Relevant bibliographies by topics / Clinical decision making

Contents

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

Academic literature on the topic 'Clinical decision making'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Clinical decision making"

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Kostbade Hughes, Katherine, and Wendy B. Young. "Decision Making Stability of Clinical Decisions." Nurse Educator 17, no. 3 (1992): 12–16. http://dx.doi.org/10.1097/00006223-199205000-00010.

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Richards, Kathy, Katherine Carroll Britt, Norma Cuellar, Yanyan Wang, and Janet Morrison. "Clinical Decision-Making." Nursing Clinics of North America 56, no. 2 (2021): 265–74. http://dx.doi.org/10.1016/j.cnur.2021.02.005.

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Goodwin, C. Rory, Nancy Abu-Bonsrah, Mark H. Bilsky, et al. "Clinical Decision Making." SPINE 41 (October 2016): S171—S177. http://dx.doi.org/10.1097/brs.0000000000001836.

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Records, Nancy L., and J. Bruce Tomblin. "Clinical Decision Making." Journal of Speech, Language, and Hearing Research 37, no. 1 (1994): 144–56. http://dx.doi.org/10.1044/jshr.3701.144.

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Ludwick, Ruth. "Clinical Decision Making." Orthopaedic Nursing 18, no. 1 (1999): 65???72. http://dx.doi.org/10.1097/00006416-199901000-00013.

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McCullough, Gary H., and Balaji Rangarathnam. "Clinical Decision Making." Seminars in Speech and Language 40, no. 03 (2019): 149–50. http://dx.doi.org/10.1055/s-0039-1688996.

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Croskerry, Pat, and Gordon Tait. "Clinical Decision Making." Academic Medicine 88, no. 2 (2013): 149–50. http://dx.doi.org/10.1097/acm.0b013e31827b258d.

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Sherbino, Jonathan, Geoffrey R. Norman, and Wolfgang Gaissmaier. "Clinical Decision Making." Academic Medicine 88, no. 2 (2013): 150–51. http://dx.doi.org/10.1097/acm.0b013e31827b2941.

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Restrepo, Daniel, Katrina A. Armstrong, and Joshua P. Metlay. "Annals Clinical Decision Making: Avoiding Cognitive Errors in Clinical Decision Making." Annals of Internal Medicine 172, no. 11 (2020): 747–51. http://dx.doi.org/10.7326/m19-3692.

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Metlay, Joshua P., and Katrina A. Armstrong. "Annals Clinical Decision Making: Incorporating Perspective Into Clinical Decisions." Annals of Internal Medicine 172, no. 11 (2020): 743–46. http://dx.doi.org/10.7326/m19-3469.

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Dissertations / Theses on the topic "Clinical decision making"

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Jensen, Jan L. "Paramedic Clinical Decision Making." BMC Emergency Medicine, 2009. http://hdl.handle.net/10222/12738.

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Paramedics are responsible for the care of patients requiring emergency assistance in the out of hospital setting. These health care providers need to make many decisions during the course of an emergency call. This thesis on paramedic clinical decision-making includes two studies, intended to determine which decisions paramedics make that are most important for patient safety and clinical outcome, and what thinking strategies paramedics rely on to make decisions. Forty-two decisions were found to be most important for outcome and safety. The highest decision density of an emergency call is du
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Wong, Thomas Kwok Shing. "Clinical decision making in nursing." Thesis, Glasgow Caledonian University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283692.

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Gurbutt, Russell. "Demonstrating nurses' clinical decision-making." Thesis, University of Central Lancashire, 2005. http://clok.uclan.ac.uk/21842/.

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The study answers the question: 'How can nurses' properly considered decisions relating to patient care be demonstrated?' Nurses in the United Kingdom have a professional requirement to demonstrate': the properly considered clinical decisions relating to patient care' (UKCC, 1994; NMC, 2002). However, their decisionmaking has been reported as complex and poorly understood, and apart from nursing records, little evidence exists to demonstrate their decisions. The development of the nurses' role as a decision-maker is traced from an origin in Nightingale's text (1860) through to the present day.
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Everitt, Sally. "Clinical decision making in veterinary practice." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12051/.

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Aim The aim of this study is to develop an understanding of the factors which influence veterinary surgeons’ clinical decision making during routine consultations. Methods The research takes a qualitative approach using video-cued interviews, in which one of the veterinary surgeon’s own consultations is used as the basis of a semi-structured interview exploring decision making in real cases. The research focuses primarily on small animal consultations in first opinion practice, however small numbers of consultations from different types of practice are included to highlight contextual influenc
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Winfield, Catherine V. "Clinical decision making in district nursing." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/2830/.

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The two studies described here address the question of how District Nurses determine patients' nursing problems and plan care. The theoretical framework for the investigation is derived from Information Processing Theory. A process tracing methodology was used to capture the content of District Nurses' thinking during an assessment visit to a newly referred patient. Data was collected in the natural setting to ensure ecological validity. The assessment visits were tape recorded and immediately following the visit a stimulated recall session was conducted in which the nurse was asked to describ
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Gil-Herrera, Eleazar. "Classification Models in Clinical Decision Making." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4895.

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In this dissertation, we present a collection of manuscripts describing the development of prognostic models designed to assist clinical decision making. This work is motivated by limitations of commonly used techniques to produce accessible prognostic models with easily interpretable and clinically credible results. Such limitations hinder prognostic model widespread utilization in medical practice. Our methodology is based on Rough Set Theory (RST) as a mathematical tool for clinical data anal- ysis. We focus on developing rule-based prognostic models for end-of life care decision making in
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Wang, Shicai. "Big tranSMART for clinical decision making." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/33348.

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Molecular profiling data based patient stratification plays a key role in clinical decision making, such as identification of disease subgroups and prediction of treatment responses of individual subjects. Many existing knowledge management systems like tranSMART enable scientists to do such analysis. But in the big data era, molecular profiling data size increases sharply due to new biological techniques, such as next generation sequencing. None of the existing storage systems work well while considering the three 'V' features of big data (Volume, Variety, and Velocity). New Key Value data st
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Ogunsanya, Oluwole Victor. "Decision support using Bayesian networks for clinical decision making." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688.

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This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly, it demonstrates the flexibility of the Dynamic Discretization Algorithm in modeling existing medical knowledge using appropriate statistical distributions. Many practical applications of BNs use the relative frequency approach while translating existing medical knowledge to a prior distribution in a BN model. This appr
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Miller, Jaclyn Nieman. "Dreaming and decision-making." Case Western Reserve University School of Graduate Studies / OhioLINK, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=case1055519665.

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Burnett, Thomas. "Bayesian decision making in adaptive clinical trials." Thesis, University of Bath, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760912.

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The key original contribution of this work is the use of a Bayes optimisation framework for the decision made at the interim analysis of Adaptive Enrichment trials. Adaptive Enrichment designs make efficient use of pre-identified patient sub-populations. They begin by recruiting from all eligible patients, then at a pre-planned interim analysis select which sub-populations will be recruited from for the remainder of the sample. We ensure strong control of the Familywise Error Rate whichever sub-populations are selected by constructing an overall hypothesis testing structure using both closed t
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Books on the topic "Clinical decision making"

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D, Cebul Randall, Beck Laurence H, and University of Pennsylvania. Section of General Medicine., eds. Teaching clinical decision making. Praeger, 1985.

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D, Cebul Randall, and Beck Laurence H, eds. Teaching clinical decision making. Praeger, 1985.

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C, Aron David, and Sowers Maryfran, eds. Epidemiology and clinical decision making. Saunders, 1997.

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Richter, Ettinger Ellen, and Rouse Michael W, eds. Clinical decision making in optometry. Butterworth-Heinemann, 1997.

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G, Sheldon Michael, Brooke John 1951-, and Rector Alan, eds. Decision-making ingeneral practice. Stockton, 1985.

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Sullivan, Patricia E. Clinical decision making in therapeutic exercise. Appleton & Lange, 1995.

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Jamison, Jennifer R. Diagnostic decision making in clinical practice. Williams & Wilkins, 1991.

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Liu, Rong, ed. Clinical Decision Making for Improving Prognosis. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2952-6.

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Steele, Scott R., Justin A. Maykel, and Steven D. Wexner, eds. Clinical Decision Making in Colorectal Surgery. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-65942-8.

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Bush, Shane S. Ethical decision-making in clinical neuropsychology. Oxford University Press, 2008.

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Book chapters on the topic "Clinical decision making"

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Ridderikhoff, J. "Clinical decision-making." In Methods in Medicine. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-1097-3_4.

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Croskerry, Pat. "Clinical Decision Making." In Pediatric and Congenital Cardiac Care. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6566-8_33.

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Swallow, Veronica, Joanna Smith, and Trish Smith. "Clinical Decision Making." In Clinical Leadership in Nursing and Healthcare. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119253785.ch8.

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Zia, Jasmine K., and John M. Inadomi. "Clinical Decision Making." In Yamada' s Textbook of Gastroenterology. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118512074.ch34.

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Turner, J. Rick. "Clinical Decision-Making." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_996.

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Downs, Stephen M., and Lydia K. Johns. "Clinical Decision-Making." In Clinical Informatics Study Guide. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22753-5_4.

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Blessing, Nathan W. "Clinical Decision-Making." In Anophthalmia. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29753-4_2.

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Cooke, Mary. "Clinical Decision Making." In Foundations of Adult Nursing. SAGE Publications Ltd, 2015. http://dx.doi.org/10.4135/9781529715071.n8.

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Hill, Kylie, and W. Darlene Reid. "Clinical Decision Making." In Cardiopulmonary Physical Therapy, 2nd ed. Routledge, 2024. http://dx.doi.org/10.4324/9781003522829-7.

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Ost, Lynn Van, Karen Lew Feirman, and Karen Manfré. "Clinical Decision Making." In Athletic Training Exam Review, 7th ed. Routledge, 2024. http://dx.doi.org/10.4324/9781003522683-4.

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Conference papers on the topic "Clinical decision making"

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Attiwi, Madi Hashem, J. O. Almass, Ayham Sameer Faaq Roukiah, et al. "Unremarkable AI: Intelligent Decision Support in Critical Clinical Decision-Making." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923113.

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Dammak, Nourchen Zaghden, Hala Bazine, Ahmed Said Nouri, and Nabil Derbel. "Clinical Decision Making in Cardiovascular Diseases Leveraging Deep Learning." In 2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2025. https://doi.org/10.1109/ssd64182.2025.10989965.

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Hadidi, Tareq. "Knowledge-Based Clinical Decision Making About Radiation in Cancer Treatement." In 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2024. https://doi.org/10.1109/healthcom60970.2024.10880816.

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Kharbas, Vikas Kumar, Y. R. Meena, and Praveen Kumar Thakur. "Enabling Clinical Decision-Making in Smart Healthcare through AI/ML Algorithms." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724925.

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Gkouzionis, Ioannis, Maxime Giot, Scarlet Nazarian, et al. "Classification of Gastrointestinal Tissue In Vivo During Upper GI Cancer Surgery Using Diffuse Reflectance Spectroscopy." In Clinical and Translational Biophotonics. Optica Publishing Group, 2025. https://doi.org/10.1364/translational.2025.tm3f.3.

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Cancers of the upper GI tract remain a major challenge. This preliminary in vivo analysis using diffuse reflectance spectroscopy demonstrated promising tissue discrimination accuracy during surgery (sensitivity: 77.36%, specificity: 95.14% for stomach; sensitivity: 85.24%, specificity: 81.47% for oesophagus), highlighting its potential to enhance surgical decision-making.
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Lin, Dun, Zhiwei Zeng, Jiaxin Liu, and Huajin Long. "Application of Multimodal Sensing and Adaptive Decision Making for Clinical Supply Automation." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695366.

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Chandrakala, Polapalli Navya, K. V. Nageswari, M. L. M. Prasad, Malathi D, and R. Senthamil Selvan. "An Intelligent healthcare system for clinical decision making using Fuzzy neural networks." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059089.

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Yang, Hang, Liang Xiao, Rujun Zhu, Ziji Liu, and Jianxia Chen. "Interlinking Clinical Guidelines via Mining Medical Literature Knowledge for Multi-Morbidity Decision-Making." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00165.

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Usanase, Natacha, Berna Uzun, Leena R. David, Dilber Uzun Ozsahin, and Ilker Ozsahin. "A Decision-Making Approach in The Clinical Diagnosis and Treatment of Vulva Cancer." In 2024 17th International Conference on Development in eSystem Engineering (DeSE). IEEE, 2024. https://doi.org/10.1109/dese63988.2024.10911949.

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Sisimayi, Chenjerai, and Maria Vivien Visaya. "Deployment of AI Models and a Mapper Algorithm to Enhance Clinical Decision-Making." In 2024 International Conference on Sustainable Technology and Engineering (i-COSTE). IEEE, 2024. https://doi.org/10.1109/i-coste63786.2024.11025118.

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Reports on the topic "Clinical decision making"

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Gathu, Michael. What are the effects of interventions to encourage the use of systematic reviews in clinical decision making? SUPPORT, 2017. http://dx.doi.org/10.30846/170111.

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Clinical decision making is often not based on the best available evidence. Reasons for this vary, and may be related to factors within the healthcare setting, patients, or health practitioners. Interventions have been designed to encourage the use of systematic reviews in making clinical decisions as one way of improving clinical decision making.
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Anderson, Richard. The effect of administrative mandate on social workers' clinical decision making. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2742.

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Taylor, Robin. Clinical decision-making and treatment escalation planning in the last year of life. BJUI Knowledge, 2019. http://dx.doi.org/10.18591/bjuik.0361.

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O'Flynn, Keiran. Making the right decision - bias in clinical practice and how to manage it. BJUI Knowledge, 2020. http://dx.doi.org/10.18591/bjuik.0695.

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Manski, Charles, John Mullahy, and Atheendar Venkataramani. Using Measures of Race to Make Clinical Predictions: Decision Making, Patient Health, and Fairness. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30700.

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Han, Nana, Yang Fang, Guozhen Zhao, and Bo Ji. The comparative efficacy and safety of acupuncture for mild and moderate Alzheimer's disease: A systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.11.0014.

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Review question / Objective: According to the current randomized clinical trials (RCT) of acupuncture therapy for Alzheimer's disease (AD), to evaluate their methodology, the quality of evidence and the report are evaluated and summarize evidence of important outcomes of randomized clinical trials. We aim to provide accurate clinical decision-making for acupuncture treatment of Alzheimer's disease. Condition being studied: According to the current randomized clinical trials (RCT) of acupuncture therapy for Alzheimer's disease (AD), to evaluate their methodology, the quality of evidence and the
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Evans, Jon, Ian Porter, Emma Cockcroft, Al-Amin Kassam, and Jose Valderas. Collecting linked patient reported and technology reported outcome measures for informing clinical decision making: a scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.10.0038.

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Review question / Objective: We aim to map out the existing research where concomitant use of patient reported and technology reported outcome measures is used for patients with musculoskeletal conditions. Condition being studied: Musculoskeletal disorders (MSD) covering injuries or disorders of the muscles, nerves, tendons, joints, cartilage, and spinal discs. Musculoskeletal manifestations of joint pathology. Eligibility criteria: 1) Peer-reviewed primary studies and literature reviews. Grey literature not included. 2) Studies which include co-administration of Patient-Reported Outcomes (PRO
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Waldfogel, Julie M., Michael Rosen, Ritu Sharma, Allen Zhang, Eric B. Bass, and Sydney M. Dy. Making Healthcare Safer IV: Opioid Stewardship. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4opioid.

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Objectives. Opioid stewardship interventions promote the appropriate use of prescribed and ordered opioids to reduce the risk of opioid adverse events. Our main objectives were to determine the effectiveness of these interventions in healthcare settings on opioid prescribing and clinical outcomes (e.g., number of opioid prescriptions, opioid dosage, overdose, emergency department visits, and hospitalizations) including unintended consequences (e.g., changes in patient-reported pain intensity), and ways these interventions can be effectively implemented. Methods. We followed rapid review proces
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Syrowatka, Ania, Aneesa Motala, Emily Lawson, and Paul Shekelle. Computerized Clinical Decision Support To Prevent Medication Errors and Adverse Drug Events. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepc_mhs4mederror.

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Objectives. To assess the evidence on the effects of computerized clinical decision support systems (CDSSs) on the prevention of medication errors and adverse drug events, related implementation outcomes such as rates of medication alert overrides, and unintended consequences of use. We also summarized the literature around the effective implementation of a CDSS. Methods. We followed the rapid review processes of the Agency for Healthcare Research and Quality Evidence-based Practice Center Program. We queried PubMed and the Cochrane Library to locate relevant systematic reviews and primary stu
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Christie, Lorna. Interpretable machine learning. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/pn633.

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Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview
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