Oops! Another medication error : a literature review of contributing factors and methods to prevent medication errors

The purpose of this bachelor's thesis was to chart scientific research articles to present contributing factors to medication errors done by nurses in a hospital setting, and introduce methods to prevent medication errors. Additionally, international and Finnish research was combined and findings were reflected in relation to the Finnish health care system. Literature review was conducted out of 23 scientific articles. Data was searched systematically from CINAHL, MEDIC and MEDLINE databases, and also manually. Literature was analysed and the findings combined using inductive content analysis. Findings revealed that both organisational and individual factors contributed to medication errors. High workload, communication breakdowns, unsuitable working environment, distractions and interruptions, and similar medication products were identified as organisational factors. Individual factors included nurses' inability to follow protocol, inadequate knowledge of medications and personal qualities of the nurse. Developing and improving the physical environment, error reporting, and medication management protocols were emphasised as methods to prevent medication errors. Investing to the staff's competence and well-being was also identified as a prevention method. The number of Finnish articles was small, and therefore the applicability of the findings to Finland is difficult to assess. However, the findings seem to fit to the Finnish health care system relatively well. Further research is needed to identify those factors that contribute to medication errors in Finland. This is a necessity for the development of methods to prevent medication errors that fit in to the Finnish health care system.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Methods for the analysis of two dimensional measurement data of paper web

This work is devoted to the problem of reconstructing the basis weight structure at paper web with black{box techniques. The data that is analyzed comes from a real paper machine and is collected by an o®-line scanner. The principal mathematical tool used in this work is Autoregressive Moving Average (ARMA) modelling. When coupled with the Discrete Fourier Transform (DFT), it gives a very flexible and interesting tool for analyzing properties of the paper web. Both ARMA and DFT are independently used to represent the given signal in a simplified version of our algorithm, but the final goal is to combine the two together. Ljung-Box Q-statistic lack-of-fit test combined with the Root Mean Squared Error coefficient gives a tool to separate significant signals from noise.

Origins of production errors and significance of employee empowerment in reducing production error amount in sheet metal fabricating industry

The market place of the twenty-first century will demand that manufacturing assumes a crucial role in a new competitive field. Two potential resources in the area of manufacturing are advanced manufacturing technology (AMT) and empowered employees. Surveys in Finland have shown the need to invest in the new AMT in the Finnish sheet metal industry in the 1990's. In this run the focus has been on hard technology and less attention is paid to the utilization of human resources. In manymanufacturing companies an appreciable portion of the profit within reach is wasted due to poor quality of planning and workmanship. The production flow production error distribution of the sheet metal part based constructions is inspectedin this thesis. The objective of the thesis is to analyze the origins of production errors in the production flow of sheet metal based constructions. Also the employee empowerment is investigated in theory and the meaning of the employee empowerment in reducing the overall production error amount is discussed in this thesis. This study is most relevant to the sheet metal part fabricating industrywhich produces sheet metal part based constructions for electronics and telecommunication industry. This study concentrates on the manufacturing function of a company and is based on a field study carried out in five Finnish case factories. In each studied case factory the most delicate work phases for production errors were detected. It can be assumed that most of the production errors are caused in manually operated work phases and in mass production work phases. However, no common theme in collected production error data for production error distribution in the production flow can be found. Most important finding was still that most of the production errors in each case factory studied belong to the 'human activity based errors-category'. This result indicates that most of the problemsin the production flow are related to employees or work organization. Development activities must therefore be focused to the development of employee skills orto the development of work organization. Employee empowerment gives the right tools and methods to achieve this.

Application of Neural Network Methods in the Prediction of Stock Markets

The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.

Evaluation of Software Development Practices through Error Analysis

This thesis studies evaluation of software development practices through an error analysis. The work presents software development process, software testing, software errors, error classification and software process improvement methods. The practical part of the work presents results from the error analysis of one software process. It also gives improvement ideas for the project. It was noticed that the classification of the error data was inadequate in the project. Because of this it was impossible to use the error data effectively. With the error analysis we were able to show that there were deficiencies in design and analyzing phases, implementation phase and in testing phase. The work gives ideas for improving error classification and for software development practices.

On Fault Tolerance Methods for Networks-on-Chip

Technology scaling has proceeded into dimensions in which the reliability of manufactured devices is becoming endangered. The reliability decrease is a consequence of physical limitations, relative increase of variations, and decreasing noise margins, among others. A promising solution for bringing the reliability of circuits back to a desired level is the use of design methods which introduce tolerance against possible faults in an integrated circuit. This thesis studies and presents fault tolerance methods for network-onchip (NoC) which is a design paradigm targeted for very large systems-onchip. In a NoC resources, such as processors and memories, are connected to a communication network; comparable to the Internet. Fault tolerance in such a system can be achieved at many abstraction levels. The thesis studies the origin of faults in modern technologies and explains the classification to transient, intermittent and permanent faults. A survey of fault tolerance methods is presented to demonstrate the diversity of available methods. Networks-on-chip are approached by exploring their main design choices: the selection of a topology, routing protocol, and flow control method. Fault tolerance methods for NoCs are studied at different layers of the OSI reference model. The data link layer provides a reliable communication link over a physical channel. Error control coding is an efficient fault tolerance method especially against transient faults at this abstraction level. Error control coding methods suitable for on-chip communication are studied and their implementations presented. Error control coding loses its effectiveness in the presence of intermittent and permanent faults. Therefore, other solutions against them are presented. The introduction of spare wires and split transmissions are shown to provide good tolerance against intermittent and permanent errors and their combination to error control coding is illustrated. At the network layer positioned above the data link layer, fault tolerance can be achieved with the design of fault tolerant network topologies and routing algorithms. Both of these approaches are presented in the thesis together with realizations in the both categories. The thesis concludes that an optimal fault tolerance solution contains carefully co-designed elements from different abstraction levels

Simulation methods in the modelling of bioaffinity assays

Computational model-based simulation methods were developed for the modelling of bioaffinity assays. Bioaffinity-based methods are widely used to quantify a biological substance in biological research, development and in routine clinical in vitro diagnostics. Bioaffinity assays are based on the high affinity and structural specificity between the binding biomolecules. The simulation methods developed are based on the mechanistic assay model, which relies on the chemical reaction kinetics and describes the forming of a bound component as a function of time from the initial binding interaction. The simulation methods were focused on studying the behaviour and the reliability of bioaffinity assay and the possibilities the modelling methods of binding reaction kinetics provide, such as predicting assay results even before the binding reaction has reached equilibrium. For example, a rapid quantitative result from a clinical bioaffinity assay sample can be very significant, e.g. even the smallest elevation of a heart muscle marker reveals a cardiac injury. The simulation methods were used to identify critical error factors in rapid bioaffinity assays. A new kinetic calibration method was developed to calibrate a measurement system by kinetic measurement data utilizing only one standard concentration. A nodebased method was developed to model multi-component binding reactions, which have been a challenge to traditional numerical methods. The node-method was also used to model protein adsorption as an example of nonspecific binding of biomolecules. These methods have been compared with the experimental data from practice and can be utilized in in vitro diagnostics, drug discovery and in medical imaging.

Methods of genetic diversity creation and functional display for directed evolution experiments

Protein engineering aims to improve the properties of enzymes and affinity reagents by genetic changes. Typical engineered properties are affinity, specificity, stability, expression, and solubility. Because proteins are complex biomolecules, the effects of specific genetic changes are seldom predictable. Consequently, a popular strategy in protein engineering is to create a library of genetic variants of the target molecule, and render the population in a selection process to sort the variants by the desired property. This technique, called directed evolution, is a central tool for trimming protein-based products used in a wide range of applications from laundry detergents to anti-cancer drugs. New methods are continuously needed to generate larger gene repertoires and compatible selection platforms to shorten the development timeline for new biochemicals. In the first study of this thesis, primer extension mutagenesis was revisited to establish higher quality gene variant libraries in Escherichia coli cells. In the second study, recombination was explored as a method to expand the number of screenable enzyme variants. A selection platform was developed to improve antigen binding fragment (Fab) display on filamentous phages in the third article and, in the fourth study, novel design concepts were tested by two differentially randomized recombinant antibody libraries. Finally, in the last study, the performance of the same antibody repertoire was compared in phage display selections as a genetic fusion to different phage capsid proteins and in different antibody formats, Fab vs. single chain variable fragment (ScFv), in order to find out the most suitable display platform for the library at hand. As a result of the studies, a novel gene library construction method, termed selective rolling circle amplification (sRCA), was developed. The method increases mutagenesis frequency close to 100% in the final library and the number of transformants over 100-fold compared to traditional primer extension mutagenesis. In the second study, Cre/loxP recombination was found to be an appropriate tool to resolve the DNA concatemer resulting from error-prone RCA (epRCA) mutagenesis into monomeric circular DNA units for higher efficiency transformation into E. coli. Library selections against antigens of various size in the fourth study demonstrated that diversity placed closer to the antigen binding site of antibodies supports generation of antibodies against haptens and peptides, whereas diversity at more peripheral locations is better suited for targeting proteins. The conclusion from a comparison of the display formats was that truncated capsid protein three (p3Δ) of filamentous phage was superior to the full-length p3 and protein nine (p9) in obtaining a high number of uniquely specific clones. Especially for digoxigenin, a difficult hapten target, the antibody repertoire as ScFv-p3Δ provided the clones with the highest affinity for binding. This thesis on the construction, design, and selection of gene variant libraries contributes to the practical know-how in directed evolution and contains useful information for scientists in the field to support their undertakings.