Approaches to reducing the most important patient errors in primary health care : patient and professional perspectives

We have previously reported a preliminary taxonomy of patient error. However, approaches to managing patients' contribution to error have received little attention in the literature. This paper aims to assess how patients and primary care professionals perceive the relative importance of different patient errors as a threat to patient safety. It also attempts to suggest what these groups believe may be done to reduce the errors, and how. It addresses these aims through original research that extends the nominal group analysis used to generate the error taxonomy. Interviews were conducted with 11 purposively selected groups of patients and primary care professionals in Auckland, New Zealand, during late 2007. The total number of participants was 83, including 64 patients. Each group ranked the importance of possible patient errors identified through the nominal group exercise. Approaches to managing the most important errors were then discussed. There was considerable variation among the groups in the importance rankings of the errors. Our general inductive analysis of participants' suggestions revealed the content of four inter-related actions to manage patient error: Grow relationships; Enable patients and professionals to recognise and manage patient error; be Responsive to their shared capacity for change; and Motivate them to act together for patient safety. Cultivation of this GERM of safe care was suggested to benefit from 'individualised community care'. In this approach, primary care professionals individualise, in community spaces, population health messages about patient safety events. This approach may help to reduce patient error and the tension between personal and population health-care.

Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errors

Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

Reliability and validity of query intent assessments

In most intent recognition studies, annotations of query intent are created post hoc by external assessors who are not the searchers themselves. It is important for the field to get a better understanding of the quality of this process as an approximation for determining the searcher's actual intent. Some studies have investigated the reliability of the query intent annotation process by measuring the interassessor agreement. However, these studies did not measure the validity of the judgments, that is, to what extent the annotations match the searcher's actual intent. In this study, we asked both the searchers themselves and external assessors to classify queries using the same intent classification scheme. We show that of the seven dimensions in our intent classification scheme, four can reliably be used for query annotation. Of these four, only the annotations on the topic and spatial sensitivity dimension are valid when compared with the searcher's annotations. The difference between the interassessor agreement and the assessor-searcher agreement was significant on all dimensions, showing that the agreement between external assessors is not a good estimator of the validity of the intent classifications. Therefore, we encourage the research community to consider using query intent classifications by the searchers themselves as test data.

A large scale quantitative study of pedestrian railway level crossing behaviour : deliberate violations, errors and deterrence

Every year a number of pedestrians are struck by trains resulting in death and serious injury. While much research has been conducted on train-vehicle collisions, very little is currently known about the aetiology of train-pedestrian collisions. To date, scant research has been undertaken to investigate the demographics of rule breakers, the frequency of deliberate violation versus error making and the influence of the classic deterrence approach on subsequent behaviours. Aim This study aimed to to identify pedestrians’ self-reported reasons for engaging in violations at crossing, the frequency and nature of rule breaking and whether the threat of sanctions influence such events. Method A questionnaire was administered to 511 participants of all ages. Results Analysis revealed that pedestrians (particularly younger groups) were more likely to commit deliberate violations rather than make crossing errors e.g., mistakes. The most frequent reasons given for deliberate violations were participants were running late and did not want to miss their train or participants believed that the gate was taking too long to open so may be malfunctioning. In regards to classical deterrence, an examination of the perceived threat of being apprehended and fined for a crossing violation revealed participants reported the highest mean scores for swiftness of punishment, which suggests they were generally aware that they would receive an “on the spot” fine. However, the overall mean scores for certainty and severity of sanctions (for violating the rules) indicate that the participants did not perceive the certainty and severity of sanctions as very high. This paper will further discuss the research findings in regards to the development of interventions designed to improve pedestrian crossing safety.

Visual performance with lenses correcting peripheral refractive errors

Purpose To design and manufacture lenses to correct peripheral refraction along the horizontal meridian and to determine whether these resulted in noticeable improvements in visual performance. Method Subjective refraction of a low myope was determined on the basis of best peripheral detection acuity along the horizontal visual field out to ±30° for both horizontal and vertical gratings. Subjective refraction was compared to objective refractions using a COAS-HD aberrometer. Special lenses were made to correct peripheral refraction, based on designs optimized with and without smoothing across a 3 mm diameter square aperture. Grating detection was retested with these lenses. Contrast thresholds of 1.25’ spots were determined across the field for the conditions of best correction, on-axis correction, and the special lenses. Results The participant had high relative peripheral hyperopia, particularly in the temporal visual field (maximum 2.9 D). There were differences > 0.5D between subjective and objective refractions at a few field angles. On-axis correction reduced peripheral detection acuity and increased peripheral contrast threshold in the peripheral visual field, relative to the best correction, by up to 0.4 and 0.5 log units, respectively. The special lenses restored most of the peripheral vision, although not all at angles to ±10°, and with the lens optimized with aperture-smoothing possibly giving better vision than the lens optimized without aperture-smoothing at some angles. Conclusion It is possible to design and manufacture lenses to give near optimum peripheral visual performance to at least ±30° along one visual field meridian. The benefit of such lenses is likely to be manifest only if a subject has a considerable relative peripheral refraction, for example of the order of 2 D.

Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows

This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

A securities market operator's use of the "please explain" price query and its impact on compliance

Market operators in New Zealand and Australia, such as the New Zealand Exchange (NZX) and the Australian Securities Exchange (ASX), have the regulatory power in their listing rules to issue queries to their market participants to explain unusual fluctuations in trading price and/or volume in the market. The operator will issue a price query where it believes that the market has not been fully informed as to price relevant information. Responsive regulation theory has informed much of the regulatory debate in securities laws in the region. Price queries map onto the lower level of the enforcement pyramid envisaged by responsive regulation and are one strategy that a market operator can use in communicating its compliance expectations to its stakeholders. The issue of a price query may be a precursor to more severe enforcement activities. The aim of this study is to investigate whether increased use of price queries by the securities market operator in New Zealand corresponded with an increase in disclosure frequency by all participating companies. The study finds that an increased use of price queries did correspond with an increase in disclosure frequency. A possible explanation for this finding is that price queries are an effective means of appealing to the factors that motivate corporations, and the individuals who control them, to comply with the law and regulatory requirements. This finding will have implications for both the NZX and the ASX as well as for regulators and policy makers generally.

A query-basis approach to parametrizing novelty-biased cumulative gain

Novelty-biased cumulative gain (α-NDCG) has become the de facto measure within the information retrieval (IR) community for evaluating retrieval systems in the context of sub-topic retrieval. Setting the incorrect value of parameter α in α-NDCG prevents the measure from behaving as desired in particular circumstances. In fact, when α is set according to common practice (i.e. α = 0.5), the measure favours systems that promote redundant relevant sub-topics rather than provide novel relevant ones. Recognising this characteristic of the measure is important because it affects the comparison and the ranking of retrieval systems. We propose an approach to overcome this problem by defining a safe threshold for the value of α on a query basis. Moreover, we study its impact on system rankings through a comprehensive simulation.

Effect of pupil dilation and constriction on the distribution of bit errors within the iris

Texture information in the iris image is not uniform in discriminatory information content for biometric identity verification. The bits in an iris code obtained from the image differ in their consistency from one sample to another for the same identity. In this work, errors in bit strings are systematically analysed in order to investigate the effect of light-induced and drug-induced pupil dilation and constriction on the consistency of iris texture information. The statistics of bit errors are computed for client and impostor distributions as functions of radius and angle. Under normal conditions, a V-shaped radial trend of decreasing bit errors towards the central region of the iris is obtained for client matching, and it is observed that the distribution of errors as a function of angle is uniform. When iris images are affected by pupil dilation or constriction the radial distribution of bit errors is altered. A decreasing trend from the pupil outwards is observed for constriction, whereas a more uniform trend is observed for dilation. The main increase in bit errors occurs closer to the pupil in both cases.

Human errors in computer related abuses

The term “Human error” can simply be defined as an error which made by a human. In fact, Human error is an explanation of malfunctions, unintended consequents from operating a system. There are many factors that cause a person to have an error due to the unwanted error of human. The aim of this paper is to investigate the relationship of human error as one of the factors to computer related abuses. The paper beings by computer-relating to human errors and followed by mechanism mitigate these errors through social and technical perspectives. We present the 25 techniques of computer crime prevention, as a heuristic device that assists. A last section discussing the ways of improving the adoption of security, and conclusion.

Sensitivity of an Elekta iView GT a-Si EPID model to delivery errors for pre-treatment verification of IMRT fields

A Monte Carlo model of an Elekta iViewGT amorphous silicon electronic portal imaging device (a-Si EPID) has been validated for pre-treatment verification of clinical IMRT treatment plans. The simulations involved the use of the BEAMnrc and DOSXYZnrc Monte Carlo codes to predict the response of the iViewGT a-Si EPID model. The predicted EPID images were compared to the measured images obtained from the experiment. The measured EPID images were obtained by delivering a photon beam from an Elekta Synergy linac to the Elekta iViewGT a-Si EPID. The a-Si EPID was used with no additional build-up material. Frame averaged EPID images were acquired and processed using in-house software. The agreement between the predicted and measured images was analyzed using the gamma analysis technique with acceptance criteria of 3% / 3 mm. The results show that the predicted EPID images for four clinical IMRT treatment plans have a good agreement with the measured EPID signal. Three prostate IMRT plans were found to have an average gamma pass rate of more than 95.0 % and a spinal IMRT plan has the average gamma pass rate of 94.3 %. During the period of performing this work a routine MLC calibration was performed and one of the IMRT treatments re-measured with the EPID. A change in the gamma pass rate for one field was observed. This was the motivation for a series of experiments to investigate the sensitivity of the method by introducing delivery errors, MLC position and dosimetric overshoot, into the simulated EPID images. The method was found to be sensitive to 1 mm leaf position errors and 10% overshoot errors.

Error and variance bounds on sigmoidal neurons with weight and input errors

Bounds on the expectation and variance of errors at the output of a multilayer feedforward neural network with perturbed weights and inputs are derived. It is assumed that errors in weights and inputs to the network are statistically independent and small. The bounds obtained are applicable to both digital and analogue network implementations and are shown to be of practical value.