards were recorded across the laboratory test ordering and results management systems in 647 practices (83.2%). A total of 45 discrete hazard categories were identified with a mean of 3.6 per practice (SD=1.94). The most frequently occurring hazard was the inadequate process for matching test requests and results received (n=350, 54.1%). Of the 1604 instances where hazards were recorded, the most frequent was at the ‘postanalytical test stage’ (n=702, 43.8%), followed closely by ‘communication outcomes issues’ (n=628, 39.1%). Conclusions Based on arguably the largest data set currently available on the subject matter, our study findings shed new light on the scale and nature of hazards related to test results handling systems, which can inform future efforts to research and improve the design and reliability of these systems

Use of COTS functional analysis software as an IVHM design tool for detection and isolation of UAV fuel system faults

This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADe™ (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis

Development of an ontology for aerospace engine components degradation in service

This paper presents the development of an ontology for component service degradation. In this paper, degradation mechanisms in gas turbine metallic components are used for a case study to explain how a taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse processes relative to various failure events. The initial evaluation reveals improvement in the performance of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The information extracted and collected is required to improve service k nowledge and performance feedback which are important for service engineers. Existing research areas such as natural language processing, knowledge management, and information extraction were also examined