Relevant bibliographies by topics / Diagnosis – Decision-making

Academic literature on the topic 'Diagnosis – Decision-making'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Diagnosis – Decision-making"

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Elce, Y. A. "Decision-making without a diagnosis." Equine Veterinary Education 28, no. 12 (September 8, 2015): 665–67. http://dx.doi.org/10.1111/eve.12439.

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Pappas, Yannis, Jitka Vseteckova, Nikolas Mastellos, Geva Greenfield, and Gurch Randhawa. "Diagnosis and Decision-Making in Telemedicine." Journal of Patient Experience 6, no. 4 (October 8, 2018): 296–304. http://dx.doi.org/10.1177/2374373518803617.

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This article provides an analysis of the skills that health professionals and patients employ in reaching diagnosis and decision-making in telemedicine consultations. As governmental priorities continue to emphasize patient involvement in the management of their disease, there is an increasing need to accurately capture the provider–patient interactions in clinical encounters. Drawing on conversation analysis of 10 video-mediated consultations in 3 National Health Service settings in England, this study examines the interaction between patients, General Practitioner (GPs), nurses, and consultants during diagnosis and decision-making, with the aim to identify the range of skills that participants use in the process and capture the interprofessional communication and patient involvement in the diagnosis and decision-making phases of telemedicine consultations. The analysis shows that teleconsultations enhance collaborative working among professionals and enable GPs and nurses to develop their skills and actively participate in diagnosis and decision-making by contributing primary care–specific knowledge to the consultation. However, interprofessional interaction may result in limited patient involvement in decision-making. The findings of this study can be used to inform training programs in telemedicine that focus on the development of effective skills for professionals and the provision of information to patients.
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Bumpous, Jeffrey, Miranda D. Celestre, Edmund Pribitkin, and Brendan C. Stack. "Decision Making for Diagnosis and Management." Otolaryngologic Clinics of North America 47, no. 4 (August 2014): 609–23. http://dx.doi.org/10.1016/j.otc.2014.04.007.

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Hoesli, Rebecca C., Andrew G. Shuman, and Carol R. Bradford. "Decision Making for Diagnosis and Management." Otolaryngologic Clinics of North America 50, no. 4 (August 2017): 783–92. http://dx.doi.org/10.1016/j.otc.2017.03.014.

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Esogbue, Augustine O. "Fuzzy medical diagnosis decision making revisited." International Journal of Approximate Reasoning 2, no. 2 (April 1988): 99. http://dx.doi.org/10.1016/0888-613x(88)90076-x.

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Swift, Marcie C., Robert Townsend, Douglas W. Edwards, and Janice K. Loudon. "Decision-Making Data." Professional Case Management 23, no. 4 (2018): 204–12. http://dx.doi.org/10.1097/ncm.0000000000000280.

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Treiger, Teresa M. "Shared Decision-Making." Professional Case Management 25, no. 2 (2020): 56–76. http://dx.doi.org/10.1097/ncm.0000000000000394.

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Campagna, Vivian. "Shared Decision-Making." Professional Case Management 25, no. 6 (November 2020): 358–60. http://dx.doi.org/10.1097/ncm.0000000000000471.

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Gelman, L., B. Murray, T. H. Patel, and A. Thomson. "Novel decision-making technique for damage diagnosis." Insight - Non-Destructive Testing and Condition Monitoring 55, no. 8 (August 1, 2013): 428–32. http://dx.doi.org/10.1784/insi.2012.55.8.428.

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Waldrop, D. P., and M. A. Meeker. "Hospice Decision Making: Diagnosis Makes a Difference." Gerontologist 52, no. 5 (March 2, 2012): 686–97. http://dx.doi.org/10.1093/geront/gnr160.

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Dissertations / Theses on the topic "Diagnosis – Decision-making"

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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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Selgelid, Michael J. "Neugenics : genetically-informed reproductive decision making /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2001. http://wwwlib.umi.com/cr/ucsd/fullcit?p3015841.

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Currey, Judy A., and mikewood@deakin edu au. "Critical care nurses' haemodynamic decision making." Deakin University. School of Nursing, 2003. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20050728.094123.

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For cardiac surgical patients, the immediate 2-hour recovery period is distinguished by potentially life-threatening haemodynamic instability. To ensure optimum patient outcomes, nurses of varying levels of experience must make rapid and accurate decisions in response to episodes of haemodynamic instability. Decision complexity, nurses’ characteristics, and environmental characteristics, have each been found to influence nurses' decision making in some form. However, the effect of the interplay between these influences on decision outcomes has not been investigated. The aim of the research reported in this thesis was to explore variability in critical care nurses' haemodynamic decision making as a function of interplay between haemodynamic decision complexity, nurses' experience, and specific environmental characteristics by applying a naturalistic decision making design. Thirty-eight nurses were observed recovering patients in the immediate 2-hour period after cardiac surgery. A follow-up semi-structured interview was conducted. A naturalistic decision making approach was used. An organising framework for the goals of therapy related to maintaining haemodynamic stability after cardiac surgery was developed to assist the observation and analysis of practice. The three goals of therapy were the optimisation of cardiovascular performance, the promotion of haemostasia, and the reestablishment of normothermia. The research was conducted in two phases. Phase One explored issues related to observation as method, and identified emergent themes. Phase Two incorporated findings of Phase 1, investigating the variability in nurses' haemodynamic decision making in relation to the three goals of therapy. The findings showed that patients had a high acuity after cardiac surgery and suffered numerous episodes of haemodynamic instability during the immediate 2-hour recovery period. The quality of nurses' decision making in relation to the three goals of therapy was influenced by the experience of the nurse and social interactions with colleagues. Experienced nurses demonstrated decision making that reflected the ability to recognise subtle changes in haemodynamic cues, integrate complex combinations of cues, and respond rapidly to instability. The quality of inexperienced nurses' decision making varied according to the level and form of decision support as well as the complexity of the task. When assistance was provided by nursing colleagues during the reception and recovery of patients, the characteristics of team decision making were observed. Team decision making in this context was categorised as either integrated or non integrated. Team decision making influenced nurses' emotions and actions and decision making practices. Findings revealed nurses' experience affected interactions with other team members and their perceptions of assuming responsibility for complex patients. Interplay between decision complexity, nurses' experience, and the environment in which decisions were made influenced the quality of nurses' decision making and created an environment of team decision making, which, in turn, influenced nurses' emotional responses and practice outcomes. The observed variability in haemodynamic decision making has implications for nurse education, nursing practice, and system processes regarding patient allocation and clinical supervision.
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D'Ambrosio, Catherine P. "Computational representation of bedside nursing decision-making processes /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/7266.

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Hickernell, Thomas Slocum. "Statistical decision making with a dual detector probe." Diss., The University of Arizona, 1988. http://hdl.handle.net/10150/184538.

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Conventional imaging techniques for cancer detection have difficulty finding small, deep tumors. Single-detector radiation probes have been developed to search for deep lesions in a patient who has been given a tumor-seeking radiopharmaceutical. These probes perform poorly, however, when the background activity in the patient varies greatly from site to site. We have developed a surgical dual-detector probe that solves the problem of background activity variation, by simultaneously monitoring counts from a region of interest and counts from adjacent normal tissue. A comparison of counts from the detectors can reveal the class of tissue, tumor or normal, in the region of interest. In this dissertation we apply methods from statistical decision theory and derive a suitable comparison of counts to help us decide whether a tumor is present in the region of interest. We use the Hotelling trace criterion with a few assumptions to find a linear discriminant function, which can be reduced to a normalized subtraction of the counts for large background count-rate variations. If area under the ROC curve is our figure of merit, the likelihood ratio is the optimum discriminant. We model likelihood functions of the data given the "tumor" and "no-tumor" hypotheses, and calculate the likelihood ratio. Using a spatial response map of the dual probe, a computer torso phantom, and estimates of activity distribution, we simulate a surgical staging procedure to test the dual probe and the discriminant functions. Results of the simulations show that the dual probe effectively solves the problem of background activity variations when used with any of the discriminant functions derived in this dissertation.
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Sequeira, R. E. "Variations in GP decision making in the diagnosis of lung cancer." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1532674/.

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Background: The United Kingdom's lung cancer patients have lower survival than patients in comparable countries. Delays in diagnosis may contribute to this. There are significant socio-demographic variations in the interval between cancer patients first presenting to their general practitioner (GP) and referral, but it is unclear why these exist. Aim: To examine patient and GP characteristics associated with GPs' referral decisions, focusing on patients with symptoms indicative of lung cancer. Methods: Study 1: Systematic literature review considering non-clinical patient, GP and practice characteristics associated with variations in GPs' referral of patients for investigations or to secondary care. Study 2: GP decision making study: a factorial experiment using interactive multimedia vignettes to examine GPs' decisions to refer patients with symptoms indicative of lung cancer, and a survey to examine factors influencing decision making. Results: Study 1: 11,791 titles were screened; 47 were of sufficient quality and relevance for inclusion. There was strong evidence that patients over 75 were less likely to be investigated or referred, and of variations by patient gender. However few higher quality studies examined associations with patient ethnicity and GP or practice characteristics, or considered why socio-demographic variations occurred. Study 2: 227 GPs completed the study. GPs were less likely to investigate older than younger patients, and black patients than white. The survey identified several factors that GPs believe affect their referral decisions (such as patients' lifestyles), some of which may explain the observed differences in GPs' referral decisions. Conclusions: My thesis identified socio-demographic variations in GP decision making that are independent of clinical characteristics (for lung cancer and more widely) and factors that may underlie these. Further research addressing the extent to which these factors contribute to socio-demographic variations, and the development of primary care interventions which address these findings, could reduce delays in lung cancer diagnosis.
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Davies, Kristen. "Decision Making and Pediatric Bipolar Disorder Assessment/Diagnosis: A Phenomenographic Study." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/539.

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Prior to the 1990s, bipolar disorder, a behavioral disorder characterized by severe mood fluctuations, was not considered an suitable diagnosis for children. However, in recent decades, an increase in pediatric bipolar disorder (PBD) diagnosis has occurred in the U.S. The purpose of this study was to explore the perceptions and lived experiences of licensed mental health clinicians regarding their decision-making processes used during assessment and diagnosis of PBD. This phenomenographic study utilized individual, semi-structured interviews to explore the perceptions and lived experiences of 14 licensed clinicians in the Commonwealth of Massachusetts who assess and diagnose PBD. Data were collected with a 7-question face to face interview. Using NVivo 10 software several key phrases and words were identified, coded, and used to locate patterns, themes, and concepts. Data analysis revealed that significant issues related to PBD assessment and diagnosis may exist, including: inconsistencies in assessment/diagnostic processes; reticence to diagnose the disorder; failure to use available assessment instruments; a lack of attention to comorbidities; and trouble differentiating between PBD symptoms and other issues, such as trauma or dysfunctional family dynamics. Given the reluctance of these mental health professionals to diagnose PBD, implications for social change underscore the important role of education, training, and ongoing clinical supervision to help other mental health professionals accurately assess and diagnose PBD. Recommendations emanating from study findings suggest further research on PBD assessment and diagnosis to help professionals develop more effective diagnostic frameworks for clinical training and practice.
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Cytryn, Kayla N. "Lay reasoning and decision making related to health and illness." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36903.

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Research in decision making has identified the importance of prior knowledge and heuristics on decision making behaviour. These develop with experience in a fashion similar to how domain experts develop specialized knowledge structures and heuristic reasoning patterns. This research is extended to the domain of health and lay decision making in a series of studies characterizing conceptualizations of health and illness, information-seeking strategies, and the impact of medical information on lay decision making. Lay subjects included those with diabetes, heart disease, and no identified ongoing medical diagnosis.
Semi-structured interviews and think aloud methodology were employed. Interviews focused on understanding of health and illness, prior knowledge and beliefs, and decision making. In Study One, subjects were presented with health-related problem scenarios and instructed to think aloud as they reasoned through them to make decisions. In Study Two, subjects (lay and medical) were presented with a telecommunications device and scenarios of data to enter into the system. All data were audiorecorded, transcribed, and analyzed for factors and strategies related to information-seeking and decision making behaviours.
Lay understanding of health and illness was characterized as feeling well and functioning in everyday life. The knowledge used in making decisions was based on experience and socio-cultural tradition. Knowledge about disease was found to be decoupled from decisions to act related to illness. Additional information was sought using four criteria grounded in common experience: accessibility, familiarity, complexity, and credibility. These characteristics influenced interactions between lay people and domain experts, such as health care providers, and with technology designed by experts for lay users.
Both technical and lay people make decisions with incomplete information and uncertain outcomes. For lay people making decisions about health-related issues, this incomplete knowledge is filled in based on everyday life rather than medical and scientific facts.
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Eva, Kevin Wayne. "The influence of differentially processing evidence on diagnostic decision-making /." *McMaster only, 2001.

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Ayres, Brennan S. "The role of teamwork in diagnosis: team diagnostic decision-making in the medical intensive care unit." Thesis, University of Iowa, 2017. https://ir.uiowa.edu/etd/5706.

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Diagnostic errors cause significant patient harm and occur among 15 percent of all clinical diagnoses, but research has yet to effectively target, prevent, and mitigate diagnostic errors from occurring. So far, literature has examined how diagnostician decision-makers perform and reach a clinical diagnosis individually. However, the impact of team-based activities on diagnosis is unknown. The purpose of this study is to describe provider perception on how providers come together as a team in order to complete a clinical diagnosis. As a qualitative descriptive study with overtones of grounded theory, 18 semi-structured interviews of medical intensive care unit providers were audio-recorded, transcribed, and coded generating themes of diagnostic teamwork structure and functioning. Diagnostic teams are described using themes of inter-professional and intra-professional teamwork among roles with and without diagnostic team identity. Novel approaches to diagnostic error research, practice implications for current providers, and applications provided for improving education and team training. By providing preliminary insights on the role of teamwork in diagnostic decision-making, this study may assist future studies that improve diagnostic teamwork and prevent diagnostic errors.
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Books on the topic "Diagnosis – Decision-making"

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Dombal, F. T. De. Surgical decision making. Oxford: Butterworth-Heinemann, 1993.

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Dombal, F. T. De. Surgical decision making. Oxford: Butterworth-Heinemann, 1993.

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1929-, Thornbury John R., and Fryback Dennis G, eds. Decision making in imaging. Chicago: Year Book Medical Publishers, 1989.

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Sox, Harold C. Medical decision making. 2nd ed. Chichester, West Sussex, UK: John Wiley & Sons, 2013.

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C, Sox Harold, ed. Medical decision making. Boston: Butterworths, 1988.

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Stephen, Berman. Pediatric decision making. 3rd ed. St. Louis: Mosby, 1996.

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Stephen, Berman. Pediatric decision making. Philadelphia: B.C. Decker Inc., 1985.

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Pediatric decision making. 2nd ed. Philadelphia, Pa: B.C. Decker, 1991.

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Bajaj, Lalit. Berman's pediatric decision making. 5th ed. Philadelphia, PA: Elsevier/Mosby, 2011.

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H, DeCherney Alan, ed. Decision making in infertility. Toronto: Decker, 1988.

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Book chapters on the topic "Diagnosis – Decision-making"

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Cosby, Karen. "Medical Decision Making." In Diagnosis, 13–39. Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315116334-2.

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Einhorn, Hillel J., and Robin M. Hogarth. "Prediction, Diagnosis, and Causal Thinking in Forecasting." In Behavioral Decision Making, 311–28. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2391-4_14.

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Croskerry, Pat. "Individual Variability in Clinical Decision Making and Diagnosis." In Diagnosis, 129–58. Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315116334-9.

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Gale, Janet, and Philip Marsden. "Diagnosis: Process not Product." In Decision-Making in General Practice, 59–90. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-07159-3_7.

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Sbissi, Samia, and Said Gattoufi. "Contextual Decision Making for Cancer Diagnosis." In Information Technology in Bio- and Medical Informatics, 55–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64265-9_5.

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Collste, Göran. "Moral Decision-Making, Narratives and Genetic Diagnostics." In Ethical Dilemmas in Prenatal Diagnosis, 167–75. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1396-3_13.

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Patel, Bobby. "Treatment Planning and the Decision-Making Process." In Endodontic Diagnosis, Pathology, and Treatment Planning, 103–15. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15591-3_8.

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Vanheule, Stijn. "Dynamics of Decision-Making: The Issue of Reliability in Diagnosis." In Psychiatric Diagnosis Revisited, 7–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44669-1_2.

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Jonas, J. B. "Morphological Aspects in the Diagnosis and Pathogenesis of Glaucomatous Optic Neuropathy." In Glaucoma: Decision Making in Therapy, 51–63. Milano: Springer Milan, 1996. http://dx.doi.org/10.1007/978-88-470-2196-9_10.

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Kiseliova, Tatiana, and Hajo Peters. "Decision Making in Diagnosis of Oral Mucosa Lesions." In Computational Intelligence. Theory and Applications, 33–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_6.

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Conference papers on the topic "Diagnosis – Decision-making"

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Fujita, Hamido. "Reasoning aspects in decision making for medical diagnosis." In 2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2012. http://dx.doi.org/10.1109/saci.2012.6249967.

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Bologna, Eduardo, Marcelo Vaiman, and Matías Alfonso. "Aligning evaluation with achievement objectives: automated exams based on bloom's taxonomy." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19404.

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How many of social sciences students passing introductory statistics courses develop the expected skills to make a meaningful use of statistics? Our diagnosis suggests that an important part of them achieve this through memorization and repetition. This communication reports the in-progress effort to improve the quality of the evaluation of an introductory statistics course in Psychology degree, National University of Córdoba (Argentina). There is a specific demand on the qualifications required of students who pass the subject, which combines with a significant volume of students, so it is necessary to ensure the validity of the evaluations and the automation of their administration and correction. The work consists of the construction of examination items classified according to three criteria: elementary thematic unit it evaluates, cognitive level and degree of difficulty, so that precision exams can be built. The proposal is applicable to classroom or on-line courses.
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Tan, T. Z., G. S. Ng, and C. Quek. "Complementary Decision Making System for Aiding Medical Image Diagnosis." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371321.

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Backory, J. K. "Diagnosis and decision-making for awareness during general anaesthesia." In UKACC International Conference on Control (CONTROL '98). IEE, 1998. http://dx.doi.org/10.1049/cp:19980204.

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Fujita, Hamido. "Fuzzy reasoning prospective on decision making in medical diagnosis of VDS." In 2012 IEEE 10th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2012. http://dx.doi.org/10.1109/sami.2012.6208939.

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Liu, Jiawei, Qi Li, Weirong Chen, Yu Yan, and Lu Jiang. "Fault Diagnosis of PEMFC Systems Based on Decision-making Tree Classifier." In 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018. http://dx.doi.org/10.1109/ei2.2018.8582454.

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Aubrun, Christophe, Jean-Philippe Georges, Dominique Sauter, and Eric Rondeau. "Network calculus based fault diagnosis decision-making for Networked Control Systems." In Factory Automation (ETFA 2008). IEEE, 2008. http://dx.doi.org/10.1109/etfa.2008.4638453.

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KACEM, SAOUSSEN BEL HADJ, AMEL BORGI, and SAMI OTHMAN. "A DIAGNOSIS AID SYSTEM OF AUTISM IN A MULTI-VALUED FRAMEWORK." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0066.

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Sabol, Patrik, Peter Sincak, Kana Ogawa, and Pitoyo Hartono. "Explainable Classifier Supporting Decision-making for Breast Cancer Diagnosis from Histopathological Images." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852070.

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Altayeva, Aigerim, Suleimenov Zharas, and Young Im Cho. "Medical decision making diagnosis system integrating k-means and Naïve Bayes algorithms." In 2016 16th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2016. http://dx.doi.org/10.1109/iccas.2016.7832446.

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Reports on the topic "Diagnosis – Decision-making"

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Marquardt, Kelli. Mis(sed) Diagnosis: Physician Decision Making and ADHD. Federal Reserve Bank of Chicago, 2022. http://dx.doi.org/10.21033/wp-2022-23.

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Boyes, Allison, Jamie Bryant, Alix Hall, and Elise Mansfield. Barriers and enablers for older people at risk of and/or living with cancer to accessing timely cancer screening, diagnosis and treatment. The Sax Institute, July 2022. http://dx.doi.org/10.57022/ieoy3254.

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• Older adults have complex and unique needs that can influence how and when cancer is diagnosed, the types of treatment that are offered, how well treatment is tolerated and treatment outcomes. • This Evidence Check review identified 41 studies that specifically addressed barriers and enablers to cancer screening, diagnosis and treatment among adults aged 65 years and older. • Question 1: The main barriers for older people at risk of and/or living with cancer to access and participate in timely cancer screening relate to lack of knowledge, fear of cancer, negative beliefs about the consequences of cancer, and hygiene concerns in completing testing. The main enablers to participation in timely cancer screening include positive/helpful beliefs about screening, social influences that encourage participation and knowledge. • Question 2: The main barriers for older people at risk of and/or living with cancer to access and/or seek timely cancer diagnosis relate to lack of knowledge of the signs and symptoms of cancer that are distinct from existing conditions and ageing, healthcare accessibility difficulties, perceived inadequate clinical response from healthcare providers, and harmful patient beliefs about risk factors and signs of cancer. The main enablers to accessing and/or seeking a timely cancer diagnosis include knowledge of the signs and symptoms of cancer, and support from family and friends that encourage help-seeking for symptoms. • Question 3: The main barriers for older people at risk of and/or living with cancer in accessing and completing cancer treatment include discrimination against patients in the form of ageism, lack of knowledge, patient concern about the adverse effects of treatment, predominantly on their independence, healthcare accessibility difficulties including travel and financial burden, and patients’ caring responsibilities. The main enablers to accessing and completing cancer treatment are social support from peers in a similar situation, family and friends, the influence of healthcare providers, and involving patients in treatment decision making. • Implications. The development of strategies to address the inequity of cancer outcomes in people aged 65 years and older in NSW should consider: ­ Increasing community members’ and patients’ knowledge and awareness by providing written information and decision support tools from a trusted source ­ Reducing travel and financial burden by widely disseminating information about existing support schemes and expanding remote patient monitoring and telehealth ­ Improving social support by promoting peer support, and building the support capacity of family carers ­ Addressing ageism by supporting patients in decision making, and disseminating education initiatives about geriatric oncology to healthcare providers ­ Providing interdisciplinary geriatric oncology care by including a geriatrician as part of multidisciplinary teams and/or expanding geriatric oncology clinics.
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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental repairs. Dental arches in detachable prosthodontics may be categorized using Convolutional neural networks (CNN). By properly positioning the teeth, machine learning in CAD/CAM software can reestablish healthy inter-maxillary connections. AI may assist with accurate color matching in challenging cosmetic scenarios that include a single central incisor or many front teeth. Intraoral detectors can identify implant placements in implant prosthodontics and instantly input them into CAD software. The design and execution of dental implants could potentially be improved by utilizing AI.
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Cassady, C. R., Heather L. Nachtmann, Edward A. Pohl, Alejandro Mendoza, Letitia Pohl, and Nick Rew. Maintenance Decision-Making Under Prognostic and Diagnostic Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada452058.

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Currie, Janet, and W. Bentley MacLeod. Diagnosing Expertise: Human Capital, Decision Making and Performance Among Physicians. Cambridge, MA: National Bureau of Economic Research, April 2013. http://dx.doi.org/10.3386/w18977.

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Marold, Juliane, Ruth Wagner, Markus Schöbel, and Dietrich Manzey. Decision-making in groups under uncertainty. Fondation pour une culture de sécurité industrielle, February 2012. http://dx.doi.org/10.57071/361udm.

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Abstract:
The authors have studied daily decision-making processes in groups under uncertainty, with an exploratory field study in the medical domain. The work follows the tradition of naturalistic decision-making (NDM) research. It aims to understand how groups in this high reliability context conceptualize and internalize uncertainties, and how they handle them in order to achieve effective decision-making in their everyday activities. Analysis of the survey data shows that uncertainty is thought of in terms of issues and sources (as identified by previous research), but also (possibly a domain-specific observation) as a lack of personal knowledge or skill. Uncertainty is accompanied by emotions of fear and shame. It arises during the diagnostic process, the treatment process and the outcome of medical decision making. The most frequently cited sources of uncertainty are partly lacking information and inadequate understanding owing to instability of information. Descriptions of typical group decisions reveal that the individual himself is a source of uncertainty when a lack of knowledge, skills and expertise is perceived. The group can serve as a source of uncertainty if divergent opinions in the decision making group exist. Three different situations of group decisions are identified: Interdisciplinary regular meetings (e.g. tumor conferences), formal ward meetings and ad hoc consultations. In all healthcare units concerned by the study, only little use of structured decision making procedures and processes is reported. Strategies used to handle uncertainty include attempts to reduce uncertainty by collecting additional information, delaying action until more information is available or by soliciting advice from other physicians. The factors which ultimately determine group decisions are hierarchy (the opinion of more senior medical staff carries more weight than that of junior staff), patients’ interest and professional competence. Important attributes of poor group decisions are the absence of consensus and the use of hierarchy as the predominant decision criterion. On the other hand, decisions judged to be effective are marked by a sufficient information base, a positive discussion culture and consensus. The authors identify four possible obstacles to effective decision making: a steep hierarchy gradient, a poor discussion culture, a strong need for consensus, and insufficient structure and guidance of group decision making processes. A number of intervention techniques which have been shown in other industries to be effective in improving some of these obstacles are presented.
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Diefenbach, Michael A. Facilitating Treatment Decision Making, Adjustment, and Coping in Men Newly Diagnosed with Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada405442.

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Diefenbach, Michael A. Facilitating Treatment Decision Making, Adjustment and Coping in Men Newly Diagnosed With Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada431744.

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Diefenbach, Michael A. Facilitating Treatment Decision Making, Adjustment and Coping in Men Newly Diagnosed with Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada413289.

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Diefenbach, Michael A. Facilitating Treatment Decision Making Adjustment and Coping in Men Newly Diagnosed with Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada421130.

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