Clinical decision making in pain management: Contributions of physician and patient characteristics to variations in practice

2003 ◽  
Vol 4 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Carmen R. Green ◽  
John R.C. Wheeler ◽  
Frankie LaPorte
Keyword(s):  
Decision Making ◽  
Pain Management ◽  
Clinical Decision Making ◽  
Patient Characteristics ◽  
Clinical Decision

Integrating the Patient and the Clinician With the Research

2016 ◽  
pp. 189-218
Author(s):  
John C. Norcross ◽  
Thomas P. Hogan ◽  
Gerald P. Koocher ◽  
Lauren A. Maggio
Keyword(s):  
Decision Making ◽  
Stages Of Change ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Expertise ◽  
Simple Extrapolation ◽  
Complex Cases

This chapter demonstrates how research is integrated with the two other pillars of evidence-based practice (EBP): clinical expertise and patient characteristics. Research alone never suffices for making clinical decisions, nor does the simple extrapolation of research qualify as EBP. The chapter begins by correcting pernicious myths about EBP and then discusses enlarging clinical decision-making by adding the clinician and the patient into the mix. The chapter describes several ways in which the three pillars of EBP can be integrated and considers what to do when they cannot be integrated, particularly in complex cases. Clinicians will ultimately adopt, adapt, or abandon a research-supported intervention for a specific case. Finally, the chapter reviews the research on being responsive to patients’ transdiagnostic features, such as preferences, stages of change, and culture.

scholarly journals Clinical Decision Making in Renal Pain Management

2012 ◽  
Vol 20 (1) ◽  
pp. 18
Author(s):  
Damir Aganovic ◽  
Alen Prcic ◽  
Benjamin Kulovac ◽  
Osman Hadziosmanovic
Keyword(s):  
Decision Making ◽  
Pain Management ◽  
Clinical Decision Making ◽  
Clinical Decision

Catalysts for Conversations About Advance Directives: The Influence of Physician And Patient Characteristics

1994 ◽  
Vol 22 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Jeremy Sugarman ◽  
Nancy E. Kass ◽  
Ruth R. Faden ◽  
Steven N. Goodman
Keyword(s):  
Decision Making ◽  
Advance Directives ◽  
Clinical Decision Making ◽  
Health Maintenance Organization ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Sociodemographic Factors ◽  
Medical Decision ◽  
Health Maintenance ◽  
Immunodeficiency Syndrome

Recent legislation, such as the Patient Self-Determination Act, establishes advance directives as an acceptable procedural means of incorporating patients’ preferences for life-sustaining treatments into their medical care. Advance directives can enhance medical decision making since they provide patients with an opportunity to communicate their preferences before suffering from an acute illness that may preclude their ability to do so.Although patients expect discussions about life-sustaining therapies to be initiated by their physicians, very little is known about what prompts physicians to discuss advance directives with their patients. As in other areas of clinical decision making, there is evidence that patients’ sociodemographic factors influence whether discussions about advance directives occur between patients and their physicians. In one study of persons with the acquired immunodeficiency syndrome (AIDS), those who had not had discussions about advance directives with their physicians tended to be non-white, have no prior hospitalization, and were more likely to have been cared for in a health maintenance organization than in a teaching hospital.

scholarly journals How Well Do Current Laboratory Biomarkers Inform Clinical Decision-Making in Chronic Pain Management?

2021 ◽  
Vol Volume 14 ◽  
pp. 3695-3710
Author(s):  
Jonathan M Hagedorn ◽  
Joshua Gunn ◽  
Ryan Budwany ◽  
Ryan S D'Souza ◽  
Krishnan Chakravarthy ◽  
...  
Keyword(s):  
Decision Making ◽  
Chronic Pain ◽  
Pain Management ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Chronic Pain Management ◽  
Laboratory Biomarkers

scholarly journals Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?

2021 ◽  
Author(s):  
Lorena Alvarez-Rodríguez ◽  
Joaquim de Moura ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Health Crisis ◽  
X Ray ◽  
Reliable Analysis ◽  
Chest X Ray

Abstract Background: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use different lung imaging modalities (like chest radiography) to explore the possible affected areas. Methods: In this work, we performed a comprehensive analysis of sex and age factors in chest X-ray images. The study of these recurrent patient characteristics in pathologies of this type is crucial, since there is a clear scarcity of data that may lead to biases when trying to develop systems that are as representative as possible, as well as to gain knowledge of the disease itself. To identify possible biases, we analyzed 3 different computational approaches for automatic COVID-19 screening: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The presented study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process in the context of this dramatic global pandemic. Results: The obtained results for the sex-related imbalance analysis indicate that this factor slightly affects the system performance in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system’s response. Regarding the age-related imbalance analysis, this factor was observed to be again influencing the system in a more consistent way than the sex factor, as it was present in all the approaches. Once again, this worsening is not a major problem for our data and system, as it is not of great magnitude. Conclusions: Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario.

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