Computer-aided DSM-IV-diagnostics – acceptance, use and perceived usefulness in relation to users' learning styles

BACKGROUND: CDSS (computerized decision support system) for medical diagnostics have been studied for long. This study was undertaken to investigate how different preferences of Learning Styles (LS) of psychiatrists might affect acceptance, use and perceived usefulness of a CDSS for diagnostics in psychiatry. METHODS: 49 psychiatrists (specialists and non-specialists) from 3 different clinics volunteered to participate in this study and to use the CDSS to diagnose a paper-based case (based on a real patient). LS, attitudes to CDSS and complementary data were obtained via questionnaires and interviews. To facilitate the study, a special version of the CDSS was created, which automatically could log interaction details. RESULTS: The LS preferences (according to Kolb) of the 49 physicians turned out as follows: 37% were Assimilating, 31% Converging, 27% Accommodating and 6% Diverging. The CDSS under study seemed to favor psychiatrists with abstract conceptualization information perceiving mode (Assimilating and Converging learning styles). A correlation between learning styles preferences and computer skill was found. Positive attitude to computer-aided diagnostics and learning styles preferences was also found to correlate. Using the CDSS, the specialists produced only 1 correct diagnosis and the non-specialists 2 correct diagnoses (median values) as compared to the three predetermined correct diagnoses of the actual case. Only 10% had all three diagnoses correct, 41 % two correct, 47 % one correct and 2 % had no correct diagnose at all. CONCLUSION: Our results indicate that the use of CDSS does not guarantee correct diagnosis and that LS might influence the results. Future research should focus on the possibility to create systems open to individuals with different LS preferences and possibility to create CDSS adapted to the level of expertise of the user

How different are students and their learning styles?

Introduction: Students, like anybody else differ from each other. As students they differ in their preferred mode of learning, i.e. their preferred modes in gathering, organizing and thinking about information. A recent classification proposed by Neil Fleming and associates state that students learning styles can be divided into Visual/graphic, Aural, Read/write and Kinesthetic types, VARK.Aim: The aim of the recent study is to investigate learning styles among dental students in two different dental colleges of India.Method: The VARK-questionnaire contains 15 multiple-choice- questions with four possibilities to select an answer. Each possibility represents one of the four modes of perception. But, one can select more than one answer to each question, which is necessary for the identification of poly modal modes of perception and learning. This is also a psychometric problem when trying to state a measure of the reliability of the questionnaire. The VARK-questionnaire was distributed among 200 students and was collected back. This sample size represents 100% response rate from the students in the class and is markedly above the level required to make conclusions about student preferences for receiving and processing information. The students spent about 10 minutes in an ordinary lesson to fill in the questionnaire. Students register number and name were used in the study and there was no blinding practiced.Study Design: Questionnaire based clinical studyResults: The responses from the students in our University where classified into multi-modal (VARK), tri-modal (VRK, VAK, VAR, ARK), bi-modal (VR, VA, VK, RK) and uni-modal (V, A, R.K) categories. Results showed that subjects had a higher preference for multimodal learning.Conclusion: We conclude that students in our set up prefer multimodal and more of Kinesthetic of learning. To meet their needs, a variation in teaching, learning and examination must be implemented. If not, these students with a high kinesthetic preference for perception and learning may be at the losing end

Automation bias and prescribing decision support – rates, mediators and mitigators

Purpose: Computerised clinical decision support systems (CDSS) are implemented within healthcare settings as a method to improve clinical decision quality, safety and effectiveness, and ultimately patient outcomes. Though CDSSs tend to improve practitioner performance and clinical outcomes, relatively little is known about specific impact of inaccurate CDSS output on clinicians. Although there is high heterogeneity between CDSS types and studies, reviews of the ability of CDSS to prevent medication errors through incorrect decisions have generally been consistently positive, working by improving clinical judgement and decision making. However, it is known that the occasional incorrect advice given may tempt users to reverse a correct decision, and thus introduce new errors. These systematic errors can stem from Automation Bias (AB), an effect which has had little investigation within the healthcare field, where users have a tendency to use automated advice heuristically. Research is required to assess the rate of AB, identify factors and situations involved in overreliance and propose says to mitigate risk and refine the appropriate usage of CDSS; this can provide information to promote awareness of the effect, and ensure the maximisation of the impact of benefits gained from the implementation of CDSS. Background: A broader literature review was carried out coupled with a systematic review of studies investigating the impact of automated decision support on user decisions over various clinical and non-clinical domains. This aimed to identify gaps in the literature and build an evidence-based model of reliance on Decision Support Systems (DSS), particularly a bias towards over-using automation. The literature review and systematic review revealed a number of postulates - that CDSS are socio-technical systems, and that factors involved in CDSS misuse can vary from overarching social or cultural factors, individual cognitive variables to more specific technology design issues. However, the systematic review revealed there is a paucity of deliberate empirical evidence for this effect. The reviews identified the variables involved in automation bias to develop a conceptual model of overreliance, the initial development of an ontology for AB, and ultimately inform an empirical study to investigate persuasive potential factors involved: task difficulty, time pressure, CDSS trust, decision confidence, CDSS experience and clinical experience. The domain of primary care prescribing was chosen within which to carry out an empirical study, due to the evidence supporting CDSS usefulness in prescribing, and the high rate of prescribing error. Empirical Study Methodology: Twenty simulated prescribing scenarios with associated correct and incorrect answers were developed and validated by prescribing experts. An online Clinical Decision Support Simulator was used to display scenarios to users. NHS General Practitioners (GPs) were contacted via emails through associates of the Centre for Health Informatics, and through a healthcare mailing list company. Twenty-six GPs participated in the empirical study. The study was designed so each participant viewed and gave prescriptions for 20 prescribing scenarios, 10 coded as “hard” and 10 coded as “medium” prescribing scenarios (N = 520 prescribing cases were answered overall). Scenarios were accompanied by correct advice 70% of the time, and incorrect advice 30% of the time (in equal proportions in either task difficulty condition). Both the order of scenario presentation and the correct/incorrect nature of advice were randomised to prevent order effects. The planned time pressure condition was dropped due to low response rate. Results: To compare with previous literature which took overall decisions into account, taking individual cases into account (N=520), the pre advice accuracy rate of the clinicians was 50.4%, which improved to 58.3% post advice. The CDSS improved the decision accuracy in 13.1% of prescribing cases. The rate of AB, as measured by decision switches from correct pre advice, to incorrect post advice was 5.2% of all cases at a CDSS accuracy rate of 70% - leading to a net improvement of 8%. However, the above by-case type of analysis may not enable generalisation of results (but illustrates rates in this specific situation); individual participant differences must be taken into account. By participant (N = 26) when advice was correct, decisions were more likely to be switched to a correct prescription, when advice was incorrect decisions were more likely to be switched to an incorrect prescription. There was a significant correlation between decision switching and AB error. By participant, more immediate factors such as trust in the specific CDSS, decision confidence, and task difficulty influenced rate of decision switching. Lower clinical experience was associated with more decision switching (but not higher AB rate). The rate of AB was somewhat problematic to analyse due to low number of instances – the effect could potentially have been greater. The between subjects effect of time pressure could not be investigated due to low response rate. Age, DSS experience and trust in CDSS generally were not significantly associated with decision switching. Conclusion: There is a gap in the current literature investigating inappropriate CDSS use, but the general literature supports an interactive multi-factorial aetiology for automation misuse. Automation bias is a consistent effect with various potential direct and indirect causal factors. It may be mitigated by altering advice characteristics to aid clinicians’ awareness of advice correctness and support their own informed judgement – this needs further empirical investigation. Users’ own clinical judgement must always be maintained, and systems should not be followed unquestioningly

Accommodating Asperger's: an autoethnography on the learning experience in an e-learning music education program

Thesis (D.M.A.)--Boston UniversityA student with Asperger's Syndrome faces a complex myriad of learning disabilities and social difficulties. The co-morbid conditions of dyslexia, Obsessive Compulsive Disorder, Attention Deficit Disorder, Attention Deficit Hyperactive Disorder and anxiety further complicate Asperger's Syndrome. Asperger's Syndrome and these conditions, singularly and in combination, have the potential to significantly hamper a student's achievement and success in learning environments. I am a person with Asperger's Syndrome, formerly diagnosed as Autism Spectrum Disorder-High Functioning, engaged in Boston University's Doctorate in Music Education Program delivered via E-learning modalities. The research question, "How does the E-learning modality serve the needs of a student with Asperger's Syndrome in the field of music education?" was a direct product of my personal experience with the convergence of E-learning, music education and Asperger's Syndrome. Autoethnography was employed as the research strategy to explore this convergence. The primary data source was a journal spanning almost three decades in conjunction with artifacts and other data sources. The data analysis and interpretation was completed through self-reflective and selfnarrative writing. The findings of this study, suggest that while E-learning modalities present both positives and negatives for students with Asperger's Syndrome; the potential to alleviate many of the challenges they face makes this is an excellent alternative to the traditional classroom educational delivery method in the field of music education. Further this research highlights the importance for educators to reflect on their own teaching methods and the profession to continually evaluate the methods utilized in delivering content and assessing achievement

Computer-aided DSM-IV-diagnostics – acceptance, use and perceived usefulness in relation to users' learning styles-0

<p><b>Copyright information:</b></p><p>Taken from "Computer-aided DSM-IV-diagnostics – acceptance, use and perceived usefulness in relation to users' learning styles"</p><p>BMC Medical Informatics and Decision Making 2005;5():1-1.</p><p>Published online 7 Jan 2005</p><p>PMCID:PMC545069.</p><p>Copyright © 2005 Bergman and Fors; licensee BioMed Central Ltd.</p

Articulating the new normal(s) : mental disability, medical discourse, and rhetorical action.

“Articulating the New Normal(s): Mental Disability, Medical Discourse, and Rhetorical Action” studies the writing of people diagnosed with autism and post- traumatic stress disorder within online discussion boards related to mental health and outlines their unique rhetorical strategies for interacting with biomedical ideologies of psychiatry and activist discourses. The opening chapter situates this dissertation in relation to previous scholarship in Rhetoric, Disability Studies, and other fields. I also provide a summary of the set of mixed methods I use to gather and analyze my data, including rhetorical analysis, corpus analysis, and qualitative interviews. In Chapter 2, “Medical Terminology and Discourse Features of Online Discussions of Mental Health,” I explore the ways in which medical discourse appears in discussions of mental disability through medical terms that writers and speakers use when discussing a diagnosis. Using methods borrowed from linguistics, I demonstrate that the writers in my study make different linguistic choices than the general public, and that the most prominent differences are related to the social construction of mental health and medicine. In Chapter 3, “Inhabiting Biological Primacy with Chiasmic Rhetoric in Mental Health Forums,” I describe and analyze a variety of common topics in online conversations that connect mental health and expert knowledge of the brain. I argue that this connection of mental experience and brain science constitutes a chiasmic rhetoric. The writers foregrounded in this chapter acknowledge and accept much of the claims of medicine and neuroscience regarding the brain but, uniquely, work to divide that knowledge from the path of normativity and optimization. Chapter 4, “Classified Conversations: Psychiatry and Technical Communication in Online Spaces,” examines the practices of participants in online mental health discussion forums conversations as they interpret technical documents. I detail four salient forms of the manipulation of medical discourse in online communities. At the close of this chapter, I explain how these insights can inform academic study of writing in mental health contexts and transform the content and application of medical and technical texts. In Chapter 5, “Re-Forming Mental Health: Rhetorical Innovation and the Language of Advocacy,” I summarize and synthesize the core arguments of earlier chapters, with an extended caveat regarding the ethical dilemmas of this study. Finally, I offer a set of practical recommendations for different communities with which my research has been conversant, the fields of Rhetoric and Rhetoric of Health and Medicine, Disability Studies, and activism related to mental disabilities

Essential Notes in Psychiatry

Psychiatry is one of the major specialties of medicine, and is concerned with the study and treatment of mental disorders. In recent times the field is growing with the discovery of effective therapies and interventions that alleviate suffering in people with mental disorders. This book of psychiatry is concise and clearly written so that it is usable for doctors in training, students and clinicians dealing with psychiatric illness in everyday practice. The book is a primer for those beginning to learn about emotional disorders and psychosocial consequences of severe physical and psychological trauma; and violence. Emphasis is placed on effective therapies and interventions for selected conditions such as dementia and suicide among others and the consequences of stress in the workplace. The book also highlights important causes of mental disorders in children

Analytics and Intuition in the Process of Selecting Talent

In management, decisions are expected to be based on rational analytics rather than intuition. But intuition, as a human evolutionary achievement, offers wisdom that, despite all the advances in rational analytics and AI, should be used constructively when recruiting and winning personnel. Integrating these inner experiential competencies with rational-analytical procedures leads to smart recruiting decisions