Can incident reporting improve safety? Healthcare practitioners' views of the effectiveness of incident reporting

OBJECTIVE: Recent critiques of incident reporting suggest that its role in managing safety has been over emphasized. The objective of this study was to examine the perceived effectiveness of incident reporting in improving safety in mental health and acute hospital settings by asking staff about their perceptions and experiences. DESIGN: /st>Qualitative research design using documentary analysis and semi-structured interviews. SETTING: /st>Two large teaching hospitals in London; one providing acute and the other mental healthcare. PARTICIPANTS: /st>Sixty-two healthcare practitioners with experience of reporting and analysing incidents. RESULTS: /st>Incident reporting was perceived as having a positive effect on safety, not only by leading to changes in care processes but also by changing staff attitudes and knowledge. Staff discussed examples of both instrumental and conceptual uses of the knowledge generated by incident reports. There are difficulties in using incident reports to improve safety in healthcare at all stages of the incident reporting process. Differences in the risks encountered and the organizational systems developed in the two hospitals to review reported incidents could be linked to the differences we found in attitudes to incident reporting between the two hospitals. CONCLUSION: /st>Incident reporting can be a powerful tool for developing and maintaining an awareness of risks in healthcare practice. Using incident reports to improve care is challenging and the study highlighted the complexities involved and the difficulties faced by staff in learning from incident data.

Government preparedness:using simulation to prepare for a terrorist attack

The heightened threat of terrorism has caused governments worldwide to plan for responding to large-scale catastrophic incidents. In England the New Dimension Programme supplies equipment, procedures and training to the Fire and Rescue Service to ensure the country's preparedness to respond to a range of major critical incidents. The Fire and Rescue Service is involved partly by virtue of being able to very quickly mobilize a large skilled workforce and specialist equipment. This paper discusses the use of discrete event simulation modeling to understand how a fire and rescue service might position its resources before an incident takes place, to best respond to a combination of different incidents at different locations if they happen. Two models are built for this purpose. The first model deals with mass decontamination of a population following a release of a hazardous substance—aiming to study resource requirements (vehicles, equipment and manpower) necessary to meet performance targets. The second model deals with the allocation of resources across regions—aiming to study cover level and response times, analyzing different allocations of resources, both centralized and decentralized. Contributions to theory and practice in other contexts (e.g. the aftermath of natural disasters such as earthquakes) are outlined.

Corporate cyberstalking:an invitation to build theory

Cyberstalking describes a relatively new form of stalking behaviour where technology is used as the medium of harassment. The term corporate cyberstalking is often used to describe incidents that involve organisations, such as companies and government departments. This paper uses a number of case studies in order to propose a typology of corporate cyberstalking. It is suggested that incidents involving corporate cyberstalking can be divided into two broad groups, depending on whether or not the organisation acts as a stalker or as a victim. Examining the motivations behind corporate cyberstalking allows these groups to be subdidvided further. The motives behind corporate cyberstalking can range from a desire for revenge against an employer to cyberterrorism. The paper also briefly discusses definitions of stalking and cyberstalking, concluding with a revised definition of cyberstalking that is more in keeping with the material discussed.

Managing crisis:the role of primary care for people with serious mental illness

Background and Objectives: More than 30% of patients with serious mental illness in the United Kingdom now receive all their health care solely from primary care. This study explored the process of managing acute mental health crises from the dual perspective of patients and primary care health professionals. Methods: Eighteen focus groups involving 45 patients, 39 general practitioners, and eight practice nurses were held between May and November 2002 in six Primary Care Trusts across the British West Midlands. The topic guide explored perceptions of gold standard care, current issues and critical incidents in receiving/providing care, and ideas on improving services. Results: Themes relevant to the management of acute crisis included issues of process, such as access, advocacy, communication, continuity, and coordination of care; the development of more structured care that might reduce the need for crisis responses; and issues raised by the development of a more structured approach to care. Conclusions: Access to services is a complicated yet crucial feature of managing care in a crisis, with patients identifying barriers at the level of primary care and health professionals at the interface with secondary care. The development of more structured systems as a solution may generate its own ethical and pragmatic challenges.

Cyberstalking:a new challenge for criminal law

Cyberstalking has recently emerged as a new and growing problem and is an area that will probably receive a higher profile within criminal law as more cases reach court (see Griffiths, 1999; Griffiths, Rogers and Sparrow, 1998; Bojic and McFarlane, 2002a; 2002b). For the purposes of this article we define cyberstalking as the use of information and communications technology (in particular the Internet) in order to harass individuals. Such harassment may include actions such as the transmission of offensive e-mail messages, identity theft and damage to data or equipment. Whilst a more comprehensive definition has been presented elsewhere (Bocij and McFarlane, 2002), it is hoped that the definition here is sufficient for those unfamiliar with this field. The stereotypical stalker conjures up images of someone harassing a victim who is the object of their affection. However, not all stalking incidents are motivated by unrequited love. Stalking can also be motivated by hate, a need for revenge, a need for power and/or racism. Similarly, cyberstalking can involve acts that begin with the issuing of threats and end in physical assault. We also make distinctions between conventional stalking and cyberstalking. Whilst some may view cyberstalking as an extension of conventional stalking, we believe cyberstalking should be regarded as an entirely new form of deviant behaviour. It is not surprising that cyberstalking is sometimes thought of as a trivial problem. A number of writers and researchers have suggested that cyberstalking and associated activities are of little genuine concern. Koch (2000), for example, goes as far as accusing those interested in cyberstalking as promoting hysteria over a problem that may be minuscule or even imaginary. The impression gained is that cyberstalking represents a relatively small problem where victims seldom suffer any real harm. Whilst there are no genuinely reliable statistics that can be used to determine how common cyberstalking incidents are, a great deal of evidence is available to show that cyberstalking is a significant and growing problem (Griffiths et al, 1998). For instance, CyberAngels (a well-known Internet safety organization) receives some 500 complaints of cyberstalking each day, of which up to 100 represent legitimate cases (Dean, 2000). Another Internet safety organization (Working to Halt Online Abuse) reports receiving an average of 100 cases per week (WHOA, 2001). To highlight the types of cyberstalking behaviours that take place and some of the major issues facing criminal law, we briefly examine four high profile cases of cyberstalking (adapted from Bocij and MacFarlane, 2002b).

24-Karat or fool’s gold? Consequences of real team and co-acting group membership in healthcare organizations

Although theory on team membership is emerging, limited empirical attention has been paid to the effects of different types of team membership on outcomes. We propose that an important but overlooked distinction is that between membership of real teams and membership of co-acting groups, with the former being characterized by members who report that their teams have shared objectives, and structural interdependence and engage in team reflexivity. We hypothesize that real team membership will be associated with more positive individual- and organizational-level outcomes. These predictions were tested in the English National Health Service, using data from 62,733 respondents from 147 acute hospitals. The results revealed that individuals reporting the characteristics of real team membership, in comparison with those reporting the characteristics of co-acting group membership, witnessed fewer errors and incidents, experienced fewer work related injuries and illness, were less likely to be victims of violence and harassment, and were less likely to intend to leave their current employment. At the organizational level, hospitals with higher proportions of staff reporting the characteristics of real team membership had lower levels of patient mortality and sickness absence. The results suggest the need to clearly delineate real team membership in order to advance scientific understanding of the processes and outcomes of organizational teamwork.

Understanding data collection behaviour of mental health practitioners

Failure to detect patients at risk of attempting suicide can result in tragic consequences. Identifying risks earlier and more accurately helps prevent serious incidents occurring and is the objective of the GRiST clinical decision support system (CDSS). One of the problems it faces is high variability in the type and quantity of data submitted for patients, who are assessed in multiple contexts along the care pathway. Although GRiST identifies up to 138 patient cues to collect, only about half of them are relevant for any one patient and their roles may not be for risk evaluation but more for risk management. This paper explores the data collection behaviour of clinicians using GRiST to see whether it can elucidate which variables are important for risk evaluations and when. The GRiST CDSS is based on a cognitive model of human expertise manifested by a sophisticated hierarchical knowledge structure or tree. This structure is used by the GRiST interface to provide top-down controlled access to the patient data. Our research explores relationships between the answers given to these higher-level 'branch' questions to see whether they can help direct assessors to the most important data, depending on the patient profile and assessment context. The outcome is a model for dynamic data collection driven by the knowledge hierarchy. It has potential for improving other clinical decision support systems operating in domains with high dimensional data that are only partially collected and in a variety of combinations.

Preface

The Semantic Web has come a long way since its inception in 2001, especially in terms of technical development and research progress. However, adoption by non- technical practitioners is still an ongoing process, and in some areas this process is just now starting. Emergency response is an area where reliability and timeliness of information and technologies is of essence. Therefore it is quite natural that more widespread adoption in this area has not been seen until now, when Semantic Web technologies are mature enough to support the high requirements of the application area. Nevertheless, to leverage the full potential of Semantic Web research results for this application area, there is need for an arena where practitioners and researchers can meet and exchange ideas and results. Our intention is for this workshop, and hopefully coming workshops in the same series, to be such an arena for discussion. The Extended Semantic Web Conference (ESWC - formerly the European Semantic Web conference) is one of the major research conferences in the Semantic Web field, whereas this is a suitable location for this workshop in order to discuss the application of Semantic Web technology to our specific area of applications. Hence, we chose to arrange our first SMILE workshop at ESWC 2013. However, this workshop does not focus solely on semantic technologies for emergency response, but rather Semantic Web technologies in combination with technologies and principles for what is sometimes called the "social web". Social media has already been used successfully in many cases, as a tool for supporting emergency response. The aim of this workshop is therefore to take this to the next level and answer questions like: "how can we make sense of, and furthermore make use of, all the data that is produced by different kinds of social media platforms in an emergency situation?" For the first edition of this workshop the chairs collected the following main topics of interest: • Semantic Annotation for understanding the content and context of social media streams. • Integration of Social Media with Linked Data. • Interactive Interfaces and visual analytics methodologies for managing multiple large-scale, dynamic, evolving datasets. • Stream reasoning and event detection. • Social Data Mining. • Collaborative tools and services for Citizens, Organisations, Communities. • Privacy, ethics, trustworthiness and legal issues in the Social Semantic Web. • Use case analysis, with specific interest for use cases that involve the application of Social Media and Linked Data methodologies in real-life scenarios. All of these, applied in the context of: • Crisis and Disaster Management • Emergency Response • Security and Citizen Journalism The workshop received 6 high-quality paper submissions and based on a thorough review process, thanks to our program committee, the decision was made to accept four of these papers for the workshop (67% acceptance rate). These four papers can be found later in this proceedings volume. Three out of four of these papers particularly discuss the integration and analysis of social media data, using Semantic Web technologies, e.g. for detecting complex events in social media streams, for visualizing and analysing sentiments with respect to certain topics in social media, or for detecting small-scale incidents entirely through the use of social media information. Finally, the fourth paper presents an architecture for using Semantic Web technologies in resource management during a disaster. Additionally, the workshop featured an invited keynote speech by Dr. Tomi Kauppinen from Aalto university. Dr. Kauppinen shared experiences from his work on applying Semantic Web technologies to application fields such as geoinformatics and scientific research, i.e. so-called Linked Science, but also recent ideas and applications in the emergency response field. His input was also highly valuable for the roadmapping discussion, which was held at the end of the workshop. A separate summary of the roadmapping session can be found at the end of these proceedings. Finally, we would like to thank our invited speaker Dr. Tomi Kauppinen, all our program committee members, as well as the workshop chair of ESWC2013, Johanna Völker (University of Mannheim), for helping us to make this first SMILE workshop a highly interesting and successful event!

Development of ``HealthServe'': A measure of customer service orientation in healthcare

An assessment tool designed to measure a customer service orientation among RN's and LPN's was developed using a content-oriented approach. Critical incidents were first developed by asking two samples of healthcare managers (n = 52 and 25) to identify various customer-contact situations. The critical incidents were then used to formulate a 121-item instrument. Patient-contact workers from 3 hospitals (n = 102) completed the instrument along with the NEO-FFI, a measure of the Big Five personality factors. Concurrently, managers completed a performance evaluation scale on the employees participating in the study in order to determine the predictive validity of the instrument.^ Through a criterion-keying approach, the instrument was scaled down to 38 items. The correlation between HealthServe and the supervisory ratings of performance evaluation data supported the instrument's criterion-related validity (r =.66, p $<$.0001). Incremental validity of HealthServe over the Big Five was found with HealthServe accounting for 46% of the variance.^ The NEO-FFI was used to assess the correlation between personality traits and HealthServe. A factor analysis of HealthServe suggested 4 factors which were correlated with the NEO-FFI scores. Results indicated that HealthServe was related to Extraversion, Openness to Experience, Agreeableness, Conscientiousness and negatively related to Neuroticism.^ The benefits of the test construction procedure used here over the use of broad-based measures of personality were discussed as well as the limitations of using a concurrent validation strategy. Recommendations for future studies were provided. ^

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Foundational forensic techniques for cellular and ad hoc multi-hop networks

The Internet has become an integral part of our nation’s critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a ‘distance metric’. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation’s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

The role of the health care organization in supporting nurses in the delivery of culturally competent care

This study was designed to explore ways in which health care organizations (HCOs) can support nurses in their delivery of culturally competent care. While cultural competence has become a priority for the federal government as well as the major health professional organizations, its integration into care delivery has not yet been realized. Health professionals cite a lack of educational preparation, time, and organizational resources as barriers. Most experts in the field agree that the cultural and linguistic needs of ethnic minorities pose challenges that individual care providers are unable to manage without the support of the health care organizations within which they practice. While several studies have identified implications for HCOs, there is a paucity of research on their role in this aspect of care delivery. Using a qualitative design with a case study approach, data collection included face-to-face interviews with 23 registered nurses, document analysis, and reports of critical incidents. The site chosen was a large health care system in South Florida that serves a culturally diverse population. Major findings from the study included language barriers, lack of training, difficulty with cultural differences, lack of organizational support, and reliance on culturally diverse staff members. Most nurses thought the ethnic mix was adequate, but rated other supports such as language services, training, and patient education materials as inadequate. Some of the recommendations for organizational performance were to provide the expectations and support for culturally competent care. Implications and recommendations for practice include nurses using trained interpreters instead of relying on coworkers or trying to "wing it", pursuing training, and advocating for organizational supports for culturally competent care. Implications and recommendations for theory included a blended model that combines both models in the conceptual framework. Recommendations for future research were for studies on the impact of language bathers on care delivery, develop and test a quantitative instrument, and to incorporate Gilbert's model into nursing research.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.