scholarly journals Reducing Delays in Diagnosing Primary Immunodeficiency Through the Development and Implementation of a Clinical Decision Support Tool: A Study Protocol (Preprint)

10.2196/32635 ◽  
2021 ◽  
Author(s):  
Bharat Kumar ◽  
Samuel Zetumer ◽  
Melissa Swee ◽  
Ellen L Keyser Endelman ◽  
Manish Suneja ◽  
...  
Keyword(s):  
Decision Support ◽  
Primary Immunodeficiency ◽  
Clinical Decision Support ◽  
Study Protocol ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

Reducing Delays in Diagnosing Primary Immunodeficiency Through the Development and Implementation of a Clinical Decision Support Tool: A Study Protocol (Preprint)

2021 ◽  
Author(s):  
Samuel Zetumer ◽  
Bharat Kumar ◽  
Melissa Swee ◽  
Ellen L Keyser Endelman ◽  
Manish Suneja ◽  
...  
Keyword(s):  
Decision Support ◽  
Primary Immunodeficiency ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Lean Six Sigma ◽  
Primary Immunodeficiencies ◽  
University Of Iowa ◽  
Support Tool ◽  
Clinical Decision Support Tool

BACKGROUND Primary immunodeficiencies are a set of heterogeneous chronic disorders characterized by immune dysfunction. They are diagnostically challenging because of their clinical heterogeneity, knowledge gaps among primary care physicians, and continuing shortages of clinically trained immunologists. As a result, patients with undiagnosed primary immunodeficiencies are at increased risk for recurrent infections, cancers, and autoimmune diseases. OBJECTIVE This article outlines a quality improvement protocol to develop and implement a clinical decision support tool that helps reduce delays in diagnosing primary immunodeficiencies. METHODS We will develop and implement a clinical decision support tool for the identification of underlying primary immunodeficiencies among patients who receive primary care through a health care provider at the University of Iowa Hospitals and Clinics. The clinical decision support tool will function through an algorithm that is based on the Immune Deficiency Foundation’s 10 Warning Signs for Primary Immunodeficiency. Through the course of a year, we will use Lean Six Sigma principles and the DMAIC (Define-Measure-Analyze-Improve-Control) Framework to guide the project. The primary measure is the number of newly diagnosed primary immunodeficiency patients per month. Secondary measures include: (1) the number of new patients identified by the CDS at high risk for PI, (2) the number of new PI cases in which IVIG or rotating antibiotics are started, (3) the cost of evaluation of each patient identified by CDS tool at high risk for PIs, (4) the number of new consults not diagnosed with a PI, and (5) patient satisfaction with the process of referral to Immunology Clinic. RESULTS This study was determined to be non-human subjects research by the Institutional Review Board at the University of Iowa. Data collection will begin in August 2021. CONCLUSIONS The development and implementation of a clinical decision support tool is a promising approach to identifying patients with underlying primary immunodeficiency. This protocol assesses whether such an approach will be able to achieve its objective of reducing diagnostic delays. The disciplined approach, using Lean Six Sigma and the Define-Measure-Analyze-Improve-Control framework, will guide the implementation to maximize opportunities for success.

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.

scholarly journals PO-0990: A Clinical Decision Support Tool Based on Active Appearance Modelling for Prostate Segmentation

2018 ◽  
Vol 127 ◽  
pp. S550-S551
Author(s):  
K. Cheng ◽  
H. Li ◽  
D. McLaren ◽  
S. McLaughlin ◽  
W. Nailon
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Prostate Segmentation ◽  
Clinical Decision Support Tool ◽  
Active Appearance

Evaluation of a handheld clinical decision support tool for evidence-based learning and practice in medical undergraduates

2004 ◽  
Vol 38 (6) ◽  
pp. 628-637 ◽  
Author(s):  
Janice M Johnston ◽  
Gabriel M Leung ◽  
Keith Y K Tin ◽  
Lai-Ming Ho ◽  
Wendy Lam ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Evidence Based ◽  
Support Tool ◽  
Medical Undergraduates ◽  
Clinical Decision Support Tool
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