scholarly journals Usability Testing and Adaptation of the Pediatric Cardiovascular Risk Reduction Clinical Decision Support Tool

2016 ◽  
Vol 3 (1) ◽  
pp. e17 ◽  
Author(s):  
Pamela A Williams ◽  
Robert D Furberg ◽  
Jacqueline E Bagwell ◽  
Kenneth A LaBresh
Keyword(s):  
Decision Support ◽  
Cardiovascular Risk ◽  
Risk Reduction ◽  
Clinical Decision Support ◽  
Usability Testing ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Cardiovascular Risk Reduction ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Effect of a Novel Clinical Decision Support Tool on the Efficiency and Accuracy of Treatment Recommendations for Cholesterol Management

2017 ◽  
Vol 26 (01) ◽  
pp. 124-136 ◽  
Author(s):  
Marianne Scheitel ◽  
Maya Kessler ◽  
Jane Shellum ◽  
Steve Peters ◽  
Dawn Milliner ◽  
...  
Keyword(s):  
Decision Support ◽  
Cardiovascular Risk ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Treatment Recommendations ◽  
Treatment Recommendation ◽  
Support Tool ◽  
Clinical Decision Support Tool ◽  
Consistent Treatment

Summary Background: The 2013 American College of Cardiology / American Heart Association Guidelines for the Treatment of Blood Cholesterol emphasize treatment based on cardiovascular risk. But finding time in a primary care visit to manually calculate cardiovascular risk and prescribe treatment based on risk is challenging. We developed an informatics-based clinical decision support tool, MayoExpertAdvisor, to deliver automated cardiovascular risk scores and guideline-based treatment recommendations based on patient-specific data in the electronic heath record. Objective: To assess the impact of our clinical decision support tool on the efficiency and accuracy of clinician calculation of cardiovascular risk and its effect on the delivery of guideline-consistent treatment recommendations. Methods: Clinicians were asked to review the EHR records of selected patients. We evaluated the amount of time and the number of clicks and keystrokes needed to calculate cardiovascular risk and provide a treatment recommendation with and without our clinical decision support tool. We also compared the treatment recommendation arrived at by clinicians with and without the use of our tool to those recommended by the guidelines. Results: Clinicians saved 3 minutes and 38 seconds in completing both tasks with MayoExpertAd-visor, used 94 fewer clicks and 23 fewer key strokes, and improved accuracy from the baseline of 60.61% to 100% for both the risk score calculation and guideline-consistent treatment recommendation. Conclusion: Informatics solution can greatly improve the efficiency and accuracy of individualized treatment recommendations and have the potential to increase guideline compliance.

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 Duplicate Laboratory Test Reduction Using a Clinical Decision Support Tool

2014 ◽  
Vol 141 (5) ◽  
pp. 718-723 ◽  
Author(s):  
Gary W. Procop ◽  
Lisa M. Yerian ◽  
Robert Wyllie ◽  
A. Marc Harrison ◽  
Kandice Kottke-Marchant
Keyword(s):  
Decision Support ◽  
Laboratory Test ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

41 Enhancing Burn Medical Care During a Disaster Using a Novel Augmented-Reality Application

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S31-S31
Author(s):  
Sena Veazey ◽  
Maria SerioMelvin ◽  
David E Luellen ◽  
Angela Samosorn ◽  
Alexandria Helms ◽  
...  
Keyword(s):  
Decision Support ◽  
Augmented Reality ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Burn Injuries ◽  
Medical Decision ◽  
Support Tool ◽  
Burn Care ◽  
Clinical Decision Support Tool

Abstract Introduction In disaster or mass casualty situations, access to remote burn care experts, communication, or resources may be limited. Furthermore, burn injuries are complex and require substantial training and knowledge beyond basic clinical care. Development and use of decision support (DS) technologies may provide a solution for addressing this need. Devices capable of delivering burn management recommendations can enhance the provider’s ability to make decisions and perform interventions in complex care settings. When coupled with merging augmented reality (AR) technologies these tools may provide additional capabilities to enhance medical decision-making, visualization, and workflow when managing burns. For this project, we developed a novel AR-based application with enhanced integrated clinical practice guidelines (CPGs) to manage large burn injuries for use in different environments, such as disasters. Methods We identified an AR system that met our requirements to include portability, infrared camera, gesture and voice control, hands-free control, head-mounted display, and customized application development abilities. Our goal was to adapt burn CPGs to make use of AR concepts as part of an AR-enabled burn clinical decision support system supporting four sub-applications to assist users with specific interventional tasks relevant to burn care. We integrated relevant CPGs and a media library with photos and videos as additional references. Results We successfully developed a clinical decision support tool that integrates burn CPGs with enhanced capabilities utilizing AR technology. The main interface allows input of patient demographics and injuries with step-by-step guidelines that follow typical burn management care and workflow. There are four sub-applications to assist with these tasks, which include: 1) semi-automated burn wound mapping to calculate total body surface area; 2) hourly burn fluid titration and recommendations for resuscitation; 3) medication calculator for accurate dosing in preparation for procedures and 4) escharotomy instructor with holographic overlays. Conclusions We developed a novel AR-based clinical decision support tool for management of burn injuries. Development included adaptation of CPGs into a format to guide the user through burn management using AR concepts. The application will be tested in a prospective research study to determine the effectiveness, timeliness, and performance of subjects using this AR-software compared to standard of care. We fully expect that the tool will reduce cognitive workload and errors, ensuring safety and proper adherence to guidelines.

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