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
Keyword(s):  
Decision Support ◽  
Blood Cell ◽  
Clinical Decision Support ◽  
Red Blood Cell ◽  
Point Of Care ◽  
Clinical Decision

How I use clinical decision support to improve red blood cell utilization

Transfusion ◽  
2016 ◽  
Vol 56 (10) ◽  
pp. 2406-2411 ◽  
Author(s):  
Lawrence Tim Goodnough ◽  
Steven Andrew Baker ◽  
Neil Shah
Keyword(s):  
Decision Support ◽  
Blood Cell ◽  
Clinical Decision Support ◽  
Red Blood Cell ◽  
Clinical Decision

Real-Time Clinical Decision Support at the Point of Care

2019 ◽  
pp. 125-133
Author(s):  
Ana Margarida Pereira ◽  
Cristina Jácome ◽  
Rita Amaral ◽  
Tiago Jacinto ◽  
João A Fonseca
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision

Red Book Point-of-Care Clinical Decision Support Tool

2018 ◽  
Keyword(s):  
Decision Support ◽  
Infectious Diseases ◽  
Clinical Decision Support ◽  
Health Care Professionals ◽  
Point Of Care ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Pediatric Health Care ◽  
Flip Chart

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/

scholarly journals Current Status and Future Prospects for Electronic Point-of-Care Clinical Decision Support in Diabetes Care

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
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Diabetes Care ◽  
Point Of Care ◽  
Clinical Decision ◽  
Current Status ◽  
Future Prospects

From Adverse Drug Event Detection to Prevention

2014 ◽  
Vol 53 (06) ◽  
pp. 482-492 ◽  
Author(s):  
P. McNair ◽  
V. Kilintzis ◽  
K. Skovhus Andersen ◽  
J. Niès ◽  
J.-C. Sarfati ◽  
...  
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Adverse Drug Event ◽  
Support Services ◽  
Medication Safety ◽  
Point Of Care ◽  
Clinical Decision ◽  
Substantial Contribution ◽  
Drug Event

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.

scholarly journals Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19

2020 ◽  
Author(s):  
Michael P. McRae ◽  
Glennon W. Simmons ◽  
Nicolaos J. Christodoulides ◽  
Zhibing Lu ◽  
Stella K. Kang ◽  
...  
Keyword(s):  
Decision Support ◽  
Disease Severity ◽  
Clinical Decision Support ◽  
Severity Score ◽  
Troponin I ◽  
Point Of Care ◽  
Clinical Decision ◽  
Adverse Outcomes ◽  
Number Of Patients ◽  
Severity Scores

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.

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