Improving red blood cell orders, utilization, and management with point-of-care clinical decision support

Transfusion ◽  
2015 ◽  
Vol 55 (9) ◽  
pp. 2086-2094 ◽  
Author(s):  
Zeke J. McKinney ◽  
Jessica M. Peters ◽  
Jed B. Gorlin ◽  
Elizabeth H. Perry
Transfusion ◽  
2016 ◽  
Vol 56 (10) ◽  
pp. 2406-2411 ◽  
Author(s):  
Lawrence Tim Goodnough ◽  
Steven Andrew Baker ◽  
Neil Shah

Author(s):  
Ana Margarida Pereira ◽  
Cristina Jácome ◽  
Rita Amaral ◽  
Tiago Jacinto ◽  
João A Fonseca

2018 ◽  

This convenient flip chart provides pediatric health care professionals with point-of-care guidance on the assessment, prevention, and treatment of childhood infectious diseases. https://shop.aap.org/red-book-pediatric-infectious-diseases-clinical-decision-support-chart/


2012 ◽  
Vol 13 (2) ◽  
pp. 172-176 ◽  
Author(s):  
Patrick J. O’Connor ◽  
Jay R. Desai ◽  
John C. Butler ◽  
Elyse O. Kharbanda ◽  
JoAnn M. Sperl-Hillen

2014 ◽  
Vol 53 (06) ◽  
pp. 482-492 ◽  
Author(s):  
P. McNair ◽  
V. Kilintzis ◽  
K. Skovhus Andersen ◽  
J. Niès ◽  
J.-C. Sarfati ◽  
...  

Summary Background: Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. Objectives: Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. Methods: The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frame-work’s services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. Results: Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing health-care IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. Conclusions: This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. None-theless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.


Author(s):  
Michael P. McRae ◽  
Glennon W. Simmons ◽  
Nicolaos J. Christodoulides ◽  
Zhibing Lu ◽  
Stella K. Kang ◽  
...  

AbstractSARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase–myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40–83) and 9 (6–17), respectively, and area under the curve of 0.94 (95% CI 0.89– 0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.


Sign in / Sign up

Export Citation Format

Share Document