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 Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

2017 ◽  
Vol 25 (5) ◽  
pp. 496-506 ◽  
Author(s):  
Adam Wright ◽  
Angela Ai ◽  
Joan Ash ◽  
Jane F Wiesen ◽  
Thu-Trang T Hickman ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Quantitative Methods ◽  
Clinical Decision ◽  
The United States ◽  
Health Record ◽  
Qualitative And Quantitative Methods ◽  
Electronic Health

Abstract Objective To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

scholarly journals Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

2011 ◽  
Vol 02 (03) ◽  
pp. 284-303 ◽  
Author(s):  
A. Wright ◽  
M. Burton ◽  
G. Fraser ◽  
M. Krall ◽  
S. Maviglia ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Health Care ◽  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Diabetes Care ◽  
Clinical Decision ◽  
The United States ◽  
Computer Based

SummaryBackground: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known.Objective: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care.Methods: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites.Results: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines.Conclusion: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

scholarly journals 1297. Examining the Successes and Challenges of Implementing HIV Testing Clinical Decision Support in the Emergency Department

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S467-S468
Author(s):  
Mariah Powell ◽  
Michael Gierlach ◽  
Sandra L Werner ◽  
David S Bar-Shain ◽  
Ann Avery
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Hiv Testing ◽  
Clinical Decision Support ◽  
Screening Tool ◽  
Clinical Decision ◽  
The United States ◽  
Hiv Screening ◽  
Implementation Challenges ◽  
Start Date

Abstract Background In 2016, MetroHealth System (MHS) launched the FOCUS (Frontlines of Communities in the United States) project to routinize HIV testing in the emergency department (ED). Before 2016, clinical decision support (CDS) for HIV testing was not in place, nor was there a policy to support the importance of opt-out, nontargeted screening. The purpose of this study was to outline the progress of HIV testing after the integration of CDS, as well as describe the implementation challenges, and how certain events impacted HIV testing. Methods HIV testing data from MHS EDs were collected from October 1, 2015 to March 31, 2019 and graphed into a run chart. The dataset was mapped with the following events: project start date, ED testing begins (without CDS), CDS implementation, the staffing of the ED Testing Coordinator (EDTC), and optimization of CDS (Figure 1). To determine whether observed variation in the dataset is due to random or special cause variation, these run chart rules were applied: Run, Shift (Figure 2), and Trend. Results There were 42 data points and 4 runs. With 42 points, the lower limit of runs was 16 and the upper limit of runs was 28. This signals that one or more special cause variations were present. A total of three distinct shifts were observed indicating special cause variation. The run chart did not include any downward or upward trends. Testing increased as much as 3971% (7 tests in October 2015 vs. 285 tests in March 2018). Conclusion HIV testing increased from 7 tests to 86 tests (Shift 1). This coincided with establishment of an ED testing policy in April 2016. Testing increased to 266 tests in October 2016 (Shift 2). This directly related to implementation of CDS in the ED. December 2017 displayed the lowest testing with 117 tests. This was due to lack of policy awareness, and to the rarely-visited location of the HIV screening tool during the triage process. Staff was re-educated and the HIV screening tool was moved to a more visible location. This resulted in 227 tests in February 2018, and was followed by the highest testing month with 285 tests (Shift 3). Continued challenges prohibit sustained upward trends in ED testing. A control chart may be the appropriate next step to identify new control limits Disclosures All authors: No reported disclosures.

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
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