1,857 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts

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    Background: Personal recovery, ‘living a satisfying, hopeful and contributing lifeeven with the limitations caused by the illness’ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support

    Improving patient safety by learning from near misses – insights from safety-critical industries

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    Background Patients are at risk of being harmed by the very processes meant to help them. To improve patient safety, healthcare organisations attempt to identify the factors that contribute to incidents and take action to optimise conditions to minimise repeats. However, improvements in patient safety have not matched those observed in other safety-critical industries. One difference between healthcare and other safety-critical industries may be how they learn from near misses when seeking to make safety improvements. Near misses are incidents that almost happened, but for an interruption in the sequence of events. Management of near misses includes their identification, reporting and investigation, and the learning that results. Safety theory suggests that acting on near misses will lead to actions to help prevent incidents. However, evidence also suggests that healthcare has yet to embrace the learning potential that patient safety near misses offer. The aims of this research, in support of this thesis, were to explore how best healthcare can learn from patient safety near misses to improve patient safety, and to identify what guidance non-healthcare safety-critical industries, which have implemented effective near-miss management systems, can offer healthcare. As this research progressed the aims were updated to include consideration of whether healthcare should seek to learn from patient safety near misses. Methods This research took a mixed-methods approach augmented by scoping reviews of the healthcare (study 1) and non-healthcare safety-critical industry (study 3) literature. A qualitative case study (study 2) was undertaken to explore the management of patient safety near misses in the English National Health Service. Seventeen interviews were undertaken with patient safety leads across acute hospitals, ambulance trusts, mental health trusts, primary care, and national bodies. A questionnaire was also used to help access the views of frontline staff. A grounded theory (study 4) was used to develop a set of principles, based on learning from non-healthcare safety-critical industries, around how best near misses can be managed. Thirty-five interviews were undertaken across aviation, maritime, and rail, with nuclear later added as per the theoretical sampling. Results The scoping reviews contributed 125 healthcare and 108 non-healthcare safety-critical industry academic articles, published internationally between 2000 and 2022, to the evidence gained from the qualitative case study and grounded theory. Safety cultures and maturity with safety management processes were found to vary in and across the different industries, and there was a reluctance for healthcare to learn about safety and near misses from other industries. Healthcare has yet to establish effective processes to manage patient safety near misses. There is an absence of evidence that learning has led to improvements in patient safety. The definition of a patient safety near miss varies, and organisations focus their efforts on reporting and investigating incidents, with limited attention to patient safety near misses. In non-healthcare safety-critical industries, near-miss management is more established, but process maturity varies in and across industries. Near misses are often defined specifically for an industry, but there is limited evidence that learning from them has improved safety. Information about near misses are commonly aggregated and may contribute to company and industry safety management systems. Exploration of the definition of a patient safety near miss led to the identification of the features of a near miss. The features have not been previously defined in the manner presented in this thesis. A patient safety near miss is context-specific and complex, involves interruptions, highlights system vulnerabilities, and is delineated from an incident by whether events reach a patient. Across healthcare and non-healthcare safety-critical industries the impact of learning from near misses is often assumed or extrapolated based on the common cause hypothesis. The hypothesis is regularly cited in safety literature and is used as the basis for justifying a focus on patient safety near misses. However, the validity of the hypothesis has been questioned and has not been validated for different patient safety near miss and incident types. Conclusions The research findings challenge long-held beliefs that learning from patient safety near misses will lead to improvements in patient safety. These beliefs are based on traditional safety theory that is unlikely to now be valid in the complexity of modern-day systems where incidents are the result of multiple factors and can emerge without apparent warning. Further research is required to understand the relationship between learning from patient safety near misses and patient safety, and whether the common cause hypothesis is valid for different types of healthcare safety event. While there are questions about the value of learning directly from patient safety near misses, the contribution of near misses to safety management systems in non-healthcare safety-critical industries looks to be beneficial for safety improvement. Safety management systems have yet to be implemented in the National Health Service and future research should look to understand how best this may be achieved and their value. In the meantime, patient safety near misses may help healthcare’s understanding of systems and their optimisation to create barriers to incidents and build resilience. This research offers an evidence-based definition of a patient safety near miss and describes principles to support identification, reporting, prioritisation, investigation, aggregation, learning, and action to help improve patient safety

    Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

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    Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

    Pharmacy Manager System Implementation Strategies to Mitigate the Cost of Prescription Errors

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    AbstractOne of the most frequent medical errors in contemporary medicine is incorrect prescriptions, and the profits from retail pharmacy operations are adversely impacted by the costs associated with prescription errors. Independent pharmacy managers are interested in finding workable strategies to mitigate the cost of prescription errors and increase profit. Using the resource-based theory of competitive advantage (RBTCA), the purpose of this qualitative multiple-case study was to explore strategies some independent pharmacy managers in Texas use to mitigate the cost of pharmacy employee prescription errors and increase profitability. The participants were five independent pharmacy managers who implemented strategies to mitigate the cost of prescription errors. Data were collected using semistructured, face-to-face interviews, a review of company documents, and site observation notes. Through thematic analysis, four themes emerged: (a) cost of prescription quality check and errors reduction strategy, (b) increased profitability strategy through error cost mitigation, (c) positive utilization of organization resources strategy, and (d) technology system implementation strategy to reduce prescription errors. A key recommendation is for independent pharmacy managers to involve pharmacy staff in developing the pharmacy system to promote user acceptance, which will assist in reducing prescription errors and raising profit. The implications for positive social change include the potential to mitigate the cost of prescription errors, prevent hospitalization and fatalities caused by medication errors, enhance patients’ quality of life, and boost the economy and employment opportunities in their communities
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