A Bibliometric Analysis of Alert Override in Clinical Decision Support

2021 ◽  
Author(s):  
Siru Liu ◽  
Jinbo Fang ◽  
Qingke Shi ◽  
Jialin Liu
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Core Collection ◽  
Clinical Decision ◽  
Research Field ◽  
Current Status ◽  
Knowledge Maps ◽  
Research Themes ◽  
The Status ◽  
The Web

The aim of this study was to understand the status and trend in alert override research over the past two decades (1999–2018). We used the Web of Science core collection (WoSCC) database to extract all papers of alert override in clinical decision support from 1999 to 2018. A total of 150 papers were identified, most (86.67%) being articles. This study presented the key bibliometric indicators such as annual publications, top 5 authors, institutions, countries, and co-occurrence of terms from the titles and abstracts. VOSviewer was used to visualize keywords knowledge maps. The results show that alert override research has a wide variety of research themes and a multidisciplinary character. This study provides a broad view of the current status and trends in alert override research. It may help researchers, clinicians and policymakers better understand alert override research field change and direction in the future.

Clinical Decision Support Systems

2021 ◽  
pp. 1-7
Author(s):  
Andreas Teufel ◽  
Harald Binder
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Medical Knowledge ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Current Status ◽  
Care Providers

<b><i>Background:</i></b> By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. <b><i>Summary and Key Messages:</i></b> Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.

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

scholarly journals Outpatient diabetes clinical decision support: current status and future directions

2016 ◽  
Vol 33 (6) ◽  
pp. 734-741 ◽  
Author(s):  
P. J. O'Connor ◽  
J. M. Sperl-Hillen ◽  
C. J. Fazio ◽  
B. M. Averbeck ◽  
B. H. Rank ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Current Status ◽  
Future Directions

scholarly journals Prospects and Challenges for Clinical Decision Support in the Era of Big Data

2018 ◽  
pp. 1-12 ◽  
Author(s):  
Issam El Naqa ◽  
Michael R. Kosorok ◽  
Judy Jin ◽  
Michelle Mierzwa ◽  
Randall K. Ten Haken
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Current Status ◽  
Analytical Approaches

Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called big data (BD), an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data, patient privacy, transformation of current analytical approaches to handle such noisy and heterogeneous data, and expanded use of advanced statistical learning methods on the basis of confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical end points, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the use and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.

scholarly journals Cancer Multidisciplinary Team Meetings: Evidence, Challenges, and the Role of Clinical Decision Support Technology

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Vivek Patkar ◽  
Dionisio Acosta ◽  
Tim Davidson ◽  
Alison Jones ◽  
John Fox ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Multidisciplinary Team ◽  
Care Delivery ◽  
Clinical Decision ◽  
Full Potential ◽  
Team Meetings ◽  
The Status ◽  
The Impact

Multidisciplinary team (MDT) model in cancer care was introduced and endorsed to ensure that care delivery is consistent with the best available evidence. Over the last few years, regular MDT meetings have become a standard practice in oncology and gained the status of the key decision-making forum for patient management. Despite the fact that cancer MDT meetings are well accepted by clinicians, concerns are raised over the paucity of good-quality evidence on their overall impact. There are also concerns over lack of the appropriate support for this important but overburdened decision-making platform. The growing acceptance by clinical community of the health information technology in recent years has created new opportunities and possibilities of using advanced clinical decision support (CDS) systems to realise full potential of cancer MDT meetings. In this paper, we present targeted summary of the available evidence on the impact of cancer MDT meetings, discuss the reported challenges, and explore the role that a CDS technology could play in addressing some of these challenges.

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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