scholarly journals Web-based Stroke Calculators in Clinical Decision Support: Retrospective Analysis of Usage Patterns (Preprint)

10.2196/28266 ◽  
2021 ◽  
Author(s):  
Benjamin R Kummer ◽  
Lubaina Shakir ◽  
Rachel Kwon ◽  
Joseph Habboushe ◽  
Nathalie Jetté
Keyword(s):  
Decision Support ◽  
Retrospective Analysis ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Web Based ◽  
Usage Patterns

scholarly journals Web-based Stroke Calculators in Clinical Decision Support: a Retrospective Analysis of Usage Patterns (Preprint)

2021 ◽  
Author(s):  
R Kummer ◽  
Lubaina Shakir ◽  
Rachel Kwon ◽  
Joseph Habboushe ◽  
Nathalie Jetté
Keyword(s):  
Decision Support ◽  
Cerebrovascular Disease ◽  
Clinical Decision Support ◽  
Stroke Risk ◽  
Clinical Decision ◽  
The United States ◽  
Web Browser ◽  
Web Based ◽  
Usage Patterns ◽  
Calculator Usage

BACKGROUND Clinical scores are frequently used in the diagnosis and management of stroke and cerebrovascular disease. While medical calculators are increasingly important clinical decision support tools, uptake and usage of commonly used medical calculators for cerebrovascular disease remain poorly characterized. OBJECTIVE To describe usage patterns in frequently used stroke-related medical calculators from a Web-based clinical decision support system. METHODS We conducted a retrospective study of calculators from MDCalc, a web-based medical calculator platform based in the United States. We analyzed metadata tags from MDCalc’s usage data to identify all calculators related to stroke. Using relative pageviews as a measure of calculator usage, we determined the 5 most frequently used stroke-related calculators between January 2016 and December 2018. For all 5 calculators, we determined cumulative and quarterly usage, mode of access (e.g., app or Web browser), as well as US geographic and international distributions in usage. We compared cumulative usage in the 2016-2018 period to usage from January 2011 to December 2015. RESULTS Over the study period, we identified 454 MDCalc calculators, of which 48 (10.6%) were related to stroke. Of these, the 5 most frequently used calculators were the CHA2DS2-VASc Score for Atrial Fibrillation Stroke Risk calculator (5.5% and 32% of total and stroke-related pageviews, respectively) the Mean Arterial Pressure (MAP) calculator (2.4%, 14.0%), the HAS-BLED Score for Major Bleeding Risk (1.9%, 11.4%), the National Institutes of Health Stroke Scale Score (NIHSS) calculator (1.7%, 10.1%), and the CHADS2 Score for Atrial Fibrillation Stroke Risk calculator (1.4%, 8.1%). Web browser was the most common mode of access, accounting for 82.7% to 91.2% of individual stroke calculator pageviews. Access originated most frequently from the most populated regions within the United States. Internationally, usage originated mostly from English-language countries. The NIHSS score calculator demonstrated the greatest increase in pageviews (238.1%) between the first and last quarter of the study period. CONCLUSIONS The most frequently used stroke calculators were for the CHA2DS2-VASc, MAP, HAS-BLED, NIHSS, and CHADS2. These were mainly accessed by Web browser, from English-speaking countries, and from highly populated areas. Further studies should investigate barriers to stroke calculator adoption and the effect of calculator usage on the application of best practices in cerebrovascular disease.

scholarly journals Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  
Keyword(s):  
Decision Support ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Centers ◽  
Web Based ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals A Retrospective Analysis of Interruptive versus Non-interruptive Clinical Decision Support for Identification of Patients Needing Contact Isolation

2013 ◽  
Vol 04 (04) ◽  
pp. 569-582 ◽  
Author(s):  
X. Li ◽  
J. Grein ◽  
D.S. Bell ◽  
P. Silka ◽  
J.M. Pevnick
Keyword(s):  
Decision Support ◽  
Retrospective Analysis ◽  
Clinical Decision Support ◽  
Primary Outcome ◽  
Objective Assessment ◽  
Clinical Decision ◽  
Second Primary ◽  
Large Hospital ◽  
Contact Isolation ◽  
The Difference

SummaryBackground: In determining whether clinical decision support (CDS) should be interruptive or non-interruptive, CDS designers need more guidance to balance the potential for interruptive CDS to overburden clinicians and the potential for non-interruptive CDS to be overlooked by clinicians.Objectives: (1)To compare performance achieved by clinicians using interruptive CDS versus using similar, non-interruptive CDS. (2)To compare performance achieved using non-interruptive CDS among clinicians exposed to interruptive CDS versus clinicians not exposed to interruptive CDS.Methods: We studied 42 emergency medicine physicians working in a large hospital where an interruptive CDS to help identify patients requiring contact isolation was replaced by a similar, but non-interruptive CDS. The first primary outcome was the change in sensitivity in identifying these patients associated with the conversion from an interruptive to a non-interruptive CDS. The second primary outcome was the difference in sensitivities yielded by the non-interruptive CDS when used by providers who had and who had not been exposed to the interruptive CDS. The reference standard was an epidemiologist-designed, structured, objective assessment.Results: In identifying patients needing contact isolation, the interruptive CDS-physician dyad had sensitivity of 24% (95% CI: 17%-32%), versus sensitivity of 14% (95% CI: 9%-21%) for the non-interruptive CDS-physician dyad (p = 0.04). Users of the non-interruptive CDS with prior exposure to the interruptive CDS were more sensitive than those without exposure (14% [95% CI: 9%-21%] versus 7% [95% CI: 3%-13%], p = 0.05).Limitations: As with all observational studies, we cannot confirm that our analysis controlled for every important difference between time periods and physician groups.Conclusions: Interruptive CDS affected clinicians more than non-interruptive CDS. Designers of CDS might explicitly weigh the benefits of interruptive CDS versus its associated increased clinician burden. Further research should study longer term effects of clinician exposure to interruptive CDS, including whether it may improve clinician performance when using a similar, subsequent non-interruptive CDS.Citation: Pevnick JM, Li X, Grein J, Bell DS, Silka P. A retrospective analysis of interruptive versus non-interruptive clinical decision support for identification of patients needing contact isolation. Appl Clin Inf 2013; 4: 569–582http://dx.doi.org/10.4338/ACI-2013-04-RA-0021

CxCaDSS: A Web-Based Clinical Decision Support System for Cervical Cancer

2015 ◽  
pp. 757-760 ◽  
Author(s):  
Panagiotis Bountris ◽  
Gregory Kotronoulas ◽  
Tassos Tagaris ◽  
Maria Haritou ◽  
Aris Spathis ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Web Based

Guidance DS: A web-based clinical decision support system for antimicrobial stewardship in hospitals

2009 ◽  
Vol 42 (12) ◽  
pp. 354-358
Author(s):  
Karin Thursky ◽  
Marion Robertson ◽  
Susan Luu ◽  
James Black ◽  
Michael Richards ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Web Based

My Cancer Genome: Web-based clinical decision support for genome-directed lung cancer treatment.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 7576-7576 ◽  
Author(s):  
M. A. Levy ◽  
C. M. Lovly ◽  
L. Horn ◽  
R. Naser ◽  
W. Pao
Keyword(s):  
Lung Cancer ◽  
Decision Support ◽  
Cancer Treatment ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Cancer Genome ◽  
Web Based ◽  
Lung Cancer Treatment
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