Journal articles: '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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Mileman, PA, and WB van den Hout. "Evidence-based diagnosis and clinical decision making." Dentomaxillofacial Radiology 38, no. 1 (January 2009): 1–10. http://dx.doi.org/10.1259/dmfr/18200441.

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Isley, Michelle M., Cynthia Shellhaas, and Laura Montgomery. "Determinants of Decision Making for Prenatal Diagnosis." Obstetrics & Gynecology 107, Supplement (April 2006): 69S. http://dx.doi.org/10.1097/00006250-200604001-00164.

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Hughes, McRoberts, and Burnett. "Decision-making and diagnosis in disease management." Plant Pathology 48, no. 2 (April 1999): 147–53. http://dx.doi.org/10.1046/j.1365-3059.1999.00327.x.

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Buhagiar, Teresa M., and Deborah S. Smith. "Ethical Decision Making." Clinical Nurse Specialist 36, no. 2 (March 2022): 74–77. http://dx.doi.org/10.1097/nur.0000000000000661.

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Grace, P. Julia, and G. Jeyakumar. "A Statistical Decision Making Approach for Disease Diagnosis." International Journal of Data Mining Techniques and Applications 1, no. 1 (June 13, 2012): 21–25. http://dx.doi.org/10.20894/ijdmta.102.001.001.006.

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Boyles, Tom H., Lutgarde Lynen, and James A. Seddon. "Decision-making in the diagnosis of tuberculous meningitis." Wellcome Open Research 5 (January 23, 2020): 11. http://dx.doi.org/10.12688/wellcomeopenres.15611.1.

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Tuberculous meningitis (TBM) is the most devastating form of tuberculosis (TB) but diagnosis is difficult and delays in initiating therapy increase mortality. All currently available tests are imperfect; culture of Mycobacterium tuberculosis from the cerebrospinal fluid (CSF) is considered the most accurate test but is often negative, even when disease is present, and takes too long to be useful for immediate decision making. Rapid tests that are frequently used are conventional Ziehl–Neelsen staining and nucleic acid amplification tests such as Xpert MTB/RIF and Xpert MTB/RIF Ultra. While positive results will often confirm the diagnosis, negative tests frequently provide insufficient evidence to withhold therapy. The conventional diagnostic approach is to determine the probability of TBM using experience and intuition, based on prevalence of TB, history, examination, analysis of basic blood and CSF parameters, imaging, and rapid test results. Treatment decisions may therefore be both variable and inaccurate, depend on the experience of the clinician, and requests for tests may be inappropriate. In this article we discuss the use of Bayes’ theorem and the threshold model of decision making as ways to improve testing and treatment decisions in TBM. Bayes’ theorem describes the process of converting the pre-test probability of disease to the post-test probability based on test results and the threshold model guides clinicians to make rational test and treatment decisions. We discuss the advantages and limitations of using these methods and suggest that new diagnostic strategies should ultimately be tested in randomised trials.

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Xu, Victor, Henry Chan, Alexander Lin, Napapon Sailasuta, Samuel Valencerina, Thao Tran, Jan Hovener, and Brian Ross. "MR Spectroscopy in Diagnosis and Neurological Decision-Making." Seminars in Neurology 28, no. 04 (September 2008): 407–22. http://dx.doi.org/10.1055/s-0028-1083685.

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Rebeccca Paul, Nirmala. "Rough Topology Based Decision Making in Medical Diagnosis." International Journal of Mathematics Trends and Technology 18, no. 1 (February 25, 2015): 40–43. http://dx.doi.org/10.14445/22315373/ijmtt-v18p507.

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Bijma, Hilmar H., Agnes van der Heide, and Hajo I. J. Wildschut. "Decision-Making after Ultrasound Diagnosis of Fetal Abnormality." Reproductive Health Matters 16, sup31 (January 2008): 82–89. http://dx.doi.org/10.1016/s0968-8080(08)31372-x.

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Ge, Yawei, and Xiqian Hou. "The probabilistic reasoning of hierarchical diagnosis decision-making." Journal of Physics: Conference Series 1507 (March 2020): 102016. http://dx.doi.org/10.1088/1742-6596/1507/10/102016.

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Alton, E. W. F. W., N. Newman, J. Hooper, D. Fok, and F. R. Vicary. "‘Soluble’: Decision-making in the diagnosis of Jaundice." International Journal of Bio-Medical Computing 27, no. 1 (January 1991): 47–57. http://dx.doi.org/10.1016/0020-7101(91)90020-f.

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Bijma, Hilmar H., Agnes van der Heide, and Hajo I. J. Wildschut. "Decision-making after ultrasound diagnosis of fetal abnormality." European Clinics in Obstetrics and Gynaecology 3, no. 2 (October 11, 2007): 89–95. http://dx.doi.org/10.1007/s11296-007-0070-0.

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Thammasitboon, Satid, and William B. Cutrer. "Diagnostic Decision-Making and Strategies to Improve Diagnosis." Current Problems in Pediatric and Adolescent Health Care 43, no. 9 (October 2013): 232–41. http://dx.doi.org/10.1016/j.cppeds.2013.07.003.

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Hoehn, K. Sarah, Gil Wernovsky, Jack Rychik, Zhi-yun Tian, Denise Donaghue, Melissa A. Alderfer, J. William Gaynor, Anne E. Kazak, Thomas L. Spray, and Robert M. Nelson. "Parental decision-making in congenital heart disease." Cardiology in the Young 14, no. 3 (June 2004): 309–14. http://dx.doi.org/10.1017/s1047951104003099.

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Objective:To explore whether prenatal diagnosis of congenital heart disease is associated with lower levels of parental distress and greater satisfaction with decisions about cardiothoracic surgery when compared to postnatal diagnosis.Methodology:A combined quantitative–qualitative design was used. Participants included the parents of 31 neonates (30 mothers and 22 fathers) admitted to the cardiac intensive care unit between 1 November 2001 and 1 May 2002 for repair of congenital cardiac malformations. Participants completed self-report measures of anxiety, optimism, and life events pre-operatively, and semi-structured qualitative interviews assessing satisfaction with decision-making within 1 week of the operation.Results:At the time of surgery, mothers of neonates receiving the diagnosis prenatally did not differ from mothers of neonates receiving the diagnosis postnatally on measures of anxiety, optimism, and life events. Fathers of neonates receiving the diagnosis prenatally, however, reported more optimism, lower state and trait anxiety, and fewer negative life events than fathers of neonates receiving the diagnosis postnatally. When we analyzed the interviews, we found that, regardless of the timing of the diagnosis, parents felt as though they made a genuine choice for their baby to have surgery.Conclusions:In this pilot study, fathers who learned prenatally that their child had a congenital cardiac malformation were less distressed than those who discovered this fact only postnatally. From the parental perspective, nonetheless, distress and urgency do not impair their ability to make decisions about neonatal cardiac surgery.

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Spiegel, Thais, and Ana Carolina P. V. Silva. "Decision-Making Cognitive Process." International Journal of Computers in Clinical Practice 3, no. 1 (January 2018): 1–12. http://dx.doi.org/10.4018/ijccp.2018010101.

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In the study of decision-making, the classical view of behavioral appropriateness or rationality was challenged by neuro and psychological reasons. The “bounded rationality” theory proposed that cognitive limitations lead decision-makers to construct simplified models for dealing with the world. Doctors' decisions, for example, are made under uncertain conditions, as without knowing precisely whether a diagnosis is correct or whether a treatment will actually cure a patient, and often under time constraints. Using cognitive heuristics are neither good nor bad per se, if applied in situations to which they have been adapted to be helpful. Therefore, this text contextualizes the human decision-making perspective to find descriptions that adhere more closely to the human decision-making process. Then, based on a literature review of cognition during decision-making, particularly in healthcare context, it addresses a model that identifies the roles of attention, categorization, memory, emotion, and their inter-relations, during the decision-making process.

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Grauel, Adolf, Lars A. Ludwig, and Georg Klene. "ECG Diagnostics by Fuzzy Decision Making." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, no. 02 (April 1998): 201–10. http://dx.doi.org/10.1142/s0218488598000185.

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The analysis of electrocardiograms (ECGs) helps physicians make their cardiac diagnosis. Therefore a large store of medical knowledge and practical experience is required. In this paper we report on our investigations of a rule-based fuzzy logic system that processes ECG data using the knowledge of a medical expert. The aim is to give support to the physician for his diagnosis. In this first consideration we discuss single modules of the rule-based system proposed and moreover we present the used input and output variables of the rulebases. The performance of the implemented rule-based fuzzy logic system is tested using ECGs with abnormalities in the P and T wave as well as in the QRS complex. The system's output corresponds to the analysis of these ECGs by a medical expert.

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Callaghan, Kathleen. "Factors influencing diagnostic decision-making." Journal of Primary Health Care 4, no. 3 (2012): 223. http://dx.doi.org/10.1071/hc12223.

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INTRODUCTION: Identifying influences on diagnostic decisions is important because diagnostic errors often have far-reaching consequences for an individuals future within the workforce and their eligibility for Accident Compensation Corporationfunded treatment. Most investigations of factors biasing decision making have used quantitative techniques rather than qualitative methods. AIM: To identify factors influencing GPs diagnostic decision-making and to develop a valid questionnaire to determine the desirability and importance of each factors influence. METHODS: Focus groups and the Delphi method were combined with Rasch analysis to identify factors influencing GPs diagnostic decision-making and then examine the strength and stability of ratings of the factors desirability and importance. RESULTS: Thirty-nine factors were identified. Factors demonstrating high stability but no consensus included the importance of evidence-based medicine, the potential ramifications of a diagnosis, and the desirability of medicolegal issues. Factors for which there was disagreement in the first Delphi round but consensus in the second round included the importance of patient advocacy/support groups and the desirability of examination findings. Rasch analysis indicated that the questionnaire was close to the model (88.6% and 86.2% of variance in the ratings of importance and desirability explained). DISCUSSION: Participants readily identified factors influencing GPs diagnostic decision-making. Their ratings did not appear to support a prescriptive model of medicine, yet two cornerstones of prescriptive medicine, clinical information and probability of disease, were rated as highly desirable and important. KEYWORDS: Decision-making; diagnosis; bias; Rasch analysis; general practitioners

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Dahmani, Sarra, Xavier Boucher, Sophie Peillon, and Béatrix Besombes. "A reliability diagnosis to support servitization decision-making process." Journal of Manufacturing Technology Management 27, no. 4 (May 3, 2016): 502–34. http://dx.doi.org/10.1108/jmtm-06-2015-0044.

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Purpose – Servitization of manufacturing is characterized by very complex decision processes within strongly unstable and uncertain decision contexts. Decision-makers are face situations of lack of internal and external information. The purpose of this paper is to develop a decision aid approach to support the management of servitization decision-making processes. Design/methodology/approach – The scientific orientation of this research consists in working at improving the efficiency of the servitization decision-making process, by identifying factors of non-reliability, in order to propose remediation actions for the whole process. Improving the final decisions taken by the managers is considered as a consequence of the improvement of the decision-making process reliability. The method, based on modeling and evaluation, requires the specification of a decision process model for servitization, used as a basis to assess decision process reliability and diagnose the enterprise’s servitization decision system. Improving the final decisions made by the managers is considered as a consequence of the improvement of the decision-making process reliability. Findings – Key added values: first, to formalize a servitization decision-making reference model; second, to specify a reliability assessment applied to the decision system; and third, to define a decision process reliability diagnosis procedure for servitization, illustrated in a case study. Research limitations/implications – A direct perspective is to complete the focus on procedural reliability, by taking into consideration the subjective rationality of decision-makers in the reliability assessment procedure. Additionally, this reliability assessment method and diagnosis could become the basis of a larger risk management approach for servitization. Practical implications – The diagnosis procedure proposed in the paper is dedicated to generating practical results for enterprise decision-makers, consisting in recommendations for decision process improvements, in the context of servitization. The approach is illustrated through an industrial SME case study. The practical implications are highly contextualized. Originality/value – The key originality of this research is to tackle servitization complexity with a decision system modeling and diagnosis orientation, including the formalization of the notion of “decision process reliability,” and the specification and implementation of a quantitative assessment procedure.

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Jordão, Ana Rita, Renato Costa, Álvaro Lopes Dias, Leandro Pereira, and José Pedro Santos. "BOUNDED RATIONALITY IN DECISION MAKING: AN ANALYSIS OF THE DECISION-MAKING BIASES." Business: Theory and Practice 21, no. 2 (October 8, 2020): 654–65. http://dx.doi.org/10.3846/btp.2020.11154.

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Decision-making is a multidisciplinary and ubiquitous phenomenon in organizations, and it can be observed at the individual, group, and organizational levels. Decision making plays, however, an increasingly important role for the manager, whose cognitive competence is reflected in his ability to identify potential opportunities, to immediately detect and solve the problems he faces, and to predict and prevent future threats. Nevertheless, to what extent do managers of the most diverse sectors and industries continue to rely on false knowledge when they have better strategies at their disposal? The present article proposes, through the application of bibliographically based instruments, the diagnosis of three prominent biases – overconfidence, optimism, and anchoring effect – in managers of the Portuguese port sector, as well as also seeking to establish a comparative analysis with the conclusions already documented in relation to the Brazilian civil construction sector. In addition, and in view of the results obtained, this paper also provides a set of measures capable of contributing to the mitigation of the effects of these and other biases, and, in this way, to the improvement of the decisions of said managers.

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Ngan, Peter, and David Musich. "Early class III treatment decision-making." APOS Trends in Orthodontics 9 (June 29, 2019): 68–72. http://dx.doi.org/10.25259/apos-60-2019.

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Clinicians make decisions for their patients everyday. Ryan Hamilton, in his course guidebook, How You Decide: The Science of Human Decision Making, summarized the current research on the 4 R's of decision-making that matter: reference points, reasons, resources, and replacement. The authors will apply this principle in the decision- making necessary for the growing Class III patients. First, the decision on whether to treat or not to treat Class III patients in the mixed dentition rely on a thorough diagnosis and objectives for early treatment. For example, elimination of a functional shift of the mandible may be a good reason to institute early treatment. Second, the decision on when to start Phase II treatment relies on the follow-up observation after Phase I treatment. The authors suggested the use of a “checklist” to decide whether patient will be benefited from surgical intervention or nonsurgical orthodontic treatment. If the checklist review has several negative checkpoints, it will help the clinicians to decide on an aggressive stage of 4–8 months therapeutic re-diagnosis to confirm the surgical or nonsurgical decision.

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Gavrilenko, A. V., G. V. Siniavin, A. M. Grabuzdov, and E. V. Barinov. "Subclavian artery aneurysms: pathogenesis, diagnosis, and therapeutic decision-making." Angiology and vascular surgery 26, no. 3 (2020): 185. http://dx.doi.org/10.33529/angio2020312.

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Dahmani, Sarra, Xavier Boucher, Didier Gourc, François Marmier, and Sophie Peillon. "Towards a Reliability Diagnosis for Servitization Decision-making Process." Procedia CIRP 16 (2014): 259–64. http://dx.doi.org/10.1016/j.procir.2014.02.013.

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Shannon-Dorcy, Kathleen, and Victoria Wolfe. "Decision-making in the diagnosis and treatment of leukemia." Seminars in Oncology Nursing 19, no. 2 (May 2003): 142–49. http://dx.doi.org/10.1016/s0749-2081(03)00009-3.

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Gambino, Orazio, Leonardo Rundo, Roberto Pirrone, and Salvatore Vitabile. "HCI for biomedical decision-making: From diagnosis to therapy." Journal of Biomedical Informatics 111 (November 2020): 103593. http://dx.doi.org/10.1016/j.jbi.2020.103593.

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Fernández Pérez, Evans R., Tilman L. Koelsch, Paolo M. Leone, Steve D. Groshong, David A. Lynch, and Kevin K. Brown. "Clinical Decision-Making in Hypersensitivity Pneumonitis: Diagnosis and Management." Seminars in Respiratory and Critical Care Medicine 41, no. 02 (April 2020): 214–28. http://dx.doi.org/10.1055/s-0040-1701250.

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AbstractThis review provides an updated approach to the diagnosis and management of hypersensitivity pneumonitis (HP). The importance of using a multidisciplinary discussion to increase diagnostic and treatment confidence is emphasized. The role of Bayesian reasoning is highlighted throughout, underscoring the importance of hypothesis generation (differential diagnosis) and diagnostic test interpretation based on the probability of HP. Probability estimates of diagnostic certainty (i.e., a confident versus a working diagnosis) and treatment thresholds are carefully examined.Therapeutically, beyond antigen avoidance and newly available antifibrotic therapy for patients with a progressive fibrosing phenotype; the role, timing, and expected response to anti-inflammatory therapy in individual patients are unanswered questions. Since the evidence and validation of testing generally performed during the diagnostic work-up and longitudinal monitoring of HP is feeble at best, the viewpoints discussed are not intended to resolve current controversies but rather to provide a conceptual framework for evaluating discordant information when evaluating and caring for patients with HP.

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Noordman, Janneke, Mariska Oosterveld-Vlug, and Jany Rademakers. "Shared Decision Making in Clinical Practice." European Journal of Health Communication 3, no. 1 (February 24, 2022): 31–52. http://dx.doi.org/10.47368/ejhc.2022.102.

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Shared Decision Making (SDM), currently considered a preferred model for making decisions in healthcare, requires that patients have the knowledge and skills to actively participate in the consultation and decision-making process. Patients with limited health literacy (LHL) often fail to do so. To gain insight into the experiences, needs and support for LHL-patients concerning SDM in clinical practice, five focus groups were held with 26 patients with LHL. A focus group discussion guide was developed based on 4-steps SDM models. Data was coded using thematic content analyses. LHL patients participating in this study had little experience with SDM in practice, but do prefer it. Important barriers for this are healthcare provider-related (involving patients too little in decision-making, using medical jargon), patient-related (feeling insecure to play a role in decision-making, inability to understand their diagnosis or information about treatment options), patient-provider interaction-related (relationship of trust) or system-related (too little consultation time). For SDM to take place more often, a shared responsibility between patients and healthcare providers is required. We recommend expanding the SDM models, by adding a step zero (patients understand their diagnosis) and a fifth step (reviewing the decision), to improve the process for LHL patients.

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Garcia, Luiz Henrique Costa, and Bruna Cortez Ferreira. "An ABC for decision making." Radiologia Brasileira 48, no. 2 (April 2015): 101–10. http://dx.doi.org/10.1590/0100-3984.2013.1846.

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The present study was aimed at proposing a systematic evaluation of cranial computed tomography, identifying the main aspects to be analyzed in order to facilitate the decision making process regarding diagnosis and management in emergency settings. The present descriptive study comprised a literature review at the following databases: Access Medicine and Access Emergency Medicine (McGraw- Hill Education); British Medical Journal Evidence Center; UptoDate; Bireme; PubMed; Lilacs; SciELO; ProQuest; Micromedex (Thomson Reuters); Embase. Once the literature review was completed, the authors identified the main diseases with tomographic repercussions and proposed the present system to evaluate cranial computed tomography images. An easy-to-memorize ABC system will facilitate the decision making in emergency settings, as it covers the main diseases encountered by intensivists and emergency physicians, and provides a sequential guidance about anatomical structures to be investigated as well as their respective alterations.

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N C, Danushri. "Clinical Decision Making using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3061–64. http://dx.doi.org/10.22214/ijraset.2022.45625.

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Abstract: Deaths due to cardiovascular diseases are increasing at an alarming rate. This led to nearly 2.1 million deaths in India in 2015. Heart disease is one of the deadliest causes of death worldwide and has a major impact on the lives of rural people. According to a recent study, cardiovascular disease mortality among rural Indians has surpassed urban Indians. Such numbers are alarming, especially when 68% of India's population lives in rural areas that have poor access to quality healthcare. This paper aims to provide a solution to this problem by introducing a new model*clinical*decision*support system*, abbreviated as CDSS, which*includes machine learning algorithms for the diagnosis of cardiovascular diseases. CDSS is intelligent enough to diagnose a patient's*disease* and help the doctor prescribe the*correct medication, reducing the cost and effort required to prescribe unnecessary treatment. *In this work, we applied correlation-based feature selection (CFS) and a multilayer perceptron classifier on a large heart disease dataset. The dataset used in this study is the "Cleveland Clinic Foundation Heart Disease Dataset" available at the UCI Machine Learning Repository. Our proposed model produced greater accuracy compared to other existing models used in this study. This system can be integrated into a public health care setting to help rural people get a correct, timely and cost-effective diagnosis.

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Senjaya, Ferry. "Decision Making in Peripheral Nerve Injury." Neurologico Spinale Medico Chirurgico 1, no. 2 (August 7, 2018): 33. http://dx.doi.org/10.15562/nsmc.v1i2.111.

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The decision making process during diagnosis and treatment of a peripheral nerve injury can be complex at times. The nature and cause of nerve injury, its location, and its severity require very distinct decisions with regards to timing and intervention chosen. For good decision-making, a good knowledge of the pathophysiological conditions of peripheral nerve and its response to injury is paramount.In this article, the decision-making process related to pre-operative, intra-operative, and post-operative periods are discussed.

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HOLT, FRIEDA M. "Evaluation, Measurement, and Decision Making." CLINICAL NURSE SPECIALIST 3, no. 2 (1989): 79. http://dx.doi.org/10.1097/00002800-198900320-00010.

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HOLT, FRIEDA M. "Evaluation, Measurement, and Decision Making." CLINICAL NURSE SPECIALIST 3, no. 2 (1989): 79. http://dx.doi.org/10.1097/00002800-198922000-00010.

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Sinopoulou, Vassiliki, Paul Summerfield, and Paul Rutter. "A qualitative study on community pharmacists' decision-making process when making a diagnosis." Journal of Evaluation in Clinical Practice 23, no. 6 (October 8, 2017): 1482–88. http://dx.doi.org/10.1111/jep.12837.

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White, Mathew P., J. Christopher Cohrs, and Anja S. Göritz. "Dynamics of Trust in Medical Decision Making." Medical Decision Making 31, no. 5 (January 25, 2011): 710–20. http://dx.doi.org/10.1177/0272989x10394463.

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Background: Patient trust in medical decision makers is a crucial facilitator of effective health care. Greater patient involvement in decision making requires improved understanding of how such trust is built, maintained, and lost in medical contexts. Objective: The study investigates how trust in clinicians is affected by the 4 main diagnostic outcomes proposed by signal detection theory: true positives, true negatives, false positives, and false negatives. Cognitive appraisals of, and affective reactions to, the decisions were measured to investigate the psychological mechanisms underpinning effects on trust. Design: Members of an Internet research panel ( N = 1162) participated in a between-participant experimental study using hypothetical cancer diagnosis scenarios. Results: Overall, correct diagnoses bolstered trust as much as incorrect ones undermined it. Consistent with recent findings in other decision-making domains, trust was not as precarious as generally believed. The influence of decisions and outcomes on trust was mediated through cognitive assessments and affective responses in line with current appraisal theories in psychology. Prior levels of trust in clinicians affected sympathy for doctors, highlighting the role that trust plays in responding to new information. Conclusions: Trust in (hypothetical) clinicians is sensitive to information about their past diagnostic performance. Greater understanding of the cognitive and affective mechanisms by which this occurs may help maintain current high levels of trust. Further research is needed to examine whether findings generalize to real medical decision-making contexts. Clinicians may want to consider the impact their diagnoses have on trust alongside medical and financial considerations.

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Han, Yan Ling, and Shou Qi Cao. "Decision-Making Model and Evaluation Architecture for Intelligent Fault Diagnosis." Advanced Materials Research 33-37 (March 2008): 1155–62. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.1155.

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Diagnosis decision-making and evaluation analysis is two closely-related contents in the process of intelligent fault diagnosis, correct decision-making could be made only based on objective and fair evaluation. This paper brought up decision-making objective and corresponding decision-making model for intelligent fault diagnosis, and gave its evaluation indicator architecture. Combining grey correlative analysis with hierarchical analysis method, multi-hierarchy grey correlative analysis was brought forward to apply into intelligent fault diagnosis, its principle, algorithm and evaluation execution process were researched deeply, and finally the analysis sample was given.

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Lanceley, A., J. Savage, U. Menon, and I. Jacobs. "Influences on multidisciplinary team decision-making." International Journal of Gynecologic Cancer 18, no. 2 (2008): 215–22. http://dx.doi.org/10.1111/j.1525-1438.2007.00991.x.

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The objective is to explore how clinical decisions are made in a cancer multidisciplinary team meeting (MDM). The study design is qualitative based on participant observation, in depth interviews, and questionnaires. The research setting was weekly cancer MDM which provides a forum for clinical debate for practitioners in the field of women's health, working within one Cancer Network in England. The participants were 53 practitioners attending a weekly MDM over a 4-month period. Analysis of nonparticipant observation data and practitioner interview narratives identified key influences on the work of the MDM, and in particular decision-making. The research identified three major influences on the conduct of the MDM. First, MDM discussions are dominated by those with surgical, medical, or diagnostic expertise with limited contributions from those with a nursing, palliative, or psychosocial background. Second, decision-making is shaped by an overriding need to comply with policy initiatives concerning the organization of diagnosis and treatment. The third influence is whether the patient is known or unknown to some degree by members of the MDM. Where there is preexisting knowledge of the patient, the discussion and decision is inclusive of a wider range of disciplines. Team working in these circumstances is an acknowledged source of satisfaction and motivation. Where the patient is not known, discussion concerns only the physical details necessary to make a diagnosis and contributions from the wider team (including those with knowledge of psychosocial care) are rare. Practitioners' sphere of expertise, Department of Health policy, and familiarity of the team with the patient are key factors in shaping decision-making in MDMs.

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Haham, Lilach Marom, Inbal Avrahami, Noam Domniz, Liat Ries-Levavi, Michal Berkenstadt, Raoul Orvieto, Yoram Cohen, and Shai E. Elizur. "Preimplantation genetic diagnosis versus prenatal diagnosis—decision-making among pregnant FMR1 premutation carriers." Journal of Assisted Reproduction and Genetics 35, no. 11 (August 22, 2018): 2071–75. http://dx.doi.org/10.1007/s10815-018-1293-3.

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Tomšík, P., and E. Svoboda. "Diagnostics and decision-making of the company management within the period of economiccrisis and recession." Agricultural Economics (Zemědělská ekonomika) 56, No. 7 (July 20, 2010): 303–9. http://dx.doi.org/10.17221/53/2010-agricecon.

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Enterprises are socio-economic entities with bonds to their environment. Their economic health is evaluated by the means of adequate diagnostic methods. When evaluating a processing business as a living organism, these diagnostic methods enable to determine their health condition. These business entities pass through individual stages of their life cycle and, in real time, their momentary condition corresponds with the homeostasis existing between the company's system and its environment. A diagnosis defined by the means of diagnostic methods must help to implement the goals which lead, on the base of business management activities, to the generation of an adequate response to the given momentary situation existing in the diagnosed enterprise. The definition of a proper diagnosis is important especially in those periods when the enterprise passes through its life crisis under the conditions of market recession. This scientific paper presents data formulated within the framework of research focused on the analysis of managerial, financial and economic risks and on the methods of their solution within the framework of strategic business management in a new business environment as influenced by the phenomena resulting from the integration processes, development of information technologies and factors of globalisation. This diagnostic method was applied when analysing in detail the operation of a company producing bioveterinary drugs and preparations. Keywords: diagnostics; homeostasis; crisis management; business environment

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Prakash, L. "The Logic and Reasoning Process for Orthopaedic Diagnosis and Decision Making: Heuristics." Acta Scientific Orthopaedics 3, no. 2 (January 20, 2020): 01–04. http://dx.doi.org/10.31080/asor.2020.03.the-logic-and-reasoning-process-for-orthopaedic-diagnosis-and-decision-making-heuristics.

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Goering, Elizabeth M., and Andrea Krause. "From sense making to decision making when living with cancer." Communication and Medicine 14, no. 3 (October 26, 2018): 268–73. http://dx.doi.org/10.1558/cam.32234.

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The diagnosis of a catastrophic illness, such as cancer, brings with it a whirlwind of decisions to be made. As healthcare systems rely increasingly on shared decision making (SDM), understanding how patients make sense of health-related information and equip themselves to participate as equal partners in health-related decision making is essential. Coordinated management of meaning’s (CMM) LUUUTT (lived, unknown, untold, unheard, told stories, telling stories) model provides a useful conceptual and methodological framework for better understanding how stories are woven together to create meaning and influence decision making. This Research Note illustrates the potential of applying the LUUUTT model to autoethnographic vignettes and personal health narratives to reach a deeper understanding of the sense-making and decision-making processes related to living with cancer.

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Baldwin, Kathleen M. "Moral distress and ethical decision making." Nursing Made Incredibly Easy! 8, no. 6 (November 2010): 5. http://dx.doi.org/10.1097/01.nme.0000388524.64122.41.

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

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