scholarly journals Back to Basics: Recognition of Sepsis with New Definition

2019 ◽  
Vol 8 (11) ◽  
pp. 1838 ◽  
Author(s):  
Horak ◽  
Martinkova ◽  
Radej ◽  
Matejovič
Keyword(s):  
Decision Making ◽  
At Risk ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Junior Doctors ◽  
Serious Infections ◽  
Sepsis Definition ◽  
Definition Of

Patients with serious infections at risk of deterioration represent highly challenging clinical situations, and in particular for junior doctors. A comprehensive clinical examination that integrates the assessment of vital signs, hemodynamics, and peripheral perfusion into clinical decision making is key to responding promptly and effectively to evolving acute medical illnesses, such as sepsis or septic shock. Against this background, the new concept of sepsis definition may provide a useful link between junior doctors and consultant decision making. The purpose of this article is to introduce the updated definition of sepsis and suggest its practical implications, with particular emphasis on integrative clinical assessment, allowing for the rapid identification of patients who are at risk of further deterioration.

scholarly journals Task Shifting: The Use of Laypersons for Acquisition of Vital Signs Data for Clinical Decision Making in the Emergency Room Following Traumatic Injury

2017 ◽  
Vol 41 (12) ◽  
pp. 3066-3073 ◽  
Author(s):  
Bryce E. Haac ◽  
Jared R. Gallaher ◽  
Charles Mabedi ◽  
Andrew J. Geyer ◽  
Anthony G. Charles
Keyword(s):  
Decision Making ◽  
Emergency Room ◽  
Clinical Decision Making ◽  
Traumatic Injury ◽  
Vital Signs ◽  
Clinical Decision ◽  
Task Shifting

scholarly journals Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elza Rechtman ◽  
Paul Curtin ◽  
Esmeralda Navarro ◽  
Sharon Nirenberg ◽  
Megan K. Horton
Keyword(s):  
Machine Learning ◽  
New York ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Vital Signs ◽  
Clinical Decision ◽  
Rapid Identification ◽  
Gradient Boosting ◽  
Hospital System ◽  
Response Strategies

AbstractTimely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

Reassessment of the definition of castrate levels of testosterone: implications for clinical decision making

Urology ◽  
2000 ◽  
Vol 56 (6) ◽  
pp. 1021-1024 ◽  
Author(s):  
Michael G Oefelein ◽  
Adrian Feng ◽  
Michael J Scolieri ◽  
Daniel Ricchiutti ◽  
Martin I Resnick
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Definition Of

New Insights in Cross-Cultural Communication

2012 ◽  
pp. e85-e88
Author(s):  
Lidia Schapira
Keyword(s):  
Decision Making ◽  
Cultural History ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Cross Cultural ◽  
Low Literacy ◽  
Cross Cultural Communication ◽  
Working Definition ◽  
Cross Cultural Education ◽  
Definition Of

Overview: Improving clinician-patient communication, improving clinical decision making, and eliminating mistrust have been identified as three key areas for reducing disparities in care. An important step is the training of cancer professionals to deliver culturally competent care in clinical settings as well as increasing the proportion of underrepresented minorities in the health care workforce. Providing care that is attuned to the patient's cultural preferences begins by talking to the patient about his or her cultural history and identifying the locus of decision making, preferences for disclosure of vital health information, and goals of care. Patients with low literacy and those with poor fluency of the dominant language require additional services. Language interpretation by trained professionals is fundamental to ensure that patients are able to provide informed consent for treatment. A working definition of culture involves multiple dimensions and levels and must be viewed as both dynamic and adaptive, rather than simply as a collection of beliefs and values. Effective cross-cultural education avoids stereotyping and promotes communication and negotiation to solve problems and minimize tension and conflict. Recent research has identified that unconscious biases held by clinicians affect their behavior and recommendations for treatment.

scholarly journals Economic evaluations of big data analytics for clinical decision-making: a scoping review

2020 ◽  
Vol 27 (9) ◽  
pp. 1466-1475
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
William Redekop
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Economic Impact ◽  
Scoping Review ◽  
Data Analytics ◽  
Clinical Decision Making ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Economic Evaluations ◽  
Definition Of

Abstract Objective Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. Materials and Methods We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed “big data analytics” based on a broad definition of this term. Results The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined “big data analytics” and only 7 reported both cost-savings and better outcomes. Discussion The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of “big data” limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

Gaps Between Alarm Capabilities and Decision-making Needs: An Observational Study of Detecting Patient Decompensation

2018 ◽  
Vol 7 (1) ◽  
pp. 112-116 ◽  
Author(s):  
Chelsea R. Horwood ◽  
Michael F. Rayo ◽  
Morgan Fitzgerald ◽  
E. Asher Balkin ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision ◽  
Unintended Consequences ◽  
Warning Signs ◽  
Medical Setting ◽  
Mental Picture ◽  
Wide Range

Decompensation is a change in the overall ability to maintain physiological function in the presence of a stressor or disease. In the medical setting, clinicians utilize a wide range of technological tools to aid in their clinical decision making and to identify early warning signals for decompensation. However, many of these technologies have underperformed and are not aligned with the actual role of practitioners, resulting in unintended consequences and adverse events. The primary aim of this study is to explore how different nurses interpret early warning signs in order to anticipate decompensation. The secondary aim is to assess which technologies nurses rely on when anticipating decompensation, and if those technologies are adequately aiding them in their clinical decision making. Two researchers performed semi-structured ethnographic interviews that were recorded and transcribed during the summer of 2017. In total, 43 nurses were interviewed from different medical and surgical floors within the same hospital. Participants were asked questions focused on how they use and respond to alarms and how they anticipate patient decompensation. Constant Comparative Analysis was used to reveal patterns of responses between participants. Based on the qualitative analysis 6 major themes emerged:  1. Anticipating patient decompensation requires creating a complete mental “picture of the patient” by the nurses  2. Nurse-to-nurse communication and expertise is essential to understanding the patient’s history  3. Warning signs for decompensation were largely determined by a patient’s baseline  4. Change over time, or trends, is informative for anticipating decompensation. Numbers (regarding vital signs and labs) alone are not  5. Consistent care of patients improved nurse’s confidence in decision making  6. Anticipating decompensation requires “staying ahead of the machines Our research suggests that there is a gap between the information practitioners need to accurately anticipate patient decompensation, and the information current alarm technologies provide. Alarms are the primary tool provided to nurses to aid them in detecting hazardous events, however, current alarms are not well-suited in supporting signals that anticipate patient decompensation before it happens.

scholarly journals New Sepsis Definition (Sepsis-3) and Community-acquired Pneumonia Mortality. A Validation and Clinical Decision-Making Study

2017 ◽  
Vol 196 (10) ◽  
pp. 1287-1297 ◽  
Author(s):  
Otavio T. Ranzani ◽  
Elena Prina ◽  
Rosario Menéndez ◽  
Adrian Ceccato ◽  
Catia Cilloniz ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Community Acquired Pneumonia ◽  
Sepsis Definition

scholarly journals Clinical decision making regarding the use of vital signs in physical therapy

2016 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Virginia G Thistle ◽  
Allison L Basskin ◽  
Eric Shamus ◽  
Renee Jeffreys-Heil
Keyword(s):  
Decision Making ◽  
Physical Therapy ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision

Treatment Decisions: Types, Models and Schools

1998 ◽  
Vol 14 (1) ◽  
pp. 2-13 ◽  
Author(s):  
Gerhard Seidenstücker ◽  
Wolfgang L. Roth
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Decision Rules ◽  
Clinical Decision ◽  
Adaptive Models ◽  
Complex Decision ◽  
State Of Affairs ◽  
Definition Of ◽  
Therapeutic Schools ◽  
Assessment Strategies

Psychotherapy can be seen as a complex decision process. First, we clarify some distinctions that have to be made in order to adequately describe and evaluate the state of affairs. Then we illustrate decisions made by clients, semi-professionals, and professionals, and mention descriptive and prescriptive studies in treatment-related decision-making. Next, we focus on questions confronting therapists before and during psychotherapy, i. e., the selection, design, and implementation of an efficient therapy offer. These questions motivate the definition of different pragmatic indication models - the selective, inventive and adaptive models. In the next to last section we describe and comment on assessment strategies and decision rules in the context of therapeutic schools (psychoanalysis, client-centered psychotherapy, cognitive-behavioral psychotherapy). In the last section we outline contributions towards general models of decision making in psychological treatments and formulate some conclusions for further research in clinical decision-making.

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