Relevant bibliographies by topics / Clinical support tools

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Clinical support tools'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Clinical support tools"

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Kosinski, Lawrence R. "Clinical Decision Support Tools." Clinical Gastroenterology and Hepatology 11, no. 7 (July 2013): 756–59. http://dx.doi.org/10.1016/j.cgh.2013.04.015.

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Sofianou, A., J. Kannry, D. M. Mann, T. G. McGinn, and L. J. McCullagh. "User Centered Clinical Decision Support Tools." Applied Clinical Informatics 05, no. 04 (2014): 1015–25. http://dx.doi.org/10.4338/aci-2014-05-ra-0048.

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Summary Background: Dissemination and adoption of clinical decision support (CDS) tools is a major initiative of the Affordable Care Act’s Meaningful Use program. Adoption of CDS tools is multipronged with personal, organizational, and clinical settings factoring into the successful utilization rates. Specifically, the diffusion of innovation theory implies that ‘early adopters’ are more inclined to use CDS tools and younger physicians tend to be ranked in this category. Objective: This study examined the differences in adoption of CDS tools across providers’ training level. Participants: From November 2010 to 2011, 168 residents and attendings from an academic medical institution were enrolled into a randomized controlled trial.Intervention: The intervention arm had access to the CDS tool through the electronic health record (EHR) system during strep and pneumonia patient visits. Main Measures: The EHR system recorded details on how intervention arm interacted with the CDS tool including acceptance of the initial CDS alert, completion of risk-score calculators and the signing of medication order sets. Using the EHR data, the study performed bivariate tests and general estimating equation (GEE) modeling to examine the differences in adoption of the CDS tool across residents and attendings. Key Results: The completion rates of the CDS calculator and medication order sets were higher amongst first year residents compared to all other training levels. Attendings were the less likely to accept the initial step of the CDS tool (29.3%) or complete the medication order sets (22.4%) that guided their prescription decisions, resulting in attendings ordering more antibiotics (37.1%) during an CDS encounter compared to residents. Conclusion: There is variation in adoption of CDS tools across training levels. Attendings tended to accept the tool less but ordered more medications. CDS tools should be tailored to clinicians’ training levels. Citation: McCullagh LJ, Sofianou A, Kannry J, Mann DM, McGinn TG. User centered clinical decision support tools: Adoption across clinician training level. Appl Clin Inf 2014; 5: 1015–1025http://dx.doi.org/10.4338/ACI-2014-05-RA-0048
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Modgil, S., and P. Hammond. "Decision support tools for clinical trial design." Artificial Intelligence in Medicine 27, no. 2 (February 2003): 181–200. http://dx.doi.org/10.1016/s0933-3657(02)00112-4.

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Stillman, Robert C. "Clinical Decision Support Tools Improving Cancer Care." Seminars in Oncology Nursing 34, no. 2 (May 2018): 158–67. http://dx.doi.org/10.1016/j.soncn.2018.03.007.

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Dighe, Anand S. "Clinical Decision Support: Tools, Strategies, and Emerging Technologies." Clinics in Laboratory Medicine 39, no. 2 (June 2019): i. http://dx.doi.org/10.1016/s0272-2712(19)30016-2.

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Sullivan, Frank, and Jeremy C. Wyatt. "How decision support tools help define clinical problems." BMJ 331, no. 7520 (October 6, 2005): 831–33. http://dx.doi.org/10.1136/bmj.331.7520.831.

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Jackups, Ronald. "Clinical Decision Support Tools for Microbiology Laboratory Testing." Clinical Microbiology Newsletter 42, no. 5 (March 2020): 35–44. http://dx.doi.org/10.1016/j.clinmicnews.2020.02.001.

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Bohen, Faye, and Ceri Woodrow. "Dynamic support database clinical support tool: inter-rater reliability." Advances in Mental Health and Intellectual Disabilities 14, no. 2 (February 3, 2020): 25–32. http://dx.doi.org/10.1108/amhid-09-2019-0027.

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Purpose The dynamic support database (DSD) clinical support tool structures the risk of admission rating for individuals with intellectual disabilities. This study aims to investigate inter-rater reliability between multi-disciplinary health care professionals within the North West of England. Design/methodology/approach A small-scale quantitative study investigated reliability between raters on the DSD clinical support tool. A data set of 60 rating tools for 30 individuals was used. Descriptive statistics and Kappa coefficient explored agreement. Findings The DSD clinical support tool was found to have strong inter-rater reliability between individual items and the differences between individual scores were spread suggesting variance found could not be attributed to specific questions. Strong inter-rater reliability was found in the overall ratings. Research limitations/implications Results suggest the DSD clinical support tool provides stratification for risk of admission ratings independent of who completes it. Future studies could investigate inter-rater reliability between organisations, i.e. health and social care professionals, and use a larger data sample to ensure generalisability. Replication of the study within child and adolescent services using the children’s DSD clinical support tool is also recommended. Originality/value The DSD clinical support tool has been implemented within the child and adult intellectual disability services across the North West. As more teams across England consider its implementation, the study provides reassurance that coding agreement is high, allowing for stratification for risk of admission independent of the rater.
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Lipton, Jonathan, and Jan A. Hazelzet. "Clinical decision support systems: Important tools when appropriately used*." Pediatric Critical Care Medicine 10, no. 1 (January 2009): 128–29. http://dx.doi.org/10.1097/pcc.0b013e31819838f9.

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Walsh, Kieran. "What patients think of online clinical decision support tools." BMJ Simulation and Technology Enhanced Learning 4, no. 1 (February 17, 2017): 41–42. http://dx.doi.org/10.1136/bmjstel-2017-000198.

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Dissertations / Theses on the topic "Clinical support tools"

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Denney, Kimberly B. "Assessing Clinical Software User Needs for Improved Clinical Decision Support Tools." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1563.

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Consolidating patient and clinical data to support better-informed clinical decisions remains a primary function of electronic health records (EHRs). In the United States, nearly 6 million patients receive care from an accountable care organization (ACO). Knowledge of clinical decision support (CDS) tool design for use by physicians participating in ACOs remains limited. The purpose of this quantitative study was to examine whether a significant correlation exists between characteristics of alert content and alert timing (the independent variables) and physician perceptions of improved ACO quality measure adherence during electronic ordering (the dependent variable). Sociotechnical theory supported the theoretical framework for this research. Sixty-nine physician executives using either a Cerner Incorporated or Epic Systems EHR in a hospital or health system affiliated ACO participated in the online survey. The results of the regression analysis were statistically significant, R2 = .108, F(2,66) = 3.99, p = .023, indicating that characteristics of alert content and timing affect physician perceptions for improving their adherence to ACO quality measures. However, analysis of each independent variable showed alert content highly correlated with the dependent variable (p = .007) with no significant correlation found between workflow timing and the dependent variable (p = .724). Understanding the factors that support physician acceptance of alerts is essential to third-party software developers and health care organizations designing CDS tools. Providing physicians with improved EHR-integrated CDS tools supports the population health goal of ACOs in delivering better patient care.
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Washington, Tiffany K. "The Effects of Using Clinical Support Tools to Prevent Treatment Failure." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2459.

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To date, outcome research suggests that providing clinicians with patient progress feedback and problem-solving tools is effective in improving therapeutic outcome for clients who are predicted to have a negative treatment outcome. To expand upon this body of research, the current study examined the efficacy of using these problem-solving tools (Clinical Support Tools; CST) to reduce the risk of treatment failure and enhance positive outcome with 118 clients who were not identified as at -risk for a negative outcome. Results of this study indicated that the intervention failed to lower the rate of becoming an at-risk case or to enhance treatment outcome. A possible explanation for the null results observed is poor treatment compliance. Based on the findings of this study, the CST cannot be recommended as an intervention across the broad range of clients who enter treatment. However, qualitative analysis results reflect positive indicators for continued research with at-risk cases.
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White, Melissa Mallory. "Deterioration in Individual Psychotherapy: The Effectiveness of theClinical Support Tools." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7479.

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Researchers have found evidence that when clinicians use an evidence-based feedback system that uses Clinical Support Tools (CST) for not-on-track clients, deterioration rates fall and success rates improve (Shimokawa et al., 2010). Despite multiple studies finding evidence in support of using the CST, there has been a discrepancy between effect sizes (i.e., d = 0.5; Simon et al., 2012). As such, further replicate of these past studies is needed to discover if small effect sizes still persist and if so, what possible variables may contribute to inconsistent findings. For the current study, it was predicted that the use of the CST would result in significantly lower OQ-45 scores at treatment termination after controlling for the intake OQ-45 score. Additionally, previous research indicated that the combined intervention of the progress feedback plus CST would significantly reduce deterioration rates with those NOT. Out of 1,122 participants, 172 were randomly assigned to one of two conditions: The CST feedback group (n = 71) and the no CST feedback group (n = 101). There was not a significant difference in the mean OQ-45 scores for the CST feedback group (M = 2.39, SD = 20.95) and the no CST feedback group (M = 4.17, SD = 19.74). The results of this study raise questions about how regularly the therapists were monitoring their clients' progress feedback and whether the CST are effective. Additionally, the author evaluates the timing of when the CST were administered to clients and when therapists reviewed the feedback.
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Slade, Karstin Lee. "Improving Psychotherapy Outcome: The Use of Immediate Electronic Feedback and Revised Clinical Support Tools." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2556.pdf.

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Harris, Mitchell Wayne. "Providing Patient Progress Information and Clinical Support Tools to Therapists: Effects on Patients at Risk for Treatment Failure." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/3079.

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Patient-focused research systems have been developed to monitor and inform therapists of patients' treatment progress in psychotherapy as a method to enhance patient outcome. The current study examined the effects of providing treatment progress information and problem-solving tools to both patients and therapists during the course of psychotherapy. Three hundred seventy patients at a hospital-based outpatient psychotherapy clinic were randomly assigned to one of two treatment groups: treatment-as-usual, or an experimental condition based on the use of patient/therapist feedback and clinical decision-support tools. Patients in the feedback condition were significantly more improved at termination than the patients in the treatment as usual condition. These findings are consistent with past research on these approaches although the effect size was smaller in this study. Treatment effects were not a consequence of different amounts of psychotherapy received by experimental and control clients. Not all therapists were aided by the feedback intervention.
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Weyand, Sabine A. "Development and Usability Testing of a Neonatal Intensive Care Unit Physician-Parent Decision Support Tool (PPADS)." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20141.

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This thesis presents the development and evaluation of a computerized physician-parent decision support tool for a neonatal intensive care unit (NICU), known as Physician and Parent Decision Support (PPADS). The NICU is a specialized hospital unit that treats very-ill neonates. Many difficult care decisions are made daily for this vulnerable population. The PPADS tool aims to augment current NICU decision-making by helping parents and physicians make more informed decisions, improving physician-parent communication, increasing parent decision-making satisfaction, decreasing conflict, and increasing decision efficiency. The development of the PPADS tool followed a five-step methodology: assessing the clinical environment, establishing the design criteria, developing the system design, implementing the system, and performing usability testing. Usability testing of the PPADS tool was performed on neonatologists and on parents of neonates who have graduated (survived) from a tertiary level NICU. The usability testing demonstrated the usefulness and ease of use of the tool.
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Kimball, Kevin Larry. "Toward Determining Best Items for Identifying Therapeutic Problem Areas." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2519.

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While most clients show improvement in therapy, anomalously, 5% to 10% actually worsen, and a significant minority of clients shows little or no response to therapy. Earlier studies developed clinical support tools (CSTs) designed to provide feedback to therapists about potential problem areas and to improve the likelihood of a positive outcome for clients identified as at-risk for a negative outcome in therapy (Harmon et. al. 2007; Slade, Lambert, Harmon, Smart, & Bailey, 2008; Whipple et al., 2003). While varying from study to study, the CSTs looked at five domains: therapeutic alliance, motivation to change, social support, life events, and perfectionism. More than 100 questions were used to assess these domains. The major goal of this study was to streamline the CST measures to increase efficiency. Toward that end, a new instrument consisting of 37 questions was developed by administering questionnaires to 169 patients at a rural Utah mental health center. In addition, the life events and social support questions were given to 76 students at Brigham Young University and 88 randomly selected residents of Utah County. Using item response analysis and mean scores for each dimension, subscale cut scores were developed for four dimensions: therapeutic alliance, motivation for therapy, social support, and life events. The perfectionism subscale was dropped from the questionnaire because perfectionism was deemed to be too stable to be useful for the intended use of the measure. Cut scores were also developed for each individual question. These subscale and individual item cut scores are intended to help clinicians identify potential problem areas to be explored during the course of therapy.
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Acevedo, Lipes Andrea Milena. "Deep Learning System for the Automatic Classification of Normal and Dysplastic Peripheral Blood Cells as a Support Tool for the Diagnosis." Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/671387.

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Clinical pathologists identify visually many morphological features to characterize the different normal cells, as well as the abnormal cell types whose presence in peripheral blood is the evidence of serious diseases. Disadvantages of visual morphological analysis are that it is time consuming, needs expertise to perform an objective review of the smears and is prone to inter-observer variability. Also, most of the morphological descriptions are given in qualitative terms and there is a lack of quantitative measures. The general objective of this thesis is the automatic recognition of normal and dysplastic cells circulating in blood in myelodysplastic syndromes using convolutional neural networks and digital image processing techniques. In order to accomplish this objective, this work starts with the design and development of a Mysql database to store information and images from patients and the development of a first classifier of four groups of cells, using convolutional neural networks as feature extractors. Then, a high- quality dataset of around 17,000 images of normal blood cells is compiled and used for the development of a recognition system of eight groups of blood cells. In this work, we compare two transfer learning approaches to find the best to classify the different cell types. In the second part of the thesis, a new convolutional neural network model for the diagnosis of myelodysplastic syndromes is developed. This model was validated by means of a proof of concept. It is considered among the first models that have been built for diagnosis support. The final work of the thesis is the integration of two convolutional networks in a modular system for the automatic classification of normal and abnormal cells. The methodology and models developed constitute a step forward to the implementation of a modular system to recognize automatically all cell types in a real setup in the laboratory.
Los especialistas de laboratorio identifican visualmente muchas características morfológicas para identificar las diferentes células normales, así como los tipos de células anormales, cuya presencia en sangre periférica es evidencia de enfermedades graves. Algunas de las desventajas del análisis morfológico visual incluyen que toma mucho tiempo, necesita experiencia para realizar una revisión objetiva de los frotis y es propenso a la variabilidad entre observadores. Además, la mayoría de las descripciones morfológicas se proporcionan en términos cualitativos. Debido a lo expuesto anteriormente, es necesario establecer medidas cuantitativas. El objetivo general de esta tesis es el reconocimiento automático de células normales y células displásicas circulantes en sangre en síndromes mielodisplásicos mediante redes neuronales convolucionales y técnicas de procesamiento digital de imágenes. Para lograr este objetivo, este trabajo comenzó con el diseño y desarrollo de una base de datos Mysql para almacenar información e imágenes de pacientes y el desarrollo de un primer clasificador de cuatro grupos de células, utilizando redes neuronales convolucionales como extractores de características. Luego, se compila un conjunto de datos de alta calidad de alrededor de 17.000 imágenes de células sanguíneas normales y se utiliza para el desarrollo de un sistema de reconocimiento de ocho grupos de células sanguíneas. En este trabajo, comparamos dos enfoques de aprendizaje por transferencia para encontrar el mejor para clasificar los diferentes tipos de células. En la segunda parte de la tesis se desarrolla un nuevo modelo de red neuronal convolucional para el diagnóstico de síndromes mielodisplásicos. Este modelo fue validado mediante prueba de concepto. Se considera uno de los primeros modelos que se han construido para apoyar el diagnóstico. El trabajo final de la tesis es la integración de dos redes convolucionales en un sistema modular para la clasificación automática de células normales y anormales. La metodología y los modelos desarrollados constituyen un paso adelante hacia la implementación de un sistema modular para reconocer automáticamente todos los tipos de células en una configuración real en el laboratorio.
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Pópulo, Jorge Miguel Martins. "High-tech diagnostic imaging clinical decision support tools adoption : study using a system dynamics approach." Dissertação, 2010. http://hdl.handle.net/10216/59802.

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Pópulo, Jorge Miguel Martins. "High-tech diagnostic imaging clinical decision support tools adoption : study using a system dynamics approach." Master's thesis, 2010. http://hdl.handle.net/10216/59802.

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Books on the topic "Clinical support tools"

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Translational research and clinical practice: Basic tools for medical decision making and self-learning. New York: Oxford University Press, 2011.

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Evans, Charlotte, Anne Creaton, Marcus Kennedy, and Terry Martin, eds. Respiratory support. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198722168.003.0008.

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The aim of the respiratory support chapter is to provide the retrievalist with an armamentarium of information regarding advanced airway management. The chapter details the approach to the difficult airway with assessment tools and clinical features. Airway devices are discussed and intubation methods outlined. Practical guidance is provided on how to set up your non-invasive and invasive modes of ventilation with sections on mechanical ventilation of the healthy lung.
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Bhugra, Dinesh, Antonio Ventriglio, and Kamaldeep S. Bhui. Assessment tools and cultural formulation. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198723196.003.0005.

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Assessment tools can support clinical assessments but cannot replace them. They can be used for a number of purposes. They are standardized tools but may require some adjustments if they are being used in cultures other than those in which they were developed. If they have been translated into other languages, it is essential that translation be carried out with proper conceptual equivalence rather than simple literal translation. The experiences of migration and acculturation need to be assessed carefully. Furthermore, for the first time DSM-5 includes concepts of cultural formulation; the key features include cultural identity of individuals, cultural explanations of their illnesses, cultural factors related to their environment and levels of functioning, various cultural elements of relationship between the clinician and the individual, and overall cultural assessment. At the heart of cultural formulation lie the principles of cultural sensitivity.
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Coyne, Imelda, Freda Neill, and Fiona Timmins, eds. Clinical Skills in Children's Nursing. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780199559039.001.0001.

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Children's Nurses require excellent clinical skills to provide high quality care to children and young people across a range of different ages. After the first year of their training, children's nursing students must master skills of increasing complexity whilst developing clinical judgement and confidence. Therefore, it is vital that links are made to children's biology and development, family needs, legal issues and problem solving but until now, it has been hard to find all this in one place. Clinical Skills for Children's Nursing is designed for children's and general nursing students in second year onwards to facilitate the transition from closely supervised beginners, to qualified professionals. By clearly explaining essential principles, evidence and special considerations, this text helps students to build up their confidence, not just in performing skills, but also in decision-making in readiness for registration and beyond. Step-by-step guides to performing core and advanced procedures are presented in tables for easy comprehension and revision, illustrated by photographs and drawings. Each skill draws on the available evidence base, which is updated regularly on the accompanying Online Resource Centre. Uniquely, this text develops students' critical thinking skills and ability to deliver child centred care by providing clear links to anatomical, physiological and child development milestones as well as regular nursing alerts which help prevent readers from making common mistakes. Clearly reflecting the Nursing and Midwifery Council's Essential Skills Clusters for registration and beyond, Clinical Skills for Children's Nursing is designed to support student nurses develop into competent practitioners. Supported by a dedicated Online Resource Centre with up-to-date evidence, realistic scenarios, and a wealth of other tools. On the Online Resource Centre: For registered lecturers and mentors: - Figures from the book, ready to download and use in teaching material For students: - Evidence, guidelines and protocols, reviewed and updated every 6 months - Over 40 interactive scenarios - Active web links provide a gateway to the articles cited in the book - Flashcard glossary to help learn key terms
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Fye, W. Bruce. The Reinvention of the American Heart Association and the Invention of Cardiac Catheterization. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199982356.003.0008.

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President Harry Truman signed the National Heart Act in 1948, which resulted in the creation of the National Heart Institute and started federal funds flowing to academic centers to support cardiovascular research. Mayo cardiologist Arlie Barnes’s term as president of the American Heart Association coincided with its transformation from a low-budget professional society into a large voluntary health organization that raised funds from the public to support its programs. World War II research into shock contributed to the development of cardiac catheterization as a clinical diagnostic tool. Mayo’s wartime research program that focused on ways to protect fighter pilots from blackouts due to high gravitational forces led to the invention of technologies to measure blood pressure and blood oxygen content. Physiologist Earl Wood used these tools in Mayo’s cardiac catheterization laboratory, which was established at the institution in 1947. The clinic helped pioneer the emerging field of cardiac catheterization.
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Feinstein, Robert E. Violence and Suicide. Edited by Robert E. Feinstein, Joseph V. Connelly, and Marilyn S. Feinstein. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190276201.003.0018.

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Patients exhibiting violent or suicidal behavior have psychiatric symptoms varying along a spectrum of risk, from minimal to fatal. Evidence supports screening patients for intimate partner violence and suicide risk. Clinical care focuses on establishing a team and a working alliance, determining the “Why now?” of dangerousness, and using clinical judgments, risk assessment tools, a critical pathway, and a risk registry. Clinical care includes assessment of (1) violent or suicidal ideation, (2) recent dangerous behaviors, (3) past history of risky behaviors, (4) support system, (5) substance use, (6) cooperation with treatment, and (7) clinician reactions (8) diagnosis of medical and neurologic comorbidities. A multidisciplinary team can optimally manage these patients by deciding on the level of care needed for each problem or episode. Care can be delivered by using a practice registry and a critical pathway and focusing care on psychotherapy, with medications as needed. Steps are outlined for managing intimate partner violence.
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Feuerstein, Seth. The Integration of New Technological Approaches in OCD Care. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0065.

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This chapter explores topics related to how technology is impacting care for patients with obsessive compulsive disorder (OCD) and providing new resources for their caregivers. It explores what has occurred and is occurring today in clinical environments with the introduction of new technologies. It goes on to describe current research into how to leverage newer technologies, and discusses what we might expect in a few years. Technology can mean many things, including new medications, novel pharmaceutical approaches such as immunotherapeutics, genetic testing to support treatment selection, and potential new diagnostic tools such as fMRI. The emphasis here is on software technology; other areas of clinically relevant technological advances are covered elsewhere in this text.
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Katritsis, Demosthenes G., Bernard J. Gersh, and A. John Camm. Clinical Cardiology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199685288.001.0001.

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The purpose of this book is to provide a useful, easily accessible, and user-friendly clinical tool that can be used in the every day clinical practice. Definition, epidemiology, aetiology, and pathophysiology and genetics of cardiac diseases are summarized according to recent evidence. Presentation of diseases, physical findings and investigations for a contemporary, evidence- based approach are organized in a clear and instructive manner. The management of the patient is presented according to most recent randomized control trials and recommendations of guidelines by the ACC/AHA and the ESC. Most recent guidelines and updates have been collected for each topic, and current recommendations have been extracted and presented in abbreviated tables. The reference list is aimed at presenting seminal studies that support statements in the book, randomized control trials that are dictating modern management, and scholarly, instructive reviews that have appeared in the major cardiology journals. Recent only articles are presented in order to guide potential further reading. The on-line editions of the book are updated on a regular basis.
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Randhawa, Gurvaneet S., and Edwin A. Lomotan. Harnessing Big Data-Based Technologies to Improve Cancer Care. Edited by David A. Chambers, Wynne E. Norton, and Cynthia A. Vinson. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190647421.003.0034.

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Big data promises to harness the power of advanced computing to transform health and health care, including cancer research and care delivery. In health care, big data can be generated by administrative and clinical processes, by patients and families, and by machines. Ultimately, the goal of big data is to transform data into actionable knowledge with attention to four dimensions: person-level data collection; data access, exchange, and aggregation; population-level analytics; and provider, researcher, or patient-facing clinical decision support. A fabric of trust forms the basis for policies for governance, privacy and security, and confidentiality. This chapter offers several examples of the application of big data along the cancer care continuum, ranging from primary prevention through diagnosis, survivorship, and end-of-life care. Challenges to the effective collection and use of big data include its integration with health care delivery; interoperability; and the need for validated, well-designed informatics tools.
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Brown, Jacqueline A., and Shane R. Jimerson, eds. Supporting Bereaved Students at School. Oxford University Press, 2017. http://dx.doi.org/10.1093/med:psych/9780190606893.001.0001.

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Given that most children will experience the death of at least one close or special person prior to high school graduation, and because the vast majority of children attend school on a regular basis, school-based mental health professionals must be prepared to effectively support bereaved students. Supporting Bereaved Students at School is a contemporary guide that provides school-based mental health professionals with the information they need to support bereaved students, with a particular emphasis on practitioners in the fields of school psychology, school counseling, school social work, and clinical child psychology. The book covers how these professionals can help children and adolescents cope with their emotional, physical, and social reactions during the period of grief, lasting months or years, following a significant death in their lives. The book is divided into two sections, the first focusing on foundational knowledge and the second offering a range of evidence-based intervention strategies. This book provides school-based professionals and graduate students with tools that can be easily integrated into their daily practice.
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Book chapters on the topic "Clinical support tools"

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Cochon, Laila, and Ramin Khorasani. "Clinical Decision Support Tools for Order Entry." In Quality and Safety in Imaging, 21–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/174_2017_162.

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Cypko, Mario A. "Tools for Guided BN Modeling." In Development of Clinical Decision Support Systems using Bayesian Networks, 81–102. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-32594-7_8.

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Wasylewicz, A. T. M., and A. M. J. W. Scheepers-Hoeks. "Clinical Decision Support Systems." In Fundamentals of Clinical Data Science, 153–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_11.

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AbstractClinical decision support (CDS) includes a variety of tools and interventions computerized as well as non- computerized. High-quality clinical decision support systems (CDSS), computerized CDS, are essential to achieve the full benefits of electronic health records and computerized physician order entry. A CDSS can take into account all data available in the EHR making it possible to notice changes outside the scope of the professional and notice changes specific for a certain patient, within normal limits. However, to use of CDSS in practice, it is important to understand the basic requirements of these systems.This chapter shows in what way CDSS can support the use of clinical data science in daily clinical practice. Moreover, it explains what types of CDSS are available and how such systems can be used. However, to achieve high-quality CDSS which is effective in use requires thoughtful design, implementation and critical evaluation. Therefore, challenges surrounding implementation of a CDSS are discussed, as well as a strategies to develop and validate CDSS.
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Febretti, Alessandro, Karen Dunn Lopez, Janet Stifter, Andrew E. Johnson, Gail M. Keenan, and Diana J. Wilkie. "A Component-Based Evaluation Protocol for Clinical Decision Support Interfaces." In Design, User Experience, and Usability. Design Philosophy, Methods, and Tools, 232–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39229-0_26.

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Kubben, Pieter. "Mobile Apps." In Fundamentals of Clinical Data Science, 171–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_12.

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AbstractMobile apps are an important source of data, but also an important tool for applying models. The goal of this chapter is to provide a short overview of relevant app development background including data collection tools, as well as provide a literature review on mobile clinical decision support systems. Regulatory issues will be touched upon to create awareness for this important topic.
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Renapurkar, Shravan, and Robert A. Strauss. "Lasers in Oral and Maxillofacial Surgery." In Oral and Maxillofacial Surgery for the Clinician, 817–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1346-6_39.

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AbstractLasers have become an integral part of Oral and Maxillofacial Surgery (OMS) since their first use decades ago. Lasers not only have multiple practical advantages over conventional cutting tools used in OMS, but also have supported the development of newer procedures, which were not possible before. Although lasers do have some disadvantages, recent technological advances and developments have helped reduce these. Vast clinical experience and literature data support their safety, reliability and efficacy in surgical applications. This chapter highlights the basic principles, advantages, disadvantages, practical applications of lasers along with safety concerns.
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Tavazzi, Erica, Camille L. Gerard, Olivier Michielin, Alexandre Wicky, Roberto Gatta, and Michel A. Cuendet. "A Process Mining Approach to Statistical Analysis: Application to a Real-World Advanced Melanoma Dataset." In Lecture Notes in Business Information Processing, 291–304. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72693-5_22.

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AbstractThanks to its ability to offer a time-oriented perspective on the clinical events that define the patient’s path of care, Process Mining (PM) is assuming an emerging role in clinical data analytics. PM’s ability to exploit time-series data and to build processes without any a priori knowledge suggests interesting synergies with the most common statistical analyses in healthcare, in particular survival analysis. In this work we demonstrate contributions of our process-oriented approach in analyzing a real-world retrospective dataset of patients treated for advanced melanoma at the Lausanne University Hospital. Addressing the clinical questions raised by our oncologists, we integrated PM in almost all the steps of a common statistical analysis. We show: (1) how PM can be leveraged to improve the quality of the data (data cleaning/pre-processing), (2) how PM can provide efficient data visualizations that support and/or suggest clinical hypotheses, also allowing to check the consistency between real and expected processes (descriptive statistics), and (3) how PM can assist in querying or re-expressing the data in terms of pre-defined reference workflows for testing survival differences among sub-cohorts (statistical inference). We exploit a rich set of PM tools for querying the event logs, inspecting the processes using statistical hypothesis testing, and performing conformance checking analyses to identify patterns in patient clinical paths and study the effects of different treatment sequences in our cohort.
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Li, David Z., Masaru Ishii, Russell H. Taylor, Gregory D. Hager, and Ayushi Sinha. "Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision." In Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures, 54–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60946-7_6.

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Rajapuri, Anushri Singh, Radhika Ravindran, Kevin Horan, Sherri Bucher, and Saptarshi Purkayastha. "Essential Care for Every Baby: Neonatal Clinical Decision Support Tool." In Advances in Intelligent Systems and Computing, 189–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50838-8_26.

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Vijayalakshmi, S., Savita, S. P. Gayathri, and S. Janarthanan. "Blockchain Security for Artificial Intelligence-Based Clinical Decision Support Tool." In Internet of Things, Artificial Intelligence and Blockchain Technology, 209–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74150-1_10.

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Conference papers on the topic "Clinical support tools"

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Fairhurst, M. C., R. M. Guest, N. Donnelly, J. Potter, A. Deighton, and M. Patel. "Engineering software tools for assessment of visuo-spatial neglect." In IEE Colloquium on Intelligent Decision Support in Clinical Practice. IEE, 1998. http://dx.doi.org/10.1049/ic:19980788.

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Ifeachor, E. C., J. M. Garibaldi, and J. Skinner. "Intelligent decision support tools for the management of labour: the INFANT and DataCare expert systems." In IEE Colloquium on Intelligent Decision Support in Clinical Practice. IEE, 1998. http://dx.doi.org/10.1049/ic:19980792.

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Gallerani, Massimo, Dario Pelizzola, Marcello Pivanti, Giovanni Guerra, Michela Boni, Evelina Lamma, and Elena Bellodi. "Reducing Laboratory Examinations by a Computer-Aided Clinical Decision Support System." In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2015. http://dx.doi.org/10.1109/ictai.2015.112.

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Kerexeta, Jon, Jordi Torres, Naiara Muro, Kristin Rebescher, and Nekane Larburu. "Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools." In 13th International Conference on Health Informatics. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008952200950105.

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Ceccarelli, Michele, Antonio Donatiello, and Dante Vitale. "KON^3: A Clinical Decision Support System, in Oncology Environment, Based on Knowledge Management." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.46.

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Aljarboa, Soliman, and Shah Jahan Miah. "Assessing the Acceptance of Clinical Decision Support Tools using an Integrated Technology Acceptance Model." In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). IEEE, 2020. http://dx.doi.org/10.1109/csde50874.2020.9411594.

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Bella, R., A. Langer, M. Csanádi, A. Zemplényi, and L. Botz. "5PSQ-110 Computerised physician order entry systems and related clinical decision support tools in inpatient care – barriers of cost-effectiveness." In 24th EAHP Congress, 27th–29th March 2019, Barcelona, Spain. British Medical Journal Publishing Group, 2019. http://dx.doi.org/10.1136/ejhpharm-2019-eahpconf.543.

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Bender, Dieter, Ali Jalali, and C. Nataraj. "Prediction of Periventricular Leukomalacia Occurrence in Neonates Using a Novel Unsupervised Learning Method." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6304.

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Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). A preceding study indicated that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This preliminary work, as well as the complex nature of the PVL data suggested integration of the active learning algorithm into the overall SVM framework. The present study supports this initial hypothesis and shows that active learning SVM type classifier performs considerably well and outperforms normal SVM type classifiers when dealing with clinical data of high dimensionality.
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Federico, Monica J., Diane Redmond, Diane Swietlik, Jennifer Reese, Jodi Thrasher, Linda Worack, and Daniel Hyman. "Using Quality Improvement Methods, Clinical Decision Support Tools, And A Multidisciplinary Team To Improve Compliance With Joint Commission Asthma Action Plan Standards." In American Thoracic Society 2011 International Conference, May 13-18, 2011 • Denver Colorado. American Thoracic Society, 2011. http://dx.doi.org/10.1164/ajrccm-conference.2011.183.1_meetingabstracts.a2600.

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Zaidi, Syed Hassan, Imran Akhtar, Syed Imran Majeed, Tahir Zaidi, and Muhammad Saif Ullah Khalid. "Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-66836.

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This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.
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Reports on the topic "Clinical support tools"

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Biesecker, Barbara, Melissa Raspa, Douglas Rupert, Rebecca Moultrie, Robert Furberg, and Lauren A. McCormack. Making Clinical Trials More Patient-Centered Using Digital Interactive E-Consent Tools. RTI Press, October 2019. http://dx.doi.org/10.3768/rtipress.2019.op.0063.1910.

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Research participants are required to give their consent to participate in clinical trials and nonexempt government-funded studies. The goal is to facilitate participant understanding of the intent of the research, its voluntary nature, and the potential benefits and harms. Ideally, participants make an informed choice whether to participate; one that is based on having sufficient relevant knowledge and that is consistent with their values and preferences. Achieving this objective can be challenging, and as such, many scholars have declared the consent process flawed or “broken.” Moreover, clinical trials are complex studies, and compelling evidence suggests that current consent processes are inadequate in achieving informed choice. E-consent offers a dynamic, engaging consent delivery mode that can effectively support making informed decisions about whether to participate in a trial.
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Schnabel, Filipina, and Danielle Aldridge. Effectiveness of EHR-Depression Screening Among Adult Diabetics in an Urban Primary Care Clinic. University of Tennessee Health Science Center, April 2021. http://dx.doi.org/10.21007/con.dnp.2021.0003.

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Background Diabetes mellitus (DM) and depression are important comorbid conditions that can lead to more serious health outcomes. The American Diabetes Association (ADA) supports routine screening for depression as part of standard diabetes management. The PHQ2 and PHQ9 questionnaires are good diagnostic screening tools used for major depressive disorders in Type 2 diabetes mellitus (DM2). This quality improvement study aims to compare the rate of depression screening, treatment, and referral to behavioral health in adult patients with DM2 pre and post-integration of depression screening tools into the electronic health record (EHR). Methods We conducted a retrospective chart review on patients aged 18 years and above with a diagnosis of DM2 and no initial diagnosis of depression or other mental illnesses. Chart reviews included those from 2018 or prior for before integration data and 2020 to present for after integration. Sixty subjects were randomly selected from a pool of 33,695 patients in the clinic with DM2 from the year 2013-2021. Thirty of the patients were prior to the integration of depression screening tools PHQ2 and PHQ9 into the EHR, while the other half were post-integration. The study population ranged from 18-83 years old. Results All subjects (100%) were screened using PHQ2 before integration and after integration. Twenty percent of patients screened had a positive PHQ2 among subjects before integration, while 10% had a positive PHQ2 after integration. Twenty percent of patients were screened with a PHQ9 pre-integration which accounted for 100% of those subjects with a positive PHQ2. However, of the 10% of patients with a positive PHQ2 post-integration, only 6.7 % of subjects were screened, which means not all patients with a positive PHQ2 were adequately screened post-integration. Interestingly, 10% of patients were treated with antidepressants before integration, while none were treated with medications in the post-integration group. There were no referrals made to the behavior team in either group. Conclusion There is no difference between the prevalence of depression screening before or after integration of depression screening tools in the EHR. The study noted that there is a decrease in the treatment using antidepressants after integration. However, other undetermined conditions could have influenced this. Furthermore, not all patients with positive PHQ2 in the after-integration group were screened with PHQ9. The authors are unsure if the integration of the depression screens influenced this change. In both groups, there is no difference between referrals to the behavior team. Implications to Nursing Practice This quality improvement study shows that providers are good at screening their DM2 patients for depression whether the screening tools were incorporated in the EHR or not. However, future studies regarding providers, support staff, and patient convenience relating to accessibility and availability of the tool should be made. Additional issues to consider are documentation reliability, hours of work to scan documents in the chart, risk of documentation getting lost, and the use of paper that requires shredding to comply with privacy.
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Clinical decision support tools. National Institute for Health Research, October 2020. http://dx.doi.org/10.3310/collection_42068.

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