A simulation model for evaluating national patient record networks in South Africa.

Includes abstract.Includes bibliographical references.This study has shown that modelling and simulation is a feasible approach for evaluating NPR solutions in the developing context. The model can represent different network models, patient types and performance metrics to aid in the evaluation of NPR solutions. Using the current model, more case studies can be investigated for various public health issues - such as the impact of disease or regional services planning

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

MS

thesisHealth information systems are networks of computers employed by health care enterprises to facilitate the delivery of their health care product. Computers originally entered the medical domain solely as tools aimed at the business functions of the hospital. Having demonstrated their utility in this area, computers were perceived by certain innovators to have usefulness in the clinical domain. As clinical computer applications were successfully developed and implemented, they have over time been merged together into systems offering multiple areas of functionality directly impacting the clinical aspects of health care delivery. Such health information systems have now assumed major importance in the provision of health care in a complex medical environment. Although the focus of substantial investment for development and implementation, relatively little work has been done to assess the value of such health information systems. The business information technology literature and the medical informatics literature each include only a small number of published reports examining the value question in an incomplete manner. No generally accepted valuation strategy has been developed for information systems in either the business or health care domains. Several valuation methods with potential applicability to health information systems have evolved: cost-effectiveness / cost- benefit analysis, return on investment, information economics, measurement systems, the Strassmann approach, the Japanese approach, and the strategic value approach. None of these valuation strategies is clearly superior; each has different strengths and weaknesses. A matrix comparing these strategies on the bases of explicitness and ease of implementation is proposed. Intermountain Health Care (IHC) has been instrumental in the development of health information systems and a leader in the application of such technology in clinical health care delivery. IHC's HELP system has played a seminal role as a catalyst to the development of the health information system industry. Although both historically and functionally important, detailed financial information regarding HELP'S origins and implementation no longer exists. Current IHC budget information demonstrates the major financial commitment underway within this health care enterprise totaling approximately $157 million over the last decade and with additional expenditures of$47 to $61 million projected annually through fiscal year 2004. The complex budgetary relationships between HELP and the other health information systems at LDS Hospital further obscure the magnitude of the information technology investment within this institution. Benefits of health information systems are potentially most substantial within the domain of clinical integration. IHC has not implemented any formal valuation strategy for its health information systems, but the ad hoc measurement systems valuation approach applied to date is practical, flexible, and the most appropriate of the available systems. Adequate valuation of health information systems cannot readily be achieved given the existing traditional hierarchical accounting structure; an alternative accounting framework patterned after a relational database is proposed

Design methodology and simulation of a fleet management system for an advanced helicopter platform

This research is part of a major helicopter acquisition and upgrade program of the Australian Defence Forces (ADF) under a 20+ year strategic plan. The ‘Air 9000’ program aims to rationalise the number of helicopter types operated, simplify operational requirements and reduce through-life-support costs. This research program developed and modelled a Fleet Management System (FMS) for the newly acquired Multi-Role Helicopter-90 (MRH-90 / NHI NH-90) platform. It assessed current practices in aerospace technology management of civil and military aircraft fleets, and established requirements of civil & military rotary-wing platforms for the development of a fleet management methodology for the MRH-90 platform. A novel approach was adopted by applying systems engineering principles to design the FMS. The systems engineering approach enabled identification and implementation of the additional rotary-wing design parameters, required for system adaptability to future network-centric military & civil operational environments from a life-cycle perspective. This approach has resulted in the development and implementation of an adaptable prototype FMS software with integrated fleet management capabilities. Subsequent simulation & validation demonstrated significant enhancements in operational effectiveness over state-of-the art rotary-wing fleet management practices, by holistically and systematically addressing the present and future system needs of helicopter life-cycle management

A tailorable framework of practices for maintenance delivery

Scope – This research focused on the development of a tailorable framework of practices for Maintenance Delivery (MD): i.e. a flexible business process design tool which was developed in order to resolve a series of specific gaps identified in the sponsor’s Asset Management System (AMS). Methodology – The framework was developed in two stages: firstly via a systematic review of existing MD practices from the literature in order to establish a preliminary version; this was then developed further via a Delphi study utilising the opinion of experts from industry to critique and improve the initial framework design. Key Findings – The framework was implemented and tested in the sponsor company in order to demonstrate its ability to successfully improve MD practices across multiple sites in different industry contexts. A post-implementation assessment demonstrated significant improvement, sufficient to close all of the high-risk gaps that were originally identified. Contribution to Industry – The framework covers the entire subject area of MD in detail and offers a wide range of optional practices throughout, complete with expert guidance to facilitate the decision-making process. This means it can be utilised by any business to design an effective MD process that is tailored to suit their specific context. Alongside a tailored MD process, the framework will also generate a fully aligned implementation specification for the supporting CMMS (Computerised Maintenance Management System), which is also tailored according to the same contextual requirements. This will enable the end user of the framework to procure, implement and configure a CMMS that has the complete range of functionality required to fully support their business requirements. Innovation – A tailorable framework that is flexible enough to be utilised in many different industries is novel, because existing MD processes are generally designed for a single, specific case and cannot adapt to different contexts. The size and scope of the framework also validates the innovation claim – i.e. a series of flowcharts covering multiple AM subject areas, with 157 core process steps, 109 contextual options, and 30,000+ words of guidance. The fact that framework has already been successfully utilised to develop and implement an effective MD process in a very specific context (i.e. a maintenance-intensive, highly regulated nuclear site with a relatively small workforce) further strengthens the claim for innovation

Bayesian Network Analysis for Diagnostics and Prognostics of Engineering Systems

Bayesian networks have been applied to many different domains to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military field and human performance data sets in an industrial environment. Methods frequently rely on a clear understanding of causal connections leading to an undesirable event and detailed understanding of the system behavior. Methods may also require large amount of analyst teams and domain experts, coupled with manual data cleansing and classification. The research performed utilized machine learning algorithms (such as Bayesian networks) and two existing data sets. The primary objective of the research was to develop a diagnostic and prognostic tool utilizing Bayesian networks that does not require the need for detailed causal understanding of the underlying system. The research yielded a predictive method with substantial benefits over reactive methods. The research indicated Bayesian networks can be trained and utilized to predict failure of several important components to include potential malfunction codes and downtime on a real-world Navy data set. The research also considered potential error within the training data set. The results provided credence to utilization of Bayesian networks in real field data – which will always contain error that is not easily quantified. Research should be replicated with additional field data sets from other aircraft. Future research should be conducted to solicit and incorporate domain expertise into subsequent models. Research should also consider incorporation of text based analytics for text fields, which was considered out of scope for this research project

PROGNOSTIC ALGORITHM DEVELOPMENT FOR PLANT MONITORING AND MAINTENANCE PLANNING

The economic goals in a typical industrial plant are to improve product quality, maximize equipment up-time, reliability, and availability, and minimize spare part inventories and maintenance costs. Modern facilities are comprised of thousands of subsystems with critical unique components. Simple components and more complex engineering systems alike are typically engineered to perform satisfactorily. Their lives can be predicted under normal operation runtime. It should be the same with chronological time lapse from the moment of installation. However, their ages accelerate faster than chronological time lapse if they are operated under unfavorable working conditions, making their remaining life predictions likely not accurate, thus making failure imminent. These components most become more sophisticated and advanced to meet supercritical demands, and unplanned critical failures of any these components can result in costly operation stoppages. Speedy repair costs of failed components during operation can be extremely costly, not only due to the failed component, but also to collateral damage to other components, which can result in significant economic loss, lost production, personal injury, and even loss of life. Today’s marketplace faces global competition, ever-changing customer perception, and evolving demand. Industrial plants are constantly retooling their operations and equipment to act in a supercritical manner, and this is happening amidst the already complex nature of mechanical structures, operational stress, and environmental influence. To address these continuous changes, early fault detection is imperative to accurately predict the Remaining Useful Life (RUL) of machinery to prevent performance degradation and malfunction, which leads to substantial damage. Predicting the RUL of degraded components and putting these components to use will reduce spare part inventories and maintenance and increase reliability, availability, and performance to minimize plant downtime and production loss while enhancing operation safety. The primary purpose of this dissertation is to create an improved prognostic algorithm and methodology to predict the time of machinery failure. Empirical wear models built using historical operating conditions are then used to monitor the RUL of machinery and components. Machinery online monitoring data are used to determine the current health state of components along their life curves

MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules