scholarly journals The Swedish MS registry – clinical support tool and scientific resource

2015 ◽  
Vol 132 (S199) ◽  
pp. 11-19 ◽  
Author(s):  
J. Hillert ◽  
L. Stawiarz
Keyword(s):  
Support Tool ◽  
Clinical Support ◽  
Clinical Support Tool

scholarly journals Artificial intelligence: a new clinical support tool for stress echocardiography

2018 ◽  
Vol 15 (8) ◽  
pp. 513-515 ◽  
Author(s):  
Maryam Alsharqi ◽  
Ross Upton ◽  
Angela Mumith ◽  
Paul Leeson
Keyword(s):  
Artificial Intelligence ◽  
Stress Echocardiography ◽  
Support Tool ◽  
Clinical Support ◽  
Clinical Support Tool

P1-576: A RISK FACTOR-BASED CLINICAL SUPPORT TOOL TO SCREEN FOR PRE-DEATH GRIEF IN FAMILY CAREGIVERS OF PERSONS WITH DEMENTIA

2006 ◽  
Vol 14 (7S_Part_10) ◽  
pp. P556-P557
Author(s):  
Tau Ming Liew ◽  
Bee Choo Tai ◽  
Philip Yap ◽  
Gerald Choon-Huat Koh
Keyword(s):  
Risk Factor ◽  
Family Caregivers ◽  
Support Tool ◽  
Clinical Support ◽  
Clinical Support Tool

scholarly journals Stakeholder Engagement in Developing an Electronic Clinical Support Tool for Tobacco Prevention in Adolescent Primary Care

Children ◽  
2018 ◽  
Vol 5 (12) ◽  
pp. 170 ◽  
Author(s):  
Ramzi G. Salloum ◽  
Ryan P. Theis ◽  
Lori Pbert ◽  
Matthew J. Gurka ◽  
Maribeth Porter ◽  
...  
Keyword(s):  
Primary Care ◽  
Stakeholder Engagement ◽  
Critical Role ◽  
Implementation Process ◽  
Feasibility Trial ◽  
Tobacco Prevention ◽  
Clinical Workflow ◽  
Support Tool ◽  
Clinical Support ◽  
Clinical Support Tool

Following guideline recommendations to promote tobacco prevention in adolescent primary care, we developed a patient-facing clinical support tool. The electronic tool screens patients for use and susceptibility to conventional and alternative tobacco products, and promotes patient–provider communication. The purpose of this paper is to describe the iterative stakeholder engagement process used in the development of the tool. During the pre-testing phase, we consulted with scientists, methodologists, clinicians, and Citizen Scientists. Throughout the development phase, we engaged providers from three clinics in focus groups. Usability testing was conducted via in-depth, cognitive interviewing of adolescent patients. Citizen Scientists (n = 7) played a critical role in the final selection of educational content and interviewer training by participating in mock-up patient interviews. Cognitive interviews with patients (n = 16) ensured that systems were in place for the feasibility trial and assessed ease of navigation. Focus group participants (n = 24) offered recommendations for integrating the tool into clinical workflow and input on acceptability and appropriateness, and anticipated barriers and facilitators for adoption and feasibility. Engaging key stakeholders to discuss implementation outcomes throughout the implementation process can improve the quality, applicability, and relevance of the research, and enhance implementation success.

scholarly journals Standard blood laboratory values as a clinical support tool to distinguish between SARS-CoV-2 positive and negative patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rainer Thell ◽  
Jascha Zimmermann ◽  
Marton Szell ◽  
Sabine Tomez ◽  
Philip Eisenburger ◽  
...  
Keyword(s):  
Negative Likelihood Ratio ◽  
Blood Parameters ◽  
Support Tool ◽  
Laboratory Parameters ◽  
Reactive Protein ◽  
Diagnostic Potential ◽  
Clinical Support ◽  
Post Test ◽  
Sensitivity Specificity ◽  
Clinical Support Tool

AbstractStandard blood laboratory parameters may have diagnostic potential, if polymerase-chain-reaction (PCR) tests are not available on time. We evaluated standard blood laboratory parameters of 655 COVID-19 patients suspected to be infected with SARS-CoV-2, who underwent PCR testing in one of five hospitals in Vienna, Austria. We compared laboratory parameters, clinical characteristics, and outcomes between positive and negative PCR-tested patients and evaluated the ability of those parameters to distinguish between groups. Of the 590 patients (20–100 years, 276 females and 314 males), 208 were PCR-positive. Positive compared to negative PCR-tested patients had significantly lower levels of leukocytes, neutrophils, basophils, eosinophils, lymphocytes, neutrophil-to-lymphocyte ratio, monocytes, and thrombocytes; while significantly higher levels were detected with erythrocytes, hemoglobin, hematocrit, C-reactive-protein, ferritin, activated-partial-thromboplastin-time, alanine-aminotransferase, aspartate-aminotransferase, lipase, creatine-kinase, and lactate-dehydrogenase. From all blood parameters, eosinophils, ferritin, leukocytes, and erythrocytes showed the highest ability to distinguish between COVID-19 positive and negative patients (area-under-curve, AUC: 72.3–79.4%). The AUC of our model was 0.915 (95% confidence intervals, 0.876–0.955). Leukopenia, eosinopenia, elevated erythrocytes, and hemoglobin were among the strongest markers regarding accuracy, sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and post-test probabilities. Our findings suggest that especially leukopenia, eosinopenia, and elevated hemoglobin are helpful to distinguish between COVID-19 positive and negative tested patients.

scholarly journals Standard Blood Laboratory Values As A Clinical Support Tool to Distinguish Between SARS-CoV-2 Positive and Negative Patients

2020 ◽  
Author(s):  
Rainer Thell ◽  
Jascha Zimmermann ◽  
Marton Szell ◽  
Sabine Tomez ◽  
Philip Eisenburger ◽  
...  
Keyword(s):  
Negative Likelihood Ratio ◽  
Blood Parameters ◽  
Support Tool ◽  
Laboratory Parameters ◽  
Reactive Protein ◽  
Diagnostic Potential ◽  
Clinical Support ◽  
Post Test ◽  
Sensitivity Specificity ◽  
Clinical Support Tool

Abstract Standard blood laboratory parameters may have diagnostic potential, if polymerase-chain-reaction (PCR) tests are not available on time. We evaluated standard blood laboratory parameters of 655 COVID-19 patients suspected to be infected with SARS-CoV-2, who underwent PCR testing in one of five hospitals in Vienna, Austria. We compared laboratory parameters, clinical characteristics, and outcomes between positive and negative PCR-tested patients and evaluated the ability of those parameters to distinguish between groups. Of the 590 patients (20-100years, 276 females and 314 males), 208 were PCR-positive. Positive compared to negative PCR-tested patients had significantly lower levels of leukocytes, neutrophils, basophils, eosinophils, lymphocytes, neutrophil-to-lymphocyte ratio, monocytes, and thrombocytes; while significantly higher levels were detected with erythrocytes, hemoglobin, hematocrit, C-reactive-protein, ferritin, activated-partial-thromboplastin-time, alanine-aminotransferase, aspartate-aminotransferase, lipase, creatine-kinase, and lactate-dehydrogenase. From all blood parameters, eosinophils, ferritin, leukocytes, and erythrocytes showed the highest ability to distinguish between COVID-19 positive and negative patients (area-under-curve: 72.3-79.4%). Leukopenia, eosinopenia, elevated erythrocytes, and hemoglobin were among the strongest markers regarding accuracy, sensitivity, specificity, positive and negative predictive value, positive and negative likelihood ratio, and post-test probabilities. Our findings suggest that especially leukopenia, eosinopenia, as well as elevated erythrocytes, hemoglobin, and ferritin are helpful to distinguish between COVID-19 positive and negative tested patients.

scholarly journals Considerations of Privacy and Confidentiality in Developing a Clinical Support Tool for Adolescent Tobacco Prevention: Qualitative Study

10.2196/12406 ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. e12406
Author(s):  
Ryan P Theis ◽  
Ali M Malik ◽  
Lindsay A Thompson ◽  
Elizabeth A Shenkman ◽  
Lori Pbert ◽  
...  
Keyword(s):  
Qualitative Study ◽  
Tobacco Prevention ◽  
Support Tool ◽  
Clinical Support ◽  
Privacy And Confidentiality ◽  
Clinical Support Tool

scholarly journals Using Augmented Reality as a Clinical Support Tool to Assist Combat Medics in the Treatment of Tension Pneumothoraces

2013 ◽  
Vol 178 (9) ◽  
pp. 981-985 ◽  
Author(s):  
Kenneth L. Wilson ◽  
Jayfus T. Doswell ◽  
Olatokunbo S. Fashola ◽  
Wayne Debeatham ◽  
Nii Darko ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Support Tool ◽  
Clinical Support ◽  
Combat Medics ◽  
Clinical Support Tool
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